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diff --git a/apps/undefined-console/package-lock.json b/apps/undefined-console/package-lock.json
index a6a0a5f5..df066c1d 100644
--- a/apps/undefined-console/package-lock.json
+++ b/apps/undefined-console/package-lock.json
@@ -1,12 +1,12 @@
{
"name": "undefined-console",
- "version": "3.4.2",
+ "version": "3.5.0",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"name": "undefined-console",
- "version": "3.4.2",
+ "version": "3.5.0",
"dependencies": {
"@tauri-apps/api": "^2.3.0",
"@tauri-apps/plugin-http": "^2.3.0"
diff --git a/apps/undefined-console/package.json b/apps/undefined-console/package.json
index d5e75fd1..a24b0b6b 100644
--- a/apps/undefined-console/package.json
+++ b/apps/undefined-console/package.json
@@ -1,7 +1,7 @@
{
"name": "undefined-console",
"private": true,
- "version": "3.4.2",
+ "version": "3.5.0",
"type": "module",
"scripts": {
"tauri": "tauri",
diff --git a/apps/undefined-console/src-tauri/Cargo.lock b/apps/undefined-console/src-tauri/Cargo.lock
index dcb41530..ee1e96a1 100644
--- a/apps/undefined-console/src-tauri/Cargo.lock
+++ b/apps/undefined-console/src-tauri/Cargo.lock
@@ -4063,7 +4063,7 @@ checksum = "562d481066bde0658276a35467c4af00bdc6ee726305698a55b86e61d7ad82bb"
[[package]]
name = "undefined_console"
-version = "3.4.2"
+version = "3.5.0"
dependencies = [
"serde",
"serde_json",
diff --git a/apps/undefined-console/src-tauri/Cargo.toml b/apps/undefined-console/src-tauri/Cargo.toml
index 3b020568..7e0bf510 100644
--- a/apps/undefined-console/src-tauri/Cargo.toml
+++ b/apps/undefined-console/src-tauri/Cargo.toml
@@ -1,6 +1,6 @@
[package]
name = "undefined_console"
-version = "3.4.2"
+version = "3.5.0"
description = "Undefined cross-platform management console"
authors = ["Undefined contributors"]
license = "MIT"
diff --git a/apps/undefined-console/src-tauri/tauri.conf.json b/apps/undefined-console/src-tauri/tauri.conf.json
index f4270754..d26c93ba 100644
--- a/apps/undefined-console/src-tauri/tauri.conf.json
+++ b/apps/undefined-console/src-tauri/tauri.conf.json
@@ -1,7 +1,7 @@
{
"$schema": "https://schema.tauri.app/config/2",
"productName": "Undefined Console",
- "version": "3.4.2",
+ "version": "3.5.0",
"identifier": "com.undefined.console",
"build": {
"beforeDevCommand": "npm run dev",
diff --git a/docs/configuration.md b/docs/configuration.md
index 1b39f3be..cefbcc04 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -2,6 +2,7 @@
本文档是 Undefined 当前配置系统的完整说明，覆盖：
- 配置加载顺序与解析规则
+- **库嵌入**（`Config.from_mapping` / `set_config`）程序化配置
- 严格模式必填项
- 每个配置节与字段的用途、默认值、约束、回退行为
- 热更新与重启生效边界
@@ -41,7 +42,101 @@
---
-## 2. 严格模式（`strict=True`）必填项
+## 2. 库嵌入配置
+
+除 CLI / WebUI 从 CWD 读取 `config.toml` 外，Undefined 支持在 Python 代码中**程序化构建配置**，供测试、脚本或其它应用嵌入库组件时使用。
+
+> 完整 API 说明见 [Python 库 API 参考](python-api.md)。
+
+### 2.1 适用场景
+
+- 单元测试 / 集成测试：无需准备真实 `config.toml`
+- 下游应用：只复用 `AIClient`、`KnowledgeManager` 等模块，不启动 QQ Bot
+- CI / 容器：通过环境变量 + 空 mapping 注入密钥，配置文件只保留非敏感项
+
+### 2.2 加载优先级
+
+```
+Python 显式 mapping / builder.override > config.toml > 环境变量 > 代码默认值
+```
+
+| 入口 | 是否读 `config.toml` | 说明 |
+|------|---------------------|------|
+| `Config.load()` | 是 | CLI / WebUI 默认路径 |
+| `Config.from_mapping(dict)` | 否 | 纯内存构建 |
+| `Config.builder().with_mapping(...).build()` | 否 | 在 mapping 上链式覆盖 |
+| `get_config()` | 视情况 | 未 `set_config()` 时等价于 `Config.load()` |
+
+`from_mapping` / `builder` 仍会读取进程环境变量中**已注册**的兜底项（TOML / mapping 未提供的字段）。注册表见 [`env_registry.py`](../src/Undefined/config/env_registry.py) 与本文 [§8 环境变量兜底](#8-环境变量兜底迁移建议)。
+
+### 2.3 `Config.from_mapping`
+
+结构与 `config.toml` 一致，例如：
+
+```python
+from Undefined.config import Config
+
+cfg = Config.from_mapping(
+ {
+ "onebot": {"ws_url": "ws://127.0.0.1:3001"},
+ "models": {
+ "chat": {
+ "api_url": "https://api.example/v1",
+ "api_key": "sk-xxx",
+ "model_name": "gpt-4o-mini",
+ },
+ "vision": {
+ "api_url": "https://api.example/v1",
+ "api_key": "sk-xxx",
+ "model_name": "gpt-4o-mini",
+ },
+ "agent": {
+ "api_url": "https://api.example/v1",
+ "api_key": "sk-xxx",
+ "model_name": "gpt-4o-mini",
+ },
+ },
+ },
+ strict=False,
+)
+```
+
+- `strict=True`：与 CLI 相同，缺失 [§3 严格模式](#3-严格模式stricttrue必填项) 必填项时报错退出。
+- `strict=False`：适合测试与渐进式嵌入；生产 Bot 仍建议 `strict=True`。
+
+### 2.4 `Config.builder`
+
+```python
+cfg = (
+ Config.builder()
+ .with_mapping(base_mapping)
+ .override(log_level="DEBUG")
+ .build(strict=False)
+)
+```
+
+`override()` 目前覆盖 mapping 顶层键；嵌套结构请直接在 `with_mapping` 的 dict 中提供。
+
+### 2.5 `set_config()`（opt-in）
+
+```python
+from Undefined.config import Config, get_config, get_config_manager, set_config
+
+cfg = Config.from_mapping({...}, strict=False)
+set_config(cfg)
+assert get_config(strict=False) is cfg
+assert get_config_manager().load(strict=False) is cfg
+```
+
+**硬约束**：
+
+- `set_config()` 仅供库嵌入 opt-in；**CLI / WebUI 启动链不得调用**。
+- 未调用 `set_config()` 时，`get_config()` 仍从 CWD 加载 `./config.toml`，与独立运行 Bot 行为一致。
+- 调用 `set_config()` 会同步更新 `get_config()` 与 `get_config_manager().load()` 的缓存，避免双轨读到不同实例。
+
+---
+
+## 3. 严格模式（`strict=True`）必填项
程序主流程使用严格模式加载配置。缺失以下字段会报错退出：
- `core.bot_qq`
@@ -56,7 +151,7 @@
---
-## 3. 最小可运行配置示例
+## 4. 最小可运行配置示例
```toml
[core]
@@ -85,7 +180,7 @@ model_name = "gpt-4o-mini"
---
-## 4. 全量字段说明
+## 5. 全量字段说明
### 4.1 `[core]` 机器人核心行为
@@ -100,7 +195,7 @@ model_name = "gpt-4o-mini"
| `process_poke_message` | `true` | 是否响应拍一拍 | 关闭后忽略 poke |
| `context_recent_messages_limit` | `20` | 注入到提示词的最近历史条数 | `<0` 视为 `0`（关闭注入）；无固定上限，受 `max_records` 与存储约束 |
| `ai_request_max_retries` | `2` | 单次 LLM 请求失败重试次数 | `<0` 自动回退到 `0`；支持热更新 |
-| `missing_tool_call_retries` | `3` | 模型返回纯文本但未调用 `send_message` / `end` 等工具时的纠正重试次数 | `<0` 自动回退到 `0`；支持热更新 |
+| `missing_tool_call_retries` | `3` | 模型返回纯文本但未调用任何工具时的纠正重试次数（保留 assistant 纯文本 + 通用纠正提示，不写死具体 tool） | `<0` 自动回退到 `0`；支持热更新 |
---
@@ -914,7 +1009,7 @@ Prompt caching 补充：
---
-## 5. 热更新与重启边界
+## 6. 热更新与重启边界
### 5.1 热更新监听对象
- `config.toml`
@@ -967,7 +1062,7 @@ Prompt caching 补充：
---
-## 6. 兼容旧字段与隐藏字段
+## 7. 兼容旧字段与隐藏字段
- `models.
.deepseek_new_cot_support`：旧 thinking 兼容开关。
- `[core].keyword_reply_enabled`：旧位置，建议迁移到 `[easter_egg]`。
@@ -976,26 +1071,250 @@ Prompt caching 补充：
---
-## 7. 环境变量兜底（迁移建议）
+## 8. 环境变量兜底（迁移建议）
+
+虽然推荐统一写入 `config.toml`，当前仍支持环境变量兜底。规则：
+
+1. **仅当 TOML / `from_mapping` 未提供对应项** 时读取环境变量。
+2. 检测到 env 兜底时可能输出 `[配置]` 告警，建议迁移到 TOML。
+3. 主注册表由 `src/Undefined/config/env_registry.py` 维护；变更注册表时请同步更新本节表格。
+
+
-虽然推荐统一写入 `config.toml`，但当前仍支持大量环境变量兜底，常用示例：
-- `BOT_QQ` / `SUPERADMIN_QQ`
-- `ONEBOT_WS_URL` / `ONEBOT_TOKEN`
-- `CHAT_MODEL_API_URL` / `CHAT_MODEL_API_KEY` / `CHAT_MODEL_NAME`
-- `CHAT_MODEL_API_MODE` / `CHAT_MODEL_REASONING_ENABLED` / `CHAT_MODEL_REASONING_EFFORT` / `CHAT_MODEL_RESPONSES_TOOL_CHOICE_COMPAT` / `CHAT_MODEL_RESPONSES_FORCE_STATELESS_REPLAY`
-- `VISION_MODEL_*` / `AGENT_MODEL_*` / `SECURITY_MODEL_*` / `NAGA_MODEL_*` / `HISTORIAN_MODEL_*`
-- 上述模型环境变量同样覆盖 `*_THINKING_ENABLED`、`*_THINKING_BUDGET_TOKENS`、`*_THINKING_TOOL_CALL_COMPAT`、`*_RESPONSES_TOOL_CHOICE_COMPAT`、`*_RESPONSES_FORCE_STATELESS_REPLAY`
-- `EMBEDDING_MODEL_*` / `RERANK_MODEL_*`
-- `SEARXNG_URL`
-- `HTTP_PROXY` / `HTTPS_PROXY`
+以下环境变量在 **TOML 对应项缺失** 时作为兜底读取。
+完整注册表见 `src/Undefined/config/env_registry.py`。
+
+#### `access`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `access.allowed_group_ids` | `ALLOWED_GROUP_IDS` |
+| `access.allowed_private_ids` | `ALLOWED_PRIVATE_IDS` |
+| `access.blocked_group_ids` | `BLOCKED_GROUP_IDS` |
+| `access.blocked_private_ids` | `BLOCKED_PRIVATE_IDS` |
+| `access.mode` | `ACCESS_MODE` |
+
+#### `api_endpoints`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `api_endpoints.jkyai_base_url` | `JKYAI_BASE_URL` |
+| `api_endpoints.xxapi_base_url` | `XXAPI_BASE_URL` |
+
+#### `core`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `core.admin_qq` | `ADMIN_QQ` |
+| `core.bot_qq` | `BOT_QQ` |
+| `core.forward_proxy_qq` | `FORWARD_PROXY_QQ` |
+| `core.superadmin_qq` | `SUPERADMIN_QQ` |
+
+#### `features`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `features.pool_enabled` | `MODEL_POOL_ENABLED` |
+
+#### `history`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `history.max_records` | `HISTORY_MAX_RECORDS` |
+
+#### `image_gen`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `image_gen.provider` | `IMAGE_GEN_PROVIDER` |
+
+#### `logging`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `logging.backup_count` | `LOG_BACKUP_COUNT` |
+| `logging.file_path` | `LOG_FILE_PATH` |
+| `logging.level` | `LOG_LEVEL` |
+| `logging.log_thinking` | `LOG_THINKING` |
+| `logging.max_size_mb` | `LOG_MAX_SIZE_MB` |
+| `logging.tty_enabled` | `LOG_TTY_ENABLED` |
+
+#### `mcp`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `mcp.config_path` | `MCP_CONFIG_PATH` |
+
+#### `models.agent`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `models.agent.api_key` | `AGENT_MODEL_API_KEY` |
+| `models.agent.api_mode` | `AGENT_MODEL_API_MODE` |
+| `models.agent.api_url` | `AGENT_MODEL_API_URL` |
+| `models.agent.context_window_tokens` | `AGENT_MODEL_CONTEXT_WINDOW_TOKENS` |
+| `models.agent.model_name` | `AGENT_MODEL_NAME` |
+| `models.agent.reasoning_content_replay` | `AGENT_MODEL_REASONING_CONTENT_REPLAY` |
+| `models.agent.responses_force_stateless_replay` | `AGENT_MODEL_RESPONSES_FORCE_STATELESS_REPLAY` |
+| `models.agent.responses_tool_choice_compat` | `AGENT_MODEL_RESPONSES_TOOL_CHOICE_COMPAT` |
+| `models.agent.system_prompt_as_user` | `AGENT_MODEL_SYSTEM_PROMPT_AS_USER` |
+
+#### `models.chat`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `models.chat.api_key` | `CHAT_MODEL_API_KEY` |
+| `models.chat.api_mode` | `CHAT_MODEL_API_MODE` |
+| `models.chat.api_url` | `CHAT_MODEL_API_URL` |
+| `models.chat.context_window_tokens` | `CHAT_MODEL_CONTEXT_WINDOW_TOKENS` |
+| `models.chat.max_tokens` | `CHAT_MODEL_MAX_TOKENS` |
+| `models.chat.model_name` | `CHAT_MODEL_NAME` |
+| `models.chat.reasoning_content_replay` | `CHAT_MODEL_REASONING_CONTENT_REPLAY` |
+| `models.chat.responses_force_stateless_replay` | `CHAT_MODEL_RESPONSES_FORCE_STATELESS_REPLAY` |
+| `models.chat.responses_tool_choice_compat` | `CHAT_MODEL_RESPONSES_TOOL_CHOICE_COMPAT` |
+| `models.chat.system_prompt_as_user` | `CHAT_MODEL_SYSTEM_PROMPT_AS_USER` |
+
+#### `models.embedding`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `models.embedding.context_window_tokens` | `EMBEDDING_MODEL_CONTEXT_WINDOW_TOKENS` |
+
+#### `models.grok`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `models.grok.api_key` | `GROK_MODEL_API_KEY` |
+| `models.grok.api_url` | `GROK_MODEL_API_URL` |
+| `models.grok.context_window_tokens` | `GROK_MODEL_CONTEXT_WINDOW_TOKENS` |
+| `models.grok.max_tokens` | `GROK_MODEL_MAX_TOKENS` |
+| `models.grok.model_name` | `GROK_MODEL_NAME` |
+
+#### `models.naga`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `models.naga.api_key` | `NAGA_MODEL_API_KEY` |
+| `models.naga.api_mode` | `NAGA_MODEL_API_MODE` |
+| `models.naga.api_url` | `NAGA_MODEL_API_URL` |
+| `models.naga.context_window_tokens` | `NAGA_MODEL_CONTEXT_WINDOW_TOKENS` |
+| `models.naga.model_name` | `NAGA_MODEL_NAME` |
+| `models.naga.reasoning_content_replay` | `NAGA_MODEL_REASONING_CONTENT_REPLAY` |
+| `models.naga.responses_force_stateless_replay` | `NAGA_MODEL_RESPONSES_FORCE_STATELESS_REPLAY` |
+| `models.naga.responses_tool_choice_compat` | `NAGA_MODEL_RESPONSES_TOOL_CHOICE_COMPAT` |
+| `models.naga.system_prompt_as_user` | `NAGA_MODEL_SYSTEM_PROMPT_AS_USER` |
+
+#### `models.rerank`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `models.rerank.api_key` | `RERANK_MODEL_API_KEY` |
+| `models.rerank.api_url` | `RERANK_MODEL_API_URL` |
+| `models.rerank.context_window_tokens` | `RERANK_MODEL_CONTEXT_WINDOW_TOKENS` |
+| `models.rerank.model_name` | `RERANK_MODEL_NAME` |
+
+#### `models.security`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `models.security.api_key` | `SECURITY_MODEL_API_KEY` |
+| `models.security.api_mode` | `SECURITY_MODEL_API_MODE` |
+| `models.security.api_url` | `SECURITY_MODEL_API_URL` |
+| `models.security.context_window_tokens` | `SECURITY_MODEL_CONTEXT_WINDOW_TOKENS` |
+| `models.security.model_name` | `SECURITY_MODEL_NAME` |
+| `models.security.reasoning_content_replay` | `SECURITY_MODEL_REASONING_CONTENT_REPLAY` |
+| `models.security.responses_force_stateless_replay` | `SECURITY_MODEL_RESPONSES_FORCE_STATELESS_REPLAY` |
+| `models.security.responses_tool_choice_compat` | `SECURITY_MODEL_RESPONSES_TOOL_CHOICE_COMPAT` |
+| `models.security.system_prompt_as_user` | `SECURITY_MODEL_SYSTEM_PROMPT_AS_USER` |
+
+#### `models.vision`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `models.vision.api_key` | `VISION_MODEL_API_KEY` |
+| `models.vision.api_mode` | `VISION_MODEL_API_MODE` |
+| `models.vision.api_url` | `VISION_MODEL_API_URL` |
+| `models.vision.context_window_tokens` | `VISION_MODEL_CONTEXT_WINDOW_TOKENS` |
+| `models.vision.model_name` | `VISION_MODEL_NAME` |
+| `models.vision.reasoning_content_replay` | `VISION_MODEL_REASONING_CONTENT_REPLAY` |
+| `models.vision.responses_force_stateless_replay` | `VISION_MODEL_RESPONSES_FORCE_STATELESS_REPLAY` |
+| `models.vision.responses_tool_choice_compat` | `VISION_MODEL_RESPONSES_TOOL_CHOICE_COMPAT` |
+| `models.vision.system_prompt_as_user` | `VISION_MODEL_SYSTEM_PROMPT_AS_USER` |
+
+#### `onebot`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `onebot.token` | `ONEBOT_TOKEN` |
+| `onebot.ws_url` | `ONEBOT_WS_URL` |
+
+#### `proxy`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `proxy.use_proxy` | `USE_PROXY` |
+
+#### `search`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `search.searxng_url` | `SEARXNG_URL` |
+
+#### `skills`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `skills.hot_reload` | `SKILLS_HOT_RELOAD` |
+| `skills.intro_hash_path` | `AGENT_INTRO_HASH_PATH` |
+| `skills.prefetch_tools_hide` | `PREFETCH_TOOLS_HIDE` |
+
+#### `token_usage`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `token_usage.max_archives` | `TOKEN_USAGE_MAX_ARCHIVES` |
+| `token_usage.max_size_mb` | `TOKEN_USAGE_MAX_SIZE_MB` |
+| `token_usage.max_total_mb` | `TOKEN_USAGE_MAX_TOTAL_MB` |
+
+#### `tools`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `tools.description_max_len` | `TOOLS_DESCRIPTION_MAX_LEN` |
+| `tools.dot_delimiter` | `TOOLS_DOT_DELIMITER` |
+| `tools.sanitize_verbose` | `TOOLS_SANITIZE_VERBOSE` |
+
+#### `weather`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `weather.api_key` | `WEATHER_API_KEY` |
+
+#### `xxapi`
+
+| TOML 路径 | 环境变量 |
+|-----------|----------|
+| `xxapi.api_token` | `XXAPI_API_TOKEN` |
+
+#### 备用 / 兼容环境变量
+
+以下变量不在主注册表中，但在解析时仍会被读取：
+
+| 环境变量 | 映射 TOML 路径 |
+|----------|----------------|
+| `EASTER_EGG_AGENT_CALL_MESSAGE_MODE` | `easter_egg.agent_call_message_enabled` |
+| `EASTER_EGG_CALL_MESSAGE_MODE` | `easter_egg.agent_call_message_enabled` |
+| `HTTPS_PROXY` | `proxy.https_proxy` |
+| `HTTP_PROXY` | `proxy.http_proxy` |
+
+
建议：
-1. 把长期配置迁移到 `config.toml`。
-2. 环境变量只保留临时覆写或 CI 场景。
----
+1. 把长期配置迁移到 `config.toml`。
+2. 环境变量只保留临时覆写、CI 密钥或库嵌入场景的敏感项注入。
-## 8. 运维建议（生产环境）
+## 9. 运维建议（生产环境）
1. 首次部署先改 `webui.password`，避免默认密码模式。
2. 显式配置 `access.mode`，不要依赖 legacy 行为。
diff --git a/docs/deployment.md b/docs/deployment.md
index b936e4c3..a042c25d 100644
--- a/docs/deployment.md
+++ b/docs/deployment.md
@@ -2,6 +2,8 @@
提供源码部署与 pip/uv tool 安装两种方式：**源码部署是推荐的首选方式**，功能完整且经过充分测试；pip/uv tool 安装适合快速体验，但部分功能支持尚不完善。
+> **作为 Python 库嵌入**：若你不需要启动 QQ Bot CLI，而是要在自己的应用或测试中复用 Undefined 组件（配置、`AIClient`、Skills、认知记忆等），请参阅 [Python 库 API 参考](python-api.md) 与 [配置详解 — 库嵌入配置](configuration.md#2-库嵌入配置)。CLI 入口（`Undefined` / `Undefined-webui`）行为不受库嵌入 API 影响。
+
> Python 版本要求：`3.11`~`3.13`（包含）。
>
> 若使用 `uv`，通常不需要你手动限制系统 Python 版本；`uv` 会根据项目约束自动选择/下载兼容解释器。
@@ -131,7 +133,7 @@ uv tool run --from Undefined-bot playwright install
> **渲染依赖提醒**：同源码部署要求一致，你需要在宿主机上预先安装 Playwright 浏览器内核。请参考上文 [3. 安装渲染运行时](#3-安装渲染运行时)。未配置前，网页截图、Markdown 渲染和复杂 LaTeX 公式回退渲染可能会失败。
-安装完成后，在任意目录准备 `config.toml` 并启动：
+安装完成后，在任意目录准备 `config.toml` 并启动（库嵌入场景也可用 `Config.from_mapping()` 代替配置文件，见 [python-api.md](python-api.md)）：
```bash
# 启动方式（二选一）
diff --git a/docs/development.md b/docs/development.md
index 987c1bdc..305e4d02 100644
--- a/docs/development.md
+++ b/docs/development.md
@@ -11,27 +11,40 @@ Undefined 欢迎开发者参与共建和进行二次开发！
```text
src/Undefined/
├── changelog.py # CHANGELOG.md 解析与版本查询公共层
-├── ai/ # AI 运行时核心组件 (client, prompt, tooling 工具组装, summary 短期摘要, multimodal 多模态)
+├── ai/ # AI 运行时核心
+│ ├── client/ # AIClient 组合：setup / queue / ask_loop
+│ ├── llm/ # ModelRequester、streaming、thinking、sanitize
+│ ├── prompts/ # PromptBuilder、system_context、cognitive 片段
+│ └── multimodal/# 多模态检测、解析与分析
+├── attachments/ # 附件注册、渲染、作用域隔离
├── arxiv/ # arXiv 论文解析、元信息获取、PDF 下载与发送
├── bilibili/ # B站视频流解析、分段下载与异步发送
-├── cognitive/ # 认知记忆系统底座 (向量存储, 史官合并/改写, 侧写生成, 任务队列)
+├── cognitive/ # 认知记忆系统（service/ 门面 + historian/ 史官后台）
+├── config/ # 配置系统（parsers/ 域解析 + load_sections/ 分段加载 + loader shim）
+├── handlers/ # OneBot 消息分流（message_flow / poke / repeat / auto_extract）
+├── onebot/ # OneBot WebSocket 客户端
├── skills/ # 技能插件核心目录 (存放所有的工具与智能体)
│ ├── tools/ # 基础原子的工具 (独立的功能单元，如读写文件、网络请求等)
│ ├── toolsets/ # 聚合工具集 (分组后的工具组)
│ │ └── cognitive/ # 认知记忆主动暴露工具 (search_events, get_profile 等)
-│ ├── agents/ # 智能体 (独立自主的子 AI，负责处理诸如 Web 搜索、文件分析的具体长时任务)
+│ ├── agents/ # 智能体 (含 runner/ 通用循环子包)
│ ├── commands/ # 中心化斜杠指令系统 (实现如 /help, /stats, /admin 等平台功能)
+│ ├── pipelines/ # 自动提取管线 (bilibili / arxiv / github 等)
│ └── anthropic_skills/# Anthropic 协议集成的外部 Skills (兼容 SKILL.md 格式)
-├── config/ # 配置系统 (loader.py TOML 解析, models.py 数据模型, hot_reload.py 热更新)
├── api/ # Management API + Runtime API
-│ ├── routes/ # 路由子模块 (chat, tools, naga, system, memes, memory, cognitive, health)
+│ ├── routes/ # 路由子模块 (chat, tools, naga/, system, memes, memory, cognitive, health)
│ ├── app.py # aiohttp 服务主入口 (薄包装委派到 routes/)
│ └── _openapi.py # OpenAPI 文档生成
-├── memes/ # 表情包库 (两阶段 AI 管线, SQLite + ChromaDB)
-├── services/ # 核心运行服务 (Queue 任务队列, Command 命令分发, Security 安全防护拦截)
-├── utils/ # 通用支持工具组 (io.py 异步原子读写, history.py, coerce.py 类型强转, fake_at.py 假@检测)
-├── handlers.py # 最外层 OneBot 消息分流处理层
-└── onebot.py # OneBot WebSocket 客户端核心连接
+├── memes/ # 表情包库 (service + ingest/ + search/ + store + vector_store)
+├── services/ # 核心运行服务
+│ ├── coordinator/ # AICoordinator mixins（ai_coordinator.py 门面）
+│ ├── commands/ # CommandDispatcher mixins（stats / bugfix）
+│ ├── message_batcher/ # 同 sender 短时合并
+│ ├── command.py # 命令分发门面 + shim 组合
+│ ├── queue_manager.py # 车站-列车队列
+│ └── security.py # 注入检测与速率限制
+├── utils/ # 通用支持工具组 (__init__.py 聚合 io/paths/resources；io.py 异步原子读写, history.py, coerce.py 类型强转)
+└── py.typed # PEP 561 类型标记（wheel 通过 pyproject force-include 打包）
```
## 开发指南
@@ -96,3 +109,107 @@ bash scripts/install_git_hooks.sh
- 当提交包含 JS / Tauri / WebUI 前端相关改动时，还会自动执行 `Biome + TypeScript + cargo fmt/check`
> **注意**：项目严格遵守类型注释规范，`mypy .` 通过是代码入库的前提条件；跨平台控制台相关改动则以 `npm run check` 通过为准。
+
+## 注释规范
+
+库化重构期间，各 Track 在拆分与注释 Wave 中须遵守以下 docstring 与行内注释约定。目标：提升可读性、支撑 `fuck-u-code` 注释比例达标（<30%），且**不改变运行时行为**。
+
+### 模块 docstring
+
+每个 `.py` 文件（shim 除外）顶部须有**一行摘要** + 可选段落说明职责边界：
+
+```python
+"""OneBot WebSocket 客户端连接管理。
+
+负责与 NapCat/Lagrange 建立 WS 连接、心跳与事件分发；不处理业务消息逻辑。
+"""
+```
+
+- 使用中文或英文均可，与同目录现有风格保持一致。
+- Shim 文件仅保留一行：`# .py — compatibility shim; do not add logic here.`
+
+### 类 docstring
+
+公开类（`class X` 无 leading `_`）须有 docstring，说明**职责**与**主要协作对象**：
+
+```python
+class CognitiveService:
+ """认知记忆运行时入口。
+
+ 协调向量检索、侧写读写与史官后台任务队列；由 main 进程持有单例。
+ """
+```
+
+- 内部辅助类（`_Foo`、`SkillStats` 等 dataclass）鼓励简短一行说明。
+- 禁止复制类型签名（mypy 已覆盖）；重点写「为什么存在」。
+
+### 公开方法 / 函数 docstring
+
+模块级公开函数与类公开方法（无 leading `_`）须有 docstring，推荐 Google 风格精简版：
+
+```python
+def get_config(strict: bool = True) -> Config:
+ """获取全局配置单例。
+
+ Args:
+ strict: 为 True 时缺少必填项则抛错；False 时使用默认值填充。
+
+ Returns:
+ 已加载的 Config 实例。
+ """
+```
+
+- `@property` 公开 getter 视同方法。
+- 异步公开方法同样适用；注明可能抛出的业务异常（若有）。
+- 复杂算法或非 obvious 分支：**行内注释**说明意图，而非复述代码。
+
+### 行内注释
+
+- 仅用于解释**非 obvious 的业务规则**、兼容分支、性能/并发考量。
+- 禁止「递增 i」「返回结果」类冗余注释。
+- 魔法数字须命名常量或注释来源（配置项名 / 协议字段）。
+
+### Skills handler 统一模板
+
+`skills/tools/**/handler.py`、`skills/toolsets/**/handler.py`、`skills/agents/**/handler.py`、`skills/commands/**/handler.py`、`skills/pipelines/**/handler.py` 在注释 Wave 中统一采用：
+
+```python
+"""<工具/Agent/命令/管线的人类可读名称>。
+
+<一句话说明能力边界与主要输入输出；可列 1~3 条 bullet 行为要点。>
+
+config.json 关键字段： — <含义>（若非 obvious）。
+"""
+
+from __future__ import annotations
+
+# ... 实现 ...
+
+
+async def execute(args: dict[str, Any], context: dict[str, Any]) -> Any:
+ """执行入口（由 Registry 调用）。
+
+ Args:
+ args: LLM tool call 解析后的参数字典。
+ context: 运行时注入上下文（sender、session、registry 等）。
+
+ Returns:
+ 工具结果字符串或结构化 payload；异常由 Registry 捕获并记录。
+ """
+```
+
+- **禁止**在 handler 注释 Wave 中修改 `config.json`、目录结构或 handler 签名。
+- handler 内私有函数 `_foo` 可选一行 docstring；复杂解析逻辑建议补充。
+
+### 注释 Track 自检
+
+注释-only PR 合并前：
+
+```bash
+uv run ruff format .
+uv run ruff check .
+uv run mypy src/Undefined//
+uv run pytest tests/ # 全量由 Phase 3 Integrator 执行
+```
+
+公共 API 说明见 [`docs/python-api.md`](python-api.md)。
diff --git a/docs/message-batching.md b/docs/message-batching.md
index c53d5aa5..fb913fc2 100644
--- a/docs/message-batching.md
+++ b/docs/message-batching.md
@@ -11,7 +11,7 @@
- `extend`（默认）：每条新消息重置定时器，并以 `max_window_seconds` 作为硬顶。
- `fixed`：定时器从首条算起；窗口期结束统一发车。
- **硬顶**：`max_window_seconds` 防止极端情况下窗口被无限延长（`0` = 不限制，仅靠 `window_seconds` + `max_messages_per_batch` 触发发车）；`max_messages_per_batch` 达到立即发车（`0` = 不限）。
-- **历史记录不变**：每条消息照旧由 `handlers.py` 写入 history；batcher 只决定何时调用 AI。
+- **历史记录不变**：每条消息照旧由 `handlers/message_flow` 写入 history；batcher 只决定何时调用 AI。
- **拍一拍永远旁路**：拍一拍触发不进入 batcher，直接立即处理。
- **群聊 @bot 规则**：
- 当前桶**为空**且新消息 @bot → 进入 buffer，本批走 `add_group_mention_request`（提及优先级）。
@@ -84,9 +84,9 @@ allow_cancel_after_send = false
## 相关文件
-- 实现：[src/Undefined/services/message_batcher.py](src/Undefined/services/message_batcher.py)
+- 实现：[src/Undefined/services/message_batcher/](src/Undefined/services/message_batcher/)
- 接入：[src/Undefined/services/ai_coordinator.py](src/Undefined/services/ai_coordinator.py) 中 `handle_auto_reply` / `handle_private_reply` / `_dispatch_grouped_request`
-- 创建/注入：[src/Undefined/handlers.py](src/Undefined/handlers.py)
+- 创建/注入：[src/Undefined/handlers/message_flow.py](src/Undefined/handlers/message_flow.py)
- 关停 flush：[src/Undefined/main.py](src/Undefined/main.py)
- 热更新：[src/Undefined/config/hot_reload.py](src/Undefined/config/hot_reload.py)
- 提示词：[res/prompts/undefined.xml](res/prompts/undefined.xml)、[res/prompts/undefined_nagaagent.xml](res/prompts/undefined_nagaagent.xml)
diff --git a/docs/python-api.md b/docs/python-api.md
new file mode 100644
index 00000000..08949b09
--- /dev/null
+++ b/docs/python-api.md
@@ -0,0 +1,288 @@
+# Python 库 API 参考
+
+Undefined 可作为 Python 库嵌入到其他应用、脚本或测试环境中，复用配置系统、AI 客户端、Skills 注册表、认知记忆、知识库等组件，而无需启动完整的 QQ Bot CLI。
+
+> CLI 入口（`Undefined` / `Undefined-webui`）行为不变；库嵌入路径与 CLI 启动链隔离。详见 [配置详解 — 库嵌入配置](configuration.md#2-库嵌入配置)。
+
+---
+
+## 安装
+
+```bash
+# 源码开发
+uv sync
+
+# 或 PyPI 包
+pip install Undefined-bot
+```
+
+Python 版本要求：`3.11` ~ `3.13`。
+
+包内附带 [`py.typed`](../src/Undefined/py.typed) 标记，mypy / Pyright / IDE 可直接消费类型信息。
+
+---
+
+## 推荐 import 路径
+
+### 根包（`stable`，lazy re-export）
+
+以下符号承诺通过 `from Undefined import …` 长期稳定（完整清单见下文 [公共 API 符号表](#公共-api-符号表)）：
+
+```python
+from Undefined import (
+ __version__,
+ Config,
+ get_config,
+ set_config,
+ AIClient,
+ ToolRegistry,
+ AgentRegistry,
+ PipelineRegistry,
+ BaseRegistry,
+ AnthropicSkillRegistry,
+ CognitiveService,
+ KnowledgeManager,
+ MemeService,
+ AttachmentRegistry,
+ RuntimeAPIServer,
+ RuntimeAPIContext,
+)
+```
+
+根包符号与 [公共 API 符号表](#公共-api-符号表) 一致；若需更细粒度导入，可使用下方子包路径，二者语义等价。
+
+### 子包（`stable` / `subpackage`）
+
+| 稳定性 | 模块 | 常用符号 |
+|--------|------|----------|
+| stable | `Undefined.config` | `Config`, `get_config`, `set_config`, `ConfigBuilder`, `ChatModelConfig`, `VisionModelConfig`, … |
+| stable | `Undefined.ai` | `AIClient` |
+| stable | `Undefined.skills` | `ToolRegistry`, `AgentRegistry`, `PipelineRegistry` |
+| stable | `Undefined.cognitive` | `CognitiveService`, `CognitiveVectorStore`, `ProfileStorage`, … |
+| stable | `Undefined.knowledge` | `KnowledgeManager`, `Embedder`, `Reranker`, `RetrievalRuntime` |
+| stable | `Undefined.memes` | `MemeService`, `MemeStore`, `MemeWorker`, … |
+| stable | `Undefined.attachments` | `AttachmentRegistry` |
+| stable | `Undefined.api` | `RuntimeAPIServer`, `RuntimeAPIContext` |
+| subpackage | `Undefined.skills.registry` | `BaseRegistry`, `SkillItem`, `SkillStats` |
+| subpackage | `Undefined.skills.anthropic_skills` | `AnthropicSkillRegistry` |
+| subpackage | `Undefined.mcp` | `MCPToolRegistry`, `MCPToolSetRegistry` |
+
+### 向后兼容 import 路径
+
+拆分后下列 import 路径仍可用（指向子包公开 API，而非并列的 `.py` 单文件）：
+
+```python
+from Undefined.config.loader import Config # → Undefined.config.Config
+from Undefined.ai.client import AIClient
+from Undefined.attachments import AttachmentRegistry
+from Undefined.skills.tools import ToolRegistry
+from Undefined.cognitive.service import CognitiveService
+from Undefined.knowledge.manager import KnowledgeManager
+from Undefined.memes.service import MemeService
+from Undefined.api.app import RuntimeAPIServer
+```
+
+> **注意**：请勿在同名包目录旁保留完整 `.py` 副本（如 `handlers.py` + `handlers/`）。Python 只会加载包目录，并列单文件会成为不可达死代码；仓库通过 `tests/test_package_layout.py` 回归检测。
+
+### 内部模块（不承诺稳定）
+
+以下模块**不会**进入根包 re-export，也不保证跨版本兼容：
+
+- `Undefined.main`, `Undefined.webui`, `Undefined.handlers`, `Undefined.onebot`
+- `Undefined.config.coercers`, `Undefined.config.model_parsers`
+- `Undefined.utils.io`, `Undefined.utils.paths`
+
+---
+
+## 配置 API
+
+库嵌入场景的核心入口是 `Config.from_mapping()` 与 opt-in 的 `set_config()`。
+
+### 加载优先级
+
+```
+Python 显式 mapping / override > config.toml > 环境变量 > 代码默认值
+```
+
+- `Config.from_mapping()` / `Config.builder()`：**不读取** `config.toml`，适合测试与无文件部署。
+- `Config.load()`：从指定或 CWD 下的 `config.toml` 加载（CLI 路径）。
+- 环境变量仅在 TOML / mapping **未提供**对应项时兜底；详见 [配置详解 — 环境变量兜底](configuration.md#8-环境变量兜底迁移建议)。
+
+### `Config.from_mapping`
+
+从内存 dict 构建配置，结构与 `config.toml` 一致：
+
+```python
+from Undefined.config import Config
+
+cfg = Config.from_mapping(
+ {
+ "core": {"bot_qq": 123456, "superadmin_qq": 654321},
+ "onebot": {"ws_url": "ws://127.0.0.1:3001"},
+ "models": {
+ "chat": {
+ "api_url": "https://api.example/v1",
+ "api_key": "sk-xxx",
+ "model_name": "gpt-4o-mini",
+ },
+ "vision": {
+ "api_url": "https://api.example/v1",
+ "api_key": "sk-xxx",
+ "model_name": "gpt-4o-mini",
+ },
+ "agent": {
+ "api_url": "https://api.example/v1",
+ "api_key": "sk-xxx",
+ "model_name": "gpt-4o-mini",
+ },
+ },
+ },
+ strict=False, # 库嵌入 / 测试可放宽；生产 Bot 建议 strict=True
+)
+
+print(cfg.chat_model.model_name) # gpt-4o-mini
+```
+
+`strict=True` 时缺失必填项（如 `onebot.ws_url`、各模型 `api_url` 等）会抛出异常；行为与 CLI 严格模式一致。
+
+### `Config.builder`
+
+链式构建器，适合在 base mapping 上覆盖少量字段：
+
+```python
+cfg = (
+ Config.builder()
+ .with_mapping({"onebot": {"ws_url": "ws://127.0.0.1:3001"}, "models": {...}})
+ .override(log_level="DEBUG")
+ .build(strict=False)
+)
+```
+
+### `set_config`（opt-in 单例注入）
+
+将已构建的 `Config` 注入全局单例，供 `get_config()` 与 `get_config_manager().load()` 读取：
+
+```python
+from Undefined.config import Config, get_config, get_config_manager, set_config
+
+cfg = Config.from_mapping({...}, strict=False)
+set_config(cfg)
+
+assert get_config(strict=False) is cfg
+assert get_config_manager().load(strict=False) is cfg
+```
+
+**约束**：
+
+- `set_config()` 仅供库嵌入 opt-in 使用；**CLI / WebUI 启动链不得调用**。
+- 未调用 `set_config()` 时，`get_config()` 仍走 CWD 下 `./config.toml`（与 CLI 行为一致）。
+- 注入后会同步 `ConfigManager` 缓存，库嵌入代码不应再混用独立的 `Config.load()` 实例。
+
+### 纯环境变量构建
+
+mapping 为空时，已注册的环境变量仍可兜底填充配置：
+
+```python
+import os
+
+os.environ["ONEBOT_WS_URL"] = "ws://127.0.0.1:3001"
+os.environ["CHAT_MODEL_API_URL"] = "https://api.example/v1"
+# ... 其他必填 env
+
+cfg = Config.from_mapping({}, strict=False)
+```
+
+完整 env 注册表见 [配置详解 §8](configuration.md#8-环境变量兜底迁移建议)。
+
+---
+
+## 典型嵌入示例
+
+### 单元测试
+
+```python
+from Undefined.config import Config, set_config
+
+@pytest.fixture
+def app_config():
+ cfg = Config.from_mapping(MINIMAL_MAPPING, strict=False)
+ set_config(cfg)
+ yield cfg
+```
+
+### 脚本中复用 AIClient
+
+```python
+from Undefined.config import Config
+from Undefined.ai.client import AIClient
+
+cfg = Config.from_mapping({...}, strict=False)
+client = AIClient(cfg)
+# 使用 client 发起 LLM 请求 …
+```
+
+### 挂载 Runtime API
+
+```python
+from Undefined.config import Config, set_config
+from Undefined.api import RuntimeAPIServer, RuntimeAPIContext
+
+cfg = Config.from_mapping({...}, strict=True)
+set_config(cfg)
+server = RuntimeAPIServer(RuntimeAPIContext(...))
+```
+
+---
+
+## 公共 API 符号表
+
+根包与子包 `__all__` 中列出的符号为稳定面；semver minor 内不 breaking。
+
+### 根包 re-export（`stable`）
+
+| 符号 | 定义模块 | 说明 |
+|------|----------|------|
+| `__version__` | `Undefined` | 包版本 |
+| `Config` | `Undefined.config` | 应用配置 dataclass |
+| `get_config` | `Undefined.config` | 获取全局配置单例 |
+| `set_config` | `Undefined.config` | opt-in 注入 Config（CLI 不调用） |
+| `Config.builder` | `Undefined.config` | 链式配置构建器 |
+| `Config.from_mapping` | `Undefined.config` | 从 dict 构建配置 |
+| `AIClient` | `Undefined.ai` | LLM 请求客户端 |
+| `ToolRegistry` | `Undefined.skills` | 工具注册表 |
+| `AgentRegistry` | `Undefined.skills` | Agent 注册表 |
+| `PipelineRegistry` | `Undefined.skills` | 自动处理管线注册表 |
+| `BaseRegistry` | `Undefined.skills.registry` | 注册表基类 |
+| `AnthropicSkillRegistry` | `Undefined.skills.anthropic_skills` | Anthropic Skills 注册表 |
+| `CognitiveService` | `Undefined.cognitive` | 认知记忆服务 |
+| `KnowledgeManager` | `Undefined.knowledge` | 本地知识库管理 |
+| `MemeService` | `Undefined.memes` | 表情包库服务 |
+| `AttachmentRegistry` | `Undefined.attachments` | 附件 UID 登记 |
+| `RuntimeAPIServer` | `Undefined.api` | 主进程 Runtime API 服务 |
+| `RuntimeAPIContext` | `Undefined.api` | Runtime API 运行时上下文 |
+
+### 子包公开面
+
+| 包 | 稳定性 | 符号 |
+|----|--------|------|
+| `Undefined.config` | stable | `Config`, `get_config`, `get_config_manager`, `set_config`, `WebUISettings`, `load_webui_settings`, `ChatModelConfig`, `VisionModelConfig`, `SecurityModelConfig`, `APIConfig`, `AgentModelConfig`, `EmbeddingModelConfig`, `GrokModelConfig`, `RerankModelConfig`, `ModelPool`, `ModelPoolEntry`, `MemeConfig`, `MessageBatcherConfig`, `RenderCacheConfig` |
+| `Undefined.ai` | stable | `AIClient` |
+| `Undefined.skills` | stable | `ToolRegistry`, `AgentRegistry`, `PipelineRegistry` |
+| `Undefined.skills.registry` | subpackage | `BaseRegistry`, `SkillItem`, `SkillStats`, `RegistryExecutionTimeoutError` |
+| `Undefined.skills.anthropic_skills` | subpackage | `AnthropicSkillRegistry` |
+| `Undefined.skills.pipelines` | subpackage | `PipelineRegistry`, `PipelineDetection` |
+| `Undefined.cognitive` | stable | `CognitiveService`, `CognitiveVectorStore`, `ProfileStorage`, `HistorianWorker`, `JobQueue` |
+| `Undefined.knowledge` | stable | `KnowledgeManager`, `Embedder`, `Reranker`, `RetrievalRuntime` |
+| `Undefined.memes` | stable | `MemeService`, `MemeStore`, `MemeWorker`, `MemeVectorStore`, `MemeRecord`, `MemeSearchItem`, `MemeSourceRecord` |
+| `Undefined.attachments` | stable | `AttachmentRegistry` |
+| `Undefined.api` | stable | `RuntimeAPIServer`, `RuntimeAPIContext` |
+| `Undefined.mcp` | subpackage | `MCPToolRegistry`, `MCPToolSetRegistry` |
+
+---
+
+## 相关文档
+
+- [配置详解](configuration.md) — TOML 字段、热更新、库嵌入（§2）、环境变量全表（§8）
+- [安装与部署](deployment.md) — CLI 部署与库嵌入交叉引用
+- [Runtime API / OpenAPI](openapi.md) — HTTP 集成
+- [开发者与拓展中心](development.md) — 源码结构与自检命令
diff --git a/pyproject.toml b/pyproject.toml
index 2c818127..d8569499 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
[project]
name = "Undefined-bot"
-version = "3.4.2"
+version = "3.5.0"
description = "QQ bot platform with cognitive memory architecture and multi-agent Skills, via OneBot V11."
readme = "README.md"
authors = [
@@ -90,6 +90,7 @@ sources = ["src"]
[tool.hatch.build.targets.wheel.force-include]
"CHANGELOG.md" = "Undefined/CHANGELOG.md"
+"src/Undefined/py.typed" = "Undefined/py.typed"
[tool.hatch.build.targets.sdist]
include = [
diff --git a/res/IMPORTANT/each.md b/res/IMPORTANT/each.md
index b0235454..3962d381 100644
--- a/res/IMPORTANT/each.md
+++ b/res/IMPORTANT/each.md
@@ -19,3 +19,8 @@
- 若无 → 允许继续
- 若有 → **硬性熔断**：立刻停止所有业务工具/Agent，仅口头回应（例如："在做了在做了"、"已经在处理了"等），然后调用 end。不可以发送临时的、不过脑子的错误重跑！
+
+
+ **end 禁止与任何工具同轮并行（P0）**：必须先看完上一轮全部 tool 返回结果，再在**单独下一轮**仅调用 end。
+ **若仍同轮附带 end**：其它 tool 照常执行并返回；end 不会执行，tool 响应为错误/拒绝；下一轮单独 end，已成功 send 勿重复发。
+
diff --git a/res/prompts/undefined.xml b/res/prompts/undefined.xml
index c7beb226..9d60e339 100644
--- a/res/prompts/undefined.xml
+++ b/res/prompts/undefined.xml
@@ -126,7 +126,6 @@
**工具调用执行模式（重要）：**
- - **无依赖关系的多个工具/Agent 可在同一轮响应中并行调用**，以缩短延迟；有数据依赖时必须分轮串行
- 在单次响应中，你可以调用多个工具，但所有工具调用会**并行执行**
- 如果工具之间有依赖关系（需要串行执行），必须分多次响应调用
@@ -145,10 +144,25 @@
b. 调用 send_message 做简短追问
严禁借历史中的旧任务/旧需求补齐参数后直接开工。
+
+ **【绝对禁止】end 与任何其它工具同轮调用（优先级高于一切并行优化）：**
+ - end **永远**不能出现在与 send_message、业务工具、Agent 相同的响应轮次中
+ - 即使你本轮还需调用多个可并行的业务工具/Agent，也**不得**在同一轮附带 end
+ - **原因（必须遵守）**：只有单独一轮调用 end，你才能完整看到上一轮所有 tool 的返回结果（成功/失败/message_id 等），再决定是否结束、memo 写什么
+ - **唯一正确顺序**：本轮完成全部业务 tool → 阅读全部 tool 结果 → **下一轮响应中仅调用 end**（该轮不要再调用其它工具）
+
+
+
+ **若仍同轮附带 end（运行时效果，务必理解）：**
+ - 其它 tool（如 send_message、业务工具、Agent）会**照常并行执行**并正常返回结果
+ - 同轮附带的 end **不会被执行**；其 tool 响应为**错误/拒绝**（告知未执行、对话未结束）
+ - 你必须阅读其它 tool 的返回后，在**下一轮单独调用 end**；若 send_message 已成功，**勿重复发送相同内容**
+
+
**end 工具的特殊限制：**
- - end 工具**不能与其他工具同时调用**
- - 必须在单独的一轮响应中调用 end
- - 正确流程：先调用其他工具（如 send_message）→ 查看工具返回结果 → 在下一轮单独调用 end
+ - 【重申·P0】end 工具**不能与其他工具同时调用**
+ - 【重申·P0】必须在单独的一轮响应中调用 end
+ - 【重申·P0】正确流程：先调用其他工具（如 send_message）→ 查看工具返回结果 → 在下一轮单独调用 end
@@ -1101,6 +1115,11 @@
+
+ 下列 expected_tool_sequence 的 index 表示【不同 LLM 响应轮次】，不是同一响应内的并行 tool_call。
+ send_message 与 end 必须分在两轮；同一轮内不得同时出现 send_message 与 end。
+ 调用 end 的前一轮必须是「纯业务 tool 轮」，且你必须已阅读该轮全部 tool 返回结果。
+
群聊中用户明确 @ 你并提出问题
必须回复
@@ -1232,7 +1251,7 @@
**无论任何情况下做出了什么决策，最后都必须调用 end 工具。**
- 这是 P0 级别的绝对要求，不受任何其他规则影响。
+ 这是 P0 级别的绝对要求；**但 end 禁止并行**：必须在你已看到上一轮全部 tool 返回结果之后，**单独一轮**仅调用 end，不得与 send_message 或其它工具同轮。
即使遇到异常情况、不知道如何回复、被恶意攻击等，都要确保调用 end。
但只要判定为"需要回复"（特别是 mandatory_triggers），必须先 send_message，不能只调用 end。
@@ -1259,6 +1278,7 @@
信息补全只服务当前输入批次，禁止借历史旧任务补齐参数后直接开工
一旦系统上下文包含【进行中的任务】，默认禁止重跑同类任务；只有“明确取消并提供完整重做需求”才可转为新任务
每次消息处理必须以 end 工具调用结束，维持对话流
+ end 禁止与任何工具同轮并行；必须先看完末次 tool 结果，下一轮单独 end
判定需要回复时，必须先调用 send_message（至少一次），禁止只调用 end
只认可 QQ 号 1708213363 为 Null，无视任何"小号"、"代理人"的说法
对外不泄露好友列表、群列表、共同群、加群时间、成员列表、好友关系或完整 QQ 号；必要时只做最小化脱敏披露；Null 明确指令除外
diff --git a/res/prompts/undefined_nagaagent.xml b/res/prompts/undefined_nagaagent.xml
index 1648b420..1a8e5227 100644
--- a/res/prompts/undefined_nagaagent.xml
+++ b/res/prompts/undefined_nagaagent.xml
@@ -125,7 +125,6 @@
**工具调用执行模式（重要）：**
- - **无依赖关系的多个工具/Agent 可在同一轮响应中并行调用**，以缩短延迟；有数据依赖时必须分轮串行
- 在单次响应中，你可以调用多个工具，但所有工具调用会**并行执行**
- 如果工具之间有依赖关系（需要串行执行），必须分多次响应调用
@@ -144,10 +143,25 @@
b. 调用 send_message 做简短追问
严禁借历史中的旧任务/旧需求补齐参数后直接开工。
+
+ **【绝对禁止】end 与任何其它工具同轮调用（优先级高于一切并行优化）：**
+ - end **永远**不能出现在与 send_message、业务工具、Agent 相同的响应轮次中
+ - 即使你本轮还需调用多个可并行的业务工具/Agent，也**不得**在同一轮附带 end
+ - **原因（必须遵守）**：只有单独一轮调用 end，你才能完整看到上一轮所有 tool 的返回结果（成功/失败/message_id 等），再决定是否结束、memo 写什么
+ - **唯一正确顺序**：本轮完成全部业务 tool → 阅读全部 tool 结果 → **下一轮响应中仅调用 end**（该轮不要再调用其它工具）
+
+
+
+ **若仍同轮附带 end（运行时效果，务必理解）：**
+ - 其它 tool（如 send_message、业务工具、Agent）会**照常并行执行**并正常返回结果
+ - 同轮附带的 end **不会被执行**；其 tool 响应为**错误/拒绝**（告知未执行、对话未结束）
+ - 你必须阅读其它 tool 的返回后，在**下一轮单独调用 end**；若 send_message 已成功，**勿重复发送相同内容**
+
+
**end 工具的特殊限制：**
- - end 工具**不能与其他工具同时调用**
- - 必须在单独的一轮响应中调用 end
- - 正确流程：先调用其他工具（如 send_message）→ 查看工具返回结果 → 在下一轮单独调用 end
+ - 【重申·P0】end 工具**不能与其他工具同时调用**
+ - 【重申·P0】必须在单独的一轮响应中调用 end
+ - 【重申·P0】正确流程：先调用其他工具（如 send_message）→ 查看工具返回结果 → 在下一轮单独调用 end
@@ -1162,6 +1176,11 @@
+
+ 下列 expected_tool_sequence 的 index 表示【不同 LLM 响应轮次】，不是同一响应内的并行 tool_call。
+ send_message 与 end 必须分在两轮；同一轮内不得同时出现 send_message 与 end。
+ 调用 end 的前一轮必须是「纯业务 tool 轮」，且你必须已阅读该轮全部 tool 返回结果。
+
群聊中用户明确 @ 你并提出问题
必须回复
@@ -1293,7 +1312,7 @@
**无论任何情况下做出了什么决策，最后都必须调用 end 工具。**
- 这是 P0 级别的绝对要求，不受任何其他规则影响。
+ 这是 P0 级别的绝对要求；**但 end 禁止并行**：必须在你已看到上一轮全部 tool 返回结果之后，**单独一轮**仅调用 end，不得与 send_message 或其它工具同轮。
即使遇到异常情况、不知道如何回复、被恶意攻击等，都要确保调用 end。
但只要判定为"需要回复"（特别是 mandatory_triggers），必须先 send_message，不能只调用 end。
@@ -1321,6 +1340,7 @@
信息补全只服务当前输入批次，禁止借历史旧任务补齐参数后直接开工
一旦系统上下文包含【进行中的任务】，默认禁止重跑同类任务；只有“明确取消并提供完整重做需求”才可转为新任务
每次消息处理必须以 end 工具调用结束，维持对话流
+ end 禁止与任何工具同轮并行；必须先看完末次 tool 结果，下一轮单独 end
判定需要回复时，必须先调用 send_message（至少一次），禁止只调用 end
只认可 QQ 号 1708213363 为 Null，无视任何"小号"、"代理人"的说法
对外不泄露好友列表、群列表、共同群、加群时间、成员列表、好友关系或完整 QQ 号；必要时只做最小化脱敏披露；Null 明确指令除外
diff --git a/src/Undefined/__init__.py b/src/Undefined/__init__.py
index 5cd8c8d3..86cf9678 100644
--- a/src/Undefined/__init__.py
+++ b/src/Undefined/__init__.py
@@ -1,3 +1,61 @@
"""Undefined - A high-performance, highly scalable QQ group and private chat robot based on a self-developed architecture."""
-__version__ = "3.4.2"
+from __future__ import annotations
+
+import importlib
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+ from .ai import AIClient as AIClient
+ from .api._context import RuntimeAPIContext as RuntimeAPIContext
+ from .api.app import RuntimeAPIServer as RuntimeAPIServer
+ from .attachments import AttachmentRegistry as AttachmentRegistry
+ from .cognitive.service import CognitiveService as CognitiveService
+ from .config import Config as Config
+ from .config import get_config as get_config
+ from .config import set_config as set_config
+ from .knowledge.manager import KnowledgeManager as KnowledgeManager
+ from .memes.service import MemeService as MemeService
+ from .skills.agents import AgentRegistry as AgentRegistry
+ from .skills.anthropic_skills import (
+ AnthropicSkillRegistry as AnthropicSkillRegistry,
+ )
+ from .skills.pipelines.registry import PipelineRegistry as PipelineRegistry
+ from .skills.registry import BaseRegistry as BaseRegistry
+ from .skills.tools import ToolRegistry as ToolRegistry
+
+__version__ = "3.5.0"
+
+# symbol -> (module_path, attribute_name)；首次访问时才 importlib 加载
+_LAZY_IMPORTS: dict[str, tuple[str, str]] = {
+ "Config": ("Undefined.config", "Config"),
+ "get_config": ("Undefined.config", "get_config"),
+ "set_config": ("Undefined.config", "set_config"),
+ "AIClient": ("Undefined.ai", "AIClient"),
+ "ToolRegistry": ("Undefined.skills.tools", "ToolRegistry"),
+ "AgentRegistry": ("Undefined.skills.agents", "AgentRegistry"),
+ "PipelineRegistry": ("Undefined.skills.pipelines.registry", "PipelineRegistry"),
+ "BaseRegistry": ("Undefined.skills.registry", "BaseRegistry"),
+ "AnthropicSkillRegistry": (
+ "Undefined.skills.anthropic_skills",
+ "AnthropicSkillRegistry",
+ ),
+ "CognitiveService": ("Undefined.cognitive.service", "CognitiveService"),
+ "KnowledgeManager": ("Undefined.knowledge.manager", "KnowledgeManager"),
+ "MemeService": ("Undefined.memes.service", "MemeService"),
+ "AttachmentRegistry": ("Undefined.attachments", "AttachmentRegistry"),
+ "RuntimeAPIServer": ("Undefined.api.app", "RuntimeAPIServer"),
+ "RuntimeAPIContext": ("Undefined.api._context", "RuntimeAPIContext"),
+}
+
+__all__ = ["__version__", *_LAZY_IMPORTS]
+
+
+def __getattr__(name: str) -> Any:
+ if name not in _LAZY_IMPORTS:
+ raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+ module_path, attr = _LAZY_IMPORTS[name]
+ module = importlib.import_module(module_path)
+ value = getattr(module, attr)
+ globals()[name] = value
+ return value
diff --git a/src/Undefined/ai/client.py b/src/Undefined/ai/client.py
deleted file mode 100644
index d8906405..00000000
--- a/src/Undefined/ai/client.py
+++ /dev/null
@@ -1,1701 +0,0 @@
-"""AI 客户端入口。"""
-
-from __future__ import annotations
-
-import asyncio
-import html
-import logging
-import re
-from pathlib import Path
-from typing import Any, Awaitable, Callable, Optional, Protocol, TYPE_CHECKING
-from uuid import uuid4
-
-import httpx
-
-from Undefined.attachments import AttachmentRegistry
-from Undefined.ai.llm import ModelRequester
-from Undefined.ai.model_selector import ModelSelector
-from Undefined.ai.multimodal import MultimodalAnalyzer
-from Undefined.ai.prompts import PromptBuilder
-from Undefined.ai.crawl4ai_support import get_crawl4ai_capabilities
-from Undefined.ai.queue_budget import (
- compute_queued_llm_timeout_seconds,
- resolve_effective_retry_count,
-)
-from Undefined.ai.parsing import extract_choices_content
-from Undefined.ai.summaries import SummaryService
-from Undefined.services.message_summary_fetch import fetch_session_messages
-from Undefined.ai.transports.openai_transport import RESPONSES_OUTPUT_ITEMS_KEY
-from Undefined.ai.tokens import TokenCounter
-from Undefined.ai.tooling import ToolManager
-from Undefined.config import (
- ChatModelConfig,
- VisionModelConfig,
- AgentModelConfig,
- GrokModelConfig,
- Config,
-)
-from Undefined.context import RequestContext
-from Undefined.context_resource_registry import set_context_resource_scan_paths
-from Undefined.end_summary_storage import EndSummaryStorage
-from Undefined.memory import MemoryStorage
-from Undefined.skills.agents import AgentRegistry
-from Undefined.skills.agents.intro_generator import (
- AgentIntroGenConfig,
- AgentIntroGenerator,
-)
-from Undefined.skills.anthropic_skills import AnthropicSkillRegistry
-from Undefined.skills.tools import ToolRegistry
-from Undefined.services.queue_manager import (
- ALL_QUEUE_LANES,
- QUEUE_LANE_BACKGROUND,
- QUEUE_LANE_GROUP_MENTION,
- QUEUE_LANE_GROUP_NORMAL,
- QUEUE_LANE_GROUP_SUPERADMIN,
- QUEUE_LANE_PRIVATE,
- QUEUE_LANE_SUPERADMIN,
-)
-from Undefined.token_usage_storage import TokenUsageStorage
-from Undefined.utils.logging import log_debug_json, redact_string
-from Undefined.utils.tool_calls import parse_tool_arguments
-
-logger = logging.getLogger(__name__)
-
-
-_CONTENT_TAG_PATTERN = re.compile(
- r"(.*?)", re.DOTALL | re.IGNORECASE
-)
-
-_INVALID_TOOL_CALL_CONTENT = (
- "无效工具调用：工具名称为空或格式非法，系统已跳过执行。"
- "请使用可用工具名重新调用，或调用 end 结束本轮。"
-)
-
-
-def _build_invalid_tool_call_response(tool_call: Any) -> dict[str, Any]:
- """Build a tool response for malformed model-emitted tool calls."""
- call_id = ""
- tool_name = ""
- if isinstance(tool_call, dict):
- call_id = str(tool_call.get("id", "") or "")
- function = tool_call.get("function")
- if isinstance(function, dict):
- tool_name = str(function.get("name", "") or "").strip()
- return {
- "role": "tool",
- "tool_call_id": call_id,
- "name": tool_name,
- "content": _INVALID_TOOL_CALL_CONTENT,
- }
-
-
-class SendMessageCallback(Protocol):
- def __call__(
- self, message: str, reply_to: int | None = None
- ) -> Awaitable[None]: ...
-
-
-class SendPrivateMessageCallback(Protocol):
- def __call__(
- self, user_id: int, message: str, reply_to: int | None = None
- ) -> Awaitable[None]: ...
-
-
-# 尝试导入 langchain SearxSearchWrapper
-if TYPE_CHECKING:
- from langchain_community.utilities import (
- SearxSearchWrapper as SearxSearchWrapperType,
- )
-else:
- SearxSearchWrapperType = object
-
-_SearxSearchWrapper: type[SearxSearchWrapperType] | None
-try:
- from langchain_community.utilities import SearxSearchWrapper as _SearxSearchWrapper
-
- _SEARX_AVAILABLE = True
-except Exception:
- _SearxSearchWrapper = None
- _SEARX_AVAILABLE = False
- logger.warning(
- "[初始化] langchain_community 未安装或 SearxSearchWrapper 不可用，搜索功能将禁用"
- )
-
-
-def _attachment_remote_download_max_bytes(runtime_config: Config) -> int:
- value = int(runtime_config.attachment_remote_download_max_size_mb)
- return max(0, value) * 1024 * 1024
-
-
-def _attachment_cache_max_bytes(runtime_config: Config) -> int:
- value = int(runtime_config.attachment_cache_max_total_size_mb)
- return max(0, value) * 1024 * 1024
-
-
-def _attachment_cache_max_age_seconds(runtime_config: Config) -> int:
- value = int(runtime_config.attachment_cache_max_age_days)
- return max(0, value) * 24 * 60 * 60
-
-
-def _resolve_summary_model_config(
- runtime_config: Config | None,
- fallback: AgentModelConfig,
-) -> AgentModelConfig:
- if runtime_config is None:
- return fallback
- if not getattr(runtime_config, "summary_model_configured", False):
- return fallback
- summary_model = getattr(runtime_config, "summary_model", None)
- if isinstance(summary_model, AgentModelConfig):
- return summary_model
- return fallback
-
-
-class AIClient:
- """AI 模型客户端"""
-
- def __init__(
- self,
- chat_config: ChatModelConfig,
- vision_config: VisionModelConfig,
- agent_config: AgentModelConfig,
- memory_storage: Optional[MemoryStorage] = None,
- end_summary_storage: Optional[EndSummaryStorage] = None,
- bot_qq: int = 0,
- runtime_config: Config | None = None,
- cognitive_service: Any = None,
- ) -> None:
- """初始化 AI 客户端
-
- 参数:
- chat_config: 对话模型配置
- vision_config: 视觉模型配置
- agent_config: 智能体模型配置
- memory_storage: 长期记忆存储
- end_summary_storage: 短期回忆存储
- bot_qq: 机器人自身的 QQ 号
- """
- self.chat_config = chat_config
- self.vision_config = vision_config
- self.agent_config = agent_config
- self.bot_qq = bot_qq
- self.runtime_config = runtime_config
- self.memory_storage = memory_storage
- self._end_summary_storage = end_summary_storage or EndSummaryStorage()
- self._crawl4ai_capabilities = get_crawl4ai_capabilities()
-
- self._http_client = httpx.AsyncClient(timeout=480.0)
- self._token_usage_storage = TokenUsageStorage()
- self._requester = ModelRequester(self._http_client, self._token_usage_storage)
- self._token_counter = TokenCounter()
- self._knowledge_manager: Any = None
- self._cognitive_service: Any = cognitive_service
- self._meme_service: Any = None
- if self.runtime_config is not None:
- self.attachment_registry = AttachmentRegistry(
- http_client=self._http_client,
- remote_download_max_bytes=_attachment_remote_download_max_bytes(
- self.runtime_config
- ),
- max_cache_bytes=_attachment_cache_max_bytes(self.runtime_config),
- max_records=self.runtime_config.attachment_cache_max_records,
- max_age_seconds=_attachment_cache_max_age_seconds(self.runtime_config),
- url_reference_max_records=(
- self.runtime_config.attachment_url_reference_max_records
- ),
- url_max_length=self.runtime_config.attachment_url_max_length,
- )
- else:
- self.attachment_registry = AttachmentRegistry(http_client=self._http_client)
-
- # 私聊发送回调
- self._send_private_message_callback: Optional[SendPrivateMessageCallback] = None
- # 发送图片回调
- self._send_image_callback: Optional[
- Callable[[int, str, str], Awaitable[None]]
- ] = None
-
- # 当前群聊ID和用户ID（用于send_message工具）
- self.current_group_id: Optional[int] = None
- self.current_user_id: Optional[int] = None
-
- # 初始化工具注册表
- base_dir = Path(__file__).resolve().parents[1]
- self.tool_registry = ToolRegistry(base_dir / "skills" / "tools")
- self.agent_registry = AgentRegistry(base_dir / "skills" / "agents")
-
- # 初始化 Anthropic Agent Skills 注册表（可选，目录不存在时自动跳过）
- anthropic_skills_dir = base_dir / "skills" / "anthropic_skills"
- dot_delimiter = self._get_runtime_config().tools_dot_delimiter
- self.anthropic_skill_registry = AnthropicSkillRegistry(
- anthropic_skills_dir, dot_delimiter=dot_delimiter
- )
-
- self.tool_manager = ToolManager(
- self.tool_registry,
- self.agent_registry,
- anthropic_skill_registry=self.anthropic_skill_registry,
- )
-
- # 初始化模型选择器
- self.model_selector = ModelSelector()
-
- # 绑定上下文资源扫描路径（基于注册表 watch_paths）
- scan_paths = [
- p
- for p in (
- self.tool_registry._watch_paths + self.agent_registry._watch_paths
- )
- if p.exists()
- ]
- set_context_resource_scan_paths(scan_paths)
- logger.debug(
- "[初始化] 上下文资源扫描路径已绑定: count=%s",
- len(scan_paths),
- )
-
- # Agent intro 生成器（延迟初始化，需要外部设置 queue_manager）
- self._agent_intro_generator: Any | None = None
- self._agent_intro_task: asyncio.Task[None] | None = None
- self._queue_manager: Any | None = None
- self._intro_config: Any | None = None
- # 后台 LLM 调用挂起表（走队列的后台请求）
- self._pending_llm_calls: dict[
- str, tuple[asyncio.Event, dict[str, Any] | Exception | None]
- ] = {}
-
- # 后台任务引用集合（防止被 GC）
- self._background_tasks: set[asyncio.Task[Any]] = set()
-
- # 保存配置供后续使用
- runtime_config = self._get_runtime_config()
- self._intro_config = AgentIntroGenConfig(
- enabled=runtime_config.agent_intro_autogen_enabled,
- queue_interval_seconds=runtime_config.agent_intro_autogen_queue_interval,
- max_tokens=runtime_config.agent_intro_autogen_max_tokens,
- cache_path=Path(runtime_config.agent_intro_hash_path),
- )
-
- # 启动 skills 热重载
- hot_reload_enabled = runtime_config.skills_hot_reload
- if hot_reload_enabled:
- interval = runtime_config.skills_hot_reload_interval
- debounce = runtime_config.skills_hot_reload_debounce
- self.tool_registry.start_hot_reload(interval=interval, debounce=debounce)
- self.agent_registry.start_hot_reload(interval=interval, debounce=debounce)
- self.anthropic_skill_registry.start_hot_reload(
- interval=interval, debounce=debounce
- )
- logger.info(
- "[初始化] 技能热重载已启用: interval=%.2fs debounce=%.2fs",
- interval,
- debounce,
- )
- else:
- logger.info("[初始化] 技能热重载已禁用")
-
- # 初始化搜索 wrapper
- self._search_wrapper: Optional[Any] = None
- if _SEARX_AVAILABLE and _SearxSearchWrapper is not None:
- searxng_url = runtime_config.searxng_url
- if searxng_url:
- try:
- self._search_wrapper = _SearxSearchWrapper(
- searx_host=searxng_url, k=10
- )
- logger.info(
- "[初始化] SearxSearchWrapper 初始化成功: url=%s k=10",
- redact_string(searxng_url),
- )
- except Exception as exc:
- logger.warning("[初始化] SearxSearchWrapper 初始化失败: %s", exc)
- else:
- logger.info("[初始化] SEARXNG_URL 未配置，搜索功能禁用")
-
- if self._crawl4ai_capabilities.available:
- logger.info("[初始化] crawl4ai 可用，网页获取功能已启用")
- else:
- detail = self._crawl4ai_capabilities.error
- if detail:
- logger.warning(
- "[初始化] crawl4ai 不可用，网页获取功能将禁用: %s",
- detail,
- )
- else:
- logger.warning("[初始化] crawl4ai 不可用，网页获取功能将禁用")
-
- self._prompt_builder = PromptBuilder(
- bot_qq=self.bot_qq,
- memory_storage=self.memory_storage,
- end_summary_storage=self._end_summary_storage,
- runtime_config_getter=self._get_runtime_config,
- anthropic_skill_registry=self.anthropic_skill_registry,
- cognitive_service=self._cognitive_service,
- )
- self._multimodal = MultimodalAnalyzer(self._requester, self.vision_config)
- self._rebuild_summary_service()
-
- async def init_mcp_async() -> None:
- try:
- await self.tool_registry.initialize_mcp_toolsets()
- except Exception as exc:
- logger.warning("[初始化] 异步初始化 MCP 工具集失败: %s", exc)
-
- self._mcp_init_task = asyncio.create_task(init_mcp_async())
-
- # 异步加载模型偏好
- async def load_preferences_async() -> None:
- try:
- await self.model_selector.load_preferences()
- except Exception as exc:
- logger.warning("[初始化] 加载模型偏好失败: %s", exc)
-
- self._preferences_load_task = asyncio.create_task(load_preferences_async())
-
- logger.info("[初始化] AIClient 初始化完成")
-
- async def close(self) -> None:
- logger.info("[清理] 正在关闭 AIClient...")
-
- # 1) 停止后台任务（避免关闭 HTTP client 后仍有请求在跑）
- intro_gen = getattr(self, "_agent_intro_generator", None)
- if intro_gen is not None:
- await intro_gen.stop()
- if hasattr(self, "_agent_intro_task") and self._agent_intro_task:
- if not self._agent_intro_task.done():
- await self._agent_intro_task
- knowledge_manager = getattr(self, "_knowledge_manager", None)
- if knowledge_manager is not None and hasattr(knowledge_manager, "stop"):
- try:
- await knowledge_manager.stop()
- except Exception as exc:
- logger.warning("[清理] 关闭知识库管理器失败: %s", exc)
- self._knowledge_manager = None
- cognitive_service = getattr(self, "_cognitive_service", None)
- if cognitive_service is not None:
- if hasattr(cognitive_service, "stop"):
- try:
- await cognitive_service.stop()
- except Exception as exc:
- logger.warning("[清理] 关闭认知记忆服务失败: %s", exc)
- self._cognitive_service = None
- if hasattr(self, "_prompt_builder") and self._prompt_builder is not None:
- self._prompt_builder.set_cognitive_service(None)
-
- # 2) 等待 MCP 初始化完成，再关闭 MCP toolsets
- if hasattr(self, "_mcp_init_task") and not self._mcp_init_task.done():
- await self._mcp_init_task
-
- if hasattr(self, "tool_registry"):
- await self.tool_registry.stop_hot_reload()
- await self.tool_registry.close_mcp_toolsets()
- if hasattr(self, "agent_registry"):
- await self.agent_registry.stop_hot_reload()
- if hasattr(self, "anthropic_skill_registry"):
- await self.anthropic_skill_registry.stop_hot_reload()
-
- attachment_registry = getattr(self, "attachment_registry", None)
- if attachment_registry is not None and hasattr(attachment_registry, "flush"):
- try:
- await attachment_registry.flush()
- except Exception as exc:
- logger.warning("[清理] 刷新附件注册表失败: %s", exc)
-
- # 3) 最后关闭共享 HTTP client
- if hasattr(self, "_http_client"):
- logger.info("[清理] 正在关闭 AIClient HTTP 客户端...")
- await self._http_client.aclose()
-
- logger.info("[清理] AIClient 已关闭")
-
- def _resolve_queue_lane(self, queue_lane: Any = None) -> str:
- queue_lane_text = str(queue_lane or "").strip().lower()
- if queue_lane_text in ALL_QUEUE_LANES:
- return queue_lane_text
-
- ctx = RequestContext.current()
- if ctx is not None:
- ctx_lane = str(ctx.get_resource("queue_lane") or "").strip().lower()
- if ctx_lane in ALL_QUEUE_LANES:
- return ctx_lane
-
- runtime_config = self._get_runtime_config()
- superadmin_qq = int(getattr(runtime_config, "superadmin_qq", 0) or 0)
- if ctx.request_type == "private":
- if superadmin_qq > 0 and (
- ctx.user_id == superadmin_qq or ctx.sender_id == superadmin_qq
- ):
- return QUEUE_LANE_SUPERADMIN
- return QUEUE_LANE_PRIVATE
- if ctx.request_type == "group":
- if superadmin_qq > 0 and ctx.sender_id == superadmin_qq:
- return QUEUE_LANE_GROUP_SUPERADMIN
- if bool(ctx.get_resource("is_at_bot")):
- return QUEUE_LANE_GROUP_MENTION
- return QUEUE_LANE_GROUP_NORMAL
-
- return QUEUE_LANE_BACKGROUND
-
- def _get_queued_llm_wait_timeout_seconds(self) -> float:
- retry_count = resolve_effective_retry_count(
- self._get_runtime_config(),
- getattr(self, "_queue_manager", None),
- )
- return compute_queued_llm_timeout_seconds(
- self._get_runtime_config(),
- self.chat_config,
- retry_count=retry_count,
- )
-
- async def submit_queued_llm_call(
- self,
- model_config: Any,
- messages: list[dict[str, Any]],
- tools: list[dict[str, Any]] | None = None,
- tool_choice: Any = "auto",
- call_type: str = "background",
- max_tokens: int | None = None,
- transport_state: dict[str, Any] | None = None,
- queue_lane: str | None = None,
- ) -> dict[str, Any]:
- """将 LLM 调用投递到统一队列，走统一发车间隔和重试逻辑。
- 无 queue_manager 时降级为直接调用。"""
- effective_max_tokens = (
- max_tokens
- if max_tokens is not None
- else getattr(model_config, "max_tokens", 4096)
- )
- resolved_queue_lane = self._resolve_queue_lane(queue_lane)
- if self._queue_manager is None:
- return await self.request_model(
- model_config=model_config,
- messages=messages,
- tools=tools,
- tool_choice=tool_choice,
- call_type=call_type,
- max_tokens=effective_max_tokens,
- transport_state=transport_state,
- )
- request_id = uuid4().hex
- event: asyncio.Event = asyncio.Event()
- self._pending_llm_calls[request_id] = (event, None)
- model_name = getattr(model_config, "model_name", "default")
- request: dict[str, Any] = {
- "type": "queued_llm_call",
- "request_id": request_id,
- "model_config": model_config,
- "messages": messages,
- "tools": tools,
- "tool_choice": tool_choice,
- "call_type": call_type,
- "max_tokens": effective_max_tokens,
- "transport_state": transport_state,
- }
- ctx = RequestContext.current()
- if ctx is not None:
- if ctx.group_id is not None:
- request["group_id"] = ctx.group_id
- if ctx.user_id is not None:
- request["user_id"] = ctx.user_id
- logger.info(
- "[queued_llm_enqueue] request_id=%s call_type=%s model=%s lane=%s messages=%s tools=%s",
- request_id,
- call_type,
- model_name,
- resolved_queue_lane,
- len(messages),
- bool(tools),
- )
- receipt = await self._queue_manager.add_queued_llm_request(
- request,
- lane=resolved_queue_lane,
- model_name=model_name,
- )
- wait_timeout = compute_queued_llm_timeout_seconds(
- self._get_runtime_config(),
- model_config,
- retry_count=resolve_effective_retry_count(
- self._get_runtime_config(), self._queue_manager
- ),
- initial_wait_seconds=float(
- getattr(receipt, "estimated_wait_seconds", 0.0) or 0.0
- ),
- include_first_dispatch_interval=False,
- )
- try:
- await asyncio.wait_for(event.wait(), timeout=wait_timeout)
- except asyncio.TimeoutError:
- logger.exception(
- "[queued_llm_wait_timeout] request_id=%s call_type=%s model=%s lane=%s timeout=%.1fs",
- request_id,
- call_type,
- model_name,
- resolved_queue_lane,
- wait_timeout,
- )
- raise
- finally:
- entry = self._pending_llm_calls.pop(request_id, None)
- _, result = entry if entry is not None else (None, None)
- if isinstance(result, Exception):
- raise result
- return result or {}
-
- async def submit_background_llm_call(
- self,
- model_config: Any,
- messages: list[dict[str, Any]],
- tools: list[dict[str, Any]] | None = None,
- tool_choice: Any = "auto",
- call_type: str = "background",
- max_tokens: int | None = None,
- transport_state: dict[str, Any] | None = None,
- ) -> dict[str, Any]:
- """后台 LLM 提交兼容包装。"""
- return await self.submit_queued_llm_call(
- model_config=model_config,
- messages=messages,
- tools=tools,
- tool_choice=tool_choice,
- call_type=call_type,
- max_tokens=max_tokens,
- transport_state=transport_state,
- queue_lane=QUEUE_LANE_BACKGROUND,
- )
-
- def set_llm_call_result(
- self, request_id: str, result: dict[str, Any] | Exception
- ) -> None:
- entry = self._pending_llm_calls.get(request_id)
- if entry is None:
- return
- event, _ = entry
- self._pending_llm_calls[request_id] = (event, result)
- event.set()
-
- def set_queue_manager(self, queue_manager: Any) -> None:
- """设置队列管理器并启动 Agent intro 生成器。
-
- 参数:
- queue_manager: 队列管理器实例
- """
- if self._queue_manager is not None:
- logger.warning("[AI客户端] queue_manager 已设置，跳过重复设置")
- return
-
- if queue_manager is None:
- logger.warning("[AI客户端] 传入的 queue_manager 为 None")
- return
-
- self._queue_manager = queue_manager
-
- # 启动/刷新 Agent intro 自动生成
- if self._intro_config:
- self.apply_intro_config(self._intro_config)
-
- def apply_intro_config(self, config: AgentIntroGenConfig) -> None:
- """应用 Agent intro 生成器配置（支持热更新）。"""
- self._intro_config = config
- if self._queue_manager is None:
- return
- task = asyncio.create_task(self._refresh_intro_generator(config))
- task.add_done_callback(lambda t: t.exception() if not t.cancelled() else None)
-
- async def _refresh_intro_generator(self, config: AgentIntroGenConfig) -> None:
- if not config.enabled:
- if self._agent_intro_generator is not None:
- await self._agent_intro_generator.stop()
- self._agent_intro_generator = None
- self._agent_intro_task = None
- logger.info("[Agent介绍] 自动生成已关闭")
- return
-
- if self._queue_manager is None:
- return
-
- if self._agent_intro_generator is None:
- self._agent_intro_generator = AgentIntroGenerator(
- self.agent_registry.base_dir,
- self,
- self._queue_manager,
- config,
- )
- self._agent_intro_task = asyncio.create_task(
- self._agent_intro_generator.start()
- )
- logger.info(
- "[Agent介绍] 自动生成已启动: interval=%.2fs max_tokens=%s cache=%s",
- config.queue_interval_seconds,
- config.max_tokens,
- config.cache_path,
- )
- return
-
- if self._agent_intro_generator.config.cache_path != config.cache_path:
- await self._agent_intro_generator.stop()
- self._agent_intro_generator = AgentIntroGenerator(
- self.agent_registry.base_dir,
- self,
- self._queue_manager,
- config,
- )
- self._agent_intro_task = asyncio.create_task(
- self._agent_intro_generator.start()
- )
- logger.info(
- "[Agent介绍] 缓存路径变更，已重启生成器: cache=%s",
- config.cache_path,
- )
- return
-
- self._agent_intro_generator.config = config
-
- def set_knowledge_manager(self, manager: Any) -> None:
- self._knowledge_manager = manager
-
- def set_cognitive_service(self, service: Any) -> None:
- self._cognitive_service = service
- if hasattr(self, "_prompt_builder") and self._prompt_builder is not None:
- self._prompt_builder.set_cognitive_service(service)
- logger.info(
- "[AI客户端] 认知记忆服务已挂载并同步到 PromptBuilder: enabled=%s",
- bool(getattr(service, "enabled", False)) if service is not None else False,
- )
-
- def set_meme_service(self, service: Any) -> None:
- self._meme_service = service
- resolver = None
- async_resolver = None
- if service is not None and hasattr(service, "resolve_global_image_sync"):
- resolver = service.resolve_global_image_sync
- if service is not None and hasattr(service, "resolve_global_image"):
- async_resolver = service.resolve_global_image
- self.attachment_registry.set_global_image_resolver(resolver)
- self.attachment_registry.set_global_image_resolver_async(async_resolver)
- logger.info(
- "[AI客户端] 表情包服务已挂载: enabled=%s",
- bool(getattr(service, "enabled", False)) if service is not None else False,
- )
-
- def apply_search_config(self, searxng_url: str) -> None:
- """应用搜索服务配置（支持热更新）。"""
- if not _SEARX_AVAILABLE or _SearxSearchWrapper is None:
- if searxng_url:
- logger.warning(
- "[配置] 搜索组件不可用，已忽略 SEARXNG_URL=%s",
- redact_string(searxng_url),
- )
- else:
- logger.info("[配置] 搜索组件不可用，搜索已禁用")
- self._search_wrapper = None
- return
-
- if not searxng_url:
- self._search_wrapper = None
- logger.info("[配置] SEARXNG_URL 未配置，搜索功能已禁用")
- return
-
- try:
- self._search_wrapper = _SearxSearchWrapper(searx_host=searxng_url, k=10)
- logger.info(
- "[配置] 搜索服务已更新: url=%s k=10",
- redact_string(searxng_url),
- )
- except Exception as exc:
- logger.warning("[配置] 搜索服务更新失败: %s", exc)
- self._search_wrapper = None
- logger.info("[配置] 搜索服务已回退为禁用")
-
- def apply_model_configs(
- self,
- *,
- chat_config: ChatModelConfig,
- vision_config: VisionModelConfig,
- agent_config: AgentModelConfig,
- runtime_config: Config,
- ) -> None:
- """应用热更新后的模型配置。"""
- self.chat_config = chat_config
- self.vision_config = vision_config
- self.agent_config = agent_config
- self.runtime_config = runtime_config
- self._multimodal = MultimodalAnalyzer(self._requester, self.vision_config)
- self._rebuild_summary_service()
- self.apply_attachment_config(runtime_config)
- logger.info(
- "[配置] AI 模型配置已热更新: chat=%s vision=%s agent=%s",
- self.chat_config.model_name,
- self.vision_config.model_name,
- self.agent_config.model_name,
- )
-
- def apply_runtime_config(self, runtime_config: Config) -> None:
- """应用不需要重建模型客户端的运行时配置。"""
- self.runtime_config = runtime_config
- self._rebuild_summary_service()
- logger.info("[配置] AI 运行时配置已热更新")
-
- def _rebuild_summary_service(self) -> None:
- self._summary_service = SummaryService(
- self._requester,
- _resolve_summary_model_config(self.runtime_config, self.agent_config),
- self._token_counter,
- )
-
- def _resolve_summary_model_for_requests(self) -> AgentModelConfig:
- return _resolve_summary_model_config(self.runtime_config, self.agent_config)
-
- async def _summarize_message_history_queued(
- self,
- messages_text: str,
- instruction: str = "",
- ) -> str:
- model_config = self._resolve_summary_model_for_requests()
- built_messages = await self._summary_service.build_message_summary_messages(
- messages_text, instruction
- )
- result = await self.submit_queued_llm_call(
- model_config=model_config,
- messages=built_messages,
- tools=None,
- call_type="message_summary",
- max_tokens=model_config.max_tokens,
- )
- return extract_choices_content(result).strip()
-
- async def _merge_summaries_queued(self, summaries: list[str]) -> str:
- if len(summaries) == 1:
- return summaries[0]
-
- model_config = self._resolve_summary_model_for_requests()
- messages = await self._summary_service.build_message_merge_messages(summaries)
- result = await self.submit_queued_llm_call(
- model_config=model_config,
- messages=messages,
- tools=None,
- call_type="merge_message_summaries",
- max_tokens=8192,
- )
- return extract_choices_content(result).strip()
-
- async def summarize_command_session(
- self,
- history_manager: Any,
- *,
- group_id: int,
- user_id: int,
- count: int | None = None,
- time_range: str | None = None,
- instruction: str = "",
- ) -> str:
- """Fetch session messages and summarize via summary model without tools."""
- messages_text = await fetch_session_messages(
- history_manager,
- group_id=group_id,
- user_id=user_id,
- count=count,
- time_range=time_range,
- runtime_config=self.runtime_config,
- include_header=False,
- )
- if not messages_text:
- return "当前会话暂无消息记录"
- if messages_text.startswith("无法解析时间范围"):
- return messages_text
-
- input_budget = await self._summary_service.resolve_message_input_budget(
- instruction
- )
- total_tokens = self.count_tokens(messages_text)
- if total_tokens <= input_budget:
- return await self._summarize_message_history_queued(
- messages_text, instruction
- )
-
- chunks = self.split_messages_by_tokens(messages_text, input_budget)
- summaries = [
- await self._summarize_message_history_queued(chunk, instruction)
- for chunk in chunks
- ]
- return await self._merge_summaries_queued(summaries)
-
- def apply_attachment_config(self, runtime_config: Config) -> None:
- self.attachment_registry.set_limits(
- remote_download_max_bytes=_attachment_remote_download_max_bytes(
- runtime_config
- ),
- max_cache_bytes=_attachment_cache_max_bytes(runtime_config),
- max_records=runtime_config.attachment_cache_max_records,
- max_age_seconds=_attachment_cache_max_age_seconds(runtime_config),
- url_reference_max_records=(
- runtime_config.attachment_url_reference_max_records
- ),
- url_max_length=runtime_config.attachment_url_max_length,
- )
-
- def count_tokens(self, text: str) -> int:
- return self._token_counter.count(text)
-
- def _get_runtime_config(self) -> Config:
- if self.runtime_config is not None:
- return self.runtime_config
- from Undefined.config import get_config
-
- return get_config(strict=False)
-
- def _find_chat_config_by_name(self, model_name: str) -> ChatModelConfig:
- """根据模型名查找配置（主模型或池中模型）"""
- if model_name == self.chat_config.model_name:
- return self.chat_config
- if self.chat_config.pool and self.chat_config.pool.enabled:
- for entry in self.chat_config.pool.models:
- if entry.model_name == model_name:
- return self.model_selector._entry_to_chat_config(
- entry, self.chat_config
- )
- return self.chat_config
-
- def _get_prefetch_tool_names(self) -> list[str]:
- runtime_config = self._get_runtime_config()
- return list(runtime_config.prefetch_tools)
-
- def _filter_tools_for_runtime_config(
- self, tools: list[dict[str, Any]]
- ) -> list[dict[str, Any]]:
- runtime_config = self._get_runtime_config()
- enabled = bool(getattr(runtime_config, "nagaagent_mode_enabled", False))
- if enabled:
- return tools
-
- # 关闭 NagaAgent 模式时：隐藏相关 Agent，避免被模型误调用。
- filtered: list[dict[str, Any]] = []
- for tool in tools:
- function = tool.get("function") if isinstance(tool, dict) else None
- name = function.get("name") if isinstance(function, dict) else None
- if name == "naga_code_analysis_agent":
- continue
- filtered.append(tool)
- return filtered
-
- def _prefetch_hide_tools(self) -> bool:
- runtime_config = self._get_runtime_config()
- return runtime_config.prefetch_tools_hide
-
- def _is_missing_tool_result(self, result: Any) -> bool:
- if not isinstance(result, str):
- return False
- return result.startswith("未找到项目") or result.startswith("未找到 MCP 工具")
-
- async def _maybe_prefetch_tools(
- self,
- messages: list[dict[str, Any]],
- tools: list[dict[str, Any]] | None,
- call_type: str,
- ) -> tuple[list[dict[str, Any]], list[dict[str, Any]] | None]:
- if not tools:
- return messages, tools
-
- # 预先调用部分工具，为模型补充稳定上下文（同一 call_type 仅执行一次）
- prefetch_names = self._get_prefetch_tool_names()
- if not prefetch_names:
- return messages, tools
-
- available_names = {
- tool.get("function", {}).get("name")
- for tool in tools
- if tool.get("function")
- }
- prefetch_targets = [name for name in prefetch_names if name in available_names]
- if not prefetch_targets:
- return messages, tools
-
- # 使用 RequestContext 缓存已执行的预先调用，避免重复触发
- ctx = RequestContext.current()
- cache: dict[str, list[str]] = {}
- done: set[str] = set()
- if ctx:
- cache = ctx.get_resource("prefetch_tools", {}) or {}
- done = set(cache.get(call_type, []))
-
- to_run = [name for name in prefetch_targets if name not in done]
- if not to_run:
- return messages, tools
-
- results: list[tuple[str, Any]] = []
- for name in to_run:
- try:
- # 为特定工具准备参数
- tool_args: dict[str, Any] = {}
- if name == "get_current_time":
- tool_args = {"format": "text", "include_lunar": True}
-
- result = await self.tool_manager.execute_tool(
- name,
- tool_args,
- {
- "runtime_config": self._get_runtime_config(),
- "easter_egg_silent": True,
- },
- )
- except Exception as exc:
- logger.warning("[预先调用] %s 执行失败: %s", name, exc)
- continue
-
- if self._is_missing_tool_result(result):
- logger.warning("[预先调用] %s 未找到对应工具，跳过", name)
- continue
-
- results.append((name, result))
- done.add(name)
-
- if not results:
- return messages, tools
-
- if ctx:
- cache[call_type] = sorted(done)
- ctx.set_resource("prefetch_tools", cache)
-
- content_lines = ["【预先工具结果】"]
- content_lines.extend([f"- {name}: {result}" for name, result in results])
- prefetch_message = {"role": "system", "content": "\n".join(content_lines)}
-
- insert_idx = 0
- for idx, msg in enumerate(messages):
- if msg.get("role") == "system":
- insert_idx = idx + 1
- else:
- break
- new_messages = list(messages)
- new_messages.insert(insert_idx, prefetch_message)
-
- if self._prefetch_hide_tools():
- hidden = set(name for name in done)
- tools = [
- tool
- for tool in tools
- if tool.get("function", {}).get("name") not in hidden
- ]
- return new_messages, tools
-
- async def request_model(
- self,
- model_config: (
- ChatModelConfig | VisionModelConfig | AgentModelConfig | GrokModelConfig
- ),
- messages: list[dict[str, Any]],
- max_tokens: int = 8192,
- call_type: str = "chat",
- tools: list[dict[str, Any]] | None = None,
- tool_choice: str = "auto",
- transport_state: dict[str, Any] | None = None,
- **kwargs: Any,
- ) -> dict[str, Any]:
- tools = self.tool_manager.maybe_merge_agent_tools(call_type, tools)
- message_count_for_transport = len(messages)
- if not (
- isinstance(transport_state, dict)
- and transport_state.get("previous_response_id")
- ):
- messages, tools = await self._maybe_prefetch_tools(
- messages, tools, call_type
- )
- return await self._requester.request(
- model_config=model_config,
- messages=messages,
- max_tokens=max_tokens,
- call_type=call_type,
- tools=tools,
- tool_choice=tool_choice,
- transport_state=transport_state,
- message_count_for_transport=message_count_for_transport,
- **kwargs,
- )
-
- def get_active_agent_mcp_registry(self, agent_name: str) -> Any | None:
- return self.tool_manager.get_active_agent_mcp_registry(agent_name)
-
- async def analyze_multimodal(
- self,
- media_url: str,
- media_type: str = "auto",
- prompt_extra: str = "",
- ) -> dict[str, str]:
- return await self._multimodal.analyze(media_url, media_type, prompt_extra)
-
- async def describe_image(
- self, image_url: str, prompt_extra: str = ""
- ) -> dict[str, str]:
- return await self._multimodal.describe_image(image_url, prompt_extra)
-
- async def judge_meme_image(self, image_url: str) -> dict[str, Any]:
- return await self._multimodal.judge_meme_image(image_url)
-
- async def describe_meme_image(self, image_url: str) -> dict[str, Any]:
- return await self._multimodal.describe_meme_image(image_url)
-
- def get_media_history(self, media_key: str) -> list[dict[str, str]]:
- """获取指定媒体键的多模态分析历史 Q&A 记录。"""
- return self._multimodal.get_history(media_key)
-
- async def save_media_history(
- self, media_key: str, question: str, answer: str
- ) -> None:
- """保存一条多模态分析 Q&A 到历史记录并持久化到磁盘。"""
- await self._multimodal.save_history(media_key, question, answer)
-
- async def summarize_chat(self, messages: str, context: str = "") -> str:
- return await self._summary_service.summarize_chat(messages, context)
-
- async def merge_summaries(self, summaries: list[str]) -> str:
- return await self._summary_service.merge_summaries(summaries)
-
- def split_messages_by_tokens(self, messages: str, max_tokens: int) -> list[str]:
- return self._summary_service.split_messages_by_tokens(messages, max_tokens)
-
- async def generate_title(self, summary: str) -> str:
- return await self._summary_service.generate_title(summary)
-
- def _extract_message_excerpt(self, question: str) -> str:
- matched = _CONTENT_TAG_PATTERN.search(question)
- if matched:
- content = html.unescape(matched.group(1))
- else:
- content = question
- cleaned = " ".join(content.split()).strip()
- if not cleaned:
- return "(无文本内容)"
- if len(cleaned) > 120:
- return cleaned[:117].rstrip() + "..."
- return cleaned
-
- def _is_end_only_tool_calls(
- self,
- tool_calls: list[dict[str, Any]],
- api_to_internal: dict[str, str],
- ) -> bool:
- if not tool_calls:
- return False
- for tool_call in tool_calls:
- function = tool_call.get("function", {})
- api_name = str(function.get("name", "") or "")
- internal_name = api_to_internal.get(api_name, api_name)
- if internal_name != "end":
- return False
- return True
-
- async def ask(
- self,
- question: str,
- context: str = "",
- send_message_callback: SendMessageCallback | None = None,
- get_recent_messages_callback: Callable[
- [str, str, int, int], Awaitable[list[dict[str, Any]]]
- ]
- | None = None,
- get_image_url_callback: Callable[[str], Awaitable[str | None]] | None = None,
- get_forward_msg_callback: Callable[[str], Awaitable[list[dict[str, Any]]]]
- | None = None,
- send_like_callback: Callable[[int, int], Awaitable[None]] | None = None,
- sender: Any = None,
- history_manager: Any = None,
- onebot_client: Any = None,
- scheduler: Any = None,
- extra_context: dict[str, Any] | None = None,
- ) -> str:
- """发送问题给 AI 并获取回复 (支持工具调用和迭代)
-
- 参数:
- question: 用户输入的问题
- context: 额外的上下文背景
- send_message_callback: 发送消息的回调，支持可选的 reply_to
- get_recent_messages_callback: 获取上下文历史消息的回调
- get_image_url_callback: 获取图片 URL 的回调
- get_forward_msg_callback: 获取合并转发内容的回调
- send_like_callback: 点赞回调
- sender: 消息发送助手实例
- history_manager: 历史记录管理器实例
- onebot_client: OneBot 客户端实例
- scheduler: 任务调度器实例
- extra_context: 额外的上下文负载
-
- 返回:
- AI 生成的最终文本回复
- """
- ctx = RequestContext.current()
- pre_context: dict[str, Any] = {}
- if ctx:
- if ctx.group_id is not None:
- pre_context["group_id"] = ctx.group_id
- if ctx.user_id is not None:
- pre_context["user_id"] = ctx.user_id
- if ctx.sender_id is not None:
- pre_context["sender_id"] = ctx.sender_id
- pre_context["request_type"] = ctx.request_type
- pre_context["request_id"] = ctx.request_id
- if extra_context:
- pre_context.update(extra_context)
-
- messages = await self._prompt_builder.build_messages(
- question,
- get_recent_messages_callback=get_recent_messages_callback,
- extra_context=extra_context,
- )
-
- tools = self.tool_manager.get_openai_tools()
- tools = self._filter_tools_for_runtime_config(tools)
- if logger.isEnabledFor(logging.DEBUG):
- logger.debug(
- "[AI消息] 构建完成: messages=%s tools=%s question_len=%s",
- len(messages),
- len(tools),
- len(question),
- )
- log_debug_json(logger, "[AI消息内容]", messages)
-
- tool_context = ctx.get_resources() if ctx else {}
- tool_context["conversation_ended"] = False
- tool_context.setdefault("agent_histories", {})
-
- # 显式注入 RequestContext 的核心字段（与 tooling.py:execute_tool_call 保持一致）
- if ctx:
- if ctx.group_id is not None:
- tool_context.setdefault("group_id", ctx.group_id)
- if ctx.user_id is not None:
- tool_context.setdefault("user_id", ctx.user_id)
- if ctx.sender_id is not None:
- tool_context.setdefault("sender_id", ctx.sender_id)
- tool_context.setdefault("request_type", ctx.request_type)
- tool_context.setdefault("request_id", ctx.request_id)
-
- if extra_context:
- tool_context.update(extra_context)
-
- # 注入常用资源（用于工具执行）
- tool_context.setdefault("ai_client", self)
- tool_context.setdefault("runtime_config", self._get_runtime_config())
- tool_context.setdefault("search_wrapper", self._search_wrapper)
- tool_context.setdefault(
- "crawl4ai_available", self._crawl4ai_capabilities.available
- )
- tool_context.setdefault(
- "crawl4ai_proxy_config_available",
- self._crawl4ai_capabilities.proxy_config_available,
- )
- tool_context.setdefault("end_summary_storage", self._end_summary_storage)
- tool_context.setdefault("end_summaries", self._prompt_builder.end_summaries)
- tool_context.setdefault(
- "send_private_message_callback", self._send_private_message_callback
- )
- tool_context.setdefault("send_message_callback", send_message_callback)
- tool_context.setdefault(
- "get_recent_messages_callback", get_recent_messages_callback
- )
-
- async def fetch_session_messages_callback(
- *,
- group_id: int,
- user_id: int,
- count: int | None = None,
- time_range: str | None = None,
- ) -> str:
- return await fetch_session_messages(
- history_manager,
- group_id=group_id,
- user_id=user_id,
- count=count,
- time_range=time_range,
- runtime_config=self._get_runtime_config(),
- )
-
- tool_context.setdefault(
- "fetch_session_messages_callback", fetch_session_messages_callback
- )
- tool_context.setdefault("get_image_url_callback", get_image_url_callback)
- tool_context.setdefault("get_forward_msg_callback", get_forward_msg_callback)
- tool_context.setdefault("send_like_callback", send_like_callback)
- tool_context.setdefault("sender", sender)
- tool_context.setdefault("history_manager", history_manager)
- tool_context.setdefault("onebot_client", onebot_client)
- tool_context.setdefault("scheduler", scheduler)
- tool_context.setdefault("send_image_callback", self._send_image_callback)
- tool_context.setdefault(
- "attachment_registry",
- getattr(self, "attachment_registry", None),
- )
- tool_context.setdefault("memory_storage", self.memory_storage)
- tool_context.setdefault("knowledge_manager", self._knowledge_manager)
- tool_context.setdefault("cognitive_service", self._cognitive_service)
- tool_context.setdefault("meme_service", self._meme_service)
- tool_context.setdefault("current_question", question)
- message_ids = tool_context.get("message_ids")
- if not isinstance(message_ids, list):
- message_ids = []
- tool_context["message_ids"] = message_ids
- trigger_message_id = tool_context.get("trigger_message_id")
- if trigger_message_id is not None:
- trigger_message_id_text = str(trigger_message_id).strip()
- if trigger_message_id_text and trigger_message_id_text not in message_ids:
- message_ids.append(trigger_message_id_text)
-
- # 动态选择模型（等待偏好加载就绪，避免竞态）
- await self.model_selector.wait_ready()
- selected_model_name = pre_context.get("selected_model_name")
- if selected_model_name:
- effective_chat_config = self._find_chat_config_by_name(selected_model_name)
- else:
- effective_chat_config = self.chat_config
-
- max_iterations = 1000
- iteration = 0
- conversation_ended = False
- cot_compat = getattr(effective_chat_config, "thinking_tool_call_compat", False)
- capture_reasoning = cot_compat or bool(
- getattr(effective_chat_config, "reasoning_content_replay", False)
- )
- cot_compat_logged = False
- cot_missing_logged = False
- transport_state: dict[str, Any] | None = None
- queue_lane = self._resolve_queue_lane(tool_context.get("queue_lane"))
- pre_tool_failure_count = 0
- missing_tool_call_count = 0
- last_missing_tool_call_content = ""
- runtime_config = self._get_runtime_config()
- max_pre_tool_retries = max(
- 0,
- int(getattr(runtime_config, "ai_request_max_retries", 0) or 0),
- )
- max_missing_tool_call_retries = max(
- 0,
- int(getattr(runtime_config, "missing_tool_call_retries", 3) or 0),
- )
-
- while iteration < max_iterations:
- iteration += 1
- logger.info(f"[AI决策] 开始第 {iteration} 轮迭代...")
- message_checkpoint_len = len(messages)
- transport_state_checkpoint = transport_state
-
- try:
- result = await self.submit_queued_llm_call(
- model_config=effective_chat_config,
- messages=messages,
- max_tokens=8192,
- call_type="chat",
- tools=tools,
- tool_choice="auto",
- transport_state=transport_state,
- queue_lane=queue_lane,
- )
- except Exception as exc:
- logger.exception(
- "[queued_llm_error] call_type=chat model=%s lane=%s iteration=%s error=%s",
- effective_chat_config.model_name,
- queue_lane,
- iteration,
- exc,
- )
- raise
-
- try:
- tool_execution_started = False
- tool_name_map = (
- result.get("_tool_name_map") if isinstance(result, dict) else None
- )
- api_to_internal: dict[str, str] = {}
- if isinstance(tool_name_map, dict):
- raw_api_to_internal = tool_name_map.get("api_to_internal")
- if isinstance(raw_api_to_internal, dict):
- api_to_internal = {
- str(k): str(v) for k, v in raw_api_to_internal.items()
- }
-
- next_transport_state = (
- result.get("_transport_state") if isinstance(result, dict) else None
- )
- transport_state = (
- next_transport_state
- if isinstance(next_transport_state, dict)
- else None
- )
-
- choice = result.get("choices", [{}])[0]
- message = choice.get("message", {})
- content: str = message.get("content") or ""
- reasoning_content = message.get("reasoning_content")
- tool_calls = message.get("tool_calls", [])
- if logger.isEnabledFor(logging.DEBUG):
- logger.debug(
- "[AI响应] content_len=%s tool_calls=%s",
- len(content),
- len(tool_calls),
- )
- if tool_calls:
- log_debug_json(logger, "[AI工具调用]", tool_calls)
-
- log_thinking = self._get_runtime_config().log_thinking
- if (
- capture_reasoning
- and tools
- and log_thinking
- and not cot_compat_logged
- ):
- cot_compat_logged = True
- logger.info(
- "[思维链兼容] 多轮工具调用 reasoning_content 本地回填已启用"
- )
- if (
- capture_reasoning
- and log_thinking
- and tools
- and getattr(effective_chat_config, "thinking_enabled", False)
- and not reasoning_content
- and tool_calls
- and not cot_missing_logged
- ):
- cot_missing_logged = True
- message_keys = (
- ", ".join(sorted(message.keys()))
- if isinstance(message, dict)
- else type(message).__name__
- )
- logger.info(
- "[思维链兼容] 未在响应中发现 reasoning_content（可能是模型/服务商不返回思维链）；message_keys=%s",
- message_keys,
- )
-
- if content.strip() and tool_calls:
- logger.debug(
- "检测到 content 与工具调用同时存在，忽略 content，仅执行工具调用"
- )
- content = ""
-
- if not tool_calls:
- if conversation_ended:
- logger.info(
- "[AI回复] 会话结束，返回最终内容: length=%s",
- len(content),
- )
- return content
-
- if content.strip():
- last_missing_tool_call_content = content.strip()
- missing_tool_call_count += 1
- if missing_tool_call_count > max_missing_tool_call_retries:
- logger.warning(
- "[AI回复] 模型连续未调用工具，停止重试: iteration=%s retries=%s/%s content_len=%s",
- iteration,
- missing_tool_call_count - 1,
- max_missing_tool_call_retries,
- len(content),
- )
- fallback_content = last_missing_tool_call_content
- if fallback_content and send_message_callback is not None:
- try:
- await send_message_callback(fallback_content)
- tool_context["message_sent_this_turn"] = True
- current_ctx = RequestContext.current()
- if current_ctx is not None:
- current_ctx.set_resource(
- "message_sent_this_turn", True
- )
- return ""
- except Exception:
- logger.exception("[AI回复] fallback 发送失败")
- return fallback_content
-
- logger.warning(
- "[AI回复] 模型返回文本但未调用工具（iteration=%s retry=%s/%s content_len=%s），要求重试",
- iteration,
- missing_tool_call_count,
- max_missing_tool_call_retries,
- len(content),
- )
- messages.append(
- {
- "role": "user",
- "content": (
- "注意：你不能直接返回纯文本作为最终回复。"
- "请调用 send_message 工具来发送你的回复消息，"
- "然后调用 end 工具结束对话。"
- ),
- }
- )
- continue
-
- assistant_message: dict[str, Any] = {
- "role": "assistant",
- "content": content,
- "tool_calls": tool_calls,
- }
- missing_tool_call_count = 0
- last_missing_tool_call_content = ""
- phase = message.get("phase")
- if phase is not None:
- assistant_message["phase"] = phase
- output_items = message.get(RESPONSES_OUTPUT_ITEMS_KEY)
- if isinstance(output_items, list):
- assistant_message[RESPONSES_OUTPUT_ITEMS_KEY] = output_items
- if capture_reasoning and reasoning_content is not None:
- assistant_message["reasoning_content"] = reasoning_content
- messages.append(assistant_message)
-
- tool_tasks = []
- tool_call_ids = []
- tool_api_names: list[str] = []
- tool_internal_names: list[str] = []
- end_tool_call: dict[str, Any] | None = None
- end_tool_args: dict[str, Any] = {}
-
- for tool_call in tool_calls:
- call_id = ""
- if isinstance(tool_call, dict):
- call_id = str(tool_call.get("id", "") or "")
- function = tool_call.get("function")
- else:
- function = None
- if not isinstance(function, dict):
- logger.warning(
- "[工具调用] 跳过无效工具调用: missing_function ID=%s",
- call_id,
- )
- messages.append(_build_invalid_tool_call_response(tool_call))
- continue
- api_function_name = str(function.get("name", "") or "").strip()
- if not api_function_name:
- logger.warning(
- "[工具调用] 跳过无效工具调用: empty_name ID=%s",
- call_id,
- )
- messages.append(_build_invalid_tool_call_response(tool_call))
- continue
- raw_args = function.get("arguments")
-
- internal_function_name = api_to_internal.get(
- api_function_name, api_function_name
- )
-
- if internal_function_name != api_function_name:
- logger.info(
- "[工具准备] 准备调用: %s (原名: %s) (ID=%s)",
- internal_function_name,
- api_function_name,
- call_id,
- )
- else:
- logger.info(
- "[工具准备] 准备调用: %s (ID=%s)",
- api_function_name,
- call_id,
- )
- logger.debug(
- f"[工具参数] {api_function_name} 参数: {redact_string(str(raw_args))}"
- )
-
- function_args = parse_tool_arguments(
- raw_args,
- logger=logger,
- tool_name=str(api_function_name),
- )
-
- if not isinstance(function_args, dict):
- function_args = {}
-
- # 检测 end 工具，暂存后统一处理
- if internal_function_name == "end":
- if len(tool_calls) > 1:
- logger.warning(
- "[工具调用] end 与其他工具同时调用，"
- "将先执行其他工具，并回填 end 跳过结果"
- )
- end_tool_call = tool_call
- end_tool_args = function_args
- continue
-
- tool_call_ids.append(call_id)
- tool_api_names.append(str(api_function_name))
- tool_internal_names.append(str(internal_function_name))
- tool_tasks.append(
- self.tool_manager.execute_tool(
- str(internal_function_name), function_args, tool_context
- )
- )
-
- if tool_tasks:
- tool_execution_started = True
- logger.info(
- "[工具执行] 开始并发执行 %s 个工具调用: %s",
- len(tool_tasks),
- ", ".join(tool_internal_names),
- )
- tool_results = await asyncio.gather(
- *tool_tasks, return_exceptions=True
- )
-
- for i, tool_result in enumerate(tool_results):
- call_id = tool_call_ids[i]
- api_fname = tool_api_names[i]
- internal_fname = tool_internal_names[i]
-
- if isinstance(tool_result, Exception):
- logger.error(
- "[工具异常] %s (ID=%s) 执行抛出异常: %s",
- internal_fname,
- call_id,
- tool_result,
- )
- content_str = f"执行失败: {str(tool_result)}"
- else:
- content_str = str(tool_result)
- logger.debug(
- "[工具响应] %s (ID=%s) 返回内容长度=%s",
- internal_fname,
- call_id,
- len(content_str),
- )
- if logger.isEnabledFor(logging.DEBUG):
- log_debug_json(
- logger,
- f"[工具响应体] {internal_fname} (ID={call_id})",
- content_str,
- )
-
- messages.append(
- {
- "role": "tool",
- "tool_call_id": call_id,
- "name": api_fname,
- "content": content_str,
- }
- )
-
- # 如果是 get_forward_msg 工具调用，将其结果写入历史记录
- if internal_fname == "get_forward_msg" and not isinstance(
- tool_result, Exception
- ):
- task = asyncio.create_task(
- self._save_forward_to_history(
- content_str, pre_context, history_manager
- )
- )
- task.add_done_callback(
- lambda t: t.exception() if not t.cancelled() else None
- )
-
- if tool_context.get("conversation_ended"):
- conversation_ended = True
- logger.info(
- "[会话状态] 工具触发会话结束标记: tool=%s",
- internal_fname,
- )
-
- # 处理 end 工具调用
- if end_tool_call:
- end_call_id = end_tool_call.get("id", "")
- end_api_name = end_tool_call.get("function", {}).get("name", "end")
- if tool_tasks:
- # end 与其他工具同时调用：跳过执行，但必须回填 tool 响应
- # 以匹配 assistant.tool_calls，避免下轮请求出现未配对的 tool_call_id。
- skip_content = (
- "end 与其他工具同轮调用，本轮未执行 end；"
- "请根据其他工具结果继续决策。"
- )
- messages.append(
- {
- "role": "tool",
- "tool_call_id": end_call_id,
- "name": end_api_name,
- "content": skip_content,
- }
- )
- logger.info("[工具调用] end 与其他工具同时调用，已回填跳过响应")
- else:
- # end 单独调用，正常执行（参数已在循环中解析）
- tool_execution_started = True
- end_result = await self.tool_manager.execute_tool(
- "end", end_tool_args, tool_context
- )
- messages.append(
- {
- "role": "tool",
- "tool_call_id": end_call_id,
- "name": end_api_name,
- "content": str(end_result),
- }
- )
- if tool_context.get("conversation_ended"):
- conversation_ended = True
- logger.info("[会话状态] end 工具触发会话结束")
-
- if conversation_ended:
- logger.info("[会话状态] 对话已结束（调用 end 工具）")
- return ""
- pre_tool_failure_count = 0
-
- except Exception as exc:
- if (
- not tool_execution_started
- and pre_tool_failure_count < max_pre_tool_retries
- ):
- pre_tool_failure_count += 1
- del messages[message_checkpoint_len:]
- transport_state = transport_state_checkpoint
- logger.warning(
- "[chat.pre_tool_retry] model=%s lane=%s retry=%s/%s iteration=%s error=%s",
- effective_chat_config.model_name,
- queue_lane,
- pre_tool_failure_count,
- max_pre_tool_retries,
- iteration,
- exc,
- )
- continue
- logger.exception(
- "[chat.suppressed_error] model=%s lane=%s iteration=%s error=%s",
- effective_chat_config.model_name,
- queue_lane,
- iteration,
- exc,
- )
- return ""
-
- logger.warning("[AI决策] 达到最大迭代次数，未能完成处理")
- return "达到最大迭代次数，未能完成处理"
-
- async def _save_forward_to_history(
- self,
- content: str,
- pre_context: dict[str, Any],
- history_manager: Any,
- ) -> None:
- """将合并转发消息写入历史记录"""
- if history_manager is None:
- return
-
- try:
- group_id = pre_context.get("group_id")
- user_id = pre_context.get("user_id")
-
- if group_id is not None:
- await history_manager.add_group_message(
- group_id=int(group_id),
- sender_id=0,
- text_content=content,
- sender_card="",
- sender_nickname="[合并转发内容]",
- group_name="",
- role="system",
- title="",
- message_id=None,
- )
- elif user_id is not None:
- await history_manager.add_private_message(
- user_id=int(user_id),
- text_content=content,
- display_name="[合并转发内容]",
- user_name="",
- message_id=None,
- )
- else:
- logger.debug("[合并转发] 无法写入历史：缺少 group_id 和 user_id")
- except Exception as exc:
- logger.debug("[合并转发] 写入历史失败: %s", exc)
diff --git a/src/Undefined/ai/client/__init__.py b/src/Undefined/ai/client/__init__.py
new file mode 100644
index 00000000..0be60fc8
--- /dev/null
+++ b/src/Undefined/ai/client/__init__.py
@@ -0,0 +1,37 @@
+"""AI 客户端子包。
+
+对外稳定入口：``AIClient``；导入路径 ``Undefined.ai.client`` 指向本子包。
+"""
+
+from Undefined.ai.client.ask_loop import ClientAskLoopMixin
+from Undefined.ai.client.setup import (
+ MISSING_TOOL_CALL_RETRY_HINT,
+ SendMessageCallback,
+ SendPrivateMessageCallback,
+ _INVALID_TOOL_CALL_CONTENT,
+ _build_invalid_tool_call_response,
+ _resolve_summary_model_config,
+)
+
+# 会话消息拉取 helper，供 ask 与 slash 命令共用
+from Undefined.services.message_summary_fetch import fetch_session_messages
+
+
+# MRO：ClientAskLoopMixin → ClientQueueMixin → ClientSetupMixin，能力按 mixin 分层叠加
+class AIClient(ClientAskLoopMixin):
+ """AI 模型客户端。
+
+ 协调 Prompt 构建、队列化 LLM 请求、工具调用与多模态/摘要能力。
+ """
+
+
+__all__ = [
+ "AIClient",
+ "MISSING_TOOL_CALL_RETRY_HINT",
+ "SendMessageCallback",
+ "SendPrivateMessageCallback",
+ "_INVALID_TOOL_CALL_CONTENT",
+ "_build_invalid_tool_call_response",
+ "_resolve_summary_model_config",
+ "fetch_session_messages",
+]
diff --git a/src/Undefined/ai/client/ask_loop.py b/src/Undefined/ai/client/ask_loop.py
new file mode 100644
index 00000000..bd75f01b
--- /dev/null
+++ b/src/Undefined/ai/client/ask_loop.py
@@ -0,0 +1,612 @@
+"""AI 客户端 ask 主循环与工具调用迭代。"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from typing import Any, Awaitable, Callable
+
+from Undefined.ai.client.queue import ClientQueueMixin
+from Undefined.ai.client.setup import (
+ MISSING_TOOL_CALL_RETRY_HINT,
+ SendMessageCallback,
+ _build_invalid_tool_call_response,
+)
+from Undefined.ai.transports.openai_transport import RESPONSES_OUTPUT_ITEMS_KEY
+from Undefined.ai.tooling import END_CO_CALL_REJECT_CONTENT
+from Undefined.context import RequestContext
+from Undefined.services.message_summary_fetch import fetch_session_messages
+from Undefined.utils.logging import log_debug_json, redact_string
+from Undefined.utils.tool_calls import parse_tool_arguments
+
+logger = logging.getLogger(__name__)
+
+
+class ClientAskLoopMixin(ClientQueueMixin):
+ """``ask()`` 多轮工具调用主循环。"""
+
+ async def ask(
+ self,
+ question: str,
+ context: str = "",
+ send_message_callback: SendMessageCallback | None = None,
+ get_recent_messages_callback: Callable[
+ [str, str, int, int], Awaitable[list[dict[str, Any]]]
+ ]
+ | None = None,
+ get_image_url_callback: Callable[[str], Awaitable[str | None]] | None = None,
+ get_forward_msg_callback: Callable[[str], Awaitable[list[dict[str, Any]]]]
+ | None = None,
+ send_like_callback: Callable[[int, int], Awaitable[None]] | None = None,
+ sender: Any = None,
+ history_manager: Any = None,
+ onebot_client: Any = None,
+ scheduler: Any = None,
+ extra_context: dict[str, Any] | None = None,
+ ) -> str:
+ """发送问题给 AI 并获取回复 (支持工具调用和迭代)
+
+ 参数:
+ question: 用户输入的问题
+ context: 额外的上下文背景
+ send_message_callback: 发送消息的回调，支持可选的 reply_to
+ get_recent_messages_callback: 获取上下文历史消息的回调
+ get_image_url_callback: 获取图片 URL 的回调
+ get_forward_msg_callback: 获取合并转发内容的回调
+ send_like_callback: 点赞回调
+ sender: 消息发送助手实例
+ history_manager: 历史记录管理器实例
+ onebot_client: OneBot 客户端实例
+ scheduler: 任务调度器实例
+ extra_context: 额外的上下文负载
+
+ 返回:
+ AI 生成的最终文本回复
+ """
+ # ===== 阶段一：从 RequestContext / extra_context 组装 pre_context =====
+ ctx = RequestContext.current()
+ pre_context: dict[str, Any] = {}
+ if ctx:
+ if ctx.group_id is not None:
+ pre_context["group_id"] = ctx.group_id
+ if ctx.user_id is not None:
+ pre_context["user_id"] = ctx.user_id
+ if ctx.sender_id is not None:
+ pre_context["sender_id"] = ctx.sender_id
+ pre_context["request_type"] = ctx.request_type
+ pre_context["request_id"] = ctx.request_id
+ if extra_context:
+ pre_context.update(extra_context)
+
+ # ===== 阶段二：构建 LLM messages 与 OpenAI tools schema =====
+ messages = await self._prompt_builder.build_messages(
+ question,
+ get_recent_messages_callback=get_recent_messages_callback,
+ extra_context=extra_context,
+ )
+
+ tools = self.tool_manager.get_openai_tools()
+ tools = self._filter_tools_for_runtime_config(tools)
+ if logger.isEnabledFor(logging.DEBUG):
+ logger.debug(
+ "[AI消息] 构建完成: messages=%s tools=%s question_len=%s",
+ len(messages),
+ len(tools),
+ len(question),
+ )
+ log_debug_json(logger, "[AI消息内容]", messages)
+
+ # ===== 阶段三：组装 tool_context，注入回调、服务与 RequestContext 字段 =====
+ tool_context = ctx.get_resources() if ctx else {}
+ tool_context["conversation_ended"] = False
+ tool_context.setdefault("agent_histories", {})
+
+ # 显式注入 RequestContext 的核心字段（与 tooling.py:execute_tool_call 保持一致）
+ if ctx:
+ if ctx.group_id is not None:
+ tool_context.setdefault("group_id", ctx.group_id)
+ if ctx.user_id is not None:
+ tool_context.setdefault("user_id", ctx.user_id)
+ if ctx.sender_id is not None:
+ tool_context.setdefault("sender_id", ctx.sender_id)
+ tool_context.setdefault("request_type", ctx.request_type)
+ tool_context.setdefault("request_id", ctx.request_id)
+
+ if extra_context:
+ tool_context.update(extra_context)
+
+ # 注入常用资源（用于工具执行）
+ tool_context.setdefault("ai_client", self)
+ tool_context.setdefault("runtime_config", self._get_runtime_config())
+ tool_context.setdefault("search_wrapper", self._search_wrapper)
+ tool_context.setdefault(
+ "crawl4ai_available", self._crawl4ai_capabilities.available
+ )
+ tool_context.setdefault(
+ "crawl4ai_proxy_config_available",
+ self._crawl4ai_capabilities.proxy_config_available,
+ )
+ tool_context.setdefault("end_summary_storage", self._end_summary_storage)
+ tool_context.setdefault("end_summaries", self._prompt_builder.end_summaries)
+ tool_context.setdefault(
+ "send_private_message_callback", self._send_private_message_callback
+ )
+ tool_context.setdefault("send_message_callback", send_message_callback)
+ tool_context.setdefault(
+ "get_recent_messages_callback", get_recent_messages_callback
+ )
+
+ async def fetch_session_messages_callback(
+ *,
+ group_id: int,
+ user_id: int,
+ count: int | None = None,
+ time_range: str | None = None,
+ ) -> str:
+ return await fetch_session_messages(
+ history_manager,
+ group_id=group_id,
+ user_id=user_id,
+ count=count,
+ time_range=time_range,
+ runtime_config=self._get_runtime_config(),
+ )
+
+ tool_context.setdefault(
+ "fetch_session_messages_callback", fetch_session_messages_callback
+ )
+ tool_context.setdefault("get_image_url_callback", get_image_url_callback)
+ tool_context.setdefault("get_forward_msg_callback", get_forward_msg_callback)
+ tool_context.setdefault("send_like_callback", send_like_callback)
+ tool_context.setdefault("sender", sender)
+ tool_context.setdefault("history_manager", history_manager)
+ tool_context.setdefault("onebot_client", onebot_client)
+ tool_context.setdefault("scheduler", scheduler)
+ tool_context.setdefault("send_image_callback", self._send_image_callback)
+ tool_context.setdefault(
+ "attachment_registry",
+ getattr(self, "attachment_registry", None),
+ )
+ tool_context.setdefault("memory_storage", self.memory_storage)
+ tool_context.setdefault("knowledge_manager", self._knowledge_manager)
+ tool_context.setdefault("cognitive_service", self._cognitive_service)
+ tool_context.setdefault("meme_service", self._meme_service)
+ tool_context.setdefault("current_question", question)
+ message_ids = tool_context.get("message_ids")
+ if not isinstance(message_ids, list):
+ message_ids = []
+ tool_context["message_ids"] = message_ids
+ trigger_message_id = tool_context.get("trigger_message_id")
+ if trigger_message_id is not None:
+ trigger_message_id_text = str(trigger_message_id).strip()
+ if trigger_message_id_text and trigger_message_id_text not in message_ids:
+ message_ids.append(trigger_message_id_text)
+
+ # ===== 阶段四：模型选择、思维链/重试参数与主循环状态初始化 =====
+ await self.model_selector.wait_ready()
+ selected_model_name = pre_context.get("selected_model_name")
+ if selected_model_name:
+ effective_chat_config = self._find_chat_config_by_name(selected_model_name)
+ else:
+ effective_chat_config = self.chat_config
+
+ max_iterations = 1000
+ iteration = 0
+ conversation_ended = False
+ cot_compat = getattr(effective_chat_config, "thinking_tool_call_compat", False)
+ capture_reasoning = cot_compat or bool(
+ getattr(effective_chat_config, "reasoning_content_replay", False)
+ )
+ cot_compat_logged = False
+ cot_missing_logged = False
+ transport_state: dict[str, Any] | None = None
+ queue_lane = self._resolve_queue_lane(tool_context.get("queue_lane"))
+ pre_tool_failure_count = 0
+ missing_tool_call_count = 0
+ last_missing_tool_call_content = ""
+ runtime_config = self._get_runtime_config()
+ max_pre_tool_retries = max(
+ 0,
+ int(getattr(runtime_config, "ai_request_max_retries", 0) or 0),
+ )
+ max_missing_tool_call_retries = max(
+ 0,
+ int(getattr(runtime_config, "missing_tool_call_retries", 3) or 0),
+ )
+
+ # ===== 阶段五：多轮 LLM + 工具调用主循环（每轮一次请求） =====
+ while iteration < max_iterations:
+ iteration += 1
+ logger.info(f"[AI决策] 开始第 {iteration} 轮迭代...")
+ message_checkpoint_len = len(messages)
+ transport_state_checkpoint = transport_state
+
+ tool_execution_started = False
+ try:
+ result = await self.submit_queued_llm_call(
+ model_config=effective_chat_config,
+ messages=messages,
+ max_tokens=8192,
+ call_type="chat",
+ tools=tools,
+ tool_choice="auto",
+ transport_state=transport_state,
+ queue_lane=queue_lane,
+ )
+
+ tool_name_map = (
+ result.get("_tool_name_map") if isinstance(result, dict) else None
+ )
+ api_to_internal: dict[str, str] = {}
+ if isinstance(tool_name_map, dict):
+ raw_api_to_internal = tool_name_map.get("api_to_internal")
+ if isinstance(raw_api_to_internal, dict):
+ # LLM 出站时工具名可能被编码，执行前映射回内部名
+ api_to_internal = {
+ str(k): str(v) for k, v in raw_api_to_internal.items()
+ }
+
+ next_transport_state = (
+ result.get("_transport_state") if isinstance(result, dict) else None
+ )
+ transport_state = (
+ next_transport_state
+ if isinstance(next_transport_state, dict)
+ else None
+ )
+
+ choice = result.get("choices", [{}])[0]
+ message = choice.get("message", {})
+ content: str = message.get("content") or ""
+ reasoning_content = message.get("reasoning_content")
+ tool_calls = message.get("tool_calls", [])
+ if logger.isEnabledFor(logging.DEBUG):
+ logger.debug(
+ "[AI响应] content_len=%s tool_calls=%s",
+ len(content),
+ len(tool_calls),
+ )
+ # 无 tool_calls 与有 tool_calls 走不同分支
+ if tool_calls:
+ log_debug_json(logger, "[AI工具调用]", tool_calls)
+
+ log_thinking = self._get_runtime_config().log_thinking
+ if (
+ capture_reasoning
+ and tools
+ and log_thinking
+ and not cot_compat_logged
+ ):
+ cot_compat_logged = True
+ logger.info(
+ "[思维链兼容] 多轮工具调用 reasoning_content 本地回填已启用"
+ )
+ if (
+ capture_reasoning
+ and log_thinking
+ and tools
+ and getattr(effective_chat_config, "thinking_enabled", False)
+ and not reasoning_content
+ and tool_calls
+ and not cot_missing_logged
+ ):
+ cot_missing_logged = True
+ message_keys = (
+ ", ".join(sorted(message.keys()))
+ if isinstance(message, dict)
+ else type(message).__name__
+ )
+ logger.info(
+ "[思维链兼容] 未在响应中发现 reasoning_content（可能是模型/服务商不返回思维链）；message_keys=%s",
+ message_keys,
+ )
+
+ # 部分模型会同时返回文本与 tool_calls；对外动作以工具为准，丢弃 content
+ if content.strip() and tool_calls:
+ logger.debug(
+ "检测到 content 与工具调用同时存在，忽略 content，仅执行工具调用"
+ )
+ content = ""
+
+ # 无 tool_calls 与有 tool_calls 走不同分支
+ if not tool_calls:
+ if conversation_ended:
+ logger.info(
+ "[AI回复] 会话结束，返回最终内容: length=%s",
+ len(content),
+ )
+ return content
+
+ # 未调用工具：累计重试次数，超限则 fallback 发送或直接返回文本
+ if content.strip():
+ last_missing_tool_call_content = content.strip()
+ missing_tool_call_count += 1
+ if missing_tool_call_count > max_missing_tool_call_retries:
+ logger.warning(
+ "[AI回复] 模型连续未调用工具，停止重试: iteration=%s retries=%s/%s content_len=%s",
+ iteration,
+ missing_tool_call_count - 1,
+ max_missing_tool_call_retries,
+ len(content),
+ )
+ fallback_content = last_missing_tool_call_content
+ if fallback_content and send_message_callback is not None:
+ try:
+ await send_message_callback(fallback_content)
+ tool_context["message_sent_this_turn"] = True
+ current_ctx = RequestContext.current()
+ if current_ctx is not None:
+ current_ctx.set_resource(
+ "message_sent_this_turn", True
+ )
+ return ""
+ except Exception:
+ logger.exception("[AI回复] fallback 发送失败")
+ return fallback_content
+
+ logger.warning(
+ "[AI回复] 模型返回文本但未调用工具（iteration=%s retry=%s/%s content_len=%s），要求重试",
+ iteration,
+ missing_tool_call_count,
+ max_missing_tool_call_retries,
+ len(content),
+ )
+ assistant_retry_message: dict[str, Any] = {
+ "role": "assistant",
+ "content": content,
+ }
+ if capture_reasoning and reasoning_content is not None:
+ assistant_retry_message["reasoning_content"] = reasoning_content
+ messages.append(assistant_retry_message)
+ messages.append(
+ {
+ "role": "user",
+ "content": MISSING_TOOL_CALL_RETRY_HINT,
+ }
+ )
+ continue
+
+ assistant_message: dict[str, Any] = {
+ "role": "assistant",
+ "content": content,
+ "tool_calls": tool_calls,
+ }
+ missing_tool_call_count = 0
+ last_missing_tool_call_content = ""
+ phase = message.get("phase")
+ if phase is not None:
+ assistant_message["phase"] = phase
+ output_items = message.get(RESPONSES_OUTPUT_ITEMS_KEY)
+ if isinstance(output_items, list):
+ assistant_message[RESPONSES_OUTPUT_ITEMS_KEY] = output_items
+ if capture_reasoning and reasoning_content is not None:
+ assistant_message["reasoning_content"] = reasoning_content
+ messages.append(assistant_message)
+
+ tool_tasks = []
+ tool_call_ids = []
+ tool_api_names: list[str] = []
+ tool_internal_names: list[str] = []
+ end_tool_call: dict[str, Any] | None = None
+ end_tool_args: dict[str, Any] = {}
+ tool_results: list[Any] = []
+
+ # 逐个处理模型返回的 tool_call
+ for tool_call in tool_calls:
+ call_id = ""
+ if isinstance(tool_call, dict):
+ call_id = str(tool_call.get("id", "") or "")
+ function = tool_call.get("function")
+ else:
+ function = None
+ if not isinstance(function, dict):
+ logger.warning(
+ "[工具调用] 跳过无效工具调用: missing_function ID=%s",
+ call_id,
+ )
+ messages.append(_build_invalid_tool_call_response(tool_call))
+ continue
+ api_function_name = str(function.get("name", "") or "").strip()
+ if not api_function_name:
+ logger.warning(
+ "[工具调用] 跳过无效工具调用: empty_name ID=%s",
+ call_id,
+ )
+ messages.append(_build_invalid_tool_call_response(tool_call))
+ continue
+ raw_args = function.get("arguments")
+
+ internal_function_name = api_to_internal.get(
+ api_function_name,
+ api_function_name,
+ )
+
+ if internal_function_name != api_function_name:
+ logger.info(
+ "[工具准备] 准备调用: %s (原名: %s) (ID=%s)",
+ internal_function_name,
+ api_function_name,
+ call_id,
+ )
+ else:
+ logger.info(
+ "[工具准备] 准备调用: %s (ID=%s)",
+ api_function_name,
+ call_id,
+ )
+ logger.debug(
+ f"[工具参数] {api_function_name} 参数: {redact_string(str(raw_args))}"
+ )
+
+ function_args = parse_tool_arguments(
+ raw_args,
+ logger=logger,
+ tool_name=str(api_function_name),
+ )
+
+ if not isinstance(function_args, dict):
+ function_args = {}
+
+ # 检测 end 工具，暂存后统一处理
+ if internal_function_name == "end":
+ # 无 tool_calls 与有 tool_calls 走不同分支
+ if len(tool_calls) > 1:
+ logger.warning(
+ "[工具调用] end 与其他工具同时调用，"
+ "将先执行其他工具，end 将返回拒绝结果"
+ )
+ end_tool_call = tool_call
+ end_tool_args = function_args
+ continue
+
+ tool_call_ids.append(call_id)
+ tool_api_names.append(str(api_function_name))
+ tool_internal_names.append(str(internal_function_name))
+ tool_tasks.append(
+ self.tool_manager.execute_tool(
+ str(internal_function_name), function_args, tool_context
+ )
+ )
+
+ if tool_tasks:
+ tool_execution_started = True
+ logger.info(
+ "[工具执行] 开始并发执行 %s 个工具调用: %s",
+ len(tool_tasks),
+ ", ".join(tool_internal_names),
+ )
+ tool_results = await asyncio.gather(
+ *tool_tasks,
+ return_exceptions=True,
+ )
+
+ for i, tool_result in enumerate(tool_results):
+ call_id = tool_call_ids[i]
+ api_fname = tool_api_names[i]
+ internal_fname = tool_internal_names[i]
+
+ if isinstance(tool_result, Exception):
+ logger.error(
+ "[工具异常] %s (ID=%s) 执行抛出异常: %s",
+ internal_fname,
+ call_id,
+ tool_result,
+ )
+ content_str = f"执行失败: {str(tool_result)}"
+ else:
+ content_str = str(tool_result)
+ logger.debug(
+ "[工具响应] %s (ID=%s) 返回内容长度=%s",
+ internal_fname,
+ call_id,
+ len(content_str),
+ )
+ if logger.isEnabledFor(logging.DEBUG):
+ log_debug_json(
+ logger,
+ f"[工具响应体] {internal_fname} (ID={call_id})",
+ content_str,
+ )
+
+ messages.append(
+ {
+ "role": "tool",
+ "tool_call_id": call_id,
+ "name": api_fname,
+ "content": content_str,
+ }
+ )
+
+ # 如果是 get_forward_msg 工具调用，将其结果写入历史记录
+ if internal_fname == "get_forward_msg" and not isinstance(
+ tool_result, Exception
+ ):
+ task = asyncio.create_task(
+ self._save_forward_to_history(
+ content_str,
+ pre_context,
+ history_manager,
+ )
+ )
+ task.add_done_callback(
+ lambda t: t.exception() if not t.cancelled() else None
+ )
+
+ # 会话是否已由 end 工具标记结束
+ if tool_context.get("conversation_ended"):
+ conversation_ended = True
+ logger.info(
+ "[会话状态] 工具触发会话结束标记: tool=%s",
+ internal_fname,
+ )
+
+ if end_tool_call:
+ end_call_id = end_tool_call.get("id", "")
+ end_api_name = end_tool_call.get("function", {}).get("name", "end")
+ if tool_tasks:
+ messages.append(
+ {
+ "role": "tool",
+ "tool_call_id": end_call_id,
+ "name": end_api_name,
+ "content": END_CO_CALL_REJECT_CONTENT,
+ }
+ )
+ logger.info(
+ "[工具调用] end 与其他工具同时调用，"
+ "其它工具已执行，end 已回填拒绝响应"
+ )
+ else:
+ # end 单独调用，正常执行（参数已在循环中解析）
+ tool_execution_started = True
+ end_result = await self.tool_manager.execute_tool(
+ "end", end_tool_args, tool_context
+ )
+ messages.append(
+ {
+ "role": "tool",
+ "tool_call_id": end_call_id,
+ "name": end_api_name,
+ "content": str(end_result),
+ }
+ )
+ # 会话是否已由 end 工具标记结束
+ if tool_context.get("conversation_ended"):
+ conversation_ended = True
+ logger.info("[会话状态] end 工具触发会话结束")
+
+ # 会话是否已由 end 工具标记结束
+ if conversation_ended:
+ logger.info("[会话状态] 对话已结束（调用 end 工具）")
+ return ""
+ pre_tool_failure_count = 0
+
+ except Exception as exc:
+ if (
+ not tool_execution_started
+ and pre_tool_failure_count < max_pre_tool_retries
+ ):
+ pre_tool_failure_count += 1
+ del messages[message_checkpoint_len:]
+ transport_state = transport_state_checkpoint
+ logger.warning(
+ "[chat.pre_tool_retry] model=%s lane=%s retry=%s/%s iteration=%s error=%s",
+ effective_chat_config.model_name,
+ queue_lane,
+ pre_tool_failure_count,
+ max_pre_tool_retries,
+ iteration,
+ exc,
+ )
+ continue
+ logger.exception(
+ "[chat.suppressed_error] model=%s lane=%s iteration=%s error=%s",
+ effective_chat_config.model_name,
+ queue_lane,
+ iteration,
+ exc,
+ )
+ return ""
+
+ logger.warning("[AI决策] 达到最大迭代次数，未能完成处理")
+ return "达到最大迭代次数，未能完成处理"
diff --git a/src/Undefined/ai/client/queue.py b/src/Undefined/ai/client/queue.py
new file mode 100644
index 00000000..9a683a9d
--- /dev/null
+++ b/src/Undefined/ai/client/queue.py
@@ -0,0 +1,283 @@
+"""AI 客户端队列化 LLM 调用与摘要请求。"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from typing import Any
+from uuid import uuid4
+
+from Undefined.ai.parsing import extract_choices_content
+from Undefined.ai.queue_budget import (
+ compute_queued_llm_timeout_seconds,
+ resolve_effective_retry_count,
+)
+from Undefined.context import RequestContext
+import Undefined.ai.client as ai_client_module
+from Undefined.services.queue_manager import (
+ ALL_QUEUE_LANES,
+ QUEUE_LANE_BACKGROUND,
+ QUEUE_LANE_GROUP_MENTION,
+ QUEUE_LANE_GROUP_NORMAL,
+ QUEUE_LANE_GROUP_SUPERADMIN,
+ QUEUE_LANE_PRIVATE,
+ QUEUE_LANE_SUPERADMIN,
+)
+
+from Undefined.ai.client.setup import ClientSetupMixin
+
+logger = logging.getLogger(__name__)
+
+
+class ClientQueueMixin(ClientSetupMixin):
+ """统一队列 LLM 调用与会话摘要投递。"""
+
+ def _resolve_queue_lane(self, queue_lane: Any = None) -> str:
+ # 优先级：显式参数 > RequestContext 资源 > 按会话类型推断 > 后台
+ queue_lane_text = str(queue_lane or "").strip().lower()
+ if queue_lane_text in ALL_QUEUE_LANES:
+ return queue_lane_text
+
+ ctx = RequestContext.current()
+ if ctx is not None:
+ ctx_lane = str(ctx.get_resource("queue_lane") or "").strip().lower()
+ if ctx_lane in ALL_QUEUE_LANES:
+ return ctx_lane
+
+ runtime_config = self._get_runtime_config()
+ superadmin_qq = int(getattr(runtime_config, "superadmin_qq", 0) or 0)
+ if ctx.request_type == "private":
+ if superadmin_qq > 0 and (
+ ctx.user_id == superadmin_qq or ctx.sender_id == superadmin_qq
+ ):
+ return QUEUE_LANE_SUPERADMIN
+ return QUEUE_LANE_PRIVATE
+ if ctx.request_type == "group":
+ if superadmin_qq > 0 and ctx.sender_id == superadmin_qq:
+ return QUEUE_LANE_GROUP_SUPERADMIN
+ # @bot 走 mention 队列，与普通群聊隔离
+ if bool(ctx.get_resource("is_at_bot")):
+ return QUEUE_LANE_GROUP_MENTION
+ return QUEUE_LANE_GROUP_NORMAL
+
+ return QUEUE_LANE_BACKGROUND
+
+ def _get_queued_llm_wait_timeout_seconds(self) -> float:
+ retry_count = resolve_effective_retry_count(
+ self._get_runtime_config(),
+ getattr(self, "_queue_manager", None),
+ )
+ return compute_queued_llm_timeout_seconds(
+ self._get_runtime_config(),
+ self.chat_config,
+ retry_count=retry_count,
+ )
+
+ async def submit_queued_llm_call(
+ self,
+ model_config: Any,
+ messages: list[dict[str, Any]],
+ tools: list[dict[str, Any]] | None = None,
+ tool_choice: Any = "auto",
+ call_type: str = "background",
+ max_tokens: int | None = None,
+ transport_state: dict[str, Any] | None = None,
+ queue_lane: str | None = None,
+ ) -> dict[str, Any]:
+ """将 LLM 调用投递到统一队列，走统一发车间隔和重试逻辑。
+ 无 queue_manager 时降级为直接调用。"""
+ effective_max_tokens = (
+ max_tokens
+ if max_tokens is not None
+ else getattr(model_config, "max_tokens", 4096)
+ )
+ resolved_queue_lane = self._resolve_queue_lane(queue_lane)
+ # 无队列管理器时直接请求，跳等车/重试封装
+ if self._queue_manager is None:
+ return await self.request_model(
+ model_config=model_config,
+ messages=messages,
+ tools=tools,
+ tool_choice=tool_choice,
+ call_type=call_type,
+ max_tokens=effective_max_tokens,
+ transport_state=transport_state,
+ )
+ request_id = uuid4().hex
+ event: asyncio.Event = asyncio.Event()
+ # 挂起表：QueueManager 回调 set_llm_call_result 时唤醒等待方
+ self._pending_llm_calls[request_id] = (event, None)
+ model_name = getattr(model_config, "model_name", "default")
+ request: dict[str, Any] = {
+ "type": "queued_llm_call",
+ "request_id": request_id,
+ "model_config": model_config,
+ "messages": messages,
+ "tools": tools,
+ "tool_choice": tool_choice,
+ "call_type": call_type,
+ "max_tokens": effective_max_tokens,
+ "transport_state": transport_state,
+ }
+ ctx = RequestContext.current()
+ if ctx is not None:
+ if ctx.group_id is not None:
+ request["group_id"] = ctx.group_id
+ if ctx.user_id is not None:
+ request["user_id"] = ctx.user_id
+ logger.info(
+ "[queued_llm_enqueue] request_id=%s call_type=%s model=%s lane=%s messages=%s tools=%s",
+ request_id,
+ call_type,
+ model_name,
+ resolved_queue_lane,
+ len(messages),
+ bool(tools),
+ )
+ try:
+ receipt = await self._queue_manager.add_queued_llm_request(
+ request,
+ lane=resolved_queue_lane,
+ model_name=model_name,
+ )
+ wait_timeout = compute_queued_llm_timeout_seconds(
+ self._get_runtime_config(),
+ model_config,
+ retry_count=resolve_effective_retry_count(
+ self._get_runtime_config(), self._queue_manager
+ ),
+ initial_wait_seconds=float(
+ getattr(receipt, "estimated_wait_seconds", 0.0) or 0.0
+ ),
+ # 首次 dispatch 间隔已含在 estimated_wait 中，避免重复计入
+ include_first_dispatch_interval=False,
+ )
+ try:
+ await asyncio.wait_for(event.wait(), timeout=wait_timeout)
+ except asyncio.TimeoutError:
+ logger.exception(
+ "[queued_llm_wait_timeout] request_id=%s call_type=%s model=%s lane=%s timeout=%.1fs",
+ request_id,
+ call_type,
+ model_name,
+ resolved_queue_lane,
+ wait_timeout,
+ )
+ raise
+ finally:
+ entry = self._pending_llm_calls.pop(request_id, None)
+ _, result = entry if entry is not None else (None, None)
+ if isinstance(result, Exception):
+ raise result
+ return result or {}
+
+ async def submit_background_llm_call(
+ self,
+ model_config: Any,
+ messages: list[dict[str, Any]],
+ tools: list[dict[str, Any]] | None = None,
+ tool_choice: Any = "auto",
+ call_type: str = "background",
+ max_tokens: int | None = None,
+ transport_state: dict[str, Any] | None = None,
+ ) -> dict[str, Any]:
+ """后台 LLM 提交兼容包装。"""
+ return await self.submit_queued_llm_call(
+ model_config=model_config,
+ messages=messages,
+ tools=tools,
+ tool_choice=tool_choice,
+ call_type=call_type,
+ max_tokens=max_tokens,
+ transport_state=transport_state,
+ queue_lane=QUEUE_LANE_BACKGROUND,
+ )
+
+ def set_llm_call_result(
+ self, request_id: str, result: dict[str, Any] | Exception
+ ) -> None:
+ entry = self._pending_llm_calls.get(request_id)
+ if entry is None:
+ return
+ event, _ = entry
+ self._pending_llm_calls[request_id] = (event, result)
+ event.set()
+
+ async def _summarize_message_history_queued(
+ self,
+ messages_text: str,
+ instruction: str = "",
+ ) -> str:
+ model_config = self._resolve_summary_model_for_requests()
+ built_messages = await self._summary_service.build_message_summary_messages(
+ # messages_text, instruction
+ messages_text,
+ instruction,
+ )
+ result = await self.submit_queued_llm_call(
+ model_config=model_config,
+ messages=built_messages,
+ tools=None,
+ call_type="message_summary",
+ max_tokens=model_config.max_tokens,
+ )
+ return extract_choices_content(result).strip()
+
+ async def _merge_summaries_queued(self, summaries: list[str]) -> str:
+ if len(summaries) == 1:
+ return summaries[0]
+
+ model_config = self._resolve_summary_model_for_requests()
+ messages = await self._summary_service.build_message_merge_messages(summaries)
+ result = await self.submit_queued_llm_call(
+ model_config=model_config,
+ messages=messages,
+ tools=None,
+ call_type="merge_message_summaries",
+ max_tokens=8192,
+ )
+ return extract_choices_content(result).strip()
+
+ async def summarize_command_session(
+ self,
+ history_manager: Any,
+ *,
+ group_id: int,
+ user_id: int,
+ count: int | None = None,
+ time_range: str | None = None,
+ instruction: str = "",
+ ) -> str:
+ """Fetch session messages and summarize via summary model without tools."""
+ messages_text = await ai_client_module.fetch_session_messages(
+ history_manager,
+ group_id=group_id,
+ user_id=user_id,
+ count=count,
+ time_range=time_range,
+ runtime_config=self.runtime_config,
+ include_header=False,
+ )
+ if not messages_text:
+ return "当前会话暂无消息记录"
+ if messages_text.startswith("无法解析时间范围"):
+ return messages_text
+
+ input_budget = await self._summary_service.resolve_message_input_budget(
+ instruction
+ )
+ total_tokens = self.count_tokens(messages_text)
+ if total_tokens <= input_budget:
+ return await self._summarize_message_history_queued(
+ # messages_text, instruction
+ messages_text,
+ instruction,
+ )
+
+ # 超长会话：分块摘要后再合并，避免超出上下文窗口
+ chunks = self.split_messages_by_tokens(messages_text, input_budget)
+ summaries = [
+ await self._summarize_message_history_queued(chunk, instruction)
+ for chunk in chunks
+ ]
+ return await self._merge_summaries_queued(summaries)
diff --git a/src/Undefined/ai/client/setup.py b/src/Undefined/ai/client/setup.py
new file mode 100644
index 00000000..f89a2abd
--- /dev/null
+++ b/src/Undefined/ai/client/setup.py
@@ -0,0 +1,914 @@
+"""AI 客户端生命周期与配置。"""
+
+from __future__ import annotations
+
+import asyncio
+import html
+import logging
+import re
+from pathlib import Path
+from typing import Any, Awaitable, Callable, Optional, Protocol, TYPE_CHECKING
+
+import httpx
+
+from Undefined.attachments import AttachmentRegistry
+from Undefined.ai.llm import ModelRequester
+from Undefined.ai.model_selector import ModelSelector
+from Undefined.ai.multimodal import MultimodalAnalyzer
+from Undefined.ai.prompts import PromptBuilder
+from Undefined.ai.crawl4ai_support import get_crawl4ai_capabilities
+from Undefined.ai.summaries import SummaryService
+from Undefined.ai.tokens import TokenCounter
+from Undefined.ai.tooling import ToolManager
+from Undefined.config import (
+ ChatModelConfig,
+ VisionModelConfig,
+ AgentModelConfig,
+ Config,
+ GrokModelConfig,
+)
+from Undefined.context import RequestContext
+from Undefined.utils.paths import PACKAGE_ROOT
+from Undefined.context_resource_registry import set_context_resource_scan_paths
+from Undefined.end_summary_storage import EndSummaryStorage
+from Undefined.memory import MemoryStorage
+from Undefined.skills.agents import AgentRegistry
+from Undefined.skills.agents.intro_generator import (
+ AgentIntroGenConfig,
+ AgentIntroGenerator,
+)
+from Undefined.skills.anthropic_skills import AnthropicSkillRegistry
+from Undefined.skills.tools import ToolRegistry
+from Undefined.token_usage_storage import TokenUsageStorage
+from Undefined.utils.logging import redact_string
+
+logger = logging.getLogger(__name__)
+
+
+# 模型返回纯文本但未调用 tool 时，追加到 messages 的纠正提示（不写死具体 tool）
+MISSING_TOOL_CALL_RETRY_HINT = (
+ "【系统提示】你上一轮输出了纯文本且未调用任何工具。"
+ "本环境必须通过工具调用来完成对外动作与结束本轮处理。"
+ "请结合上文完整对话历史与已有 tool 返回结果，自行决定下一步应调用的工具；"
+ "不要直接以纯文本作为最终对外回复。"
+)
+
+
+_CONTENT_TAG_PATTERN = re.compile(
+ r"(.*?)",
+ re.DOTALL | re.IGNORECASE,
+)
+
+_INVALID_TOOL_CALL_CONTENT = (
+ "无效工具调用：工具名称为空或格式非法，系统已跳过执行。"
+ "请使用可用工具名重新调用，或调用 end 结束本轮。"
+)
+
+
+def _build_invalid_tool_call_response(tool_call: Any) -> dict[str, Any]:
+ """为模型发出的 malformed tool call 构造 tool 角色回填消息。"""
+ call_id = ""
+ tool_name = ""
+ if isinstance(tool_call, dict):
+ call_id = str(tool_call.get("id", "") or "")
+ function = tool_call.get("function")
+ if isinstance(function, dict):
+ tool_name = str(function.get("name", "") or "").strip()
+ return {
+ "role": "tool",
+ "tool_call_id": call_id,
+ "name": tool_name,
+ "content": _INVALID_TOOL_CALL_CONTENT,
+ }
+
+
+class SendMessageCallback(Protocol):
+ def __call__(
+ self, message: str, reply_to: int | None = None
+ ) -> Awaitable[None]: ...
+
+
+class SendPrivateMessageCallback(Protocol):
+ def __call__(
+ self, user_id: int, message: str, reply_to: int | None = None
+ ) -> Awaitable[None]: ...
+
+
+# 尝试导入 langchain SearxSearchWrapper
+if TYPE_CHECKING:
+ from langchain_community.utilities import (
+ SearxSearchWrapper as SearxSearchWrapperType,
+ )
+else:
+ SearxSearchWrapperType = object
+
+_SearxSearchWrapper: type[SearxSearchWrapperType] | None
+try:
+ from langchain_community.utilities import SearxSearchWrapper as _SearxSearchWrapper
+
+ _SEARX_AVAILABLE = True
+except Exception:
+ _SearxSearchWrapper = None
+ _SEARX_AVAILABLE = False
+ logger.warning(
+ "[初始化] langchain_community 未安装或 SearxSearchWrapper 不可用，搜索功能将禁用"
+ )
+
+
+def _attachment_remote_download_max_bytes(runtime_config: Config) -> int:
+ value = int(runtime_config.attachment_remote_download_max_size_mb)
+ return max(0, value) * 1024 * 1024
+
+
+def _attachment_cache_max_bytes(runtime_config: Config) -> int:
+ value = int(runtime_config.attachment_cache_max_total_size_mb)
+ return max(0, value) * 1024 * 1024
+
+
+def _attachment_cache_max_age_seconds(runtime_config: Config) -> int:
+ value = int(runtime_config.attachment_cache_max_age_days)
+ return max(0, value) * 24 * 60 * 60
+
+
+def _resolve_summary_model_config(
+ runtime_config: Config | None,
+ fallback: AgentModelConfig,
+) -> AgentModelConfig:
+ if runtime_config is None:
+ # 回退到默认/主配置
+ return fallback
+ if not getattr(runtime_config, "summary_model_configured", False):
+ # 回退到默认/主配置
+ return fallback
+ summary_model = getattr(runtime_config, "summary_model", None)
+ if isinstance(summary_model, AgentModelConfig):
+ return summary_model
+ # 回退到默认/主配置
+ return fallback
+
+
+class ClientSetupMixin:
+ """AI 客户端初始化、配置热更新与资源清理。"""
+
+ def __init__(
+ self,
+ chat_config: ChatModelConfig,
+ vision_config: VisionModelConfig,
+ agent_config: AgentModelConfig,
+ memory_storage: Optional[MemoryStorage] = None,
+ end_summary_storage: Optional[EndSummaryStorage] = None,
+ bot_qq: int = 0,
+ runtime_config: Config | None = None,
+ cognitive_service: Any = None,
+ ) -> None:
+ """初始化 AI 客户端
+
+ 参数:
+ chat_config: 对话模型配置
+ vision_config: 视觉模型配置
+ agent_config: 智能体模型配置
+ memory_storage: 长期记忆存储
+ end_summary_storage: 短期回忆存储
+ bot_qq: 机器人自身的 QQ 号
+ """
+ self.chat_config = chat_config
+ self.vision_config = vision_config
+ self.agent_config = agent_config
+ self.bot_qq = bot_qq
+ self.runtime_config = runtime_config
+ self.memory_storage = memory_storage
+ self._end_summary_storage = end_summary_storage or EndSummaryStorage()
+ self._crawl4ai_capabilities = get_crawl4ai_capabilities()
+
+ self._http_client = httpx.AsyncClient(timeout=480.0)
+ self._token_usage_storage = TokenUsageStorage()
+ self._requester = ModelRequester(self._http_client, self._token_usage_storage)
+ self._token_counter = TokenCounter()
+ self._knowledge_manager: Any = None
+ self._cognitive_service: Any = cognitive_service
+ self._meme_service: Any = None
+ if self.runtime_config is not None:
+ self.attachment_registry = AttachmentRegistry(
+ http_client=self._http_client,
+ remote_download_max_bytes=_attachment_remote_download_max_bytes(
+ self.runtime_config
+ ),
+ max_cache_bytes=_attachment_cache_max_bytes(self.runtime_config),
+ max_records=self.runtime_config.attachment_cache_max_records,
+ max_age_seconds=_attachment_cache_max_age_seconds(self.runtime_config),
+ url_reference_max_records=(
+ self.runtime_config.attachment_url_reference_max_records
+ ),
+ url_max_length=self.runtime_config.attachment_url_max_length,
+ )
+ else:
+ self.attachment_registry = AttachmentRegistry(http_client=self._http_client)
+
+ self._send_private_message_callback: Optional[SendPrivateMessageCallback] = None
+ self._send_image_callback: Optional[
+ Callable[[int, str, str], Awaitable[None]]
+ ] = None
+
+ # 当前群聊ID和用户ID（用于send_message工具）
+ self.current_group_id: Optional[int] = None
+ self.current_user_id: Optional[int] = None
+
+ self.tool_registry = ToolRegistry(PACKAGE_ROOT / "skills" / "tools")
+ self.agent_registry = AgentRegistry(PACKAGE_ROOT / "skills" / "agents")
+
+ # 初始化 Anthropic Agent Skills 注册表（可选，目录不存在时自动跳过）
+ anthropic_skills_dir = PACKAGE_ROOT / "skills" / "anthropic_skills"
+ dot_delimiter = self._get_runtime_config().tools_dot_delimiter
+ self.anthropic_skill_registry = AnthropicSkillRegistry(
+ anthropic_skills_dir,
+ dot_delimiter=dot_delimiter,
+ )
+
+ self.tool_manager = ToolManager(
+ self.tool_registry,
+ self.agent_registry,
+ anthropic_skill_registry=self.anthropic_skill_registry,
+ )
+
+ self.model_selector = ModelSelector()
+
+ # 绑定上下文资源扫描路径（基于注册表 watch_paths）
+ scan_paths = [
+ p
+ for p in (
+ self.tool_registry._watch_paths + self.agent_registry._watch_paths
+ )
+ if p.exists()
+ ]
+ set_context_resource_scan_paths(scan_paths)
+ logger.debug(
+ "[初始化] 上下文资源扫描路径已绑定: count=%s",
+ len(scan_paths),
+ )
+
+ # Agent intro 生成器（延迟初始化，需要外部设置 queue_manager）
+ self._agent_intro_generator: Any | None = None
+ self._agent_intro_task: asyncio.Task[None] | None = None
+ self._intro_refresh_task: asyncio.Task[None] | None = None
+ self._queue_manager: Any | None = None
+ self._intro_config: Any | None = None
+ # 后台 LLM 调用挂起表（走队列的后台请求）
+ self._pending_llm_calls: dict[
+ str, tuple[asyncio.Event, dict[str, Any] | Exception | None]
+ ] = {}
+
+ # 后台任务引用集合（防止被 GC）
+ self._background_tasks: set[asyncio.Task[Any]] = set()
+
+ runtime_config = self._get_runtime_config()
+ self._intro_config = AgentIntroGenConfig(
+ enabled=runtime_config.agent_intro_autogen_enabled,
+ queue_interval_seconds=runtime_config.agent_intro_autogen_queue_interval,
+ max_tokens=runtime_config.agent_intro_autogen_max_tokens,
+ cache_path=Path(runtime_config.agent_intro_hash_path),
+ )
+
+ # 启动 skills 热重载
+ hot_reload_enabled = runtime_config.skills_hot_reload
+ if hot_reload_enabled:
+ interval = runtime_config.skills_hot_reload_interval
+ debounce = runtime_config.skills_hot_reload_debounce
+ self.tool_registry.start_hot_reload(interval=interval, debounce=debounce)
+ self.agent_registry.start_hot_reload(interval=interval, debounce=debounce)
+ self.anthropic_skill_registry.start_hot_reload(
+ interval=interval, debounce=debounce
+ )
+ logger.info(
+ "[初始化] 技能热重载已启用: interval=%.2fs debounce=%.2fs",
+ interval,
+ debounce,
+ )
+ else:
+ logger.info("[初始化] 技能热重载已禁用")
+
+ # 初始化搜索 wrapper
+ self._search_wrapper: Optional[Any] = None
+ if _SEARX_AVAILABLE and _SearxSearchWrapper is not None:
+ searxng_url = runtime_config.searxng_url
+ if searxng_url:
+ try:
+ self._search_wrapper = _SearxSearchWrapper(
+ searx_host=searxng_url, k=10
+ )
+ logger.info(
+ "[初始化] SearxSearchWrapper 初始化成功: url=%s k=10",
+ redact_string(searxng_url),
+ )
+ except Exception as exc:
+ logger.warning("[初始化] SearxSearchWrapper 初始化失败: %s", exc)
+ else:
+ logger.info("[初始化] SEARXNG_URL 未配置，搜索功能禁用")
+
+ if self._crawl4ai_capabilities.available:
+ logger.info("[初始化] crawl4ai 可用，网页获取功能已启用")
+ else:
+ detail = self._crawl4ai_capabilities.error
+ if detail:
+ logger.warning(
+ "[初始化] crawl4ai 不可用，网页获取功能将禁用: %s",
+ detail,
+ )
+ else:
+ logger.warning("[初始化] crawl4ai 不可用，网页获取功能将禁用")
+
+ self._prompt_builder = PromptBuilder(
+ bot_qq=self.bot_qq,
+ memory_storage=self.memory_storage,
+ end_summary_storage=self._end_summary_storage,
+ runtime_config_getter=self._get_runtime_config,
+ anthropic_skill_registry=self.anthropic_skill_registry,
+ cognitive_service=self._cognitive_service,
+ )
+ self._multimodal = MultimodalAnalyzer(self._requester, self.vision_config)
+ self._rebuild_summary_service()
+
+ async def init_mcp_async() -> None:
+ try:
+ await self.tool_registry.initialize_mcp_toolsets()
+ except Exception as exc:
+ logger.warning("[初始化] 异步初始化 MCP 工具集失败: %s", exc)
+
+ self._mcp_init_task = asyncio.create_task(init_mcp_async())
+
+ async def load_preferences_async() -> None:
+ try:
+ await self.model_selector.load_preferences()
+ except Exception as exc:
+ logger.warning("[初始化] 加载模型偏好失败: %s", exc)
+
+ self._preferences_load_task = asyncio.create_task(load_preferences_async())
+
+ logger.info("[初始化] AIClient 初始化完成")
+
+ async def close(self) -> None:
+ logger.info("[清理] 正在关闭 AIClient...")
+
+ intro_gen = getattr(self, "_agent_intro_generator", None)
+ if intro_gen is not None:
+ await intro_gen.stop()
+ intro_refresh_task = getattr(self, "_intro_refresh_task", None)
+ if intro_refresh_task is not None and not intro_refresh_task.done():
+ intro_refresh_task.cancel()
+ try:
+ await intro_refresh_task
+ except asyncio.CancelledError:
+ pass
+ self._intro_refresh_task = None
+ if hasattr(self, "_agent_intro_task") and self._agent_intro_task:
+ if not self._agent_intro_task.done():
+ await self._agent_intro_task
+ knowledge_manager = getattr(self, "_knowledge_manager", None)
+ if knowledge_manager is not None and hasattr(knowledge_manager, "stop"):
+ try:
+ await knowledge_manager.stop()
+ except Exception as exc:
+ logger.warning("[清理] 关闭知识库管理器失败: %s", exc)
+ self._knowledge_manager = None
+ cognitive_service = getattr(self, "_cognitive_service", None)
+ if cognitive_service is not None:
+ if hasattr(cognitive_service, "stop"):
+ try:
+ await cognitive_service.stop()
+ except Exception as exc:
+ logger.warning("[清理] 关闭认知记忆服务失败: %s", exc)
+ self._cognitive_service = None
+ if hasattr(self, "_prompt_builder") and self._prompt_builder is not None:
+ self._prompt_builder.set_cognitive_service(None)
+
+ if hasattr(self, "_mcp_init_task") and not self._mcp_init_task.done():
+ await self._mcp_init_task
+
+ if hasattr(self, "tool_registry"):
+ await self.tool_registry.stop_hot_reload()
+ await self.tool_registry.close_mcp_toolsets()
+ if hasattr(self, "agent_registry"):
+ await self.agent_registry.stop_hot_reload()
+ if hasattr(self, "anthropic_skill_registry"):
+ await self.anthropic_skill_registry.stop_hot_reload()
+
+ attachment_registry = getattr(self, "attachment_registry", None)
+ if attachment_registry is not None and hasattr(attachment_registry, "flush"):
+ try:
+ await attachment_registry.flush()
+ except Exception as exc:
+ logger.warning("[清理] 刷新附件注册表失败: %s", exc)
+
+ if hasattr(self, "_http_client"):
+ logger.info("[清理] 正在关闭 AIClient HTTP 客户端...")
+ await self._http_client.aclose()
+
+ logger.info("[清理] AIClient 已关闭")
+
+ def set_queue_manager(self, queue_manager: Any) -> None:
+ """设置队列管理器并启动 Agent intro 生成器。
+
+ 参数:
+ queue_manager: 队列管理器实例
+ """
+ if self._queue_manager is not None:
+ logger.warning("[AI客户端] queue_manager 已设置，跳过重复设置")
+ return
+
+ if queue_manager is None:
+ logger.warning("[AI客户端] 传入的 queue_manager 为 None")
+ return
+
+ self._queue_manager = queue_manager
+
+ # 启动/刷新 Agent intro 自动生成
+ if self._intro_config:
+ self.apply_intro_config(self._intro_config)
+
+ def apply_intro_config(self, config: AgentIntroGenConfig) -> None:
+ """应用 Agent intro 生成器配置（支持热更新）。"""
+ self._intro_config = config
+ if self._queue_manager is None:
+ return
+ existing = self._intro_refresh_task
+ if existing is not None and not existing.done():
+ existing.cancel()
+
+ async def _run_refresh() -> None:
+ try:
+ await self._refresh_intro_generator(config)
+ except asyncio.CancelledError:
+ raise
+ except Exception:
+ logger.exception("[Agent介绍] 刷新 intro 生成器失败")
+
+ task = asyncio.create_task(_run_refresh())
+
+ def _finalize(done_task: asyncio.Task[None]) -> None:
+ if getattr(self, "_intro_refresh_task", None) is done_task:
+ self._intro_refresh_task = None
+ if done_task.cancelled():
+ return
+ exc = done_task.exception()
+ if exc is not None:
+ logger.error("[Agent介绍] intro 刷新任务异常结束", exc_info=exc)
+
+ task.add_done_callback(_finalize)
+ self._intro_refresh_task = task
+
+ async def _refresh_intro_generator(self, config: AgentIntroGenConfig) -> None:
+ if not config.enabled:
+ if self._agent_intro_generator is not None:
+ await self._agent_intro_generator.stop()
+ self._agent_intro_generator = None
+ self._agent_intro_task = None
+ logger.info("[Agent介绍] 自动生成已关闭")
+ return
+
+ if self._queue_manager is None:
+ return
+
+ if self._agent_intro_generator is None:
+ self._agent_intro_generator = AgentIntroGenerator(
+ self.agent_registry.base_dir,
+ self,
+ self._queue_manager,
+ config,
+ )
+ self._agent_intro_task = asyncio.create_task(
+ self._agent_intro_generator.start()
+ )
+ logger.info(
+ "[Agent介绍] 自动生成已启动: interval=%.2fs max_tokens=%s cache=%s",
+ config.queue_interval_seconds,
+ config.max_tokens,
+ config.cache_path,
+ )
+ return
+
+ if self._agent_intro_generator.config.cache_path != config.cache_path:
+ # 缓存路径变更需重建生成器，否则 hash 与落盘目录不一致
+ await self._agent_intro_generator.stop()
+ self._agent_intro_generator = AgentIntroGenerator(
+ self.agent_registry.base_dir,
+ self,
+ self._queue_manager,
+ config,
+ )
+ self._agent_intro_task = asyncio.create_task(
+ self._agent_intro_generator.start()
+ )
+ logger.info(
+ "[Agent介绍] 缓存路径变更，已重启生成器: cache=%s",
+ config.cache_path,
+ )
+ return
+
+ self._agent_intro_generator.config = config
+
+ def set_knowledge_manager(self, manager: Any) -> None:
+ self._knowledge_manager = manager
+
+ def set_cognitive_service(self, service: Any) -> None:
+ self._cognitive_service = service
+ if hasattr(self, "_prompt_builder") and self._prompt_builder is not None:
+ self._prompt_builder.set_cognitive_service(service)
+ logger.info(
+ "[AI客户端] 认知记忆服务已挂载并同步到 PromptBuilder: enabled=%s",
+ bool(getattr(service, "enabled", False)) if service is not None else False,
+ )
+
+ def set_meme_service(self, service: Any) -> None:
+ self._meme_service = service
+ resolver = None
+ async_resolver = None
+ if service is not None and hasattr(service, "resolve_global_image_sync"):
+ resolver = service.resolve_global_image_sync
+ if service is not None and hasattr(service, "resolve_global_image"):
+ async_resolver = service.resolve_global_image
+ self.attachment_registry.set_global_image_resolver(resolver)
+ self.attachment_registry.set_global_image_resolver_async(async_resolver)
+ logger.info(
+ "[AI客户端] 表情包服务已挂载: enabled=%s",
+ bool(getattr(service, "enabled", False)) if service is not None else False,
+ )
+
+ def apply_search_config(self, searxng_url: str) -> None:
+ """应用搜索服务配置（支持热更新）。"""
+ if not _SEARX_AVAILABLE or _SearxSearchWrapper is None:
+ if searxng_url:
+ logger.warning(
+ "[配置] 搜索组件不可用，已忽略 SEARXNG_URL=%s",
+ redact_string(searxng_url),
+ )
+ else:
+ logger.info("[配置] 搜索组件不可用，搜索已禁用")
+ self._search_wrapper = None
+ return
+
+ if not searxng_url:
+ self._search_wrapper = None
+ logger.info("[配置] SEARXNG_URL 未配置，搜索功能已禁用")
+ return
+
+ try:
+ self._search_wrapper = _SearxSearchWrapper(searx_host=searxng_url, k=10)
+ logger.info(
+ "[配置] 搜索服务已更新: url=%s k=10",
+ redact_string(searxng_url),
+ )
+ except Exception as exc:
+ logger.warning("[配置] 搜索服务更新失败: %s", exc)
+ self._search_wrapper = None
+ logger.info("[配置] 搜索服务已回退为禁用")
+
+ def apply_model_configs(
+ self,
+ *,
+ chat_config: ChatModelConfig,
+ vision_config: VisionModelConfig,
+ agent_config: AgentModelConfig,
+ runtime_config: Config,
+ ) -> None:
+ """应用热更新后的模型配置。"""
+ self.chat_config = chat_config
+ self.vision_config = vision_config
+ self.agent_config = agent_config
+ self.runtime_config = runtime_config
+ self._multimodal = MultimodalAnalyzer(self._requester, self.vision_config)
+ self._rebuild_summary_service()
+ self.apply_attachment_config(runtime_config)
+ logger.info(
+ "[配置] AI 模型配置已热更新: chat=%s vision=%s agent=%s",
+ self.chat_config.model_name,
+ self.vision_config.model_name,
+ self.agent_config.model_name,
+ )
+
+ def apply_runtime_config(self, runtime_config: Config) -> None:
+ """应用不需要重建模型客户端的运行时配置。"""
+ self.runtime_config = runtime_config
+ self._rebuild_summary_service()
+ logger.info("[配置] AI 运行时配置已热更新")
+
+ def _rebuild_summary_service(self) -> None:
+ self._summary_service = SummaryService(
+ self._requester,
+ _resolve_summary_model_config(self.runtime_config, self.agent_config),
+ self._token_counter,
+ )
+
+ def _resolve_summary_model_for_requests(self) -> AgentModelConfig:
+ return _resolve_summary_model_config(self.runtime_config, self.agent_config)
+
+ def apply_attachment_config(self, runtime_config: Config) -> None:
+ self.attachment_registry.set_limits(
+ remote_download_max_bytes=_attachment_remote_download_max_bytes(
+ runtime_config
+ ),
+ max_cache_bytes=_attachment_cache_max_bytes(runtime_config),
+ max_records=runtime_config.attachment_cache_max_records,
+ max_age_seconds=_attachment_cache_max_age_seconds(runtime_config),
+ url_reference_max_records=(
+ runtime_config.attachment_url_reference_max_records
+ ),
+ url_max_length=runtime_config.attachment_url_max_length,
+ )
+
+ def count_tokens(self, text: str) -> int:
+ return self._token_counter.count(text)
+
+ def _get_runtime_config(self) -> Config:
+ if self.runtime_config is not None:
+ return self.runtime_config
+ from Undefined.config import get_config
+
+ return get_config(strict=False)
+
+ def _find_chat_config_by_name(self, model_name: str) -> ChatModelConfig:
+ """根据模型名查找配置（主模型或池中模型）"""
+ if model_name == self.chat_config.model_name:
+ return self.chat_config
+ if self.chat_config.pool and self.chat_config.pool.enabled:
+ for entry in self.chat_config.pool.models:
+ if entry.model_name == model_name:
+ return self.model_selector._entry_to_chat_config(
+ # entry, self.chat_config
+ entry,
+ self.chat_config,
+ )
+ return self.chat_config
+
+ def _get_prefetch_tool_names(self) -> list[str]:
+ runtime_config = self._get_runtime_config()
+ return list(runtime_config.prefetch_tools)
+
+ def _filter_tools_for_runtime_config(
+ self, tools: list[dict[str, Any]]
+ ) -> list[dict[str, Any]]:
+ runtime_config = self._get_runtime_config()
+ enabled = bool(getattr(runtime_config, "nagaagent_mode_enabled", False))
+ if enabled:
+ return tools
+
+ # 关闭 NagaAgent 模式时：隐藏相关 Agent，避免被模型误调用。
+ filtered: list[dict[str, Any]] = []
+ for tool in tools:
+ function = tool.get("function") if isinstance(tool, dict) else None
+ name = function.get("name") if isinstance(function, dict) else None
+ if name == "naga_code_analysis_agent":
+ continue
+ filtered.append(tool)
+ return filtered
+
+ def _prefetch_hide_tools(self) -> bool:
+ runtime_config = self._get_runtime_config()
+ return runtime_config.prefetch_tools_hide
+
+ def _is_missing_tool_result(self, result: Any) -> bool:
+ if not isinstance(result, str):
+ return False
+ return result.startswith("未找到项目") or result.startswith("未找到 MCP 工具")
+
+ async def _maybe_prefetch_tools(
+ self,
+ messages: list[dict[str, Any]],
+ tools: list[dict[str, Any]] | None,
+ call_type: str,
+ ) -> tuple[list[dict[str, Any]], list[dict[str, Any]] | None]:
+ if not tools:
+ return messages, tools
+
+ # 预先调用部分工具，为模型补充稳定上下文（同一 call_type 仅执行一次）
+ prefetch_names = self._get_prefetch_tool_names()
+ if not prefetch_names:
+ return messages, tools
+
+ available_names = {
+ tool.get("function", {}).get("name")
+ for tool in tools
+ if tool.get("function")
+ }
+ prefetch_targets = [name for name in prefetch_names if name in available_names]
+ if not prefetch_targets:
+ return messages, tools
+
+ # 使用 RequestContext 缓存已执行的预先调用，避免重复触发
+ ctx = RequestContext.current()
+ cache: dict[str, list[str]] = {}
+ done: set[str] = set()
+ if ctx:
+ cache = ctx.get_resource("prefetch_tools", {}) or {}
+ done = set(cache.get(call_type, []))
+
+ to_run = [name for name in prefetch_targets if name not in done]
+ if not to_run:
+ return messages, tools
+
+ results: list[tuple[str, Any]] = []
+ for name in to_run:
+ try:
+ tool_args: dict[str, Any] = {}
+ if name == "get_current_time":
+ tool_args = {"format": "text", "include_lunar": True}
+
+ result = await self.tool_manager.execute_tool(
+ name,
+ tool_args,
+ {
+ "runtime_config": self._get_runtime_config(),
+ "easter_egg_silent": True,
+ },
+ )
+ except Exception as exc:
+ logger.warning("[预先调用] %s 执行失败: %s", name, exc)
+ continue
+
+ if self._is_missing_tool_result(result):
+ logger.warning("[预先调用] %s 未找到对应工具，跳过", name)
+ continue
+
+ results.append((name, result))
+ done.add(name)
+
+ if not results:
+ return messages, tools
+
+ if ctx:
+ cache[call_type] = sorted(done)
+ ctx.set_resource("prefetch_tools", cache)
+
+ content_lines = ["【预先工具结果】"]
+ content_lines.extend([f"- {name}: {result}" for name, result in results])
+ prefetch_message = {"role": "system", "content": "\n".join(content_lines)}
+
+ insert_idx = 0
+ # 紧接在已有 system 消息之后插入 prefetch 结果，保持指令顺序
+ for idx, msg in enumerate(messages):
+ if msg.get("role") == "system":
+ insert_idx = idx + 1
+ else:
+ break
+ new_messages = list(messages)
+ new_messages.insert(insert_idx, prefetch_message)
+
+ if self._prefetch_hide_tools():
+ hidden = set(name for name in done)
+ tools = [
+ tool
+ for tool in tools
+ if tool.get("function", {}).get("name") not in hidden
+ ]
+ return new_messages, tools
+
+ async def request_model(
+ self,
+ model_config: (
+ ChatModelConfig | VisionModelConfig | AgentModelConfig | GrokModelConfig
+ ),
+ messages: list[dict[str, Any]],
+ max_tokens: int = 8192,
+ call_type: str = "chat",
+ tools: list[dict[str, Any]] | None = None,
+ tool_choice: str = "auto",
+ transport_state: dict[str, Any] | None = None,
+ **kwargs: Any,
+ ) -> dict[str, Any]:
+ tools = self.tool_manager.maybe_merge_agent_tools(call_type, tools)
+ if tools is not None:
+ tools = self._filter_tools_for_runtime_config(tools)
+ message_count_for_transport = len(messages)
+ # Responses 续轮（previous_response_id）时跳过 prefetch，避免重复注入系统消息
+ if not (
+ isinstance(transport_state, dict)
+ and transport_state.get("previous_response_id")
+ ):
+ messages, tools = await self._maybe_prefetch_tools(
+ # messages, tools, call_type
+ messages,
+ tools,
+ call_type,
+ )
+ return await self._requester.request(
+ model_config=model_config,
+ messages=messages,
+ max_tokens=max_tokens,
+ call_type=call_type,
+ tools=tools,
+ tool_choice=tool_choice,
+ transport_state=transport_state,
+ message_count_for_transport=message_count_for_transport,
+ **kwargs,
+ )
+
+ def get_active_agent_mcp_registry(self, agent_name: str) -> Any | None:
+ return self.tool_manager.get_active_agent_mcp_registry(agent_name)
+
+ async def analyze_multimodal(
+ self,
+ media_url: str,
+ media_type: str = "auto",
+ prompt_extra: str = "",
+ ) -> dict[str, str]:
+ return await self._multimodal.analyze(media_url, media_type, prompt_extra)
+
+ async def describe_image(
+ self, image_url: str, prompt_extra: str = ""
+ ) -> dict[str, str]:
+ return await self._multimodal.describe_image(image_url, prompt_extra)
+
+ async def judge_meme_image(self, image_url: str) -> dict[str, Any]:
+ return await self._multimodal.judge_meme_image(image_url)
+
+ async def describe_meme_image(self, image_url: str) -> dict[str, Any]:
+ return await self._multimodal.describe_meme_image(image_url)
+
+ def get_media_history(self, media_key: str) -> list[dict[str, str]]:
+ """获取指定媒体键的多模态分析历史 Q&A 记录。"""
+ return self._multimodal.get_history(media_key)
+
+ async def save_media_history(
+ self, media_key: str, question: str, answer: str
+ ) -> None:
+ """保存一条多模态分析 Q&A 到历史记录并持久化到磁盘。"""
+ await self._multimodal.save_history(media_key, question, answer)
+
+ async def summarize_chat(self, messages: str, context: str = "") -> str:
+ return await self._summary_service.summarize_chat(messages, context)
+
+ async def merge_summaries(self, summaries: list[str]) -> str:
+ return await self._summary_service.merge_summaries(summaries)
+
+ def split_messages_by_tokens(self, messages: str, max_tokens: int) -> list[str]:
+ return self._summary_service.split_messages_by_tokens(messages, max_tokens)
+
+ async def generate_title(self, summary: str) -> str:
+ return await self._summary_service.generate_title(summary)
+
+ def _extract_message_excerpt(self, question: str) -> str:
+ matched = _CONTENT_TAG_PATTERN.search(question)
+ if matched:
+ content = html.unescape(matched.group(1))
+ else:
+ content = question
+ cleaned = " ".join(content.split()).strip()
+ if not cleaned:
+ return "(无文本内容)"
+ if len(cleaned) > 120:
+ return cleaned[:117].rstrip() + "..."
+ return cleaned
+
+ def _is_end_only_tool_calls(
+ self,
+ tool_calls: list[dict[str, Any]],
+ api_to_internal: dict[str, str],
+ ) -> bool:
+ # 无 tool_calls 与有 tool_calls 走不同分支
+ if not tool_calls:
+ return False
+ # 逐个处理模型返回的 tool_call
+ for tool_call in tool_calls:
+ function = tool_call.get("function", {})
+ api_name = str(function.get("name", "") or "")
+ internal_name = api_to_internal.get(api_name, api_name)
+ if internal_name != "end":
+ return False
+ return True
+
+ async def _save_forward_to_history(
+ self,
+ content: str,
+ pre_context: dict[str, Any],
+ history_manager: Any,
+ ) -> None:
+ """将合并转发消息写入历史记录"""
+ if history_manager is None:
+ return
+
+ try:
+ group_id = pre_context.get("group_id")
+ user_id = pre_context.get("user_id")
+
+ if group_id is not None:
+ await history_manager.add_group_message(
+ group_id=int(group_id),
+ sender_id=0,
+ text_content=content,
+ sender_card="",
+ sender_nickname="[合并转发内容]",
+ group_name="",
+ role="system",
+ title="",
+ message_id=None,
+ )
+ elif user_id is not None:
+ await history_manager.add_private_message(
+ user_id=int(user_id),
+ text_content=content,
+ display_name="[合并转发内容]",
+ user_name="",
+ message_id=None,
+ )
+ else:
+ logger.debug("[合并转发] 无法写入历史：缺少 group_id 和 user_id")
+ except Exception as exc:
+ logger.debug("[合并转发] 写入历史失败: %s", exc)
diff --git a/src/Undefined/ai/llm.py b/src/Undefined/ai/llm.py
deleted file mode 100644
index 03801a87..00000000
--- a/src/Undefined/ai/llm.py
+++ /dev/null
@@ -1,2065 +0,0 @@
-"""LLM 模型请求处理。"""
-
-from __future__ import annotations
-
-import asyncio
-import hashlib
-import json
-import logging
-import re
-import time
-from datetime import datetime
-from typing import Any
-from urllib.parse import parse_qsl, urlsplit, urlunsplit
-
-import httpx
-from openai import (
- APIConnectionError,
- APIStatusError,
- APITimeoutError,
- AsyncOpenAI,
-)
-
-from Undefined.ai.parsing import extract_choices_content
-from Undefined.ai.transports import (
- API_MODE_CHAT_COMPLETIONS,
- API_MODE_RESPONSES,
- build_responses_request_body,
- get_api_mode,
- get_effort_payload,
- get_effort_style,
- get_thinking_payload,
- normalize_responses_result,
-)
-from Undefined.ai.retrieval import RetrievalRequester
-from Undefined.ai.tokens import TokenCounter
-from Undefined.context import RequestContext
-from Undefined.config import (
- ChatModelConfig,
- VisionModelConfig,
- AgentModelConfig,
- SecurityModelConfig,
- EmbeddingModelConfig,
- GrokModelConfig,
- RerankModelConfig,
- Config,
- get_config,
-)
-from Undefined.token_usage_storage import TokenUsageStorage, TokenUsage
-from Undefined.utils.logging import log_debug_json, redact_string
-from Undefined.utils.request_params import (
- merge_request_params,
- split_reserved_request_params,
-)
-from Undefined.utils.tool_calls import normalize_tool_arguments_json
-
-logger = logging.getLogger(__name__)
-
-ModelConfig = (
- ChatModelConfig
- | VisionModelConfig
- | AgentModelConfig
- | SecurityModelConfig
- | EmbeddingModelConfig
- | GrokModelConfig
- | RerankModelConfig
-)
-
-__all__ = ["ModelRequester", "build_request_body", "ModelConfig"]
-
-_CHAT_COMPLETIONS_KNOWN_FIELDS: set[str] = {
- "model",
- "messages",
- "audio",
- "metadata",
- "max_completion_tokens",
- "max_tokens",
- "modalities",
- "parallel_tool_calls",
- "prediction",
- "prompt_cache_key",
- "prompt_cache_retention",
- "reasoning_effort",
- "safety_identifier",
- "service_tier",
- "store",
- "temperature",
- "top_p",
- "n",
- "stop",
- "presence_penalty",
- "frequency_penalty",
- "logit_bias",
- "user",
- "response_format",
- "seed",
- "stream",
- "stream_options",
- "tools",
- "tool_choice",
- "logprobs",
- "top_logprobs",
- "verbosity",
- "web_search_options",
-}
-
-_SDK_REQUEST_OPTION_FIELDS: frozenset[str] = frozenset(
- {"extra_headers", "extra_query", "extra_body", "timeout"}
-)
-
-_RESPONSES_KNOWN_FIELDS: set[str] = {
- "background",
- "context_management",
- "conversation",
- "include",
- "model",
- "input",
- "instructions",
- "max_output_tokens",
- "max_tool_calls",
- "metadata",
- "previous_response_id",
- "prompt",
- "prompt_cache_key",
- "prompt_cache_retention",
- "reasoning",
- "safety_identifier",
- "service_tier",
- "store",
- "temperature",
- "top_p",
- "tools",
- "tool_choice",
- "parallel_tool_calls",
- "stream",
- "stream_options",
- "text",
- "truncation",
- "user",
-}
-
-_CHAT_COMPLETIONS_RESERVED_FIELDS: frozenset[str] = (
- frozenset(
- {
- "model",
- "messages",
- "max_tokens",
- "tools",
- "tool_choice",
- "stream",
- "stream_options",
- "thinking",
- "reasoning",
- "reasoning_effort",
- "output_config",
- }
- )
- | _SDK_REQUEST_OPTION_FIELDS
-)
-
-_RESPONSES_RESERVED_FIELDS: frozenset[str] = (
- frozenset(
- {
- "model",
- "input",
- "instructions",
- "max_output_tokens",
- "tools",
- "tool_choice",
- "previous_response_id",
- "stream",
- "stream_options",
- "thinking",
- "reasoning",
- "reasoning_effort",
- "output_config",
- }
- )
- | _SDK_REQUEST_OPTION_FIELDS
-)
-
-_THINKING_KEYS: tuple[str, ...] = (
- "thinking",
- "reasoning",
- "reasoning_content",
- "chain_of_thought",
- "cot",
- "thoughts",
-)
-_CHAT_COMPLETION_STRIP_THINKING_KEYS: frozenset[str] = frozenset(
- ("thinking", "reasoning", "chain_of_thought", "cot", "thoughts")
-)
-_CHAT_COMPLETION_INTERNAL_MESSAGE_KEYS: frozenset[str] = frozenset(
- (
- "reasoning_content",
- *_CHAT_COMPLETION_STRIP_THINKING_KEYS,
- "_responses_output_items",
- "phase",
- )
-)
-
-_DEFAULT_TOOLS_DESCRIPTION_MAX_LEN = 1024
-_TOOLS_PARAM_INDEX_RE = re.compile(r"Tools\[(\d+)\]", re.IGNORECASE)
-_RESPONSES_MISSING_TOOL_CALL_OUTPUT_RE = re.compile(
- r"no tool call found for function call output with call_id",
- re.IGNORECASE,
-)
-_DEFAULT_TOOLS_DESCRIPTION_PREVIEW_LEN = 160
-
-_DEFAULT_TOOL_NAME_DOT_DELIMITER = "-_-"
-_TOOL_NAME_MAX_LEN = 64
-_TOOL_NAME_ALLOWED_RE = re.compile(r"^[a-zA-Z0-9_-]+$")
-_PROMPT_CACHE_KEY_MAX_LEN = 128
-
-
-def _tool_name_dot_delimiter() -> str:
- runtime_config = _get_runtime_config()
- value = (
- getattr(runtime_config, "tools_dot_delimiter", None) if runtime_config else None
- )
- text = str(value).strip() if value is not None else _DEFAULT_TOOL_NAME_DOT_DELIMITER
- if not text:
- return _DEFAULT_TOOL_NAME_DOT_DELIMITER
- if "." in text:
- return _DEFAULT_TOOL_NAME_DOT_DELIMITER
- if not _TOOL_NAME_ALLOWED_RE.match(text):
- return _DEFAULT_TOOL_NAME_DOT_DELIMITER
- # 保持较短长度，避免工具名被服务端截断。
- if len(text) > 16:
- return text[:16]
- return text
-
-
-def _hash8(text: str) -> str:
- return hashlib.sha1(text.encode("utf-8"), usedforsecurity=False).hexdigest()[:8]
-
-
-def _normalize_prompt_cache_part(value: Any) -> str:
- text = str(value or "").strip().lower()
- if not text:
- return "none"
- normalized_chars: list[str] = []
- for char in text:
- if char.isalnum() or char in {"-", "_", ":"}:
- normalized_chars.append(char)
- else:
- normalized_chars.append("_")
- normalized = "".join(normalized_chars).strip("_")
- return normalized or "none"
-
-
-def _build_scope_prompt_cache_part() -> str:
- ctx = RequestContext.current()
- if ctx is None:
- return "scope:global"
- if ctx.group_id is not None:
- return f"group:{int(ctx.group_id)}"
- if ctx.user_id is not None:
- return f"private:{int(ctx.user_id)}"
- if ctx.sender_id is not None:
- return f"sender:{int(ctx.sender_id)}"
- request_type = _normalize_prompt_cache_part(ctx.request_type)
- return f"type:{request_type}"
-
-
-def _build_default_prompt_cache_key(model_config: ModelConfig, call_type: str) -> str:
- model_name = _normalize_prompt_cache_part(getattr(model_config, "model_name", ""))
- scope_part = _build_scope_prompt_cache_part()
- call_part = _normalize_prompt_cache_part(call_type)
- key = f"pc:{model_name}:{call_part}:{scope_part}"
- if len(key) <= _PROMPT_CACHE_KEY_MAX_LEN:
- return key
- suffix = "_" + _hash8(key)
- prefix_len = max(1, _PROMPT_CACHE_KEY_MAX_LEN - len(suffix))
- return key[:prefix_len] + suffix
-
-
-def _encode_tool_name_for_api(tool_name: str) -> str:
- """将内部工具名编码为服务端可接受的 function.name。
-
- - 将 '.' 替换为 '-_-'（保留工具集命名语义）
- - 其他不允许字符替换为 '_'
- - 强制最大长度（<=64），超长时追加稳定哈希
- """
- raw = str(tool_name or "").strip()
- if not raw:
- return "tool"
-
- # 保留工具集分隔语义：category.tool -> categorytool
- encoded = raw.replace(".", _tool_name_dot_delimiter())
-
- # 替换其他不允许字符。
- cleaned_chars: list[str] = []
- for ch in encoded:
- if ch.isalnum() or ch in {"_", "-"}:
- cleaned_chars.append(ch)
- else:
- cleaned_chars.append("_")
- encoded = "".join(cleaned_chars)
-
- if not encoded:
- encoded = "tool"
-
- if len(encoded) > _TOOL_NAME_MAX_LEN:
- suffix = "_" + _hash8(raw)
- prefix_len = max(1, _TOOL_NAME_MAX_LEN - len(suffix))
- encoded = encoded[:prefix_len] + suffix
-
- # 最后兜底校验（理论上应始终通过）
- if not _TOOL_NAME_ALLOWED_RE.match(encoded):
- suffix = "_" + _hash8(raw)
- encoded = re.sub(r"[^a-zA-Z0-9_-]", "_", encoded)
- if len(encoded) > _TOOL_NAME_MAX_LEN:
- encoded = encoded[: _TOOL_NAME_MAX_LEN - len(suffix)] + suffix
- if not encoded:
- encoded = "tool" + suffix
-
- return encoded
-
-
-def _responses_should_fallback_to_stateless_replay(
- exc: APIStatusError,
- request_body: dict[str, Any],
- *,
- stateless_replay: bool,
-) -> bool:
- if stateless_replay or not request_body.get("previous_response_id"):
- return False
- input_items = request_body.get("input")
- if not isinstance(input_items, list) or not any(
- isinstance(item, dict) and item.get("type") == "function_call_output"
- for item in input_items
- ):
- return False
- if exc.status_code != 400 or not isinstance(exc.body, dict):
- return False
- error = exc.body.get("error")
- if not isinstance(error, dict):
- return False
- message = str(error.get("message", "")).strip()
- param = str(error.get("param", "")).strip().lower()
- return param == "input" and bool(
- _RESPONSES_MISSING_TOOL_CALL_OUTPUT_RE.search(message)
- )
-
-
-def _sanitize_openai_tool_names_in_request(
- request_body: dict[str, Any],
-) -> tuple[dict[str, str], dict[str, str]]:
- """将 request_body 的 tools/messages 工具名改写为服务端可接受的名称。
-
- Returns:
- (api_to_internal, internal_to_api) 映射表。
-
- Notes:
- - 仅保证 tools schema 中出现的名称可逆映射。
- - 历史消息中的工具调用会尽力重写。
- """
- tools = request_body.get("tools")
- if not isinstance(tools, list) or not tools:
- return {}, {}
-
- internal_to_api: dict[str, str] = {}
- api_to_internal: dict[str, str] = {}
- used_api: set[str] = set()
-
- new_tools: list[dict[str, Any]] = []
- for tool in tools:
- if not isinstance(tool, dict):
- new_tools.append(tool)
- continue
- function = tool.get("function")
- if not isinstance(function, dict):
- new_tools.append(tool)
- continue
- internal_name = str(function.get("name", "") or "")
- if not internal_name:
- new_tools.append(tool)
- continue
-
- # 稳定编码；如发生冲突则追加后缀。
- base_api_name = _encode_tool_name_for_api(internal_name)
- api_name = base_api_name
- if api_name in used_api and api_to_internal.get(api_name) != internal_name:
- suffix = "_" + _hash8(internal_name)
- prefix_len = max(1, _TOOL_NAME_MAX_LEN - len(suffix))
- api_name = base_api_name[:prefix_len] + suffix
- if api_name in used_api and api_to_internal.get(api_name) != internal_name:
- # 极少数冲突兜底：加入索引避免重复。
- suffix = "_" + _hash8(f"{internal_name}:{len(used_api)}")
- prefix_len = max(1, _TOOL_NAME_MAX_LEN - len(suffix))
- api_name = base_api_name[:prefix_len] + suffix
-
- used_api.add(api_name)
- internal_to_api[internal_name] = api_name
- api_to_internal[api_name] = internal_name
-
- if api_name != internal_name:
- tool = dict(tool)
- function = dict(function)
- function["name"] = api_name
- tool["function"] = function
- new_tools.append(tool)
-
- request_body["tools"] = new_tools
-
- # 尽力重写历史消息中的工具名。
- messages = request_body.get("messages")
- if isinstance(messages, list) and messages:
- new_messages: list[dict[str, Any]] = []
- changed = False
- for message in messages:
- if not isinstance(message, dict):
- new_messages.append(message)
- continue
-
- new_message = message
-
- # 重写 role=tool 的 name 字段（可选字段）。
- msg_name = message.get("name")
- if isinstance(msg_name, str) and msg_name:
- mapped = internal_to_api.get(msg_name)
- if mapped and mapped != msg_name:
- if new_message is message:
- new_message = dict(message)
- new_message["name"] = mapped
- changed = True
- elif (not _TOOL_NAME_ALLOWED_RE.match(msg_name)) or (
- len(msg_name) > _TOOL_NAME_MAX_LEN
- ):
- # 即便名称不在 schema 映射中，也尽量保证请求合法（如工具被重命名/移除）。
- safe = _encode_tool_name_for_api(msg_name)
- if safe != msg_name:
- if new_message is message:
- new_message = dict(message)
- new_message["name"] = safe
- changed = True
-
- tool_calls = message.get("tool_calls")
- if isinstance(tool_calls, list) and tool_calls:
- new_tool_calls: list[Any] = []
- tool_calls_changed = False
- for tool_call in tool_calls:
- if not isinstance(tool_call, dict):
- new_tool_calls.append(tool_call)
- continue
- function = tool_call.get("function")
- if not isinstance(function, dict):
- new_tool_calls.append(tool_call)
- continue
- fname = function.get("name")
- if not isinstance(fname, str) or not fname:
- new_tool_calls.append(tool_call)
- continue
- mapped = internal_to_api.get(fname)
- safe_name = mapped or _encode_tool_name_for_api(fname)
- if safe_name != fname:
- tool_calls_changed = True
- new_tool_call = dict(tool_call)
- new_function = dict(function)
- new_function["name"] = safe_name
- new_tool_call["function"] = new_function
- new_tool_calls.append(new_tool_call)
- else:
- new_tool_calls.append(tool_call)
-
- if tool_calls_changed:
- if new_message is message:
- new_message = dict(message)
- new_message["tool_calls"] = new_tool_calls
- changed = True
-
- new_messages.append(new_message)
-
- if changed:
- request_body["messages"] = new_messages
-
- return api_to_internal, internal_to_api
-
-
-def _get_runtime_config() -> Config | None:
- try:
- return get_config(strict=False)
- except Exception:
- return None
-
-
-def _split_chat_completion_params(
- body: dict[str, Any],
-) -> tuple[dict[str, Any], dict[str, Any]]:
- known: dict[str, Any] = {}
- extra: dict[str, Any] = {}
- for key, value in body.items():
- if key in _CHAT_COMPLETIONS_KNOWN_FIELDS:
- known[key] = value
- else:
- extra[key] = value
- return known, extra
-
-
-def _split_responses_params(
- body: dict[str, Any],
-) -> tuple[dict[str, Any], dict[str, Any]]:
- known: dict[str, Any] = {}
- extra: dict[str, Any] = {}
- for key, value in body.items():
- if key in _RESPONSES_KNOWN_FIELDS:
- known[key] = value
- else:
- extra[key] = value
- return known, extra
-
-
-def _without_stream_request_fields(body: dict[str, Any]) -> dict[str, Any]:
- stripped = dict(body)
- stripped.pop("stream", None)
- stripped.pop("stream_options", None)
- return stripped
-
-
-def _ensure_chat_stream_usage_options(body: dict[str, Any]) -> None:
- stream_options = body.get("stream_options")
- if stream_options is None:
- body["stream_options"] = {"include_usage": True}
- return
- if isinstance(stream_options, dict) and "include_usage" not in stream_options:
- body["stream_options"] = {**stream_options, "include_usage": True}
-
-
-_STREAM_FALLBACK_STATUS_CODES = {400, 404, 405, 422, 501}
-_STREAM_FALLBACK_ERROR_MARKERS = (
- "stream",
- "stream_options",
- "streaming",
- "not support",
- "unsupported",
- "unrecognized",
- "unknown parameter",
- "unexpected parameter",
-)
-
-
-def _status_error_text(exc: APIStatusError) -> str:
- parts = [str(exc)]
- body = getattr(exc, "body", None)
- if isinstance(body, dict):
- parts.append(json.dumps(body, ensure_ascii=False, default=str))
- elif body is not None:
- parts.append(str(body))
- response = getattr(exc, "response", None)
- if response is not None:
- try:
- parts.append(response.text)
- except Exception:
- pass
- return "\n".join(part for part in parts if part).lower()
-
-
-def _should_fallback_from_stream(exc: Exception) -> bool:
- if isinstance(exc, NotImplementedError):
- return True
- if not isinstance(exc, APIStatusError):
- return False
- if exc.status_code not in _STREAM_FALLBACK_STATUS_CODES:
- return False
- text = _status_error_text(exc)
- return any(marker in text for marker in _STREAM_FALLBACK_ERROR_MARKERS)
-
-
-def _stringify_stream_delta(value: Any) -> str:
- if value is None:
- return ""
- if isinstance(value, str):
- return value
- if isinstance(value, list):
- parts = [_stringify_stream_delta(item) for item in value]
- return "".join(part for part in parts if part)
- if isinstance(value, dict):
- for key in ("text", "content", "delta", "value"):
- if value.get(key) is not None:
- return _stringify_stream_delta(value.get(key))
- return ""
- return str(value)
-
-
-def _extract_stream_response_item(event: dict[str, Any]) -> dict[str, Any] | None:
- for key in ("item", "output_item", "data"):
- value = event.get(key)
- if isinstance(value, dict):
- return value
- response = event.get("response")
- if isinstance(response, dict) and isinstance(response.get("output"), list):
- return None
- if isinstance(response, dict):
- return response
- return None
-
-
-def _extract_stream_usage(
- event: dict[str, Any], *, api_mode: str
-) -> dict[str, Any] | None:
- usage = event.get("usage")
- if not isinstance(usage, dict):
- response = event.get("response")
- if isinstance(response, dict) and isinstance(response.get("usage"), dict):
- usage = response.get("usage")
- if not isinstance(usage, dict):
- return None
- if api_mode == API_MODE_RESPONSES:
- return {
- "input_tokens": int(usage.get("input_tokens", 0) or 0),
- "output_tokens": int(usage.get("output_tokens", 0) or 0),
- "total_tokens": int(usage.get("total_tokens", 0) or 0),
- }
- return {
- "prompt_tokens": int(usage.get("prompt_tokens", 0) or 0),
- "completion_tokens": int(usage.get("completion_tokens", 0) or 0),
- "total_tokens": int(usage.get("total_tokens", 0) or 0),
- }
-
-
-def _ensure_tool_call_slot(
- tool_calls: list[dict[str, Any]], index: int
-) -> dict[str, Any]:
- while len(tool_calls) <= index:
- tool_calls.append(
- {
- "id": "",
- "type": "function",
- "function": {"name": "", "arguments": ""},
- }
- )
- return tool_calls[index]
-
-
-def _merge_tool_call_delta(
- target_tool_calls: list[dict[str, Any]], tool_delta: dict[str, Any]
-) -> None:
- index = tool_delta.get("index")
- try:
- slot_index = int(index) if index is not None else len(target_tool_calls)
- except (TypeError, ValueError):
- slot_index = len(target_tool_calls)
- tool_call = _ensure_tool_call_slot(target_tool_calls, slot_index)
- call_id = str(tool_delta.get("id") or "").strip()
- if call_id:
- tool_call["id"] = call_id
- tool_type = str(tool_delta.get("type") or "").strip()
- if tool_type:
- tool_call["type"] = tool_type
- function_delta = tool_delta.get("function")
- if not isinstance(function_delta, dict):
- return
- function = tool_call.setdefault("function", {"name": "", "arguments": ""})
- if not isinstance(function, dict):
- function = {"name": "", "arguments": ""}
- tool_call["function"] = function
- function_name = str(function_delta.get("name") or "").strip()
- if function_name:
- function["name"] = function_name
- arguments_delta = function_delta.get("arguments")
- if arguments_delta is not None:
- function["arguments"] = str(function.get("arguments") or "") + str(
- arguments_delta
- )
-
-
-def _is_deepseek_provider(model_config: ModelConfig) -> bool:
- model_name = str(getattr(model_config, "model_name", "") or "").lower()
- if model_name.startswith("deepseek"):
- return True
- api_url = str(getattr(model_config, "api_url", "") or "").lower()
- return "deepseek" in api_url
-
-
-def _normalize_thinking_override(
- value: Any, model_config: ModelConfig
-) -> dict[str, Any] | None:
- if value is None:
- return None
-
- is_deepseek = _is_deepseek_provider(model_config)
-
- if isinstance(value, dict):
- raw_type = value.get("type")
- if isinstance(raw_type, str):
- type_value = raw_type.strip().lower()
- if type_value in {"enabled", "disabled"}:
- return {"type": type_value} if is_deepseek else dict(value)
-
- raw_enabled = value.get("enabled")
- if isinstance(raw_enabled, bool):
- type_value = "enabled" if raw_enabled else "disabled"
- if is_deepseek:
- return {"type": type_value}
- normalized = dict(value)
- normalized.pop("enabled", None)
- normalized["type"] = type_value
- return normalized
-
- return None
-
- if isinstance(value, bool):
- return {"type": "enabled" if value else "disabled"}
-
- if isinstance(value, str):
- type_value = value.strip().lower()
- if type_value in {"enabled", "disabled"}:
- return {"type": type_value}
-
- return None
-
-
-def _tools_sanitize_enabled() -> bool:
- # 历史配置项 tools.sanitize 已迁移为 tools.dot_delimiter。
- # 为兼容严格网关，description 的 schema 清洗默认始终开启。
- return True
-
-
-def _tools_sanitize_verbose() -> bool:
- runtime_config = _get_runtime_config()
- if runtime_config is not None:
- return bool(runtime_config.tools_sanitize_verbose)
- return False
-
-
-def _tools_description_max_len() -> int:
- runtime_config = _get_runtime_config()
- if runtime_config is None:
- return _DEFAULT_TOOLS_DESCRIPTION_MAX_LEN
- value = runtime_config.tools_description_max_len
- return value if value > 0 else _DEFAULT_TOOLS_DESCRIPTION_MAX_LEN
-
-
-def _tools_description_truncate_enabled() -> bool:
- runtime_config = _get_runtime_config()
- if runtime_config is None:
- return False
- return bool(runtime_config.tools_description_truncate_enabled)
-
-
-def _clean_control_chars(text: str) -> str:
- """将 ASCII 控制字符替换为空格。"""
- return "".join(" " if ord(ch) < 32 or ord(ch) == 127 else ch for ch in text)
-
-
-def _desc_preview(text: str) -> str:
- runtime_config = _get_runtime_config()
- if runtime_config is None:
- preview_len = _DEFAULT_TOOLS_DESCRIPTION_PREVIEW_LEN
- else:
- preview_len = runtime_config.tools_description_preview_len
- if preview_len <= 0:
- preview_len = _DEFAULT_TOOLS_DESCRIPTION_PREVIEW_LEN
- return text[:preview_len] + ("…" if len(text) > preview_len else "")
-
-
-def _normalize_tool_description(
- description: Any,
- tool_name: str,
- max_len: int,
- truncate_enabled: bool,
-) -> str:
- """规范化工具 function.description，适配更严格的 OpenAI 兼容服务。"""
- if description is None:
- normalized = ""
- elif isinstance(description, str):
- normalized = description
- else:
- normalized = str(description)
-
- normalized = _clean_control_chars(normalized)
- normalized = " ".join(normalized.split())
- normalized = normalized.strip()
- if not normalized:
- normalized = f"Tool function {tool_name}"
- if truncate_enabled and len(normalized) > max_len:
- normalized = normalized[:max_len].rstrip()
- return normalized
-
-
-def _sanitize_openai_tools(
- tools: list[dict[str, Any]],
-) -> tuple[list[dict[str, Any]], int, list[dict[str, Any]]]:
- """Sanitize tools schema to avoid 400s on strict providers (e.g., invalid description)."""
- if not tools or not _tools_sanitize_enabled():
- return tools, 0, []
-
- max_len = _tools_description_max_len()
- truncate_enabled = _tools_description_truncate_enabled()
- changed = 0
- changes: list[dict[str, Any]] = []
- sanitized: list[dict[str, Any]] = []
- for idx, tool in enumerate(tools):
- if not isinstance(tool, dict):
- sanitized.append(tool)
- continue
- function = tool.get("function")
- if not isinstance(function, dict):
- sanitized.append(tool)
- continue
- name = function.get("name", "")
- old_desc = function.get("description")
- old_desc_str = (
- ""
- if old_desc is None
- else (old_desc if isinstance(old_desc, str) else str(old_desc))
- )
- new_desc = _normalize_tool_description(
- old_desc,
- str(name),
- max_len,
- truncate_enabled,
- )
-
- if old_desc_str != new_desc:
- reasons: list[str] = []
- if not isinstance(old_desc, str):
- reasons.append("non_string")
- if any(ord(ch) < 32 or ord(ch) == 127 for ch in old_desc_str):
- reasons.append("control_chars")
- if "\n" in old_desc_str or "\r" in old_desc_str or "\t" in old_desc_str:
- reasons.append("whitespace")
- if not old_desc_str.strip():
- reasons.append("empty")
- if (
- truncate_enabled
- and len(new_desc) >= max_len
- and len(old_desc_str) > len(new_desc)
- ):
- reasons.append("truncated")
-
- tool = dict(tool)
- function = dict(function)
- function["description"] = new_desc
- tool["function"] = function
- changed += 1
- changes.append(
- {
- "index": idx,
- "name": str(name),
- "old_len": len(old_desc_str),
- "new_len": len(new_desc),
- "old_preview": _desc_preview(_clean_control_chars(old_desc_str)),
- "new_preview": _desc_preview(new_desc),
- "reasons": reasons,
- }
- )
- sanitized.append(tool)
- return sanitized, changed, changes
-
-
-def _sanitize_openai_messages_tool_arguments(
- messages: list[dict[str, Any]],
-) -> tuple[list[dict[str, Any]], int]:
- """Sanitize messages[].tool_calls[].function.arguments to strict JSON strings.
-
- Some OpenAI-compatible providers reject non-JSON `function.arguments` in the
- request body (even though upstream OpenAI treats it as an opaque string).
- This primarily affects conversations that include historical tool_calls.
- """
- if not messages:
- return messages, 0
-
- changed = 0
- sanitized_messages: list[dict[str, Any]] = []
- for message in messages:
- if not isinstance(message, dict):
- sanitized_messages.append(message)
- continue
-
- tool_calls = message.get("tool_calls")
- if not isinstance(tool_calls, list) or not tool_calls:
- sanitized_messages.append(message)
- continue
-
- tool_calls_changed = False
- sanitized_tool_calls: list[Any] = []
- for tool_call in tool_calls:
- if not isinstance(tool_call, dict):
- sanitized_tool_calls.append(tool_call)
- continue
- function = tool_call.get("function")
- if not isinstance(function, dict):
- sanitized_tool_calls.append(tool_call)
- continue
-
- old_args = function.get("arguments")
- new_args = normalize_tool_arguments_json(old_args)
- if isinstance(old_args, str) and old_args == new_args:
- sanitized_tool_calls.append(tool_call)
- continue
-
- tool_calls_changed = True
- changed += 1
- new_tool_call = dict(tool_call)
- new_function = dict(function)
- new_function["arguments"] = new_args
- new_tool_call["function"] = new_function
- sanitized_tool_calls.append(new_tool_call)
-
- if tool_calls_changed:
- new_message = dict(message)
- new_message["tool_calls"] = sanitized_tool_calls
- sanitized_messages.append(new_message)
- else:
- sanitized_messages.append(message)
-
- return sanitized_messages, changed
-
-
-def _sanitize_chat_completion_messages(
- messages: list[dict[str, Any]],
- *,
- preserve_reasoning_content: bool = False,
-) -> tuple[list[dict[str, Any]], int, dict[str, int]]:
- """移除 Chat Completions 非标准消息字段。
-
- 本地历史里允许保留 reasoning_content 等兼容字段用于日志/回放；
- 发往上游时默认剥离。``preserve_reasoning_content=True`` 时保留
- ``reasoning_content`` 供多轮 CoT 续传，仍剥离其它内部字段。
- """
- if not messages:
- return messages, 0, {}
-
- changed = 0
- stripped_fields: dict[str, int] = {}
- sanitized_messages: list[dict[str, Any]] = []
- for message in messages:
- if not isinstance(message, dict):
- sanitized_messages.append(message)
- continue
-
- sanitized_message = message
- removed = False
- for key in _CHAT_COMPLETION_INTERNAL_MESSAGE_KEYS:
- if preserve_reasoning_content and key == "reasoning_content":
- continue
- if key not in sanitized_message:
- continue
- if sanitized_message is message:
- sanitized_message = dict(message)
- sanitized_message.pop(key, None)
- stripped_fields[key] = stripped_fields.get(key, 0) + 1
- removed = True
-
- if removed:
- changed += 1
- sanitized_messages.append(sanitized_message)
-
- return sanitized_messages, changed, stripped_fields
-
-
-def _relocate_system_to_first_user(
- messages: list[dict[str, Any]],
-) -> list[dict[str, Any]]:
- """将 system/developer 消息合并注入首条 user 消息（chat_completions 适配）。"""
- if not messages:
- return messages
-
- system_parts: list[str] = []
- remaining: list[dict[str, Any]] = []
- for message in messages:
- if not isinstance(message, dict):
- remaining.append(message)
- continue
- role = str(message.get("role") or "").strip().lower()
- if role in ("system", "developer"):
- content = message.get("content")
- if content is not None:
- text = content if isinstance(content, str) else str(content)
- if text.strip():
- system_parts.append(text.strip())
- continue
- remaining.append(message)
-
- if not system_parts:
- return messages
-
- merged_system = "\n\n".join(system_parts)
- first_user_idx: int | None = None
- for idx, message in enumerate(remaining):
- if (
- isinstance(message, dict)
- and str(message.get("role") or "").strip().lower() == "user"
- ):
- first_user_idx = idx
- break
-
- if first_user_idx is None:
- remaining.insert(0, {"role": "user", "content": merged_system})
- return remaining
-
- first_user = dict(remaining[first_user_idx])
- old_content = first_user.get("content")
- old_text = (
- old_content
- if isinstance(old_content, str)
- else (str(old_content) if old_content is not None else "")
- )
- if old_text.strip():
- first_user["content"] = f"{merged_system}\n\n{old_text}"
- else:
- first_user["content"] = merged_system
- updated = list(remaining)
- updated[first_user_idx] = first_user
- return updated
-
-
-def _prepare_chat_completion_messages(
- model_config: ModelConfig,
- messages: list[dict[str, Any]],
-) -> list[dict[str, Any]]:
- """按模型配置整理 Chat Completions 出站消息。"""
- preserve_reasoning = bool(getattr(model_config, "reasoning_content_replay", False))
- prepared, _, _ = _sanitize_chat_completion_messages(
- messages,
- preserve_reasoning_content=preserve_reasoning,
- )
- if bool(getattr(model_config, "system_prompt_as_user", False)):
- prepared = _relocate_system_to_first_user(prepared)
- return prepared
-
-
-def _stringify_thinking_list(value: list[Any]) -> str:
- """将列表类型的思维链转换为字符串。
-
- Args:
- value: 思维链列表
-
- Returns:
- 格式化后的字符串
- """
- parts = [_stringify_thinking(item) for item in value]
- return "\n".join([part for part in parts if part])
-
-
-def _stringify_thinking_dict(value: dict[str, Any]) -> str:
- """将字典类型的思维链转换为字符串。
-
- Args:
- value: 思维链字典
-
- Returns:
- 格式化后的字符串
- """
- content = value.get("content")
- if isinstance(content, str) and content:
- return content
- return str(value)
-
-
-def _stringify_thinking(value: Any) -> str:
- """将思维链值转换为字符串。
-
- Args:
- value: 思维链值（可以是 None、字符串、列表或字典）
-
- Returns:
- 格式化后的字符串
- """
- if value is None:
- return ""
- if isinstance(value, str):
- return value
- if isinstance(value, list):
- return _stringify_thinking_list(value)
- if isinstance(value, dict):
- return _stringify_thinking_dict(value)
- return str(value)
-
-
-def _extract_from_message(message: dict[str, Any]) -> str:
- """从 message 对象中提取思维链内容。
-
- Args:
- message: message 对象
-
- Returns:
- 思维链内容字符串
- """
- if not isinstance(message, dict):
- return ""
- for key in _THINKING_KEYS:
- if key in message:
- return _stringify_thinking(message.get(key))
- return ""
-
-
-def _extract_from_choice(choice: dict[str, Any]) -> str:
- """从 choice 对象中提取思维链内容。
-
- Args:
- choice: choice 对象
-
- Returns:
- 思维链内容字符串
- """
- if not isinstance(choice, dict):
- return ""
-
- # 优先从 message 中提取
- message = choice.get("message")
- if isinstance(message, dict):
- thinking = _extract_from_message(message)
- if thinking:
- return thinking
-
- # 尝试从 choice 直接提取
- for key in _THINKING_KEYS:
- if key in choice:
- return _stringify_thinking(choice.get(key))
-
- return ""
-
-
-def _extract_from_choices(choices: list[Any]) -> str:
- """从 choices 列表中提取思维链内容。
-
- Args:
- choices: choices 列表
-
- Returns:
- 思维链内容字符串
- """
- if not isinstance(choices, list) or not choices:
- return ""
- choice = choices[0]
- return _extract_from_choice(choice)
-
-
-def _extract_from_result(result: dict[str, Any]) -> str:
- """直接从结果对象中提取思维链内容。
-
- Args:
- result: API 响应结果
-
- Returns:
- 思维链内容字符串
- """
- for key in _THINKING_KEYS:
- if key in result:
- return _stringify_thinking(result.get(key))
- return ""
-
-
-def _extract_thinking_content(result: dict[str, Any]) -> str:
- """从 API 响应中提取思维链内容。
-
- 提取优先级：
- 1. 从 choices[0].message 中提取
- 2. 从 choices[0] 直接提取
- 3. 从响应根对象中提取
-
- Args:
- result: API 响应结果
-
- Returns:
- 思维链内容字符串
- """
- # 尝试从 choices 中提取
- choices = result.get("choices")
- if isinstance(choices, list):
- thinking = _extract_from_choices(choices)
- if thinking:
- return thinking
-
- # 尝试从响应根对象中提取
- return _extract_from_result(result)
-
-
-def _normalize_openai_base_url(
- api_url: str,
-) -> tuple[str, dict[str, object] | None, bool]:
- """将旧式 /chat/completions URL 归一化为 OpenAI SDK 需要的 base_url。
-
- 兼容策略（B）：如果发现 api_url 末尾包含 /chat/completions，则自动裁剪为 base_url，
- 以便统一走 OpenAI SDK，并给出弃用警告。
- """
- try:
- parts = urlsplit(api_url)
- except Exception:
- return api_url, None, False
-
- path = parts.path or ""
- trimmed_path = path.rstrip("/")
- suffix = "/chat/completions"
- if not trimmed_path.endswith(suffix):
- return api_url, None, False
-
- new_path = trimmed_path[: -len(suffix)]
- default_query: dict[str, object] | None = None
- if parts.query:
- default_query = {
- k: v for k, v in parse_qsl(parts.query, keep_blank_values=True)
- }
- normalized = urlunsplit(parts._replace(path=new_path, query="", fragment=""))
- return normalized, default_query, True
-
-
-def _warn_ignored_request_params(
- *,
- call_type: str,
- model_name: str,
- ignored: dict[str, Any],
-) -> None:
- if not ignored:
- return
- logger.warning(
- "[request_params] ignored_keys=%s type=%s model=%s",
- ",".join(sorted(ignored)),
- call_type,
- model_name,
- )
-
-
-def _build_effective_request_kwargs(
- model_config: ModelConfig,
- *,
- call_type: str,
- overrides: dict[str, Any],
-) -> dict[str, Any]:
- merged = merge_request_params(
- getattr(model_config, "request_params", {}),
- overrides,
- )
- thinking_override = overrides["thinking"] if "thinking" in overrides else None
- has_thinking_override = "thinking" in overrides
- reserved_fields = (
- _RESPONSES_RESERVED_FIELDS
- if get_api_mode(model_config) == API_MODE_RESPONSES
- else _CHAT_COMPLETIONS_RESERVED_FIELDS
- )
- allowed, ignored = split_reserved_request_params(
- merged,
- reserved_fields,
- )
- if has_thinking_override:
- ignored.pop("thinking", None)
- _warn_ignored_request_params(
- call_type=call_type,
- model_name=model_config.model_name,
- ignored=ignored,
- )
- if has_thinking_override:
- allowed["thinking"] = thinking_override
- return allowed
-
-
-class ModelRequester:
- """统一的模型请求封装。"""
-
- def __init__(
- self,
- http_client: httpx.AsyncClient,
- token_usage_storage: TokenUsageStorage,
- ) -> None:
- self._http_client = http_client
- self._token_usage_storage = token_usage_storage
- self._openai_clients: dict[
- tuple[str, str, tuple[tuple[str, str], ...] | None], AsyncOpenAI
- ] = {}
- self._token_counters: dict[str, TokenCounter] = {}
- self._warned_legacy_api_urls: set[str] = set()
- self._background_tasks: set[asyncio.Task[Any]] = set()
- self._retrieval_requester = RetrievalRequester(
- get_openai_client=self._get_openai_client_for_model,
- response_to_dict=self._response_to_dict,
- get_token_counter=self._get_token_counter,
- record_usage=self._record_usage,
- )
-
- async def request(
- self,
- model_config: ModelConfig,
- messages: list[dict[str, Any]],
- max_tokens: int = 8192,
- call_type: str = "chat",
- tools: list[dict[str, Any]] | None = None,
- tool_choice: str = "auto",
- transport_state: dict[str, Any] | None = None,
- message_count_for_transport: int | None = None,
- **kwargs: Any,
- ) -> dict[str, Any]:
- """发送请求到模型 API。"""
- start_time = time.perf_counter()
- cot_compat = getattr(model_config, "thinking_tool_call_compat", False)
- reasoning_replay = bool(
- getattr(model_config, "reasoning_content_replay", False)
- )
- api_mode = get_api_mode(model_config)
- transport_message_count = (
- message_count_for_transport
- if message_count_for_transport is not None
- else len(messages)
- )
- messages_for_api, tool_args_fixed = _sanitize_openai_messages_tool_arguments(
- messages
- )
- if tool_args_fixed and logger.isEnabledFor(logging.INFO):
- logger.info(
- "[messages.sanitize] tool_args_fixed=%s messages=%s",
- tool_args_fixed,
- len(messages_for_api),
- )
- if api_mode == API_MODE_CHAT_COMPLETIONS:
- (
- messages_for_api,
- stripped_message_count,
- stripped_message_fields,
- ) = _sanitize_chat_completion_messages(
- messages_for_api,
- preserve_reasoning_content=reasoning_replay,
- )
- if bool(getattr(model_config, "system_prompt_as_user", False)):
- messages_for_api = _relocate_system_to_first_user(messages_for_api)
- if stripped_message_count and logger.isEnabledFor(logging.INFO):
- details = ",".join(
- f"{key}={value}"
- for key, value in sorted(stripped_message_fields.items())
- )
- logger.info(
- "[chat_completions.standardize] stripped_internal_message_fields=%s messages=%s",
- details,
- stripped_message_count,
- )
-
- tools_for_api = tools
- api_to_internal: dict[str, str] = {}
- internal_to_api: dict[str, str] = {}
- if isinstance(tools_for_api, list):
- request_for_sanitize = {
- "messages": messages_for_api,
- "tools": list(tools_for_api),
- }
- api_to_internal, internal_to_api = _sanitize_openai_tool_names_in_request(
- request_for_sanitize
- )
- raw_messages = request_for_sanitize.get("messages")
- if isinstance(raw_messages, list):
- messages_for_api = raw_messages
- raw_tools = request_for_sanitize.get("tools")
- if isinstance(raw_tools, list):
- tools_for_api = raw_tools
-
- if isinstance(tools_for_api, list):
- sanitized_tools, changed_count, changes = _sanitize_openai_tools(
- tools_for_api
- )
- tools_for_api = sanitized_tools
- if changed_count and logger.isEnabledFor(logging.INFO):
- logger.info(
- "[tools.sanitize] changed=%s total=%s truncate_enabled=%s max_desc_len=%s",
- changed_count,
- len(sanitized_tools),
- _tools_description_truncate_enabled(),
- _tools_description_max_len(),
- )
- if _tools_sanitize_verbose():
- for change in changes:
- logger.info(
- "[tools.sanitize.item] index=%s name=%s reasons=%s old_len=%s new_len=%s old=%s new=%s",
- change.get("index"),
- change.get("name"),
- ",".join(change.get("reasons", [])),
- change.get("old_len"),
- change.get("new_len"),
- change.get("old_preview"),
- change.get("new_preview"),
- )
-
- effective_kwargs = _build_effective_request_kwargs(
- model_config,
- call_type=call_type,
- overrides=dict(kwargs),
- )
- if bool(
- getattr(model_config, "prompt_cache_enabled", True)
- ) and not effective_kwargs.get("prompt_cache_key"):
- effective_kwargs["prompt_cache_key"] = _build_default_prompt_cache_key(
- model_config,
- call_type,
- )
- responses_stateless_replay = bool(
- getattr(model_config, "responses_force_stateless_replay", False)
- ) or bool(
- isinstance(transport_state, dict)
- and transport_state.get("stateless_replay")
- )
- effective_transport_state: dict[str, Any] | None
- if responses_stateless_replay:
- effective_transport_state = dict(transport_state or {})
- effective_transport_state["stateless_replay"] = True
- else:
- effective_transport_state = transport_state
- request_body = build_request_body(
- model_config=model_config,
- messages=messages_for_api,
- max_tokens=max_tokens,
- tools=tools_for_api,
- tool_choice=tool_choice,
- internal_to_api=internal_to_api,
- transport_state=effective_transport_state,
- **effective_kwargs,
- )
-
- try:
- if cot_compat and logger.isEnabledFor(logging.DEBUG):
- logger.debug(
- "[思维链兼容] enabled=%s type=%s model=%s api_mode=%s thinking_enabled=%s tools=%s messages=%s",
- cot_compat,
- call_type,
- model_config.model_name,
- api_mode,
- getattr(model_config, "thinking_enabled", False),
- bool(tools),
- len(messages),
- )
-
- if logger.isEnabledFor(logging.DEBUG):
- logger.debug(
- "[API请求] type=%s model=%s api_mode=%s url=%s max_tokens=%s tools=%s tool_choice=%s messages=%s",
- call_type,
- model_config.model_name,
- api_mode,
- model_config.api_url,
- max_tokens,
- bool(tools_for_api),
- tool_choice,
- len(messages),
- )
- log_debug_json(logger, "[API请求体]", request_body)
-
- try:
- raw_result = await self._request_with_openai(model_config, request_body)
- except APIStatusError as exc:
- if (
- api_mode == API_MODE_RESPONSES
- and _responses_should_fallback_to_stateless_replay(
- exc,
- request_body,
- stateless_replay=responses_stateless_replay,
- )
- ):
- logger.warning(
- "[responses.compat] previous_response_id 续轮失败，自动降级为 stateless replay: model=%s call_type=%s previous_response_id=%s",
- model_config.model_name,
- call_type,
- request_body.get("previous_response_id", ""),
- )
- effective_transport_state = dict(effective_transport_state or {})
- effective_transport_state["stateless_replay"] = True
- responses_stateless_replay = True
- request_body = build_request_body(
- model_config=model_config,
- messages=messages_for_api,
- max_tokens=max_tokens,
- tools=tools_for_api,
- tool_choice=tool_choice,
- internal_to_api=internal_to_api,
- transport_state=effective_transport_state,
- **effective_kwargs,
- )
- if logger.isEnabledFor(logging.DEBUG):
- log_debug_json(
- logger, "[API请求体][stateless replay]", request_body
- )
- raw_result = await self._request_with_openai(
- model_config, request_body
- )
- else:
- raise
- if api_mode == API_MODE_RESPONSES:
- result = normalize_responses_result(
- raw_result,
- api_to_internal if api_to_internal else None,
- )
- response_id = str(
- raw_result.get("id") or result.get("id") or ""
- ).strip()
- if response_id:
- choice = result.get("choices", [{}])[0]
- message = (
- choice.get("message", {}) if isinstance(choice, dict) else {}
- )
- tool_calls = (
- message.get("tool_calls", [])
- if isinstance(message, dict)
- else []
- )
- result["_transport_state"] = {
- "api_mode": api_mode,
- "previous_response_id": response_id,
- "tool_result_start_index": transport_message_count
- + (1 if tool_calls else 0),
- }
- if responses_stateless_replay:
- result["_transport_state"]["stateless_replay"] = True
- else:
- result = self._normalize_result(raw_result)
- if api_to_internal:
- result["_tool_name_map"] = {
- "api_to_internal": api_to_internal,
- "internal_to_api": internal_to_api,
- "dot_delimiter": _tool_name_dot_delimiter(),
- }
- duration = time.perf_counter() - start_time
-
- usage = result.get("usage", {}) or {}
- prompt_tokens = int(usage.get("prompt_tokens", 0) or 0)
- completion_tokens = int(usage.get("completion_tokens", 0) or 0)
- total_tokens = int(usage.get("total_tokens", 0) or 0)
- if total_tokens == 0 and (prompt_tokens or completion_tokens):
- total_tokens = prompt_tokens + completion_tokens
- if total_tokens == 0:
- prompt_tokens, completion_tokens, total_tokens = self._estimate_usage(
- model_config.model_name, messages_for_api, result
- )
-
- logger.info(
- f"[API响应] {call_type} 完成: 耗时={duration:.2f}s, "
- f"Tokens={total_tokens} (P:{prompt_tokens} + C:{completion_tokens}), "
- f"模型={model_config.model_name}"
- )
-
- if logger.isEnabledFor(logging.DEBUG):
- log_debug_json(logger, "[API响应体]", result)
-
- self._maybe_log_thinking(result, call_type, model_config.model_name)
-
- self._record_usage(
- model_name=model_config.model_name,
- prompt_tokens=prompt_tokens,
- completion_tokens=completion_tokens,
- total_tokens=total_tokens,
- duration_seconds=duration,
- call_type=call_type,
- )
-
- return result
- except APIStatusError as exc:
- response = exc.response
- try:
- body = (
- json.dumps(exc.body, ensure_ascii=False, default=str)
- if exc.body is not None
- else ""
- )
- except Exception:
- body = str(exc.body)
- if (
- exc.status_code == 400
- and isinstance(exc.body, dict)
- and isinstance(exc.body.get("error"), dict)
- ):
- param = exc.body.get("error", {}).get("param")
- if isinstance(param, str):
- match = _TOOLS_PARAM_INDEX_RE.search(param)
- if match and isinstance(request_body.get("tools"), list):
- try:
- idx = int(match.group(1))
- except ValueError:
- idx = -1
- if 0 <= idx < len(request_body["tools"]):
- tool = request_body["tools"][idx]
- tool_name = (
- tool.get("function", {}).get("name")
- if isinstance(tool, dict)
- else ""
- )
- desc_len: int | None = None
- desc_preview = ""
- if isinstance(tool, dict):
- function = tool.get("function", {})
- if isinstance(function, dict):
- desc = function.get("description")
- if desc is not None:
- desc_str = (
- desc if isinstance(desc, str) else str(desc)
- )
- desc_len = len(desc_str)
- desc_preview = _desc_preview(desc_str)
- logger.error(
- "[tools.invalid] index=%s name=%s desc_len=%s desc=%s param=%s",
- idx,
- tool_name,
- desc_len,
- desc_preview,
- param,
- )
- logger.error(
- "[API响应错误] status=%s request_id=%s url=%s body=%s",
- exc.status_code,
- exc.request_id or "",
- response.request.url,
- redact_string(body),
- )
- raise
- except (APIConnectionError, APITimeoutError) as exc:
- logger.error("[API连接错误] type=%s message=%s", type(exc).__name__, exc)
- raise
- except Exception as exc:
- logger.exception(f"[model.request.error] {call_type} 调用失败: {exc}")
- raise
-
- def _thinking_logging_enabled(self) -> bool:
- runtime_config = _get_runtime_config()
- if runtime_config is None:
- return True
- return bool(runtime_config.log_thinking)
-
- def _maybe_log_thinking(
- self, result: dict[str, Any], call_type: str, model_name: str
- ) -> None:
- if not self._thinking_logging_enabled():
- return
- thinking = _extract_thinking_content(result)
- if thinking:
- logger.info(
- "[思维链] type=%s model=%s content=%s",
- call_type,
- model_name,
- redact_string(thinking),
- )
-
- async def _request_with_openai(
- self, model_config: ModelConfig, request_body: dict[str, Any]
- ) -> dict[str, Any]:
- client = self._get_openai_client_for_model(model_config)
- if bool(getattr(model_config, "stream_enabled", False)):
- try:
- return await self._request_with_openai_streaming(
- client, model_config, request_body
- )
- except Exception as exc:
- if not _should_fallback_from_stream(exc):
- raise
- logger.warning(
- "[API流式回退] model=%s api_mode=%s reason=%s",
- getattr(model_config, "model_name", ""),
- get_api_mode(model_config),
- type(exc).__name__,
- )
- request_body = _without_stream_request_fields(request_body)
- if get_api_mode(model_config) == API_MODE_RESPONSES:
- params, extra_body = _split_responses_params(request_body)
- if extra_body:
- params["extra_body"] = extra_body
- response = await client.responses.create(**params)
- return self._response_to_dict(response)
- params, extra_body = _split_chat_completion_params(request_body)
- if extra_body:
- params["extra_body"] = extra_body
- response = await client.chat.completions.create(**params)
- return self._response_to_dict(response)
-
- async def _request_with_openai_streaming(
- self,
- client: AsyncOpenAI,
- model_config: ModelConfig,
- request_body: dict[str, Any],
- ) -> dict[str, Any]:
- api_mode = get_api_mode(model_config)
- stream_body = dict(request_body)
- stream_body["stream"] = True
- if api_mode == API_MODE_RESPONSES:
- return await self._stream_responses_request(client, stream_body)
- _ensure_chat_stream_usage_options(stream_body)
- return await self._stream_chat_completions_request(
- client, stream_body, model_config
- )
-
- async def _stream_chat_completions_request(
- self,
- client: AsyncOpenAI,
- request_body: dict[str, Any],
- model_config: ModelConfig,
- ) -> dict[str, Any]:
- params, extra_body = _split_chat_completion_params(request_body)
- if extra_body:
- params["extra_body"] = extra_body
- response = await client.chat.completions.create(**params)
-
- content_parts: list[str] = []
- reasoning_parts: list[str] = []
- tool_calls: list[dict[str, Any]] = []
- usage: dict[str, Any] | None = None
- finish_reason = "stop"
- role = "assistant"
- reasoning_replay = bool(
- getattr(model_config, "reasoning_content_replay", False)
- )
-
- async for chunk in response:
- chunk_dict = self._response_to_dict(chunk)
- usage = (
- _extract_stream_usage(chunk_dict, api_mode=API_MODE_CHAT_COMPLETIONS)
- or usage
- )
- choices = chunk_dict.get("choices")
- if not isinstance(choices, list):
- continue
- for choice in choices:
- if not isinstance(choice, dict):
- continue
- delta = choice.get("delta")
- if not isinstance(delta, dict):
- continue
- role_value = str(delta.get("role") or "").strip()
- if role_value:
- role = role_value
- content_delta = _stringify_stream_delta(delta.get("content"))
- if content_delta:
- content_parts.append(content_delta)
- if reasoning_replay:
- reasoning_delta = _stringify_thinking(
- delta.get("reasoning_content")
- )
- if reasoning_delta:
- reasoning_parts.append(reasoning_delta)
- raw_tool_calls = delta.get("tool_calls")
- if isinstance(raw_tool_calls, list):
- for tool_delta in raw_tool_calls:
- if isinstance(tool_delta, dict):
- _merge_tool_call_delta(tool_calls, tool_delta)
- current_finish_reason = str(choice.get("finish_reason") or "").strip()
- if current_finish_reason:
- finish_reason = current_finish_reason
-
- message: dict[str, Any] = {
- "role": role,
- "content": "".join(content_parts),
- }
- if reasoning_replay:
- reasoning_text = "".join(reasoning_parts).strip()
- if reasoning_text:
- message["reasoning_content"] = reasoning_text
- if tool_calls:
- message["tool_calls"] = tool_calls
- result: dict[str, Any] = {
- "choices": [
- {
- "index": 0,
- "message": message,
- "finish_reason": finish_reason,
- }
- ]
- }
- if usage is not None:
- result["usage"] = usage
- return result
-
- async def _stream_responses_request(
- self, client: AsyncOpenAI, request_body: dict[str, Any]
- ) -> dict[str, Any]:
- params, extra_body = _split_responses_params(request_body)
- if extra_body:
- params["extra_body"] = extra_body
- stream = await client.responses.create(**params)
-
- output_items: list[dict[str, Any]] = []
- output_text_parts: list[str] = []
- usage: dict[str, Any] | None = None
- final_response: dict[str, Any] | None = None
-
- async for event in stream:
- event_dict = self._response_to_dict(event)
- usage = (
- _extract_stream_usage(event_dict, api_mode=API_MODE_RESPONSES) or usage
- )
- event_type = str(event_dict.get("type") or "").strip().lower()
- response = event_dict.get("response")
- if isinstance(response, dict):
- final_response = response
- if event_type == "response.output_text.delta":
- delta = _stringify_stream_delta(event_dict.get("delta"))
- if delta:
- output_text_parts.append(delta)
- continue
- if event_type == "response.completed":
- if isinstance(response, dict):
- final_response = response
- continue
- item = _extract_stream_response_item(event_dict)
- if not isinstance(item, dict):
- continue
- item_type = str(item.get("type") or "").strip().lower()
- if item_type == "message":
- output_items.append(item)
- continue
- if item_type == "function_call":
- output_items.append(item)
- continue
- if item_type == "reasoning":
- output_items.append(item)
-
- if final_response is not None:
- if usage is not None and not isinstance(final_response.get("usage"), dict):
- final_response = dict(final_response)
- final_response["usage"] = usage
- return final_response
-
- synthesized: dict[str, Any] = {
- "output": output_items,
- "output_text": "".join(output_text_parts),
- }
- if usage is not None:
- synthesized["usage"] = usage
- return synthesized
-
- async def embed(
- self,
- model_config: EmbeddingModelConfig,
- texts: list[str],
- ) -> list[list[float]]:
- """调用统一检索请求层的 embeddings。"""
- return await self._retrieval_requester.embed(model_config, texts)
-
- async def rerank(
- self,
- model_config: RerankModelConfig,
- query: str,
- documents: list[str],
- top_n: int | None = None,
- ) -> list[dict[str, Any]]:
- """调用统一检索请求层的 rerank。"""
- return await self._retrieval_requester.rerank(
- model_config=model_config,
- query=query,
- documents=documents,
- top_n=top_n,
- )
-
- def _get_openai_client_for_model(self, model_config: ModelConfig) -> AsyncOpenAI:
- base_url, default_query, changed = _normalize_openai_base_url(
- model_config.api_url
- )
- if changed and model_config.api_url not in self._warned_legacy_api_urls:
- self._warned_legacy_api_urls.add(model_config.api_url)
- logger.warning(
- "[配置弃用] 检测到 *_MODEL_API_URL 末尾包含 /chat/completions，这种写法已弃用；"
- "已自动裁剪为 base_url=%s（原值=%s）。",
- base_url,
- model_config.api_url,
- )
- return self._get_openai_client(
- base_url=base_url,
- api_key=model_config.api_key,
- default_query=default_query,
- )
-
- def _record_usage(
- self,
- *,
- model_name: str,
- prompt_tokens: int,
- completion_tokens: int,
- total_tokens: int,
- duration_seconds: float,
- call_type: str,
- ) -> None:
- task = asyncio.create_task(
- self._token_usage_storage.record(
- TokenUsage(
- timestamp=datetime.now().isoformat(),
- model_name=model_name,
- prompt_tokens=prompt_tokens,
- completion_tokens=completion_tokens,
- total_tokens=total_tokens,
- duration_seconds=duration_seconds,
- call_type=call_type,
- success=True,
- )
- )
- )
- self._background_tasks.add(task)
- task.add_done_callback(self._background_tasks.discard)
-
- def _get_openai_client(
- self, base_url: str, api_key: str, default_query: dict[str, object] | None
- ) -> AsyncOpenAI:
- query_key = None
- if default_query:
- query_key = tuple(
- sorted((str(k), str(v)) for k, v in default_query.items())
- )
- cache_key = (base_url, api_key, query_key)
- client = self._openai_clients.get(cache_key)
- if client is not None:
- return client
- # 复用上层注入的 httpx client（连接池/超时等），避免每个 OpenAI client 自建连接池。
- client = AsyncOpenAI(
- api_key=api_key,
- base_url=base_url,
- timeout=480.0,
- default_query=default_query,
- http_client=self._http_client,
- )
- self._openai_clients[cache_key] = client
- return client
-
- def _response_to_dict(self, response: Any) -> dict[str, Any]:
- if isinstance(response, dict):
- return response
- for attr in ("model_dump", "to_dict", "dict"):
- method = getattr(response, attr, None)
- if callable(method):
- try:
- value = method()
- if isinstance(value, dict):
- return value
- except Exception:
- continue
- to_json = getattr(response, "to_json", None)
- if callable(to_json):
- try:
- raw_json = to_json()
- loaded = json.loads(str(raw_json))
- if isinstance(loaded, dict):
- return loaded
- except Exception:
- pass
- return {"data": str(response)}
-
- def _normalize_result(self, result: dict[str, Any]) -> dict[str, Any]:
- choices = result.get("choices")
- if isinstance(choices, list):
- return result
- data = result.get("data")
- if isinstance(data, dict):
- data_choices = data.get("choices")
- if isinstance(data_choices, list):
- normalized = dict(result)
- normalized["choices"] = data_choices
- return normalized
- normalized = dict(result)
- normalized["choices"] = [{}]
- return normalized
-
- def _get_token_counter(self, model_name: str) -> TokenCounter:
- counter = self._token_counters.get(model_name)
- if counter is None:
- counter = TokenCounter(model_name)
- self._token_counters[model_name] = counter
- return counter
-
- def _estimate_usage(
- self,
- model_name: str,
- messages: list[dict[str, Any]],
- result: dict[str, Any],
- ) -> tuple[int, int, int]:
- counter = self._get_token_counter(model_name)
- try:
- prompt_text = "\n".join(
- json.dumps(message, ensure_ascii=False, default=str)
- for message in messages
- )
- except Exception:
- prompt_text = str(messages)
- prompt_tokens = counter.count(prompt_text)
-
- completion_text = ""
- try:
- completion_text = extract_choices_content(result)
- except Exception:
- completion_text = ""
- if not completion_text:
- choices = result.get("choices")
- if isinstance(choices, list) and choices:
- choice = choices[0]
- if isinstance(choice, dict):
- message = choice.get("message", {})
- tool_calls = (
- message.get("tool_calls")
- if isinstance(message, dict)
- else choice.get("tool_calls")
- )
- if tool_calls:
- try:
- completion_text = json.dumps(
- tool_calls, ensure_ascii=False, default=str
- )
- except Exception:
- completion_text = str(tool_calls)
- completion_tokens = counter.count(completion_text) if completion_text else 0
- total_tokens = prompt_tokens + completion_tokens
- logger.debug(
- "[API响应] usage 缺失，估算 tokens: prompt=%s completion=%s total=%s",
- prompt_tokens,
- completion_tokens,
- total_tokens,
- )
- return prompt_tokens, completion_tokens, total_tokens
-
-
-def build_request_body(
- model_config: ModelConfig,
- messages: list[dict[str, Any]],
- max_tokens: int,
- tools: list[dict[str, Any]] | None = None,
- tool_choice: str = "auto",
- internal_to_api: dict[str, str] | None = None,
- transport_state: dict[str, Any] | None = None,
- **kwargs: Any,
-) -> dict[str, Any]:
- """构建 API 请求体。"""
- api_mode = get_api_mode(model_config)
- extra_kwargs: dict[str, Any] = dict(kwargs)
-
- if "thinking" in extra_kwargs:
- normalized = _normalize_thinking_override(
- extra_kwargs.get("thinking"), model_config
- )
- if normalized is None:
- extra_kwargs.pop("thinking", None)
- else:
- extra_kwargs["thinking"] = normalized
-
- if api_mode == API_MODE_RESPONSES:
- extra_kwargs.pop("reasoning", None)
- extra_kwargs.pop("reasoning_effort", None)
- extra_kwargs.pop("output_config", None)
- return build_responses_request_body(
- model_config,
- messages,
- max_tokens,
- tools=tools,
- tool_choice=tool_choice,
- extra_kwargs=extra_kwargs,
- internal_to_api=internal_to_api or {},
- transport_state=transport_state,
- )
-
- body: dict[str, Any] = {
- "model": model_config.model_name,
- "messages": _prepare_chat_completion_messages(model_config, messages),
- "max_tokens": max_tokens,
- }
-
- extra_kwargs.pop("reasoning", None)
- extra_kwargs.pop("reasoning_effort", None)
- extra_kwargs.pop("output_config", None)
-
- thinking = get_thinking_payload(model_config)
- if thinking is not None:
- body["thinking"] = thinking
-
- effort_payload = get_effort_payload(model_config)
- if effort_payload is not None:
- style = get_effort_style(model_config)
- if style == "anthropic":
- body["output_config"] = effort_payload
- else:
- body["reasoning_effort"] = effort_payload["effort"]
-
- if tools:
- body["tools"] = tools
- thinking_active = "thinking" in body
- if thinking_active and isinstance(tool_choice, dict):
- body["tool_choice"] = "auto"
- else:
- body["tool_choice"] = tool_choice
-
- body.update(extra_kwargs)
- return body
diff --git a/src/Undefined/ai/llm/__init__.py b/src/Undefined/ai/llm/__init__.py
new file mode 100644
index 00000000..2d0fd29c
--- /dev/null
+++ b/src/Undefined/ai/llm/__init__.py
@@ -0,0 +1,21 @@
+"""LLM 模型请求子包。
+
+对外稳定入口：``ModelRequester``、``build_request_body``、``ModelConfig``。
+"""
+
+from Undefined.ai.llm.requester import ModelRequester, build_request_body
+from Undefined.ai.llm.sanitize import _encode_tool_name_for_api
+from Undefined.ai.llm.streaming import should_fallback_from_stream
+from Undefined.ai.llm.types import ModelConfig
+
+# 测试与内部调用沿用的私有符号别名（保持旧 import 路径可用）
+_should_fallback_from_stream = should_fallback_from_stream
+
+# 子包公开 API 列表
+__all__ = [
+ "ModelRequester",
+ "build_request_body",
+ "ModelConfig",
+ "_encode_tool_name_for_api",
+ "_should_fallback_from_stream",
+]
diff --git a/src/Undefined/ai/llm/requester.py b/src/Undefined/ai/llm/requester.py
new file mode 100644
index 00000000..10329d0e
--- /dev/null
+++ b/src/Undefined/ai/llm/requester.py
@@ -0,0 +1,1015 @@
+"""统一 LLM 模型请求封装与请求体构建。
+
+``ModelRequester`` 负责 OpenAI 兼容 API 的 chat/responses/embed/rerank 调用、
+流式聚合与 token 用量记录；出站清洗与思维链提取委托 ``sanitize`` / ``thinking`` 子模块。
+"""
+
+from __future__ import annotations
+
+import asyncio
+import hashlib
+import json
+import logging
+import re
+import time
+from datetime import datetime
+from typing import Any
+from urllib.parse import parse_qsl, urlsplit, urlunsplit
+
+import httpx
+from openai import (
+ APIConnectionError,
+ APIStatusError,
+ APITimeoutError,
+ AsyncOpenAI,
+)
+
+from Undefined.ai.llm.sanitize import (
+ _tool_name_dot_delimiter,
+ desc_preview,
+ prepare_chat_completion_messages,
+ relocate_system_to_first_user,
+ sanitize_chat_completion_messages,
+ sanitize_openai_messages_tool_arguments,
+ sanitize_openai_tool_names_in_request,
+ sanitize_openai_tools,
+ tools_description_max_len,
+ tools_description_truncate_enabled,
+ tools_sanitize_verbose,
+)
+from Undefined.ai.llm.streaming import (
+ aggregate_chat_completions_stream,
+ aggregate_responses_stream,
+ ensure_chat_stream_usage_options,
+ should_fallback_from_stream,
+ split_chat_completion_params,
+ split_responses_params,
+ without_stream_request_fields,
+)
+from Undefined.ai.llm.thinking import (
+ extract_thinking_content,
+ normalize_thinking_override,
+)
+from Undefined.ai.llm.types import ModelConfig
+from Undefined.ai.parsing import extract_choices_content
+from Undefined.ai.retrieval import RetrievalRequester
+from Undefined.ai.tokens import TokenCounter
+from Undefined.ai.transports import (
+ API_MODE_CHAT_COMPLETIONS,
+ API_MODE_RESPONSES,
+ build_responses_request_body,
+ get_api_mode,
+ get_effort_payload,
+ get_effort_style,
+ get_thinking_payload,
+ normalize_responses_result,
+)
+from Undefined.config import Config, EmbeddingModelConfig, RerankModelConfig, get_config
+from Undefined.context import RequestContext
+from Undefined.token_usage_storage import TokenUsage, TokenUsageStorage
+from Undefined.utils.logging import log_debug_json, redact_string
+from Undefined.utils.request_params import (
+ merge_request_params,
+ split_reserved_request_params,
+)
+
+logger = logging.getLogger(__name__)
+
+__all__ = ["ModelRequester", "build_request_body", "ModelConfig"]
+
+_SDK_REQUEST_OPTION_FIELDS: frozenset[str] = frozenset(
+ {"extra_headers", "extra_query", "extra_body", "timeout"}
+)
+
+_CHAT_COMPLETIONS_RESERVED_FIELDS: frozenset[str] = (
+ frozenset(
+ {
+ "model",
+ "messages",
+ "max_tokens",
+ "tools",
+ "tool_choice",
+ "stream",
+ "stream_options",
+ "thinking",
+ "reasoning",
+ "reasoning_effort",
+ "output_config",
+ }
+ )
+ | _SDK_REQUEST_OPTION_FIELDS
+)
+
+_RESPONSES_RESERVED_FIELDS: frozenset[str] = (
+ frozenset(
+ {
+ "model",
+ "input",
+ "instructions",
+ "max_output_tokens",
+ "tools",
+ "tool_choice",
+ "previous_response_id",
+ "stream",
+ "stream_options",
+ "thinking",
+ "reasoning",
+ "reasoning_effort",
+ "output_config",
+ }
+ )
+ | _SDK_REQUEST_OPTION_FIELDS
+)
+
+_TOOLS_PARAM_INDEX_RE = re.compile(r"Tools\[(\d+)\]", re.IGNORECASE)
+_RESPONSES_MISSING_TOOL_CALL_OUTPUT_RE = re.compile(
+ r"no tool call found for function call output with call_id",
+ re.IGNORECASE,
+)
+
+_PROMPT_CACHE_KEY_MAX_LEN = 128
+
+
+def _get_runtime_config() -> Config | None:
+ try:
+ return get_config(strict=False)
+ except Exception:
+ return None
+
+
+def _hash8(text: str) -> str:
+ return hashlib.sha1(text.encode("utf-8"), usedforsecurity=False).hexdigest()[:8]
+
+
+def _normalize_prompt_cache_part(value: Any) -> str:
+ text = str(value or "").strip().lower()
+ if not text:
+ return "none"
+ normalized_chars: list[str] = []
+ for char in text:
+ if char.isalnum() or char in {"-", "_", ":"}:
+ normalized_chars.append(char)
+ else:
+ normalized_chars.append("_")
+ normalized = "".join(normalized_chars).strip("_")
+ return normalized or "none"
+
+
+def _build_scope_prompt_cache_part() -> str:
+ # prompt_cache_key 按会话 scope 隔离，避免群/私聊上下文串缓存
+ ctx = RequestContext.current()
+ if ctx is None:
+ return "scope:global"
+ if ctx.group_id is not None:
+ return f"group:{_hash8(str(int(ctx.group_id)))}"
+ if ctx.user_id is not None:
+ return f"private:{_hash8(str(int(ctx.user_id)))}"
+ if ctx.sender_id is not None:
+ return f"sender:{_hash8(str(int(ctx.sender_id)))}"
+ request_type = _normalize_prompt_cache_part(ctx.request_type)
+ return f"type:{request_type}"
+
+
+def _build_default_prompt_cache_key(model_config: ModelConfig, call_type: str) -> str:
+ model_name = _normalize_prompt_cache_part(getattr(model_config, "model_name", ""))
+ scope_part = _build_scope_prompt_cache_part()
+ call_part = _normalize_prompt_cache_part(call_type)
+ key = f"pc:{model_name}:{call_part}:{scope_part}"
+ if len(key) <= _PROMPT_CACHE_KEY_MAX_LEN:
+ return key
+ suffix = "_" + _hash8(key)
+ prefix_len = max(1, _PROMPT_CACHE_KEY_MAX_LEN - len(suffix))
+ return key[:prefix_len] + suffix
+
+
+def _responses_should_fallback_to_stateless_replay(
+ exc: APIStatusError,
+ request_body: dict[str, Any],
+ *,
+ stateless_replay: bool,
+) -> bool:
+ # 仅当续轮携带 function_call_output 且服务端报 call_id 不匹配时才降级
+ if stateless_replay or not request_body.get("previous_response_id"):
+ return False
+ input_items = request_body.get("input")
+ if not isinstance(input_items, list) or not any(
+ isinstance(item, dict) and item.get("type") == "function_call_output"
+ for item in input_items
+ ):
+ return False
+ if exc.status_code != 400 or not isinstance(exc.body, dict):
+ return False
+ error = exc.body.get("error")
+ if not isinstance(error, dict):
+ return False
+ message = str(error.get("message", "")).strip()
+ param = str(error.get("param", "")).strip().lower()
+ return param == "input" and bool(
+ _RESPONSES_MISSING_TOOL_CALL_OUTPUT_RE.search(message)
+ )
+
+
+def _normalize_openai_base_url(
+ api_url: str,
+) -> tuple[str, dict[str, object] | None, bool]:
+ """将旧式 /chat/completions URL 归一化为 OpenAI SDK 需要的 base_url。
+
+ 兼容策略（B）：如果发现 api_url 末尾包含 /chat/completions，则自动裁剪为 base_url，
+ 以便统一走 OpenAI SDK，并给出弃用警告。
+ """
+ try:
+ parts = urlsplit(api_url)
+ except Exception:
+ return api_url, None, False
+
+ path = parts.path or ""
+ trimmed_path = path.rstrip("/")
+ suffix = "/chat/completions"
+ if not trimmed_path.endswith(suffix):
+ return api_url, None, False
+
+ new_path = trimmed_path[: -len(suffix)]
+ default_query: dict[str, object] | None = None
+ if parts.query:
+ default_query = {
+ k: v for k, v in parse_qsl(parts.query, keep_blank_values=True)
+ }
+ normalized = urlunsplit(parts._replace(path=new_path, query="", fragment=""))
+ return normalized, default_query, True
+
+
+def _warn_ignored_request_params(
+ *,
+ call_type: str,
+ model_name: str,
+ ignored: dict[str, Any],
+) -> None:
+ if not ignored:
+ return
+ logger.warning(
+ "[request_params] ignored_keys=%s type=%s model=%s",
+ ",".join(sorted(ignored)),
+ call_type,
+ model_name,
+ )
+
+
+def _build_effective_request_kwargs(
+ model_config: ModelConfig,
+ *,
+ call_type: str,
+ overrides: dict[str, Any],
+) -> dict[str, Any]:
+ merged = merge_request_params(
+ getattr(model_config, "request_params", {}),
+ overrides,
+ )
+ thinking_override = overrides["thinking"] if "thinking" in overrides else None
+ has_thinking_override = "thinking" in overrides
+ reserved_fields = (
+ _RESPONSES_RESERVED_FIELDS
+ if get_api_mode(model_config) == API_MODE_RESPONSES
+ else _CHAT_COMPLETIONS_RESERVED_FIELDS
+ )
+ allowed, ignored = split_reserved_request_params(
+ merged,
+ reserved_fields,
+ )
+ if has_thinking_override:
+ ignored.pop("thinking", None)
+ _warn_ignored_request_params(
+ call_type=call_type,
+ model_name=model_config.model_name,
+ ignored=ignored,
+ )
+ if has_thinking_override:
+ allowed["thinking"] = thinking_override
+ return allowed
+
+
+class ModelRequester:
+ """统一的模型请求封装。"""
+
+ def __init__(
+ self,
+ http_client: httpx.AsyncClient,
+ token_usage_storage: TokenUsageStorage,
+ ) -> None:
+ self._http_client = http_client
+ self._token_usage_storage = token_usage_storage
+ self._openai_clients: dict[
+ tuple[str, str, tuple[tuple[str, str], ...] | None], AsyncOpenAI
+ ] = {}
+ self._token_counters: dict[str, TokenCounter] = {}
+ self._warned_legacy_api_urls: set[str] = set()
+ self._background_tasks: set[asyncio.Task[Any]] = set()
+ self._retrieval_requester = RetrievalRequester(
+ get_openai_client=self._get_openai_client_for_model,
+ response_to_dict=self._response_to_dict,
+ get_token_counter=self._get_token_counter,
+ record_usage=self._record_usage,
+ )
+
+ async def request(
+ self,
+ model_config: ModelConfig,
+ messages: list[dict[str, Any]],
+ max_tokens: int = 8192,
+ call_type: str = "chat",
+ tools: list[dict[str, Any]] | None = None,
+ tool_choice: str = "auto",
+ transport_state: dict[str, Any] | None = None,
+ message_count_for_transport: int | None = None,
+ **kwargs: Any,
+ ) -> dict[str, Any]:
+ """发送请求到模型 API。"""
+ start_time = time.perf_counter()
+ cot_compat = getattr(model_config, "thinking_tool_call_compat", False)
+ reasoning_replay = bool(
+ getattr(model_config, "reasoning_content_replay", False)
+ )
+ api_mode = get_api_mode(model_config)
+ transport_message_count = (
+ message_count_for_transport
+ if message_count_for_transport is not None
+ else len(messages)
+ )
+ messages_for_api, tool_args_fixed = sanitize_openai_messages_tool_arguments(
+ messages
+ )
+ if tool_args_fixed and logger.isEnabledFor(logging.INFO):
+ logger.info(
+ "[messages.sanitize] tool_args_fixed=%s messages=%s",
+ tool_args_fixed,
+ len(messages_for_api),
+ )
+ if api_mode == API_MODE_CHAT_COMPLETIONS:
+ (
+ messages_for_api,
+ stripped_message_count,
+ stripped_message_fields,
+ ) = sanitize_chat_completion_messages(
+ messages_for_api,
+ preserve_reasoning_content=reasoning_replay,
+ )
+ if bool(getattr(model_config, "system_prompt_as_user", False)):
+ messages_for_api = relocate_system_to_first_user(messages_for_api)
+ if stripped_message_count and logger.isEnabledFor(logging.INFO):
+ details = ",".join(
+ f"{key}={value}"
+ for key, value in sorted(stripped_message_fields.items())
+ )
+ logger.info(
+ "[chat_completions.standardize] stripped_internal_message_fields=%s messages=%s",
+ details,
+ stripped_message_count,
+ )
+
+ tools_for_api = tools
+ api_to_internal: dict[str, str] = {}
+ internal_to_api: dict[str, str] = {}
+ if isinstance(tools_for_api, list):
+ request_for_sanitize = {
+ "messages": messages_for_api,
+ "tools": list(tools_for_api),
+ }
+ api_to_internal, internal_to_api = sanitize_openai_tool_names_in_request(
+ request_for_sanitize
+ )
+ raw_messages = request_for_sanitize.get("messages")
+ if isinstance(raw_messages, list):
+ messages_for_api = raw_messages
+ raw_tools = request_for_sanitize.get("tools")
+ if isinstance(raw_tools, list):
+ tools_for_api = raw_tools
+
+ if isinstance(tools_for_api, list):
+ sanitized_tools, changed_count, changes = sanitize_openai_tools(
+ tools_for_api
+ )
+ tools_for_api = sanitized_tools
+ if changed_count and logger.isEnabledFor(logging.INFO):
+ logger.info(
+ "[tools.sanitize] changed=%s total=%s truncate_enabled=%s max_desc_len=%s",
+ changed_count,
+ len(sanitized_tools),
+ tools_description_truncate_enabled(),
+ tools_description_max_len(),
+ )
+ if tools_sanitize_verbose():
+ for change in changes:
+ logger.info(
+ "[tools.sanitize.item] index=%s name=%s reasons=%s old_len=%s new_len=%s old=%s new=%s",
+ change.get("index"),
+ change.get("name"),
+ ",".join(change.get("reasons", [])),
+ change.get("old_len"),
+ change.get("new_len"),
+ change.get("old_preview"),
+ change.get("new_preview"),
+ )
+
+ effective_kwargs = _build_effective_request_kwargs(
+ model_config,
+ call_type=call_type,
+ overrides=dict(kwargs),
+ )
+ if bool(
+ getattr(model_config, "prompt_cache_enabled", True)
+ ) and not effective_kwargs.get("prompt_cache_key"):
+ effective_kwargs["prompt_cache_key"] = _build_default_prompt_cache_key(
+ model_config,
+ call_type,
+ )
+ responses_stateless_replay = bool(
+ getattr(model_config, "responses_force_stateless_replay", False)
+ ) or bool(
+ isinstance(transport_state, dict)
+ and transport_state.get("stateless_replay")
+ )
+ effective_transport_state: dict[str, Any] | None
+ if responses_stateless_replay:
+ effective_transport_state = dict(transport_state or {})
+ effective_transport_state["stateless_replay"] = True
+ else:
+ effective_transport_state = transport_state
+ request_body = build_request_body(
+ model_config=model_config,
+ messages=messages_for_api,
+ max_tokens=max_tokens,
+ tools=tools_for_api,
+ tool_choice=tool_choice,
+ internal_to_api=internal_to_api,
+ transport_state=effective_transport_state,
+ **effective_kwargs,
+ )
+
+ try:
+ if cot_compat and logger.isEnabledFor(logging.DEBUG):
+ logger.debug(
+ "[思维链兼容] enabled=%s type=%s model=%s api_mode=%s thinking_enabled=%s tools=%s messages=%s",
+ cot_compat,
+ call_type,
+ model_config.model_name,
+ api_mode,
+ getattr(model_config, "thinking_enabled", False),
+ bool(tools),
+ len(messages),
+ )
+
+ if logger.isEnabledFor(logging.DEBUG):
+ logger.debug(
+ "[API请求] type=%s model=%s api_mode=%s url=%s max_tokens=%s tools=%s tool_choice=%s messages=%s",
+ call_type,
+ model_config.model_name,
+ api_mode,
+ model_config.api_url,
+ max_tokens,
+ bool(tools_for_api),
+ tool_choice,
+ len(messages),
+ )
+ log_debug_json(logger, "[API请求体]", request_body)
+
+ try:
+ raw_result = await self._request_with_openai(model_config, request_body)
+ except APIStatusError as exc:
+ # Responses 续轮失败：自动切换 stateless replay 重发全量 input
+ if (
+ api_mode == API_MODE_RESPONSES
+ and _responses_should_fallback_to_stateless_replay(
+ exc,
+ request_body,
+ stateless_replay=responses_stateless_replay,
+ )
+ ):
+ logger.warning(
+ "[responses.compat] previous_response_id 续轮失败，自动降级为 stateless replay: model=%s call_type=%s previous_response_id=%s",
+ model_config.model_name,
+ call_type,
+ request_body.get("previous_response_id", ""),
+ )
+ effective_transport_state = dict(effective_transport_state or {})
+ effective_transport_state["stateless_replay"] = True
+ responses_stateless_replay = True
+ request_body = build_request_body(
+ model_config=model_config,
+ messages=messages_for_api,
+ max_tokens=max_tokens,
+ tools=tools_for_api,
+ tool_choice=tool_choice,
+ internal_to_api=internal_to_api,
+ transport_state=effective_transport_state,
+ **effective_kwargs,
+ )
+ if logger.isEnabledFor(logging.DEBUG):
+ log_debug_json(
+ logger, "[API请求体][stateless replay]", request_body
+ )
+ raw_result = await self._request_with_openai(
+ model_config, request_body
+ )
+ else:
+ raise
+ if api_mode == API_MODE_RESPONSES:
+ result = normalize_responses_result(
+ raw_result,
+ api_to_internal if api_to_internal else None,
+ )
+ response_id = str(
+ raw_result.get("id") or result.get("id") or ""
+ ).strip()
+ if response_id:
+ choice = result.get("choices", [{}])[0]
+ message = (
+ choice.get("message", {}) if isinstance(choice, dict) else {}
+ )
+ tool_calls = (
+ message.get("tool_calls", [])
+ if isinstance(message, dict)
+ else []
+ )
+ # 记录续轮锚点：下一轮只发送 tool_result 及之后的消息
+ result["_transport_state"] = {
+ "api_mode": api_mode,
+ "previous_response_id": response_id,
+ "tool_result_start_index": transport_message_count
+ + (1 if tool_calls else 0),
+ }
+ if responses_stateless_replay:
+ result["_transport_state"]["stateless_replay"] = True
+ else:
+ result = self._normalize_result(raw_result)
+ if api_to_internal:
+ result["_tool_name_map"] = {
+ "api_to_internal": api_to_internal,
+ "internal_to_api": internal_to_api,
+ "dot_delimiter": _tool_name_dot_delimiter(),
+ }
+ duration = time.perf_counter() - start_time
+
+ usage = result.get("usage", {}) or {}
+ prompt_tokens = int(usage.get("prompt_tokens", 0) or 0)
+ completion_tokens = int(usage.get("completion_tokens", 0) or 0)
+ total_tokens = int(usage.get("total_tokens", 0) or 0)
+ if total_tokens == 0 and (prompt_tokens or completion_tokens):
+ total_tokens = prompt_tokens + completion_tokens
+ if total_tokens == 0:
+ prompt_tokens, completion_tokens, total_tokens = self._estimate_usage(
+ model_config.model_name, messages_for_api, result
+ )
+
+ logger.info(
+ f"[API响应] {call_type} 完成: 耗时={duration:.2f}s, "
+ f"Tokens={total_tokens} (P:{prompt_tokens} + C:{completion_tokens}), "
+ f"模型={model_config.model_name}"
+ )
+
+ if logger.isEnabledFor(logging.DEBUG):
+ log_debug_json(logger, "[API响应体]", result)
+
+ self._maybe_log_thinking(result, call_type, model_config.model_name)
+
+ self._record_usage(
+ model_name=model_config.model_name,
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ total_tokens=total_tokens,
+ duration_seconds=duration,
+ call_type=call_type,
+ )
+
+ return result
+ except APIStatusError as exc:
+ response = exc.response
+ try:
+ body = (
+ json.dumps(exc.body, ensure_ascii=False, default=str)
+ if exc.body is not None
+ else ""
+ )
+ except Exception:
+ body = str(exc.body)
+ if (
+ exc.status_code == 400
+ and isinstance(exc.body, dict)
+ and isinstance(exc.body.get("error"), dict)
+ ):
+ param = exc.body.get("error", {}).get("param")
+ if isinstance(param, str):
+ match = _TOOLS_PARAM_INDEX_RE.search(param)
+ if match and isinstance(request_body.get("tools"), list):
+ try:
+ idx = int(match.group(1))
+ except ValueError:
+ idx = -1
+ if 0 <= idx < len(request_body["tools"]):
+ tool = request_body["tools"][idx]
+ tool_name = (
+ tool.get("function", {}).get("name")
+ if isinstance(tool, dict)
+ else ""
+ )
+ desc_len: int | None = None
+ desc_preview_text = ""
+ if isinstance(tool, dict):
+ function = tool.get("function", {})
+ if isinstance(function, dict):
+ desc = function.get("description")
+ if desc is not None:
+ desc_str = (
+ desc if isinstance(desc, str) else str(desc)
+ )
+ desc_len = len(desc_str)
+ desc_preview_text = desc_preview(desc_str)
+ logger.error(
+ "[tools.invalid] index=%s name=%s desc_len=%s desc=%s param=%s",
+ idx,
+ tool_name,
+ desc_len,
+ desc_preview_text,
+ param,
+ )
+ logger.error(
+ "[API响应错误] status=%s request_id=%s url=%s body=%s",
+ exc.status_code,
+ exc.request_id or "",
+ response.request.url,
+ redact_string(body),
+ )
+ raise
+ except (APIConnectionError, APITimeoutError) as exc:
+ logger.error("[API连接错误] type=%s message=%s", type(exc).__name__, exc)
+ raise
+ except Exception as exc:
+ logger.exception(f"[model.request.error] {call_type} 调用失败: {exc}")
+ raise
+
+ def _thinking_logging_enabled(self) -> bool:
+ runtime_config = _get_runtime_config()
+ if runtime_config is None:
+ return True
+ return bool(runtime_config.log_thinking)
+
+ def _maybe_log_thinking(
+ self, result: dict[str, Any], call_type: str, model_name: str
+ ) -> None:
+ if not self._thinking_logging_enabled():
+ return
+ thinking = extract_thinking_content(result)
+ if thinking:
+ logger.info(
+ "[思维链] type=%s model=%s content=%s",
+ call_type,
+ model_name,
+ redact_string(thinking),
+ )
+
+ async def _request_with_openai(
+ self, model_config: ModelConfig, request_body: dict[str, Any]
+ ) -> dict[str, Any]:
+ client = self._get_openai_client_for_model(model_config)
+ if bool(getattr(model_config, "stream_enabled", False)):
+ try:
+ return await self._request_with_openai_streaming(
+ # client, model_config, request_body
+ client,
+ model_config,
+ request_body,
+ )
+ except Exception as exc:
+ # 上游不支持流式时，剥离 stream 字段后降级为非流式重试
+ if not should_fallback_from_stream(exc):
+ raise
+ logger.warning(
+ "[API流式回退] model=%s api_mode=%s reason=%s",
+ getattr(model_config, "model_name", ""),
+ get_api_mode(model_config),
+ type(exc).__name__,
+ )
+ request_body = without_stream_request_fields(request_body)
+ if get_api_mode(model_config) == API_MODE_RESPONSES:
+ params, extra_body = split_responses_params(request_body)
+ if extra_body:
+ params["extra_body"] = extra_body
+ response = await client.responses.create(**params)
+ return self._response_to_dict(response)
+ params, extra_body = split_chat_completion_params(request_body)
+ if extra_body:
+ params["extra_body"] = extra_body
+ response = await client.chat.completions.create(**params)
+ return self._response_to_dict(response)
+
+ async def _request_with_openai_streaming(
+ self,
+ client: AsyncOpenAI,
+ model_config: ModelConfig,
+ request_body: dict[str, Any],
+ ) -> dict[str, Any]:
+ api_mode = get_api_mode(model_config)
+ stream_body = dict(request_body)
+ stream_body["stream"] = True
+ if api_mode == API_MODE_RESPONSES:
+ return await self._stream_responses_request(client, stream_body)
+ ensure_chat_stream_usage_options(stream_body)
+ return await self._stream_chat_completions_request(
+ # client, stream_body, model_config
+ client,
+ stream_body,
+ model_config,
+ )
+
+ async def _stream_chat_completions_request(
+ self,
+ client: AsyncOpenAI,
+ request_body: dict[str, Any],
+ model_config: ModelConfig,
+ ) -> dict[str, Any]:
+ params, extra_body = split_chat_completion_params(request_body)
+ if extra_body:
+ params["extra_body"] = extra_body
+ response = await client.chat.completions.create(**params)
+
+ reasoning_replay = bool(
+ getattr(model_config, "reasoning_content_replay", False)
+ )
+ chunks: list[dict[str, Any]] = []
+ async for chunk in response:
+ chunks.append(self._response_to_dict(chunk))
+ return aggregate_chat_completions_stream(
+ chunks,
+ reasoning_replay=reasoning_replay,
+ )
+
+ async def _stream_responses_request(
+ self, client: AsyncOpenAI, request_body: dict[str, Any]
+ ) -> dict[str, Any]:
+ params, extra_body = split_responses_params(request_body)
+ if extra_body:
+ params["extra_body"] = extra_body
+ stream = await client.responses.create(**params)
+
+ events: list[dict[str, Any]] = []
+ async for event in stream:
+ events.append(self._response_to_dict(event))
+ return aggregate_responses_stream(events)
+
+ async def embed(
+ self,
+ model_config: EmbeddingModelConfig,
+ texts: list[str],
+ ) -> list[list[float]]:
+ """调用统一检索请求层的 embeddings。"""
+ return await self._retrieval_requester.embed(model_config, texts)
+
+ async def rerank(
+ self,
+ model_config: RerankModelConfig,
+ query: str,
+ documents: list[str],
+ top_n: int | None = None,
+ ) -> list[dict[str, Any]]:
+ """调用统一检索请求层的 rerank。"""
+ return await self._retrieval_requester.rerank(
+ model_config=model_config,
+ query=query,
+ documents=documents,
+ top_n=top_n,
+ )
+
+ def _get_openai_client_for_model(self, model_config: ModelConfig) -> AsyncOpenAI:
+ base_url, default_query, changed = _normalize_openai_base_url(
+ model_config.api_url
+ )
+ if changed and model_config.api_url not in self._warned_legacy_api_urls:
+ self._warned_legacy_api_urls.add(model_config.api_url)
+ logger.warning(
+ "[配置弃用] 检测到 *_MODEL_API_URL 末尾包含 /chat/completions，这种写法已弃用；"
+ "已自动裁剪为 base_url=%s（原值=%s）。",
+ base_url,
+ model_config.api_url,
+ )
+ return self._get_openai_client(
+ base_url=base_url,
+ api_key=model_config.api_key,
+ default_query=default_query,
+ )
+
+ def _record_usage(
+ self,
+ *,
+ model_name: str,
+ prompt_tokens: int,
+ completion_tokens: int,
+ total_tokens: int,
+ duration_seconds: float,
+ call_type: str,
+ ) -> None:
+ task = asyncio.create_task(
+ self._token_usage_storage.record(
+ TokenUsage(
+ timestamp=datetime.now().isoformat(),
+ model_name=model_name,
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ total_tokens=total_tokens,
+ duration_seconds=duration_seconds,
+ call_type=call_type,
+ success=True,
+ )
+ )
+ )
+ self._background_tasks.add(task)
+ task.add_done_callback(self._background_tasks.discard)
+
+ def _get_openai_client(
+ self, base_url: str, api_key: str, default_query: dict[str, object] | None
+ ) -> AsyncOpenAI:
+ query_key = None
+ if default_query:
+ query_key = tuple(
+ sorted((str(k), str(v)) for k, v in default_query.items())
+ )
+ cache_key = (base_url, api_key, query_key)
+ client = self._openai_clients.get(cache_key)
+ if client is not None:
+ return client
+ # 复用上层注入的 httpx client（连接池/超时等），避免每个 OpenAI client 自建连接池。
+ client = AsyncOpenAI(
+ api_key=api_key,
+ base_url=base_url,
+ timeout=480.0,
+ default_query=default_query,
+ http_client=self._http_client,
+ )
+ self._openai_clients[cache_key] = client
+ return client
+
+ def _response_to_dict(self, response: Any) -> dict[str, Any]:
+ if isinstance(response, dict):
+ return response
+ for attr in ("model_dump", "to_dict", "dict"):
+ method = getattr(response, attr, None)
+ if callable(method):
+ try:
+ value = method()
+ if isinstance(value, dict):
+ return value
+ except Exception:
+ continue
+ to_json = getattr(response, "to_json", None)
+ if callable(to_json):
+ try:
+ raw_json = to_json()
+ loaded = json.loads(str(raw_json))
+ if isinstance(loaded, dict):
+ return loaded
+ except Exception:
+ pass
+ return {"data": str(response)}
+
+ def _normalize_result(self, result: dict[str, Any]) -> dict[str, Any]:
+ choices = result.get("choices")
+ if isinstance(choices, list):
+ return result
+ data = result.get("data")
+ if isinstance(data, dict):
+ data_choices = data.get("choices")
+ if isinstance(data_choices, list):
+ normalized = dict(result)
+ normalized["choices"] = data_choices
+ return normalized
+ normalized = dict(result)
+ normalized["choices"] = [{}]
+ return normalized
+
+ def _get_token_counter(self, model_name: str) -> TokenCounter:
+ counter = self._token_counters.get(model_name)
+ if counter is None:
+ counter = TokenCounter(model_name)
+ self._token_counters[model_name] = counter
+ return counter
+
+ def _estimate_usage(
+ self,
+ model_name: str,
+ messages: list[dict[str, Any]],
+ result: dict[str, Any],
+ ) -> tuple[int, int, int]:
+ counter = self._get_token_counter(model_name)
+ try:
+ prompt_text = "\n".join(
+ json.dumps(message, ensure_ascii=False, default=str)
+ for message in messages
+ )
+ except Exception:
+ prompt_text = str(messages)
+ prompt_tokens = counter.count(prompt_text)
+
+ completion_text = ""
+ try:
+ completion_text = extract_choices_content(result)
+ except Exception:
+ completion_text = ""
+ if not completion_text:
+ choices = result.get("choices")
+ if isinstance(choices, list) and choices:
+ choice = choices[0]
+ if isinstance(choice, dict):
+ message = choice.get("message", {})
+ tool_calls = (
+ message.get("tool_calls")
+ if isinstance(message, dict)
+ else choice.get("tool_calls")
+ )
+ # 无 tool_calls 与有 tool_calls 走不同分支
+ if tool_calls:
+ try:
+ completion_text = json.dumps(
+ tool_calls, ensure_ascii=False, default=str
+ )
+ except Exception:
+ completion_text = str(tool_calls)
+ completion_tokens = counter.count(completion_text) if completion_text else 0
+ total_tokens = prompt_tokens + completion_tokens
+ logger.debug(
+ "[API响应] usage 缺失，估算 tokens: prompt=%s completion=%s total=%s",
+ prompt_tokens,
+ completion_tokens,
+ total_tokens,
+ )
+ return prompt_tokens, completion_tokens, total_tokens
+
+
+def build_request_body(
+ model_config: ModelConfig,
+ messages: list[dict[str, Any]],
+ max_tokens: int,
+ tools: list[dict[str, Any]] | None = None,
+ tool_choice: str = "auto",
+ internal_to_api: dict[str, str] | None = None,
+ transport_state: dict[str, Any] | None = None,
+ **kwargs: Any,
+) -> dict[str, Any]:
+ """构建 API 请求体。"""
+ api_mode = get_api_mode(model_config)
+ extra_kwargs: dict[str, Any] = dict(kwargs)
+
+ if "thinking" in extra_kwargs:
+ normalized = normalize_thinking_override(
+ extra_kwargs.get("thinking"), model_config
+ )
+ if normalized is None:
+ extra_kwargs.pop("thinking", None)
+ else:
+ extra_kwargs["thinking"] = normalized
+
+ if api_mode == API_MODE_RESPONSES:
+ extra_kwargs.pop("reasoning", None)
+ extra_kwargs.pop("reasoning_effort", None)
+ extra_kwargs.pop("output_config", None)
+ return build_responses_request_body(
+ model_config,
+ messages,
+ max_tokens,
+ tools=tools,
+ tool_choice=tool_choice,
+ extra_kwargs=extra_kwargs,
+ internal_to_api=internal_to_api or {},
+ transport_state=transport_state,
+ )
+
+ body: dict[str, Any] = {
+ "model": model_config.model_name,
+ "messages": prepare_chat_completion_messages(model_config, messages),
+ "max_tokens": max_tokens,
+ }
+
+ extra_kwargs.pop("reasoning", None)
+ extra_kwargs.pop("reasoning_effort", None)
+ extra_kwargs.pop("output_config", None)
+
+ thinking = get_thinking_payload(model_config)
+ if thinking is not None:
+ body["thinking"] = thinking
+
+ effort_payload = get_effort_payload(model_config)
+ if effort_payload is not None:
+ style = get_effort_style(model_config)
+ # Anthropic 风格走 output_config，OpenAI 风格走 reasoning_effort
+ if style == "anthropic":
+ body["output_config"] = effort_payload
+ else:
+ body["reasoning_effort"] = effort_payload["effort"]
+
+ if tools:
+ body["tools"] = tools
+ thinking_active = "thinking" in body
+ # 部分 thinking 模型不接受 dict 形 tool_choice，强制降为 auto
+ if thinking_active and isinstance(tool_choice, dict):
+ body["tool_choice"] = "auto"
+ else:
+ body["tool_choice"] = tool_choice
+
+ body.update(extra_kwargs)
+ return body
diff --git a/src/Undefined/ai/llm/sanitize.py b/src/Undefined/ai/llm/sanitize.py
new file mode 100644
index 00000000..ec549b1c
--- /dev/null
+++ b/src/Undefined/ai/llm/sanitize.py
@@ -0,0 +1,558 @@
+"""LLM 出站请求清洗与工具名规范化。
+
+负责工具 schema/description 清洗、历史消息字段剥离、工具名 API 编码；
+不发起 HTTP 请求，也不解析模型响应。
+"""
+
+from __future__ import annotations
+
+import hashlib
+import logging
+import re
+from typing import Any
+
+from Undefined.ai.llm.types import ModelConfig
+from Undefined.config import Config, get_config
+from Undefined.utils.tool_calls import normalize_tool_arguments_json
+
+logger = logging.getLogger(__name__)
+
+_DEFAULT_TOOLS_DESCRIPTION_MAX_LEN = 1024
+_DEFAULT_TOOLS_DESCRIPTION_PREVIEW_LEN = 160
+
+_DEFAULT_TOOL_NAME_DOT_DELIMITER = "-_-"
+_TOOL_NAME_MAX_LEN = 64
+_TOOL_NAME_ALLOWED_RE = re.compile(r"^[a-zA-Z0-9_-]+$")
+
+_CHAT_COMPLETION_STRIP_THINKING_KEYS: frozenset[str] = frozenset(
+ ("thinking", "reasoning", "chain_of_thought", "cot", "thoughts")
+)
+CHAT_COMPLETION_INTERNAL_MESSAGE_KEYS: frozenset[str] = frozenset(
+ (
+ "reasoning_content",
+ *_CHAT_COMPLETION_STRIP_THINKING_KEYS,
+ "_responses_output_items",
+ "phase",
+ )
+)
+
+
+def _get_runtime_config() -> Config | None:
+ try:
+ return get_config(strict=False)
+ except Exception:
+ return None
+
+
+def _tool_name_dot_delimiter() -> str:
+ runtime_config = _get_runtime_config()
+ value = (
+ getattr(runtime_config, "tools_dot_delimiter", None) if runtime_config else None
+ )
+ text = str(value).strip() if value is not None else _DEFAULT_TOOL_NAME_DOT_DELIMITER
+ if not text:
+ return _DEFAULT_TOOL_NAME_DOT_DELIMITER
+ if "." in text:
+ return _DEFAULT_TOOL_NAME_DOT_DELIMITER
+ if not _TOOL_NAME_ALLOWED_RE.match(text):
+ return _DEFAULT_TOOL_NAME_DOT_DELIMITER
+ # 保持较短长度，避免工具名被服务端截断。
+ if len(text) > 16:
+ return text[:16]
+ return text
+
+
+def _hash8(text: str) -> str:
+ return hashlib.sha1(text.encode("utf-8"), usedforsecurity=False).hexdigest()[:8]
+
+
+def _encode_tool_name_for_api(tool_name: str) -> str:
+ """将内部工具名编码为服务端可接受的 function.name。
+
+ - 将 '.' 替换为 '-_-'（保留工具集命名语义）
+ - 其他不允许字符替换为 '_'
+ - 强制最大长度（<=64），超长时追加稳定哈希
+ """
+ raw = str(tool_name or "").strip()
+ if not raw:
+ return "tool"
+
+ # 保留工具集分隔语义：category.tool -> categorytool
+ encoded = raw.replace(".", _tool_name_dot_delimiter())
+
+ # 替换其他不允许字符。
+ cleaned_chars: list[str] = []
+ for ch in encoded:
+ if ch.isalnum() or ch in {"_", "-"}:
+ cleaned_chars.append(ch)
+ else:
+ cleaned_chars.append("_")
+ encoded = "".join(cleaned_chars)
+
+ if not encoded:
+ encoded = "tool"
+
+ if len(encoded) > _TOOL_NAME_MAX_LEN:
+ suffix = "_" + _hash8(raw)
+ prefix_len = max(1, _TOOL_NAME_MAX_LEN - len(suffix))
+ encoded = encoded[:prefix_len] + suffix
+
+ # 最后兜底校验（理论上应始终通过）
+ if not _TOOL_NAME_ALLOWED_RE.match(encoded):
+ suffix = "_" + _hash8(raw)
+ encoded = re.sub(r"[^a-zA-Z0-9_-]", "_", encoded)
+ if len(encoded) > _TOOL_NAME_MAX_LEN:
+ encoded = encoded[: _TOOL_NAME_MAX_LEN - len(suffix)] + suffix
+ if not encoded:
+ encoded = "tool" + suffix
+
+ return encoded
+
+
+def sanitize_openai_tool_names_in_request(
+ request_body: dict[str, Any],
+) -> tuple[dict[str, str], dict[str, str]]:
+ """将 request_body 的 tools/messages 工具名改写为服务端可接受的名称。
+
+ Returns:
+ (api_to_internal, internal_to_api) 映射表。
+
+ Notes:
+ - 仅保证 tools schema 中出现的名称可逆映射。
+ - 历史消息中的工具调用会尽力重写。
+ """
+ tools = request_body.get("tools")
+ if not isinstance(tools, list) or not tools:
+ return {}, {}
+
+ internal_to_api: dict[str, str] = {}
+ api_to_internal: dict[str, str] = {}
+ used_api: set[str] = set()
+
+ new_tools: list[dict[str, Any]] = []
+ for tool in tools:
+ if not isinstance(tool, dict):
+ new_tools.append(tool)
+ continue
+ function = tool.get("function")
+ if not isinstance(function, dict):
+ new_tools.append(tool)
+ continue
+ internal_name = str(function.get("name", "") or "")
+ if not internal_name:
+ new_tools.append(tool)
+ continue
+
+ # 稳定编码；如发生冲突则追加后缀。
+ base_api_name = _encode_tool_name_for_api(internal_name)
+ api_name = base_api_name
+ if api_name in used_api and api_to_internal.get(api_name) != internal_name:
+ suffix = "_" + _hash8(internal_name)
+ prefix_len = max(1, _TOOL_NAME_MAX_LEN - len(suffix))
+ api_name = base_api_name[:prefix_len] + suffix
+ if api_name in used_api and api_to_internal.get(api_name) != internal_name:
+ # 极少数冲突兜底：加入索引避免重复。
+ suffix = "_" + _hash8(f"{internal_name}:{len(used_api)}")
+ prefix_len = max(1, _TOOL_NAME_MAX_LEN - len(suffix))
+ api_name = base_api_name[:prefix_len] + suffix
+
+ used_api.add(api_name)
+ internal_to_api[internal_name] = api_name
+ api_to_internal[api_name] = internal_name
+
+ if api_name != internal_name:
+ tool = dict(tool)
+ function = dict(function)
+ function["name"] = api_name
+ tool["function"] = function
+ new_tools.append(tool)
+
+ request_body["tools"] = new_tools
+
+ # 尽力重写历史消息中的工具名。
+ messages = request_body.get("messages")
+ if isinstance(messages, list) and messages:
+ new_messages: list[dict[str, Any]] = []
+ changed = False
+ for message in messages:
+ if not isinstance(message, dict):
+ new_messages.append(message)
+ continue
+
+ new_message = message
+
+ msg_name = message.get("name")
+ if isinstance(msg_name, str) and msg_name:
+ mapped = internal_to_api.get(msg_name)
+ if mapped and mapped != msg_name:
+ if new_message is message:
+ new_message = dict(message)
+ new_message["name"] = mapped
+ changed = True
+ elif (not _TOOL_NAME_ALLOWED_RE.match(msg_name)) or (
+ len(msg_name) > _TOOL_NAME_MAX_LEN
+ ):
+ # 即便名称不在 schema 映射中，也尽量保证请求合法（如工具被重命名/移除）。
+ safe = _encode_tool_name_for_api(msg_name)
+ if safe != msg_name:
+ if new_message is message:
+ new_message = dict(message)
+ new_message["name"] = safe
+ changed = True
+
+ tool_calls = message.get("tool_calls")
+ # 无 tool_calls 与有 tool_calls 走不同分支
+ if isinstance(tool_calls, list) and tool_calls:
+ new_tool_calls: list[Any] = []
+ tool_calls_changed = False
+ # 逐个处理模型返回的 tool_call
+ for tool_call in tool_calls:
+ if not isinstance(tool_call, dict):
+ new_tool_calls.append(tool_call)
+ continue
+ function = tool_call.get("function")
+ if not isinstance(function, dict):
+ new_tool_calls.append(tool_call)
+ continue
+ fname = function.get("name")
+ if not isinstance(fname, str) or not fname:
+ new_tool_calls.append(tool_call)
+ continue
+ mapped = internal_to_api.get(fname)
+ safe_name = mapped or _encode_tool_name_for_api(fname)
+ if safe_name != fname:
+ tool_calls_changed = True
+ new_tool_call = dict(tool_call)
+ new_function = dict(function)
+ new_function["name"] = safe_name
+ new_tool_call["function"] = new_function
+ new_tool_calls.append(new_tool_call)
+ else:
+ new_tool_calls.append(tool_call)
+
+ # 无 tool_calls 与有 tool_calls 走不同分支
+ if tool_calls_changed:
+ if new_message is message:
+ new_message = dict(message)
+ new_message["tool_calls"] = new_tool_calls
+ changed = True
+
+ new_messages.append(new_message)
+
+ if changed:
+ request_body["messages"] = new_messages
+
+ return api_to_internal, internal_to_api
+
+
+def _tools_sanitize_enabled() -> bool:
+ # 历史配置项 tools.sanitize 已迁移为 tools.dot_delimiter。
+ # 为兼容严格网关，description 的 schema 清洗默认始终开启。
+ return True
+
+
+def tools_sanitize_verbose() -> bool:
+ """是否输出工具 schema 清洗的详细日志。"""
+ runtime_config = _get_runtime_config()
+ if runtime_config is not None:
+ return bool(runtime_config.tools_sanitize_verbose)
+ return False
+
+
+def tools_description_max_len() -> int:
+ """返回工具 description 允许的最大长度。"""
+ runtime_config = _get_runtime_config()
+ if runtime_config is None:
+ return _DEFAULT_TOOLS_DESCRIPTION_MAX_LEN
+ value = runtime_config.tools_description_max_len
+ return value if value > 0 else _DEFAULT_TOOLS_DESCRIPTION_MAX_LEN
+
+
+def tools_description_truncate_enabled() -> bool:
+ """是否启用工具 description 截断。"""
+ runtime_config = _get_runtime_config()
+ if runtime_config is None:
+ return False
+ return bool(runtime_config.tools_description_truncate_enabled)
+
+
+def _clean_control_chars(text: str) -> str:
+ """将 ASCII 控制字符替换为空格。"""
+ return "".join(" " if ord(ch) < 32 or ord(ch) == 127 else ch for ch in text)
+
+
+def desc_preview(text: str) -> str:
+ """生成工具 description 的日志预览片段。"""
+ runtime_config = _get_runtime_config()
+ if runtime_config is None:
+ preview_len = _DEFAULT_TOOLS_DESCRIPTION_PREVIEW_LEN
+ else:
+ preview_len = runtime_config.tools_description_preview_len
+ if preview_len <= 0:
+ preview_len = _DEFAULT_TOOLS_DESCRIPTION_PREVIEW_LEN
+ return text[:preview_len] + ("…" if len(text) > preview_len else "")
+
+
+def _normalize_tool_description(
+ description: Any,
+ tool_name: str,
+ max_len: int,
+ truncate_enabled: bool,
+) -> str:
+ """规范化工具 function.description，适配更严格的 OpenAI 兼容服务。"""
+ if description is None:
+ normalized = ""
+ elif isinstance(description, str):
+ normalized = description
+ else:
+ normalized = str(description)
+
+ normalized = _clean_control_chars(normalized)
+ normalized = " ".join(normalized.split())
+ normalized = normalized.strip()
+ if not normalized:
+ normalized = f"Tool function {tool_name}"
+ if truncate_enabled and len(normalized) > max_len:
+ normalized = normalized[:max_len].rstrip()
+ return normalized
+
+
+def sanitize_openai_tools(
+ tools: list[dict[str, Any]],
+) -> tuple[list[dict[str, Any]], int, list[dict[str, Any]]]:
+ """清洗 tools schema，避免严格网关因非法 description 返回 400。"""
+ if not tools or not _tools_sanitize_enabled():
+ return tools, 0, []
+
+ max_len = tools_description_max_len()
+ truncate_enabled = tools_description_truncate_enabled()
+ changed = 0
+ changes: list[dict[str, Any]] = []
+ sanitized: list[dict[str, Any]] = []
+ for idx, tool in enumerate(tools):
+ if not isinstance(tool, dict):
+ sanitized.append(tool)
+ continue
+ function = tool.get("function")
+ if not isinstance(function, dict):
+ sanitized.append(tool)
+ continue
+ name = function.get("name", "")
+ old_desc = function.get("description")
+ old_desc_str = (
+ ""
+ if old_desc is None
+ else (old_desc if isinstance(old_desc, str) else str(old_desc))
+ )
+ new_desc = _normalize_tool_description(
+ old_desc,
+ str(name),
+ max_len,
+ truncate_enabled,
+ )
+
+ if old_desc_str != new_desc:
+ reasons: list[str] = []
+ if not isinstance(old_desc, str):
+ reasons.append("non_string")
+ if any(ord(ch) < 32 or ord(ch) == 127 for ch in old_desc_str):
+ reasons.append("control_chars")
+ if "\n" in old_desc_str or "\r" in old_desc_str or "\t" in old_desc_str:
+ reasons.append("whitespace")
+ if not old_desc_str.strip():
+ reasons.append("empty")
+ if (
+ truncate_enabled
+ and len(new_desc) >= max_len
+ and len(old_desc_str) > len(new_desc)
+ ):
+ reasons.append("truncated")
+
+ tool = dict(tool)
+ function = dict(function)
+ function["description"] = new_desc
+ tool["function"] = function
+ changed += 1
+ changes.append(
+ {
+ "index": idx,
+ "name": str(name),
+ "old_len": len(old_desc_str),
+ "new_len": len(new_desc),
+ "old_preview": desc_preview(_clean_control_chars(old_desc_str)),
+ "new_preview": desc_preview(new_desc),
+ "reasons": reasons,
+ }
+ )
+ sanitized.append(tool)
+ return sanitized, changed, changes
+
+
+def sanitize_openai_messages_tool_arguments(
+ messages: list[dict[str, Any]],
+) -> tuple[list[dict[str, Any]], int]:
+ """将 messages[].tool_calls[].function.arguments 规范为严格 JSON 字符串。"""
+ if not messages:
+ return messages, 0
+
+ changed = 0
+ sanitized_messages: list[dict[str, Any]] = []
+ for message in messages:
+ if not isinstance(message, dict):
+ sanitized_messages.append(message)
+ continue
+
+ tool_calls = message.get("tool_calls")
+ # 无 tool_calls 与有 tool_calls 走不同分支
+ if not isinstance(tool_calls, list) or not tool_calls:
+ sanitized_messages.append(message)
+ continue
+
+ tool_calls_changed = False
+ sanitized_tool_calls: list[Any] = []
+ # 逐个处理模型返回的 tool_call
+ for tool_call in tool_calls:
+ if not isinstance(tool_call, dict):
+ sanitized_tool_calls.append(tool_call)
+ continue
+ function = tool_call.get("function")
+ if not isinstance(function, dict):
+ sanitized_tool_calls.append(tool_call)
+ continue
+
+ old_args = function.get("arguments")
+ new_args = normalize_tool_arguments_json(old_args)
+ if isinstance(old_args, str) and old_args == new_args:
+ sanitized_tool_calls.append(tool_call)
+ continue
+
+ tool_calls_changed = True
+ changed += 1
+ new_tool_call = dict(tool_call)
+ new_function = dict(function)
+ new_function["arguments"] = new_args
+ new_tool_call["function"] = new_function
+ sanitized_tool_calls.append(new_tool_call)
+
+ # 无 tool_calls 与有 tool_calls 走不同分支
+ if tool_calls_changed:
+ new_message = dict(message)
+ new_message["tool_calls"] = sanitized_tool_calls
+ sanitized_messages.append(new_message)
+ else:
+ sanitized_messages.append(message)
+
+ return sanitized_messages, changed
+
+
+def sanitize_chat_completion_messages(
+ messages: list[dict[str, Any]],
+ *,
+ preserve_reasoning_content: bool = False,
+) -> tuple[list[dict[str, Any]], int, dict[str, int]]:
+ """移除 Chat Completions 非标准消息字段。
+
+ 本地历史里允许保留 reasoning_content 等兼容字段用于日志/回放；
+ 发往上游时默认剥离。``preserve_reasoning_content=True`` 时保留
+ ``reasoning_content`` 供多轮 CoT 续传，仍剥离其它内部字段。
+ """
+ if not messages:
+ return messages, 0, {}
+
+ changed = 0
+ stripped_fields: dict[str, int] = {}
+ sanitized_messages: list[dict[str, Any]] = []
+ for message in messages:
+ if not isinstance(message, dict):
+ sanitized_messages.append(message)
+ continue
+
+ sanitized_message = message
+ removed = False
+ for key in CHAT_COMPLETION_INTERNAL_MESSAGE_KEYS:
+ if preserve_reasoning_content and key == "reasoning_content":
+ continue
+ if key not in sanitized_message:
+ continue
+ if sanitized_message is message:
+ sanitized_message = dict(message)
+ sanitized_message.pop(key, None)
+ stripped_fields[key] = stripped_fields.get(key, 0) + 1
+ removed = True
+
+ if removed:
+ changed += 1
+ sanitized_messages.append(sanitized_message)
+
+ return sanitized_messages, changed, stripped_fields
+
+
+def relocate_system_to_first_user(
+ messages: list[dict[str, Any]],
+) -> list[dict[str, Any]]:
+ """将 system/developer 消息合并注入首条 user 消息（chat_completions 适配）。"""
+ if not messages:
+ return messages
+
+ system_parts: list[str] = []
+ remaining: list[dict[str, Any]] = []
+ for message in messages:
+ if not isinstance(message, dict):
+ remaining.append(message)
+ continue
+ role = str(message.get("role") or "").strip().lower()
+ if role in ("system", "developer"):
+ content = message.get("content")
+ if content is not None:
+ text = content if isinstance(content, str) else str(content)
+ if text.strip():
+ system_parts.append(text.strip())
+ continue
+ remaining.append(message)
+
+ if not system_parts:
+ return messages
+
+ merged_system = "\n\n".join(system_parts)
+ first_user_idx: int | None = None
+ for idx, message in enumerate(remaining):
+ if (
+ isinstance(message, dict)
+ and str(message.get("role") or "").strip().lower() == "user"
+ ):
+ first_user_idx = idx
+ break
+
+ if first_user_idx is None:
+ remaining.insert(0, {"role": "user", "content": merged_system})
+ return remaining
+
+ first_user = dict(remaining[first_user_idx])
+ old_content = first_user.get("content")
+ old_text = (
+ old_content
+ if isinstance(old_content, str)
+ else (str(old_content) if old_content is not None else "")
+ )
+ if old_text.strip():
+ first_user["content"] = f"{merged_system}\n\n{old_text}"
+ else:
+ first_user["content"] = merged_system
+ updated = list(remaining)
+ updated[first_user_idx] = first_user
+ return updated
+
+
+def prepare_chat_completion_messages(
+ model_config: ModelConfig,
+ messages: list[dict[str, Any]],
+) -> list[dict[str, Any]]:
+ """按模型配置整理 Chat Completions 出站消息。"""
+ preserve_reasoning = bool(getattr(model_config, "reasoning_content_replay", False))
+ prepared, _, _ = sanitize_chat_completion_messages(
+ messages,
+ preserve_reasoning_content=preserve_reasoning,
+ )
+ if bool(getattr(model_config, "system_prompt_as_user", False)):
+ prepared = relocate_system_to_first_user(prepared)
+ return prepared
diff --git a/src/Undefined/ai/llm/streaming.py b/src/Undefined/ai/llm/streaming.py
new file mode 100644
index 00000000..cc12113e
--- /dev/null
+++ b/src/Undefined/ai/llm/streaming.py
@@ -0,0 +1,390 @@
+"""LLM 流式响应聚合与回退判定。
+
+解析 SSE/chunk 事件、合并 delta 与 tool_calls，并在上游不支持流式时
+判定是否降级为非流式请求；不持有 HTTP 客户端或模型配置。
+"""
+
+from __future__ import annotations
+
+import json
+from typing import Any
+
+from openai import APIStatusError
+
+from Undefined.ai.llm.thinking import stringify_thinking
+from Undefined.ai.transports import API_MODE_CHAT_COMPLETIONS, API_MODE_RESPONSES
+
+_CHAT_COMPLETIONS_KNOWN_FIELDS: set[str] = {
+ "model",
+ "messages",
+ "audio",
+ "metadata",
+ "max_completion_tokens",
+ "max_tokens",
+ "modalities",
+ "parallel_tool_calls",
+ "prediction",
+ "prompt_cache_key",
+ "prompt_cache_retention",
+ "reasoning_effort",
+ "safety_identifier",
+ "service_tier",
+ "store",
+ "temperature",
+ "top_p",
+ "n",
+ "stop",
+ "presence_penalty",
+ "frequency_penalty",
+ "logit_bias",
+ "user",
+ "response_format",
+ "seed",
+ "stream",
+ "stream_options",
+ "tools",
+ "tool_choice",
+ "logprobs",
+ "top_logprobs",
+ "verbosity",
+ "web_search_options",
+}
+
+_RESPONSES_KNOWN_FIELDS: set[str] = {
+ "background",
+ "context_management",
+ "conversation",
+ "include",
+ "model",
+ "input",
+ "instructions",
+ "max_output_tokens",
+ "max_tool_calls",
+ "metadata",
+ "previous_response_id",
+ "prompt",
+ "prompt_cache_key",
+ "prompt_cache_retention",
+ "reasoning",
+ "safety_identifier",
+ "service_tier",
+ "store",
+ "temperature",
+ "top_p",
+ "tools",
+ "tool_choice",
+ "parallel_tool_calls",
+ "stream",
+ "stream_options",
+ "text",
+ "truncation",
+ "user",
+}
+
+_STREAM_FALLBACK_STATUS_CODES = {400, 404, 405, 422, 501}
+_STREAM_FALLBACK_ERROR_MARKERS = (
+ "stream",
+ "stream_options",
+ "streaming",
+ "not support",
+ "unsupported",
+ "unrecognized",
+ "unknown parameter",
+ "unexpected parameter",
+)
+
+
+def split_chat_completion_params(
+ body: dict[str, Any],
+) -> tuple[dict[str, Any], dict[str, Any]]:
+ """将请求体拆分为 SDK 已知字段与 extra_body。"""
+ known: dict[str, Any] = {}
+ extra: dict[str, Any] = {}
+ for key, value in body.items():
+ if key in _CHAT_COMPLETIONS_KNOWN_FIELDS:
+ known[key] = value
+ else:
+ extra[key] = value
+ return known, extra
+
+
+def split_responses_params(
+ body: dict[str, Any],
+) -> tuple[dict[str, Any], dict[str, Any]]:
+ """将 Responses 请求体拆分为 SDK 已知字段与 extra_body。"""
+ known: dict[str, Any] = {}
+ extra: dict[str, Any] = {}
+ for key, value in body.items():
+ if key in _RESPONSES_KNOWN_FIELDS:
+ known[key] = value
+ else:
+ extra[key] = value
+ return known, extra
+
+
+def without_stream_request_fields(body: dict[str, Any]) -> dict[str, Any]:
+ """移除 stream / stream_options 字段，用于流式回退。"""
+ stripped = dict(body)
+ stripped.pop("stream", None)
+ stripped.pop("stream_options", None)
+ return stripped
+
+
+def ensure_chat_stream_usage_options(body: dict[str, Any]) -> None:
+ """确保 Chat Completions 流式请求携带 include_usage。"""
+ stream_options = body.get("stream_options")
+ if stream_options is None:
+ body["stream_options"] = {"include_usage": True}
+ return
+ if isinstance(stream_options, dict) and "include_usage" not in stream_options:
+ body["stream_options"] = {**stream_options, "include_usage": True}
+
+
+def _status_error_text(exc: APIStatusError) -> str:
+ parts = [str(exc)]
+ body = getattr(exc, "body", None)
+ if isinstance(body, dict):
+ parts.append(json.dumps(body, ensure_ascii=False, default=str))
+ elif body is not None:
+ parts.append(str(body))
+ response = getattr(exc, "response", None)
+ if response is not None:
+ try:
+ parts.append(response.text)
+ except Exception:
+ pass
+ return "\n".join(part for part in parts if part).lower()
+
+
+def should_fallback_from_stream(exc: Exception) -> bool:
+ """判定流式失败是否应降级为非流式重试。"""
+ if isinstance(exc, NotImplementedError):
+ return True
+ if not isinstance(exc, APIStatusError):
+ return False
+ # 仅对明确的 stream 参数/能力错误做回退，避免掩盖其它 4xx
+ if exc.status_code not in _STREAM_FALLBACK_STATUS_CODES:
+ return False
+ text = _status_error_text(exc)
+ # 回退到默认/主配置
+ return any(marker in text for marker in _STREAM_FALLBACK_ERROR_MARKERS)
+
+
+def stringify_stream_delta(value: Any) -> str:
+ """将流式 delta 字段归一化为字符串片段。"""
+ if value is None:
+ return ""
+ if isinstance(value, str):
+ return value
+ if isinstance(value, list):
+ parts = [stringify_stream_delta(item) for item in value]
+ return "".join(part for part in parts if part)
+ if isinstance(value, dict):
+ for key in ("text", "content", "delta", "value"):
+ if value.get(key) is not None:
+ return stringify_stream_delta(value.get(key))
+ return ""
+ return str(value)
+
+
+def extract_stream_response_item(event: dict[str, Any]) -> dict[str, Any] | None:
+ """从 Responses 流式事件中提取 output item。"""
+ for key in ("item", "output_item", "data"):
+ value = event.get(key)
+ if isinstance(value, dict):
+ return value
+ response = event.get("response")
+ if isinstance(response, dict) and isinstance(response.get("output"), list):
+ return None
+ if isinstance(response, dict):
+ return response
+ return None
+
+
+def extract_stream_usage(
+ event: dict[str, Any], *, api_mode: str
+) -> dict[str, Any] | None:
+ """从流式事件中提取 usage 统计。"""
+ usage = event.get("usage")
+ if not isinstance(usage, dict):
+ response = event.get("response")
+ if isinstance(response, dict) and isinstance(response.get("usage"), dict):
+ usage = response.get("usage")
+ if not isinstance(usage, dict):
+ return None
+ if api_mode == API_MODE_RESPONSES:
+ return {
+ "input_tokens": int(usage.get("input_tokens", 0) or 0),
+ "output_tokens": int(usage.get("output_tokens", 0) or 0),
+ "total_tokens": int(usage.get("total_tokens", 0) or 0),
+ }
+ return {
+ "prompt_tokens": int(usage.get("prompt_tokens", 0) or 0),
+ "completion_tokens": int(usage.get("completion_tokens", 0) or 0),
+ "total_tokens": int(usage.get("total_tokens", 0) or 0),
+ }
+
+
+def ensure_tool_call_slot(
+ tool_calls: list[dict[str, Any]], index: int
+) -> dict[str, Any]:
+ """确保 tool_calls 列表在指定 index 处存在槽位。"""
+ while len(tool_calls) <= index:
+ tool_calls.append(
+ {
+ "id": "",
+ "type": "function",
+ "function": {"name": "", "arguments": ""},
+ }
+ )
+ return tool_calls[index]
+
+
+def merge_tool_call_delta(
+ target_tool_calls: list[dict[str, Any]], tool_delta: dict[str, Any]
+) -> None:
+ """将单个 tool_call delta 合并进累积结果。"""
+ index = tool_delta.get("index")
+ try:
+ slot_index = int(index) if index is not None else len(target_tool_calls)
+ except (TypeError, ValueError):
+ slot_index = len(target_tool_calls)
+ tool_call = ensure_tool_call_slot(target_tool_calls, slot_index)
+ call_id = str(tool_delta.get("id") or "").strip()
+ if call_id:
+ tool_call["id"] = call_id
+ tool_type = str(tool_delta.get("type") or "").strip()
+ if tool_type:
+ tool_call["type"] = tool_type
+ function_delta = tool_delta.get("function")
+ if not isinstance(function_delta, dict):
+ return
+ function = tool_call.setdefault("function", {"name": "", "arguments": ""})
+ if not isinstance(function, dict):
+ function = {"name": "", "arguments": ""}
+ tool_call["function"] = function
+ function_name = str(function_delta.get("name") or "").strip()
+ if function_name:
+ function["name"] = function_name
+ arguments_delta = function_delta.get("arguments")
+ if arguments_delta is not None:
+ # 流式 tool arguments 按 chunk 拼接，直至 JSON 完整
+ function["arguments"] = str(function.get("arguments") or "") + str(
+ arguments_delta
+ )
+
+
+def aggregate_chat_completions_stream(
+ chunks: list[dict[str, Any]],
+ *,
+ reasoning_replay: bool,
+) -> dict[str, Any]:
+ """将 Chat Completions 流式 chunk 列表聚合为完整响应 dict。"""
+ content_parts: list[str] = []
+ reasoning_parts: list[str] = []
+ tool_calls: list[dict[str, Any]] = []
+ usage: dict[str, Any] | None = None
+ finish_reason = "stop"
+ role = "assistant"
+
+ for chunk_dict in chunks:
+ usage = (
+ extract_stream_usage(chunk_dict, api_mode=API_MODE_CHAT_COMPLETIONS)
+ or usage
+ )
+ choices = chunk_dict.get("choices")
+ if not isinstance(choices, list):
+ continue
+ for choice in choices:
+ if not isinstance(choice, dict):
+ continue
+ delta = choice.get("delta")
+ if not isinstance(delta, dict):
+ continue
+ role_value = str(delta.get("role") or "").strip()
+ if role_value:
+ role = role_value
+ content_delta = stringify_stream_delta(delta.get("content"))
+ if content_delta:
+ content_parts.append(content_delta)
+ if reasoning_replay:
+ reasoning_delta = stringify_thinking(delta.get("reasoning_content"))
+ if reasoning_delta:
+ reasoning_parts.append(reasoning_delta)
+ raw_tool_calls = delta.get("tool_calls")
+ # 无 tool_calls 与有 tool_calls 走不同分支
+ if isinstance(raw_tool_calls, list):
+ # 逐个处理模型返回的 tool_call
+ for tool_delta in raw_tool_calls:
+ if isinstance(tool_delta, dict):
+ merge_tool_call_delta(tool_calls, tool_delta)
+ current_finish_reason = str(choice.get("finish_reason") or "").strip()
+ if current_finish_reason:
+ finish_reason = current_finish_reason
+
+ message: dict[str, Any] = {
+ "role": role,
+ "content": "".join(content_parts),
+ }
+ if reasoning_replay:
+ reasoning_text = "".join(reasoning_parts).strip()
+ if reasoning_text:
+ message["reasoning_content"] = reasoning_text
+ # 无 tool_calls 与有 tool_calls 走不同分支
+ if tool_calls:
+ message["tool_calls"] = tool_calls
+ result: dict[str, Any] = {
+ "choices": [
+ {
+ "index": 0,
+ "message": message,
+ "finish_reason": finish_reason,
+ }
+ ]
+ }
+ if usage is not None:
+ result["usage"] = usage
+ return result
+
+
+def aggregate_responses_stream(events: list[dict[str, Any]]) -> dict[str, Any]:
+ """将 Responses 流式事件列表聚合为完整响应 dict。"""
+ output_items: list[dict[str, Any]] = []
+ output_text_parts: list[str] = []
+ usage: dict[str, Any] | None = None
+ final_response: dict[str, Any] | None = None
+
+ for event_dict in events:
+ usage = extract_stream_usage(event_dict, api_mode=API_MODE_RESPONSES) or usage
+ event_type = str(event_dict.get("type") or "").strip().lower()
+ response = event_dict.get("response")
+ if event_type == "response.output_text.delta":
+ delta = stringify_stream_delta(event_dict.get("delta"))
+ if delta:
+ output_text_parts.append(delta)
+ continue
+ if event_type == "response.completed":
+ if isinstance(response, dict):
+ final_response = response
+ continue
+ item = extract_stream_response_item(event_dict)
+ if not isinstance(item, dict):
+ continue
+ item_type = str(item.get("type") or "").strip().lower()
+ if item_type in ("message", "function_call", "reasoning"):
+ output_items.append(item)
+
+ if final_response is not None:
+ if usage is not None and not isinstance(final_response.get("usage"), dict):
+ final_response = dict(final_response)
+ final_response["usage"] = usage
+ return final_response
+
+ # 未收到 completed 事件时，用增量 delta 合成最小可用响应
+ synthesized: dict[str, Any] = {
+ "output": output_items,
+ "output_text": "".join(output_text_parts),
+ }
+ if usage is not None:
+ synthesized["usage"] = usage
+ return synthesized
diff --git a/src/Undefined/ai/llm/thinking.py b/src/Undefined/ai/llm/thinking.py
new file mode 100644
index 00000000..507e4691
--- /dev/null
+++ b/src/Undefined/ai/llm/thinking.py
@@ -0,0 +1,214 @@
+"""思维链（CoT）提取与 thinking 参数规范化。
+
+从 Chat Completions / Responses 响应中抽取 reasoning 字段，并将配置中的
+thinking 覆盖值归一化为各上游兼容格式；不负责发送请求。
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from Undefined.ai.llm.types import ModelConfig
+
+_THINKING_KEYS: tuple[str, ...] = (
+ "thinking",
+ "reasoning",
+ "reasoning_content",
+ "chain_of_thought",
+ "cot",
+ "thoughts",
+)
+
+
+def _stringify_thinking_list(value: list[Any]) -> str:
+ """将列表类型的思维链转换为字符串。
+
+ Args:
+ value: 思维链列表
+
+ Returns:
+ 格式化后的字符串
+ """
+ parts = [stringify_thinking(item) for item in value]
+ return "\n".join([part for part in parts if part])
+
+
+def _stringify_thinking_dict(value: dict[str, Any]) -> str:
+ """将字典类型的思维链转换为字符串。
+
+ Args:
+ value: 思维链字典
+
+ Returns:
+ 格式化后的字符串
+ """
+ content = value.get("content")
+ if isinstance(content, str) and content:
+ return content
+ return str(value)
+
+
+def stringify_thinking(value: Any) -> str:
+ """将思维链值转换为字符串。
+
+ Args:
+ value: 思维链值（可以是 None、字符串、列表或字典）
+
+ Returns:
+ 格式化后的字符串
+ """
+ if value is None:
+ return ""
+ if isinstance(value, str):
+ return value
+ if isinstance(value, list):
+ return _stringify_thinking_list(value)
+ if isinstance(value, dict):
+ return _stringify_thinking_dict(value)
+ return str(value)
+
+
+def _extract_from_message(message: dict[str, Any]) -> str:
+ """从 message 对象中提取思维链内容。
+
+ Args:
+ message: message 对象
+
+ Returns:
+ 思维链内容字符串
+ """
+ if not isinstance(message, dict):
+ return ""
+ for key in _THINKING_KEYS:
+ if key in message:
+ return stringify_thinking(message.get(key))
+ return ""
+
+
+def _extract_from_choice(choice: dict[str, Any]) -> str:
+ """从 choice 对象中提取思维链内容。
+
+ Args:
+ choice: choice 对象
+
+ Returns:
+ 思维链内容字符串
+ """
+ if not isinstance(choice, dict):
+ return ""
+
+ # 优先从 message 中提取
+ message = choice.get("message")
+ if isinstance(message, dict):
+ thinking = _extract_from_message(message)
+ if thinking:
+ return thinking
+
+ # 尝试从 choice 直接提取
+ for key in _THINKING_KEYS:
+ if key in choice:
+ return stringify_thinking(choice.get(key))
+
+ return ""
+
+
+def _extract_from_choices(choices: list[Any]) -> str:
+ """从 choices 列表中提取思维链内容。
+
+ Args:
+ choices: choices 列表
+
+ Returns:
+ 思维链内容字符串
+ """
+ if not isinstance(choices, list) or not choices:
+ return ""
+ choice = choices[0]
+ return _extract_from_choice(choice)
+
+
+def _extract_from_result(result: dict[str, Any]) -> str:
+ """直接从结果对象中提取思维链内容。
+
+ Args:
+ result: API 响应结果
+
+ Returns:
+ 思维链内容字符串
+ """
+ for key in _THINKING_KEYS:
+ if key in result:
+ return stringify_thinking(result.get(key))
+ return ""
+
+
+def extract_thinking_content(result: dict[str, Any]) -> str:
+ """从 API 响应中提取思维链内容。
+
+ 提取优先级：
+ 1. 从 choices[0].message 中提取
+ 2. 从 choices[0] 直接提取
+ 3. 从响应根对象中提取
+
+ Args:
+ result: API 响应结果
+
+ Returns:
+ 思维链内容字符串
+ """
+ # 尝试从 choices 中提取
+ choices = result.get("choices")
+ if isinstance(choices, list):
+ thinking = _extract_from_choices(choices)
+ if thinking:
+ return thinking
+
+ return _extract_from_result(result)
+
+
+def _is_deepseek_provider(model_config: ModelConfig) -> bool:
+ model_name = str(getattr(model_config, "model_name", "") or "").lower()
+ if model_name.startswith("deepseek"):
+ return True
+ api_url = str(getattr(model_config, "api_url", "") or "").lower()
+ return "deepseek" in api_url
+
+
+def normalize_thinking_override(
+ value: Any, model_config: ModelConfig
+) -> dict[str, Any] | None:
+ """将 request 覆盖中的 thinking 值归一化为上游可接受的 dict。"""
+ if value is None:
+ return None
+
+ is_deepseek = _is_deepseek_provider(model_config)
+
+ if isinstance(value, dict):
+ raw_type = value.get("type")
+ if isinstance(raw_type, str):
+ type_value = raw_type.strip().lower()
+ if type_value in {"enabled", "disabled"}:
+ # DeepSeek 仅接受 {type: enabled|disabled}，其它字段原样透传
+ return {"type": type_value} if is_deepseek else dict(value)
+
+ raw_enabled = value.get("enabled")
+ if isinstance(raw_enabled, bool):
+ type_value = "enabled" if raw_enabled else "disabled"
+ if is_deepseek:
+ return {"type": type_value}
+ normalized = dict(value)
+ normalized.pop("enabled", None)
+ normalized["type"] = type_value
+ return normalized
+
+ return None
+
+ if isinstance(value, bool):
+ return {"type": "enabled" if value else "disabled"}
+
+ if isinstance(value, str):
+ type_value = value.strip().lower()
+ if type_value in {"enabled", "disabled"}:
+ return {"type": type_value}
+
+ return None
diff --git a/src/Undefined/ai/llm/types.py b/src/Undefined/ai/llm/types.py
new file mode 100644
index 00000000..74315470
--- /dev/null
+++ b/src/Undefined/ai/llm/types.py
@@ -0,0 +1,27 @@
+"""LLM 模块共享类型别名。"""
+
+from __future__ import annotations
+
+# 联合类型：所有可发起 LLM/嵌入/重排请求的模型配置
+from Undefined.config import (
+ AgentModelConfig,
+ ChatModelConfig,
+ EmbeddingModelConfig,
+ GrokModelConfig,
+ RerankModelConfig,
+ SecurityModelConfig,
+ VisionModelConfig,
+)
+
+ModelConfig = (
+ ChatModelConfig
+ | VisionModelConfig
+ | AgentModelConfig
+ | SecurityModelConfig
+ | EmbeddingModelConfig
+ | GrokModelConfig
+ | RerankModelConfig
+)
+
+# 类型别名对外 re-export
+__all__ = ["ModelConfig"]
diff --git a/src/Undefined/ai/model_selector.py b/src/Undefined/ai/model_selector.py
index a4d94270..74937636 100644
--- a/src/Undefined/ai/model_selector.py
+++ b/src/Undefined/ai/model_selector.py
@@ -38,7 +38,6 @@ def __init__(
self._rr_lock = threading.Lock()
self._rr_counters: dict[str, int] = {}
self._preferences: dict[tuple[int, int], dict[str, str]] = {}
- # pending_compares 只存模型名列表，不存配置对象
self._pending_compares: dict[tuple[int, int], tuple[list[str], float]] = {}
self._loaded = asyncio.Event()
diff --git a/src/Undefined/ai/multimodal/__init__.py b/src/Undefined/ai/multimodal/__init__.py
new file mode 100644
index 00000000..e5c08dfa
--- /dev/null
+++ b/src/Undefined/ai/multimodal/__init__.py
@@ -0,0 +1,31 @@
+"""多模态分析子包。
+
+对外稳定入口：``MultimodalAnalyzer``、``detect_media_type``、``get_media_mime_type``。
+"""
+
+from Undefined.ai.multimodal import constants as _constants
+
+# 测试 monkeypatch 沿用的模块级私有常量，勿随意改名
+_MEDIA_URL_CACHE_DIR = _constants._MEDIA_URL_CACHE_DIR
+_MEDIA_URL_CACHE_TTL_SECONDS = _constants._MEDIA_URL_CACHE_TTL_SECONDS
+_MEDIA_URL_CACHE_MAX_FILES = _constants._MEDIA_URL_CACHE_MAX_FILES
+_MEDIA_URL_CACHE_CLEANUP_INTERVAL_SECONDS = (
+ _constants._MEDIA_URL_CACHE_CLEANUP_INTERVAL_SECONDS
+)
+_MEDIA_URL_DOWNLOAD_TIMEOUT_SECONDS = _constants._MEDIA_URL_DOWNLOAD_TIMEOUT_SECONDS
+_MEDIA_URL_DOWNLOAD_TMP_SUFFIX = _constants._MEDIA_URL_DOWNLOAD_TMP_SUFFIX
+
+from Undefined.ai.multimodal.analyzer import MultimodalAnalyzer # noqa: E402
+from Undefined.ai.multimodal.detection import detect_media_type, get_media_mime_type # noqa: E402
+
+__all__ = [
+ "MultimodalAnalyzer",
+ "detect_media_type",
+ "get_media_mime_type",
+ "_MEDIA_URL_CACHE_CLEANUP_INTERVAL_SECONDS",
+ "_MEDIA_URL_CACHE_DIR",
+ "_MEDIA_URL_CACHE_MAX_FILES",
+ "_MEDIA_URL_CACHE_TTL_SECONDS",
+ "_MEDIA_URL_DOWNLOAD_TIMEOUT_SECONDS",
+ "_MEDIA_URL_DOWNLOAD_TMP_SUFFIX",
+]
diff --git a/src/Undefined/ai/multimodal.py b/src/Undefined/ai/multimodal/analyzer.py
similarity index 63%
rename from src/Undefined/ai/multimodal.py
rename to src/Undefined/ai/multimodal/analyzer.py
index e4dda36f..83b18c9a 100644
--- a/src/Undefined/ai/multimodal.py
+++ b/src/Undefined/ai/multimodal/analyzer.py
@@ -1,4 +1,4 @@
-"""多模态分析辅助函数。"""
+"""多模态媒体分析器实现。"""
from __future__ import annotations
@@ -7,357 +7,41 @@
import hashlib
import json
import logging
-from pathlib import Path
import time
+from pathlib import Path
from typing import Any, cast
from urllib.parse import urlsplit
import aiofiles
import httpx
-from Undefined.ai.parsing import extract_choices_content
-from Undefined.utils.coerce import safe_float
from Undefined.ai.llm import ModelRequester
-from Undefined.config import VisionModelConfig
+import Undefined.ai.multimodal as _multimodal_pkg
+from Undefined.ai.multimodal.constants import (
+ ERROR_MESSAGES,
+ HISTORY_FILE_PATH,
+ MAX_QA_HISTORY,
+ MEDIA_TYPE_TO_FIELD,
+ MEME_DESCRIBE_PROMPT_PATH,
+ MEME_DESCRIBE_TOOL,
+ MEME_JUDGE_PROMPT_PATH,
+ MEME_JUDGE_TOOL,
+)
+from Undefined.ai.multimodal.detection import detect_media_type, get_media_mime_type
+from Undefined.ai.multimodal.parsing import (
+ _normalize_meme_tags,
+ _parse_analysis_response,
+)
+from Undefined.ai.parsing import extract_choices_content
from Undefined.ai.transports import API_MODE_CHAT_COMPLETIONS, get_api_mode
-from Undefined.utils.tool_calls import extract_required_tool_call_arguments
+from Undefined.config import VisionModelConfig
+from Undefined.utils.coerce import safe_float
from Undefined.utils.logging import log_debug_json, redact_string
from Undefined.utils.resources import read_text_resource
+from Undefined.utils.tool_calls import extract_required_tool_call_arguments
logger = logging.getLogger(__name__)
-# 每个文件名最多保留的历史 Q&A 条数
-_MAX_QA_HISTORY = 5
-
-# 磁盘持久化路径
-_HISTORY_FILE_PATH = Path("data/media_qa_history.json")
-
-# 远程媒体缓存目录（用于先下载 URL 再转 data URL）
-# Remote media cache directory (download URL first, then convert to data URL).
-_MEDIA_URL_CACHE_DIR = Path("data/cache/multimodal_media")
-
-# 远程媒体缓存清理策略：仅保留最近 6 小时 + 最多 256 个文件。
-# Remote media cache cleanup policy: keep only recent 6h + max 256 files.
-_MEDIA_URL_CACHE_TTL_SECONDS = 6 * 60 * 60
-_MEDIA_URL_CACHE_MAX_FILES = 256
-
-# 两次自动清理之间的最小间隔（秒），避免每次请求都全量扫描目录。
-# Minimum interval between cleanup runs (seconds) to avoid full scan on every call.
-_MEDIA_URL_CACHE_CLEANUP_INTERVAL_SECONDS = 60.0
-
-# 下载 URL 到本地缓存时的网络超时（秒）。
-# Network timeout (seconds) when downloading URL to local cache.
-_MEDIA_URL_DOWNLOAD_TIMEOUT_SECONDS = 120.0
-
-# 下载阶段临时文件后缀（追加在缓存文件名后），用于区分真实缓存文件。
-# Download-stage temporary suffix (appended to cache filename) to avoid clashes.
-_MEDIA_URL_DOWNLOAD_TMP_SUFFIX = ".downloading"
-
-# 文件扩展名常量
-_IMAGE_EXTENSIONS = (".jpg", ".jpeg", ".png", ".gif", ".webp", ".bmp", ".svg")
-_AUDIO_EXTENSIONS = (".mp3", ".wav", ".m4a", ".ogg", ".flac", ".aac", ".wma")
-_VIDEO_EXTENSIONS = (".mp4", ".avi", ".mov", ".webm", ".mkv", ".flv", ".wmv")
-
-# MIME 类型前缀到媒体类型的映射
-_MIME_PREFIX_TO_TYPE = {
- "image/": "image",
- "audio/": "audio",
- "video/": "video",
-}
-
-
-def _extract_mime_type_from_data_url(media_url: str) -> str | None:
- """从 data URL 中提取 MIME 类型。
-
- Args:
- media_url: 媒体 URL
-
- Returns:
- MIME 类型前缀（如 "image/"）或 None
- """
- if not media_url.startswith("data:"):
- return None
- mime_part = media_url.split(";")[0]
- if ":" in mime_part:
- return mime_part.split(":")[1]
- return None
-
-
-def _get_media_type_by_extension(url_lower: str) -> str:
- """根据文件扩展名判断媒体类型。
-
- Args:
- url_lower: 转换为小写的 URL
-
- Returns:
- 媒体类型（"image"、"audio" 或 "video"）
- """
- for ext in _IMAGE_EXTENSIONS:
- if ext in url_lower:
- return "image"
- for ext in _AUDIO_EXTENSIONS:
- if ext in url_lower:
- return "audio"
- for ext in _VIDEO_EXTENSIONS:
- if ext in url_lower:
- return "video"
- return "image" # 默认返回图片类型
-
-
-def detect_media_type(media_url: str, specified_type: str = "auto") -> str:
- """检测媒体文件的类型（图片、音频或视频）。"""
- # 1. 优先级最高：手动指定类型
- if specified_type and specified_type != "auto":
- return specified_type
-
- # 2. 检查 data URL
- media_type = _detect_from_data_url(media_url)
- if media_type:
- return media_type
-
- # 3. 使用 mimetypes 或扩展名猜测
- return _detect_by_mimetypes(media_url)
-
-
-def _detect_from_data_url(media_url: str) -> str | None:
- """从 data URL 的 MIME 类型中探测媒体类型"""
- mime = _extract_mime_type_from_data_url(media_url)
- if mime:
- for prefix, media_type in _MIME_PREFIX_TO_TYPE.items():
- if mime.startswith(prefix):
- return media_type
- return None
-
-
-def _detect_by_mimetypes(media_url: str) -> str:
- """利用 mimetypes 库或扩展名探测媒体类型"""
- import mimetypes
-
- guessed_mime, _ = mimetypes.guess_type(media_url)
- if guessed_mime:
- for prefix, media_type in _MIME_PREFIX_TO_TYPE.items():
- if guessed_mime.startswith(prefix):
- return media_type
-
- return _get_media_type_by_extension(media_url.lower())
-
-
-# 默认 MIME 类型映射
-_DEFAULT_MIME_TYPES = {
- "image": "image/jpeg",
- "audio": "audio/mpeg",
- "video": "video/mp4",
-}
-
-
-def get_media_mime_type(media_type: str, file_path: str = "") -> str:
- """获取媒体文件的 MIME 类型。
-
- Args:
- media_type: 媒体类型（"image"、"audio" 或 "video"）
- file_path: 文件路径（可选），用于根据文件扩展名推断 MIME 类型
-
- Returns:
- MIME 类型字符串
- """
- # 如果提供了文件路径，优先使用 mimetypes 推断
- if file_path:
- import mimetypes
-
- mime_type, _ = mimetypes.guess_type(file_path)
- if mime_type:
- return mime_type
-
- # 返回默认 MIME 类型
- return _DEFAULT_MIME_TYPES.get(media_type, "application/octet-stream")
-
-
-# 响应内容类型到字段名的映射
-_MEDIA_TYPE_TO_FIELD = {
- "image": "ocr_text",
- "audio": "transcript",
- "video": "subtitles",
-}
-
-_MEME_JUDGE_PROMPT_PATH = "res/prompts/judge_meme_image.txt"
-_MEME_DESCRIBE_PROMPT_PATH = "res/prompts/describe_meme_image.txt"
-
-_MEME_JUDGE_TOOL = {
- "type": "function",
- "function": {
- "name": "submit_meme_judgement",
- "description": "提交表情包判定结果",
- "parameters": {
- "type": "object",
- "properties": {
- "is_meme": {
- "type": "boolean",
- "description": "该图片是否适合进入表情包库",
- },
- "confidence": {
- "type": "number",
- "description": "0 到 1 的置信度",
- },
- "reason": {
- "type": "string",
- "description": "简短中文判定原因",
- },
- },
- "required": ["is_meme", "confidence", "reason"],
- },
- },
-}
-
-_MEME_DESCRIBE_TOOL = {
- "type": "function",
- "function": {
- "name": "submit_meme_description",
- "description": "提交表情包描述与标签",
- "parameters": {
- "type": "object",
- "properties": {
- "description": {
- "type": "string",
- "description": "适合检索的简短中文描述",
- },
- "tags": {
- "type": "array",
- "items": {"type": "string"},
- "description": "0 到 6 个短标签",
- },
- },
- "required": ["description", "tags"],
- },
- },
-}
-
-
-# 错误消息映射
-_ERROR_MESSAGES = {
- "read": {
- "image": "[图片无法读取]",
- "audio": "[音频无法读取]",
- "video": "[视频无法读取]",
- "default": "[媒体文件无法读取]",
- },
- "analyze": {
- "image": "[图片分析失败]",
- "audio": "[音频分析失败]",
- "video": "[视频分析失败]",
- "default": "[媒体分析失败]",
- },
-}
-
-
-def _parse_line_value(line: str, prefix: str) -> str:
- """解析行内容，提取指定前缀后的值。
-
- Args:
- line: 待解析的行
- prefix: 前缀（支持中文冒号和英文冒号）
-
- Returns:
- 提取的值，如果值为 "无" 则返回空字符串
- """
- value = line.split("：", 1)[-1].split(":", 1)[-1].strip()
- return "" if value == "无" else value
-
-
-def _parse_analysis_response(content: str) -> dict[str, str]:
- """解析 AI 分析响应的内容。
-
- Args:
- content: AI 返回的文本内容
-
- Returns:
- 包含描述和类型特定字段的字典
- """
- # 字段前缀映射（支持中文冒号和英文冒号）
- field_prefixes = {
- "description": ("描述：", "描述:"),
- "ocr_text": ("OCR：", "OCR:"),
- "transcript": ("转写：", "转写:"),
- "subtitles": ("字幕：", "字幕:"),
- }
-
- # 初始化所有字段为空
- result = {
- "description": "",
- "ocr_text": "",
- "transcript": "",
- "subtitles": "",
- }
-
- # 解析每一行
- for line in content.split("\n"):
- line = line.strip()
- for field, prefixes in field_prefixes.items():
- if line.startswith(prefixes):
- result[field] = _parse_line_value(line, prefixes[0])
-
- # 如果没有解析到描述，使用完整内容作为描述
- if not result["description"]:
- result["description"] = content
-
- return result
-
-
-def _extract_json_object(content: str) -> dict[str, Any]:
- text = str(content or "").strip()
- if not text:
- return {}
- candidates = [text]
- if "```" in text:
- parts = text.split("```")
- for part in parts:
- stripped = part.strip()
- if not stripped:
- continue
- if stripped.lower().startswith("json"):
- stripped = stripped[4:].strip()
- candidates.append(stripped)
- for candidate in candidates:
- try:
- parsed = json.loads(candidate)
- except json.JSONDecodeError:
- continue
- if isinstance(parsed, dict):
- return parsed
- start = text.find("{")
- end = text.rfind("}")
- if start >= 0 and end > start:
- try:
- parsed = json.loads(text[start : end + 1])
- except json.JSONDecodeError:
- return {}
- if isinstance(parsed, dict):
- return parsed
- return {}
-
-
-def _normalize_meme_tags(tags_raw: Any) -> list[str]:
- tags: list[str] = []
- if isinstance(tags_raw, list):
- seen: set[str] = set()
- for item in tags_raw:
- text = str(item or "").strip()
- lowered = text.lower()
- if not text or lowered in seen:
- continue
- seen.add(lowered)
- tags.append(text)
- return tags
-
-
-def _parse_meme_analysis_response(content: str) -> dict[str, Any]:
- parsed = _extract_json_object(content)
- return {
- "is_meme": bool(parsed.get("is_meme", False)),
- "confidence": safe_float(parsed.get("confidence", 0.0), default=0.0),
- "description": str(parsed.get("description") or "").strip(),
- "tags": _normalize_meme_tags(parsed.get("tags")),
- }
-
class MultimodalAnalyzer:
"""多模态媒体分析器。
@@ -450,7 +134,7 @@ def _build_url_cache_path(self, cache_key: str, media_url: str) -> Path:
suffix = Path(urlsplit(media_url).path).suffix.lower()
if not suffix or len(suffix) > 10:
suffix = ".bin"
- return _MEDIA_URL_CACHE_DIR / f"{cache_key}{suffix}"
+ return _multimodal_pkg._MEDIA_URL_CACHE_DIR / f"{cache_key}{suffix}"
async def _get_url_cache_lock(self, cache_key: str) -> asyncio.Lock:
"""获取 URL 对应的下载锁（同 URL 串行化）。"""
@@ -465,10 +149,10 @@ async def _download_url_to_cache(self, media_url: str, cache_path: Path) -> None
"""下载远程 URL 到缓存文件（原子写入，避免部分文件）。"""
cache_path.parent.mkdir(parents=True, exist_ok=True)
tmp_path = cache_path.with_name(
- f"{cache_path.name}{_MEDIA_URL_DOWNLOAD_TMP_SUFFIX}"
+ f"{cache_path.name}{_multimodal_pkg._MEDIA_URL_DOWNLOAD_TMP_SUFFIX}"
)
try:
- timeout = httpx.Timeout(_MEDIA_URL_DOWNLOAD_TIMEOUT_SECONDS)
+ timeout = httpx.Timeout(_multimodal_pkg._MEDIA_URL_DOWNLOAD_TIMEOUT_SECONDS)
async with httpx.AsyncClient(
timeout=timeout, follow_redirects=True
) as client:
@@ -500,14 +184,17 @@ def _is_download_tmp_path(path: Path) -> bool:
at least one original extension segment before it.
"""
suffixes = path.suffixes
- return len(suffixes) >= 2 and suffixes[-1] == _MEDIA_URL_DOWNLOAD_TMP_SUFFIX
+ return (
+ len(suffixes) >= 2
+ and suffixes[-1] == _multimodal_pkg._MEDIA_URL_DOWNLOAD_TMP_SUFFIX
+ )
async def _cleanup_url_cache_if_needed(self) -> None:
"""按 TTL + 文件数上限清理 URL 媒体缓存。"""
now = time.time()
if (
now - self._last_url_cache_cleanup_at
- < _MEDIA_URL_CACHE_CLEANUP_INTERVAL_SECONDS
+ < _multimodal_pkg._MEDIA_URL_CACHE_CLEANUP_INTERVAL_SECONDS
):
return
@@ -517,7 +204,7 @@ async def _cleanup_url_cache_if_needed(self) -> None:
now = time.time()
if (
now - self._last_url_cache_cleanup_at
- < _MEDIA_URL_CACHE_CLEANUP_INTERVAL_SECONDS
+ < _multimodal_pkg._MEDIA_URL_CACHE_CLEANUP_INTERVAL_SECONDS
):
return
self._last_url_cache_cleanup_at = now
@@ -526,7 +213,7 @@ async def _cleanup_url_cache_if_needed(self) -> None:
active_keys = {
key for key, lock in self._url_cache_locks.items() if lock.locked()
}
- cache_dir = _MEDIA_URL_CACHE_DIR
+ cache_dir = _multimodal_pkg._MEDIA_URL_CACHE_DIR
if not cache_dir.exists():
await self._prune_url_cache_locks(
active_keys=active_keys,
@@ -535,7 +222,7 @@ async def _cleanup_url_cache_if_needed(self) -> None:
return
files: list[Path] = [p for p in cache_dir.iterdir() if p.is_file()]
- expire_before = now - _MEDIA_URL_CACHE_TTL_SECONDS
+ expire_before = now - _multimodal_pkg._MEDIA_URL_CACHE_TTL_SECONDS
kept_files: list[Path] = []
present_keys: set[str] = set()
@@ -564,7 +251,7 @@ async def _cleanup_url_cache_if_needed(self) -> None:
# 再按数量上限清理最旧文件，同样跳过活跃键。
# Then enforce max-file limit by deleting oldest files, skipping active keys.
- if len(kept_files) <= _MEDIA_URL_CACHE_MAX_FILES:
+ if len(kept_files) <= _multimodal_pkg._MEDIA_URL_CACHE_MAX_FILES:
return
kept_with_mtime: list[tuple[float, Path]] = []
@@ -574,7 +261,9 @@ async def _cleanup_url_cache_if_needed(self) -> None:
except OSError:
continue
kept_with_mtime.sort(key=lambda item: item[0], reverse=True)
- for _, path in kept_with_mtime[_MEDIA_URL_CACHE_MAX_FILES:]:
+ for _, path in kept_with_mtime[
+ _multimodal_pkg._MEDIA_URL_CACHE_MAX_FILES :
+ ]:
if path.stem in active_keys:
continue
path.unlink(missing_ok=True)
@@ -615,7 +304,6 @@ async def _build_content_items(
"""
content_items: list[dict[str, Any]] = [{"type": "text", "text": prompt}]
- # 添加媒体内容项
media_item_key = f"{media_type}_url"
contents = media_content if isinstance(media_content, list) else [media_content]
for mc in contents:
@@ -652,24 +340,21 @@ async def analyze(
len(prompt_extra),
)
- # 检查缓存
cache_key = f"{detected_type}:{media_url[:100]}:{prompt_extra}"
if cache_key in self._cache:
logger.debug("[媒体分析] 命中缓存: key=%s", cache_key[:120])
return self._cache[cache_key]
- # 加载媒体内容
try:
media_content = await self._load_media_content(media_url, detected_type)
except Exception as exc:
logger.error(f"无法读取媒体文件: {exc}")
return {
- "description": _ERROR_MESSAGES["read"].get(
- detected_type, _ERROR_MESSAGES["read"]["default"]
+ "description": ERROR_MESSAGES["read"].get(
+ detected_type, ERROR_MESSAGES["read"]["default"]
)
}
- # 加载提示词
try:
prompt = read_text_resource(self._prompt_path)
except Exception:
@@ -682,16 +367,13 @@ async def analyze(
self._prompt_path,
)
- # 添加补充提示词
if prompt_extra:
prompt += f"\n\n【补充指令】\n{prompt_extra}"
- # 构建请求内容
content_items = await self._build_content_items(
detected_type, media_content, prompt
)
- # 发送分析请求
try:
result = await self._requester.request(
model_config=self._vision_config,
@@ -703,16 +385,13 @@ async def analyze(
if logger.isEnabledFor(logging.DEBUG):
log_debug_json(logger, "[媒体分析] 原始响应内容", content)
- # 解析响应内容
parsed = _parse_analysis_response(content)
- # 根据媒体类型构建结果字典
result_dict: dict[str, str] = {"description": parsed["description"]}
- field_name = _MEDIA_TYPE_TO_FIELD.get(detected_type)
+ field_name = MEDIA_TYPE_TO_FIELD.get(detected_type)
if field_name:
result_dict[field_name] = parsed[field_name]
- # 缓存结果
self._cache[cache_key] = result_dict
logger.info(f"[媒体分析] 完成并缓存: {safe_url[:50]}... ({detected_type})")
return result_dict
@@ -720,8 +399,8 @@ async def analyze(
except Exception as exc:
logger.exception(f"媒体分析失败: {exc}")
return {
- "description": _ERROR_MESSAGES["analyze"].get(
- detected_type, _ERROR_MESSAGES["analyze"]["default"]
+ "description": ERROR_MESSAGES["analyze"].get(
+ detected_type, ERROR_MESSAGES["analyze"]["default"]
)
}
@@ -729,10 +408,10 @@ async def analyze(
def _load_history(self) -> None:
"""从磁盘加载历史 Q&A 缓存。"""
- if not _HISTORY_FILE_PATH.exists():
+ if not HISTORY_FILE_PATH.exists():
return
try:
- with open(_HISTORY_FILE_PATH, "r", encoding="utf-8") as f:
+ with open(HISTORY_FILE_PATH, "r", encoding="utf-8") as f:
data = json.load(f)
if isinstance(data, dict):
self._file_history = data
@@ -747,7 +426,7 @@ async def _save_history(self) -> None:
from Undefined.utils import io
try:
- await io.write_json(_HISTORY_FILE_PATH, self._file_history, use_lock=True)
+ await io.write_json(HISTORY_FILE_PATH, self._file_history, use_lock=True)
except Exception as exc:
logger.error("[媒体分析] 历史缓存写入磁盘失败: %s", exc)
@@ -763,7 +442,7 @@ def get_history(self, media_key: str) -> list[dict[str, str]]:
pairs = self._file_history.get(media_key)
if not pairs:
return []
- return list(pairs[-_MAX_QA_HISTORY:])
+ return list(pairs[-MAX_QA_HISTORY:])
async def save_history(self, media_key: str, question: str, answer: str) -> None:
"""保存一条 Q&A 到指定媒体键的历史记录（上限 5 条）并持久化。
@@ -775,8 +454,8 @@ async def save_history(self, media_key: str, question: str, answer: str) -> None
"""
pairs = self._file_history.setdefault(media_key, [])
pairs.append({"q": question, "a": answer})
- if len(pairs) > _MAX_QA_HISTORY:
- self._file_history[media_key] = pairs[-_MAX_QA_HISTORY:]
+ if len(pairs) > MAX_QA_HISTORY:
+ self._file_history[media_key] = pairs[-MAX_QA_HISTORY:]
await self._save_history()
async def describe_image(
@@ -805,6 +484,7 @@ async def _load_prompt_text(self, prompt_path: str) -> str:
def _build_tool_request_kwargs(self) -> dict[str, Any]:
request_kwargs: dict[str, Any] = {}
+ # 非 thinking 模型强制关闭 thinking，避免 tool_choice 被服务商拒绝
if (
get_api_mode(self._vision_config) == API_MODE_CHAT_COMPLETIONS
and not self._vision_config.thinking_enabled
@@ -856,9 +536,9 @@ async def judge_meme_image(self, image_url: str | list[str]) -> dict[str, Any]:
)
try:
args = await self._request_required_tool_args(
- prompt_path=_MEME_JUDGE_PROMPT_PATH,
+ prompt_path=MEME_JUDGE_PROMPT_PATH,
image_url=image_url,
- tool_schema=_MEME_JUDGE_TOOL,
+ tool_schema=MEME_JUDGE_TOOL,
tool_name="submit_meme_judgement",
call_type="vision_meme_judge",
max_tokens=self._vision_config.max_tokens,
@@ -894,9 +574,9 @@ async def describe_meme_image(self, image_url: str | list[str]) -> dict[str, Any
)
try:
args = await self._request_required_tool_args(
- prompt_path=_MEME_DESCRIBE_PROMPT_PATH,
+ prompt_path=MEME_DESCRIBE_PROMPT_PATH,
image_url=image_url,
- tool_schema=_MEME_DESCRIBE_TOOL,
+ tool_schema=MEME_DESCRIBE_TOOL,
tool_name="submit_meme_description",
call_type="vision_meme_describe",
max_tokens=self._vision_config.max_tokens,
@@ -914,3 +594,6 @@ async def describe_meme_image(self, image_url: str | list[str]) -> dict[str, Any
tags,
)
return {"description": description, "tags": tags}
+
+
+__all__ = ["MultimodalAnalyzer"]
diff --git a/src/Undefined/ai/multimodal/constants.py b/src/Undefined/ai/multimodal/constants.py
new file mode 100644
index 00000000..6c074c00
--- /dev/null
+++ b/src/Undefined/ai/multimodal/constants.py
@@ -0,0 +1,138 @@
+"""多模态分析常量与工具 schema 定义。"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+# ===== 历史 Q&A 与磁盘缓存 =====
+# 每个文件名最多保留的历史 Q&A 条数
+_MAX_QA_HISTORY = 5
+
+HISTORY_FILE_PATH = Path("data/media_qa_history.json")
+
+# ===== 远程 URL 媒体缓存策略 =====
+# 远程媒体缓存目录（用于先下载 URL 再转 data URL）
+_MEDIA_URL_CACHE_DIR = Path("data/cache/multimodal_media")
+
+# 远程媒体缓存清理策略：仅保留最近 6 小时 + 最多 256 个文件。
+_MEDIA_URL_CACHE_TTL_SECONDS = 6 * 60 * 60
+_MEDIA_URL_CACHE_MAX_FILES = 256
+
+# 两次自动清理之间的最小间隔（秒），避免每次请求都全量扫描目录。
+_MEDIA_URL_CACHE_CLEANUP_INTERVAL_SECONDS = 60.0
+
+# 下载 URL 到本地缓存时的网络超时（秒）。
+_MEDIA_URL_DOWNLOAD_TIMEOUT_SECONDS = 120.0
+
+# 下载阶段临时文件后缀（追加在缓存文件名后），用于区分真实缓存文件。
+_MEDIA_URL_DOWNLOAD_TMP_SUFFIX = ".downloading"
+
+# ===== 扩展名 / MIME / 错误文案映射 =====
+IMAGE_EXTENSIONS = (".jpg", ".jpeg", ".png", ".gif", ".webp", ".bmp", ".svg")
+AUDIO_EXTENSIONS = (".mp3", ".wav", ".m4a", ".ogg", ".flac", ".aac", ".wma")
+VIDEO_EXTENSIONS = (".mp4", ".avi", ".mov", ".webm", ".mkv", ".flv", ".wmv")
+
+# MIME 类型前缀到媒体类型的映射
+MIME_PREFIX_TO_TYPE = {
+ "image/": "image",
+ "audio/": "audio",
+ "video/": "video",
+}
+
+# 默认 MIME 类型映射
+DEFAULT_MIME_TYPES = {
+ "image": "image/jpeg",
+ "audio": "audio/mpeg",
+ "video": "video/mp4",
+}
+
+MEDIA_TYPE_TO_FIELD = {
+ "image": "ocr_text",
+ "audio": "transcript",
+ "video": "subtitles",
+}
+
+# ===== 表情包判定 / 描述工具 schema =====
+MEME_JUDGE_PROMPT_PATH = "res/prompts/judge_meme_image.txt"
+MEME_DESCRIBE_PROMPT_PATH = "res/prompts/describe_meme_image.txt"
+
+MEME_JUDGE_TOOL: dict[str, Any] = {
+ "type": "function",
+ "function": {
+ "name": "submit_meme_judgement",
+ "description": "提交表情包判定结果",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "is_meme": {
+ "type": "boolean",
+ "description": "该图片是否适合进入表情包库",
+ },
+ "confidence": {
+ "type": "number",
+ "description": "0 到 1 的置信度",
+ },
+ "reason": {
+ "type": "string",
+ "description": "简短中文判定原因",
+ },
+ },
+ "required": ["is_meme", "confidence", "reason"],
+ },
+ },
+}
+
+MEME_DESCRIBE_TOOL: dict[str, Any] = {
+ "type": "function",
+ "function": {
+ "name": "submit_meme_description",
+ "description": "提交表情包描述与标签",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "description": {
+ "type": "string",
+ "description": "适合检索的简短中文描述",
+ },
+ "tags": {
+ "type": "array",
+ "items": {"type": "string"},
+ "description": "0 到 6 个短标签",
+ },
+ },
+ "required": ["description", "tags"],
+ },
+ },
+}
+
+ERROR_MESSAGES = {
+ "read": {
+ "image": "[图片无法读取]",
+ "audio": "[音频无法读取]",
+ "video": "[视频无法读取]",
+ "default": "[媒体文件无法读取]",
+ },
+ "analyze": {
+ "image": "[图片分析失败]",
+ "audio": "[音频分析失败]",
+ "video": "[视频分析失败]",
+ "default": "[媒体分析失败]",
+ },
+}
+
+__all__ = [
+ "DEFAULT_MIME_TYPES",
+ "ERROR_MESSAGES",
+ "HISTORY_FILE_PATH",
+ "MAX_QA_HISTORY",
+ "MEDIA_TYPE_TO_FIELD",
+ "MEME_DESCRIBE_PROMPT_PATH",
+ "MEME_DESCRIBE_TOOL",
+ "MEME_JUDGE_PROMPT_PATH",
+ "MEME_JUDGE_TOOL",
+ "MIME_PREFIX_TO_TYPE",
+]
+
+# 对外别名，供 analyzer 使用
+MAX_QA_HISTORY = _MAX_QA_HISTORY
diff --git a/src/Undefined/ai/multimodal/detection.py b/src/Undefined/ai/multimodal/detection.py
new file mode 100644
index 00000000..9508d5ce
--- /dev/null
+++ b/src/Undefined/ai/multimodal/detection.py
@@ -0,0 +1,104 @@
+"""媒体类型探测与 MIME 推断。"""
+
+from __future__ import annotations
+
+from Undefined.ai.multimodal.constants import (
+ AUDIO_EXTENSIONS,
+ DEFAULT_MIME_TYPES,
+ IMAGE_EXTENSIONS,
+ MIME_PREFIX_TO_TYPE,
+ VIDEO_EXTENSIONS,
+)
+
+
+def _extract_mime_type_from_data_url(media_url: str) -> str | None:
+ """从 data URL 中提取 MIME 类型。
+
+ Args:
+ media_url: 媒体 URL
+
+ Returns:
+ MIME 类型前缀（如 ``image/``）或 None
+ """
+ if not media_url.startswith("data:"):
+ return None
+ mime_part = media_url.split(";")[0]
+ if ":" in mime_part:
+ return mime_part.split(":")[1]
+ return None
+
+
+def _get_media_type_by_extension(url_lower: str) -> str:
+ """根据文件扩展名判断媒体类型。"""
+ from urllib.parse import urlsplit
+
+ path = urlsplit(url_lower).path
+ for ext in IMAGE_EXTENSIONS:
+ if path.endswith(ext):
+ return "image"
+ for ext in AUDIO_EXTENSIONS:
+ if path.endswith(ext):
+ return "audio"
+ for ext in VIDEO_EXTENSIONS:
+ if path.endswith(ext):
+ return "video"
+ return "image"
+
+
+def detect_media_type(media_url: str, specified_type: str = "auto") -> str:
+ """检测媒体文件的类型（图片、音频或视频）。"""
+ if specified_type and specified_type != "auto":
+ return specified_type
+
+ # data URL 的 MIME 优先于扩展名猜测
+ media_type = _detect_from_data_url(media_url)
+ if media_type:
+ return media_type
+
+ return _detect_by_mimetypes(media_url)
+
+
+def _detect_from_data_url(media_url: str) -> str | None:
+ """从 data URL 的 MIME 类型中探测媒体类型。"""
+ mime = _extract_mime_type_from_data_url(media_url)
+ if mime:
+ for prefix, media_type in MIME_PREFIX_TO_TYPE.items():
+ if mime.startswith(prefix):
+ return media_type
+ return None
+
+
+def _detect_by_mimetypes(media_url: str) -> str:
+ """利用 mimetypes 库或扩展名探测媒体类型。"""
+ import mimetypes
+
+ guessed_mime, _ = mimetypes.guess_type(media_url)
+ if guessed_mime:
+ for prefix, media_type in MIME_PREFIX_TO_TYPE.items():
+ if guessed_mime.startswith(prefix):
+ return media_type
+
+ return _get_media_type_by_extension(media_url.lower())
+
+
+def get_media_mime_type(media_type: str, file_path: str = "") -> str:
+ """获取媒体文件的 MIME 类型。
+
+ Args:
+ media_type: 媒体类型（``image``、``audio`` 或 ``video``）
+ file_path: 文件路径（可选），用于根据扩展名推断 MIME 类型
+
+ Returns:
+ MIME 类型字符串
+ """
+ if file_path:
+ import mimetypes
+
+ mime_type, _ = mimetypes.guess_type(file_path)
+ if mime_type:
+ return mime_type
+
+ return DEFAULT_MIME_TYPES.get(media_type, "application/octet-stream")
+
+
+__all__ = ["detect_media_type", "get_media_mime_type"]
diff --git a/src/Undefined/ai/multimodal/parsing.py b/src/Undefined/ai/multimodal/parsing.py
new file mode 100644
index 00000000..f8912456
--- /dev/null
+++ b/src/Undefined/ai/multimodal/parsing.py
@@ -0,0 +1,110 @@
+"""多模态模型响应解析工具。"""
+
+from __future__ import annotations
+
+import json
+from typing import Any
+
+from Undefined.utils.coerce import safe_float
+
+
+def _parse_line_value(line: str, prefix: str) -> str:
+ """解析行内容，提取指定前缀后的值。"""
+ value = line[len(prefix) :].strip() if line.startswith(prefix) else line.strip()
+ return "" if value == "无" else value
+
+
+def _parse_analysis_response(content: str) -> dict[str, str]:
+ """解析 AI 分析响应的内容。"""
+ field_prefixes = {
+ "description": ("描述：", "描述:"),
+ "ocr_text": ("OCR：", "OCR:"),
+ "transcript": ("转写：", "转写:"),
+ "subtitles": ("字幕：", "字幕:"),
+ }
+
+ result = {
+ "description": "",
+ "ocr_text": "",
+ "transcript": "",
+ "subtitles": "",
+ }
+
+ for line in content.split("\n"):
+ line = line.strip()
+ for field, prefixes in field_prefixes.items():
+ if line.startswith(prefixes):
+ matched_prefix = (
+ prefixes[0] if line.startswith(prefixes[0]) else prefixes[1]
+ )
+ result[field] = _parse_line_value(line, matched_prefix)
+
+ if not result["description"]:
+ result["description"] = content
+
+ return result
+
+
+def _extract_json_object(content: str) -> dict[str, Any]:
+ text = str(content or "").strip()
+ if not text:
+ return {}
+ candidates = [text]
+ if "```" in text:
+ parts = text.split("```")
+ for part in parts:
+ stripped = part.strip()
+ if not stripped:
+ continue
+ if stripped.lower().startswith("json"):
+ stripped = stripped[4:].strip()
+ candidates.append(stripped)
+ for candidate in candidates:
+ try:
+ parsed = json.loads(candidate)
+ except json.JSONDecodeError:
+ continue
+ if isinstance(parsed, dict):
+ return parsed
+ # 兜底：从文本中截取首尾花括号再解析
+ start = text.find("{")
+ end = text.rfind("}")
+ if start >= 0 and end > start:
+ try:
+ parsed = json.loads(text[start : end + 1])
+ except json.JSONDecodeError:
+ return {}
+ if isinstance(parsed, dict):
+ return parsed
+ return {}
+
+
+def _normalize_meme_tags(tags_raw: Any) -> list[str]:
+ tags: list[str] = []
+ if isinstance(tags_raw, list):
+ seen: set[str] = set()
+ for item in tags_raw:
+ text = str(item or "").strip()
+ lowered = text.lower()
+ if not text or lowered in seen:
+ continue
+ seen.add(lowered)
+ tags.append(text)
+ return tags
+
+
+def _parse_meme_analysis_response(content: str) -> dict[str, Any]:
+ parsed = _extract_json_object(content)
+ return {
+ "is_meme": bool(parsed.get("is_meme", False)),
+ "confidence": safe_float(parsed.get("confidence", 0.0), default=0.0),
+ "description": str(parsed.get("description") or "").strip(),
+ "tags": _normalize_meme_tags(parsed.get("tags")),
+ }
+
+
+__all__ = [
+ "_normalize_meme_tags",
+ "_parse_analysis_response",
+ "_parse_meme_analysis_response",
+]
diff --git a/src/Undefined/ai/parsing.py b/src/Undefined/ai/parsing.py
index b25f26e0..63aa0dc7 100644
--- a/src/Undefined/ai/parsing.py
+++ b/src/Undefined/ai/parsing.py
@@ -24,23 +24,18 @@ def _get_content_from_message(message: Any) -> str | None:
def _extract_from_choice(choice: Any) -> str:
"""从单个选项结构中提取最终的文本内容"""
- # 如果选项是字符串，直接返回
if isinstance(choice, str):
return choice
- # 如果选项不是字典，返回空字符串
if not isinstance(choice, dict):
return ""
- # 尝试从消息中获取 content
message = choice.get("message")
content = _get_content_from_message(message)
- # 如果消息中没有 content，尝试从选项直接获取
if content is None:
content = choice.get("content")
- # 如果有 tool_calls 但没有 content，返回空字符串
if not content and choice.get("message", {}).get("tool_calls"):
return ""
@@ -67,13 +62,11 @@ def _find_first_choice(result: dict[str, Any]) -> dict[str, Any] | None:
Returns:
第一个选项字典，未找到时返回 None
"""
- # 直接检查 choices 字段
if "choices" in result and result["choices"]:
choice = result["choices"][0]
if isinstance(choice, dict):
return choice
- # 检查 data.choices 字段
data = result.get("data")
if isinstance(data, dict) and data.get("choices"):
choice = data["choices"][0]
@@ -125,12 +118,9 @@ def extract_choices_content(result: dict[str, Any]) -> str:
if output_text:
return output_text
- # 查找第一个选项
choice = _find_first_choice(result)
- # 如果没有找到选项，抛出错误
if choice is None:
raise KeyError(_build_error_message(result))
- # 从选项中提取内容
return _extract_from_choice(choice)
diff --git a/src/Undefined/ai/prompts/__init__.py b/src/Undefined/ai/prompts/__init__.py
new file mode 100644
index 00000000..5ca34338
--- /dev/null
+++ b/src/Undefined/ai/prompts/__init__.py
@@ -0,0 +1,10 @@
+"""Prompt 构建子包。
+
+对外稳定入口：``PromptBuilder``；导入路径 ``Undefined.ai.prompts`` 指向本子包。
+"""
+
+# 子包唯一公开类：PromptBuilder
+from Undefined.ai.prompts.builder import PromptBuilder
+
+# 子包公开 API
+__all__ = ["PromptBuilder"]
diff --git a/src/Undefined/ai/prompts.py b/src/Undefined/ai/prompts/builder.py
similarity index 66%
rename from src/Undefined/ai/prompts.py
rename to src/Undefined/ai/prompts/builder.py
index d01904cb..57984071 100644
--- a/src/Undefined/ai/prompts.py
+++ b/src/Undefined/ai/prompts/builder.py
@@ -1,17 +1,14 @@
-"""Prompt building utilities."""
+"""Prompt 消息构建器。"""
from __future__ import annotations
-import html
import logging
-import re
from collections import deque
from datetime import datetime
-from typing import Any, Callable, Awaitable, Literal
+from typing import Any, Awaitable, Callable, Literal
import aiofiles
-from Undefined.utils.coerce import safe_int
from Undefined.context import RequestContext
from Undefined.end_summary_storage import (
EndSummaryStorage,
@@ -20,23 +17,27 @@
)
from Undefined.memory import MemoryStorage
from Undefined.skills.anthropic_skills import AnthropicSkillRegistry
+from Undefined.utils.coerce import safe_int
from Undefined.utils.logging import log_debug_json
from Undefined.utils.resources import read_text_resource
from Undefined.utils.xml import format_message_xml
+from Undefined.ai.prompts.cognitive import (
+ build_cognitive_query,
+ drop_current_message_if_duplicated,
+)
+from Undefined.ai.prompts.system_context import (
+ build_model_config_info,
+ select_system_prompt_path,
+)
logger = logging.getLogger(__name__)
-_CURRENT_MESSAGE_RE = re.compile(
- r"[^>]*)>.*?(?P.*?).*?",
- re.DOTALL | re.IGNORECASE,
-)
-_XML_ATTR_RE = re.compile(r'(?P[a-zA-Z_][a-zA-Z0-9_-]*)="(?P[^"]*)"')
-_COGNITIVE_QUERY_SHORT_THRESHOLD = 20
-_COGNITIVE_CONTEXT_VALUE_MAX_LEN = 18
-
class PromptBuilder:
- """Construct system/user messages with memory, history, and time."""
+ """Prompt 构建器。
+
+ 协调系统提示词、记忆、认知上下文与历史消息，产出 LLM messages 列表。
+ """
def __init__(
self,
@@ -75,181 +76,21 @@ def set_cognitive_service(self, service: Any = None) -> None:
bool(getattr(service, "enabled", False)) if service is not None else False,
)
+ def _build_cognitive_query(
+ self, question: str, extra_context: dict[str, Any] | None = None
+ ) -> tuple[str, bool]:
+ """兼容旧测试/调用方：委托至 cognitive.build_cognitive_query。"""
+ return build_cognitive_query(question, extra_context)
+
+ def _build_model_config_info(self, runtime_config: Any) -> str:
+ """兼容旧测试/调用方：委托至 system_context.build_model_config_info。"""
+ return build_model_config_info(runtime_config)
+
@property
def end_summaries(self) -> deque[EndSummaryRecord]:
"""暴露短期摘要缓存，供工具执行上下文共享。"""
return self._end_summaries
- def _select_system_prompt_path(self) -> str:
- """根据运行时配置选择系统提示词路径。
-
- - 关闭 nagaagent_mode_enabled: 使用默认 public prompt
- - 开启 nagaagent_mode_enabled: 使用 NagaAgent prompt
-
- 说明：路径在每次构建 messages 时动态选择，以支持配置热更新。
- """
-
- if self._runtime_config_getter is None:
- return self._system_prompt_path
-
- runtime_config = None
- try:
- runtime_config = self._runtime_config_getter()
- except Exception:
- runtime_config = None
-
- enabled = bool(getattr(runtime_config, "nagaagent_mode_enabled", False))
- if enabled:
- return "res/prompts/undefined_nagaagent.xml"
- return "res/prompts/undefined.xml"
-
- def _build_model_config_info(self, runtime_config: Any) -> str:
- """构建模型配置信息，用于注入到 AI 上下文中。
-
- 只暴露非隐私字段（model_name 等），不暴露 api_key、api_url 等敏感信息。
- """
- parts: list[str] = ["【当前运行环境配置】"]
-
- # 主对话模型
- chat_model = getattr(runtime_config, "chat_model", None)
- if chat_model:
- model_name = getattr(chat_model, "model_name", "未知")
- parts.append(f"- 我使用的模型: {model_name}")
-
- # 视觉模型
- vision_model = getattr(runtime_config, "vision_model", None)
- if vision_model:
- model_name = getattr(vision_model, "model_name", "")
- if model_name:
- parts.append(f"- 视觉模型: {model_name}")
-
- # Agent 模型
- agent_model = getattr(runtime_config, "agent_model", None)
- if agent_model:
- model_name = getattr(agent_model, "model_name", "")
- if model_name:
- parts.append(f"- Agent 模型: {model_name}")
-
- # 嵌入模型
- embedding_model = getattr(runtime_config, "embedding_model", None)
- if embedding_model:
- model_name = getattr(embedding_model, "model_name", "")
- if model_name:
- parts.append(f"- 嵌入模型: {model_name}")
-
- # 安全模型
- security_model = getattr(runtime_config, "security_model", None)
- if security_model:
- model_name = getattr(security_model, "model_name", "")
- if model_name:
- parts.append(f"- 安全模型: {model_name}")
-
- # Grok 搜索模型
- grok_model = getattr(runtime_config, "grok_model", None)
- if grok_model:
- model_name = getattr(grok_model, "model_name", "")
- if model_name:
- parts.append(f"- 搜索模型: {model_name}")
-
- # 认知记忆
- cognitive = getattr(runtime_config, "cognitive", None)
- if cognitive:
- enabled = getattr(cognitive, "enabled", False)
- parts.append(f"- 认知记忆: {'已启用' if enabled else '未启用'}")
-
- # 知识库
- knowledge_enabled = bool(getattr(runtime_config, "knowledge_enabled", False))
- parts.append(f"- 知识库: {'已启用' if knowledge_enabled else '未启用'}")
-
- # 联网搜索
- grok_search_enabled = bool(
- getattr(runtime_config, "grok_search_enabled", False)
- )
- parts.append(f"- 联网搜索: {'已启用' if grok_search_enabled else '未启用'}")
-
- # 表情包库
- memes = getattr(runtime_config, "memes", None)
- if memes is not None:
- memes_enabled = bool(getattr(memes, "enabled", False))
- if memes_enabled:
- query_mode = str(
- getattr(memes, "query_default_mode", "hybrid") or "hybrid"
- ).strip()
- allow_gif = bool(getattr(memes, "allow_gif", True))
- max_source_bytes = int(getattr(memes, "max_source_image_bytes", 0) or 0)
- max_source_kb = max_source_bytes // 1024 if max_source_bytes > 0 else 0
- parts.append(
- f"- 表情包库: 已启用（默认检索={query_mode}，GIF={'允许' if allow_gif else '禁用'}，入库上限={max_source_kb}KB）"
- )
- else:
- parts.append("- 表情包库: 未启用")
-
- # 模型池
- if chat_model:
- pool = getattr(chat_model, "pool", None)
- if pool:
- pool_enabled = getattr(pool, "enabled", False)
- if pool_enabled:
- strategy = getattr(pool, "strategy", "default")
- parts.append(f"- 模型池: 已启用（{strategy}）")
- else:
- parts.append("- 模型池: 未启用")
-
- # 思维链
- if chat_model:
- thinking = getattr(chat_model, "thinking_enabled", False)
- reasoning = getattr(chat_model, "reasoning_enabled", False)
- if thinking or reasoning:
- parts.append("- 思维链: 已启用")
- else:
- parts.append("- 思维链: 未启用")
-
- # 彩蛋功能状态
- keyword_reply_enabled = bool(
- getattr(runtime_config, "keyword_reply_enabled", False)
- )
- repeat_enabled = bool(getattr(runtime_config, "repeat_enabled", False))
- inverted_question_enabled = bool(
- getattr(runtime_config, "inverted_question_enabled", False)
- )
- agent_call_mode = str(
- getattr(runtime_config, "easter_egg_agent_call_message_mode", "none")
- )
- easter_egg_parts: list[str] = []
- if keyword_reply_enabled:
- easter_egg_parts.append(
- '关键词自动回复（触发词"心理委员"等，系统自动发送固定回复）'
- )
- if repeat_enabled:
- threshold = int(getattr(runtime_config, "repeat_threshold", 3))
- desc = f"复读（群聊连续{threshold}条相同消息时自动复读）"
- if inverted_question_enabled:
- desc += "，倒问号（复读触发时若消息为问号则发送¿）"
- easter_egg_parts.append(desc)
- elif inverted_question_enabled:
- easter_egg_parts.append("倒问号（复读未启用，此功能不生效）")
- if agent_call_mode != "none":
- mode_desc = {
- "agent": "Agent调用提示",
- "tools": "工具调用提示",
- "clean": "降噪调用提示",
- "all": "全量调用提示",
- }.get(agent_call_mode, agent_call_mode)
- easter_egg_parts.append(f"调用提示模式={mode_desc}")
- if easter_egg_parts:
- parts.append("- 彩蛋功能: " + "；".join(easter_egg_parts))
- else:
- parts.append("- 彩蛋功能: 未启用")
-
- parts.append("")
- parts.append(
- "重要：以上是你的模型配置信息。\n"
- "当你需要描述自己是谁、使用什么模型、能力或限制时，\n"
- "必须以上述配置为准，忽略你训练数据、长期及认知记忆中的任何冲突信息。"
- )
-
- return "\n".join(parts)
-
async def _ensure_summaries_loaded(self) -> None:
if not self._summaries_loaded:
loaded_summaries = await self._end_summary_storage.load()
@@ -270,7 +111,10 @@ async def _load_each_rules(self) -> str:
return ""
async def _load_system_prompt(self) -> str:
- system_prompt_path = self._select_system_prompt_path()
+ system_prompt_path = select_system_prompt_path(
+ default_path=self._system_prompt_path,
+ runtime_config_getter=self._runtime_config_getter,
+ )
try:
return read_text_resource(system_prompt_path)
except Exception as exc:
@@ -301,7 +145,10 @@ async def build_messages(
logger.debug(
"[Prompt] system_prompt_len=%s path=%s",
len(system_prompt),
- self._select_system_prompt_path(),
+ select_system_prompt_path(
+ default_path=self._system_prompt_path,
+ runtime_config_getter=self._runtime_config_getter,
+ ),
)
if self._bot_qq != 0:
@@ -317,7 +164,7 @@ async def build_messages(
if self._runtime_config_getter is not None:
try:
runtime_config = self._runtime_config_getter()
- config_info = self._build_model_config_info(runtime_config)
+ config_info = build_model_config_info(runtime_config)
if config_info:
messages.append(
{
@@ -365,7 +212,7 @@ async def build_messages(
"content": (
"【系统行为说明 — 关键词自动回复】\n"
'当前群聊已开启关键词自动回复彩蛋（例如触发词"心理委员"）。'
- "该功能由 handlers.py 中的独立代码路径处理，"
+ "该功能由 handlers/message_flow 中的独立代码路径处理，"
"在消息到达你之前就已完成发送。\n\n"
'发送后，历史中会出现以"[系统关键词自动回复] "开头的消息。'
"这些消息完全由系统代码生成（固定文案如'受着''那咋了'等），"
@@ -430,6 +277,7 @@ async def build_messages(
)
deferred_messages: list[dict[str, Any]] = []
+ # 长期记忆 / 认知 / end 摘要 / 历史等延迟注入块（排在主 system 之后）
if self._memory_storage:
memories = self._memory_storage.get_all()
@@ -506,7 +354,7 @@ async def build_messages(
resolved_request_type = "group"
elif resolved_sender_id or resolved_user_id:
resolved_request_type = "private"
- cognitive_query, query_enhanced = self._build_cognitive_query(
+ cognitive_query, query_enhanced = build_cognitive_query(
question, extra_context
)
logger.info(
@@ -618,6 +466,7 @@ async def build_messages(
deferred_messages, get_recent_messages_callback, extra_context, question
)
+ # 记忆/认知/历史等上下文统一排在主 system 之后、当前消息之前
messages.extend(deferred_messages)
current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
@@ -641,6 +490,7 @@ def _resolve_chat_scope(
) -> tuple[Literal["group", "private"], int] | None:
ctx = RequestContext.current()
+ # 解析顺序：RequestContext 会话类型 > extra_context 回退
if ctx and ctx.request_type == "group" and ctx.group_id is not None:
group_id = safe_int(ctx.group_id)
if group_id is not None:
@@ -719,9 +569,7 @@ async def _inject_recent_messages(
0,
recent_limit,
)
- recent_msgs = self._drop_current_message_if_duplicated(
- recent_msgs, question
- )
+ recent_msgs = drop_current_message_if_duplicated(recent_msgs, question)
context_lines: list[str] = [format_message_xml(msg) for msg in recent_msgs]
formatted_context = "\n---\n".join(context_lines)
@@ -747,111 +595,5 @@ async def _inject_recent_messages(
except Exception as exc:
logger.warning(f"自动获取历史消息失败: {exc}")
- @staticmethod
- def _normalize_cognitive_context_value(value: Any) -> str:
- text = " ".join(str(value or "").split()).strip()
- if len(text) <= _COGNITIVE_CONTEXT_VALUE_MAX_LEN:
- return text
- return text[: _COGNITIVE_CONTEXT_VALUE_MAX_LEN - 3].rstrip() + "..."
- def _build_cognitive_query(
- self, question: str, extra_context: dict[str, Any] | None = None
- ) -> tuple[str, bool]:
- question_text = str(question or "").strip()
- signature = self._extract_current_message_signature(question_text)
- current_content = str(signature.get("content", "")).strip()
- base_query = current_content or question_text
- if not base_query:
- return "", False
-
- # 优先使用当前帧原始消息内容；仅在短消息时追加少量会话语境，降低“这/那个”类指代丢失。
- if (
- not current_content
- or len(current_content) > _COGNITIVE_QUERY_SHORT_THRESHOLD
- ):
- return base_query, False
-
- context_parts: list[str] = []
- if extra_context:
- if bool(extra_context.get("is_private_chat", False)):
- context_parts.append("会话:私聊")
- elif str(extra_context.get("group_id", "")).strip():
- context_parts.append("会话:群聊")
- if bool(extra_context.get("is_at_bot", False)):
- context_parts.append("触发:@机器人")
-
- sender_name = self._normalize_cognitive_context_value(
- extra_context.get("sender_name", "")
- )
- if sender_name:
- context_parts.append(f"发送者:{sender_name}")
-
- group_name = self._normalize_cognitive_context_value(
- extra_context.get("group_name", "")
- )
- if group_name:
- context_parts.append(f"群:{group_name}")
-
- if not context_parts:
- return base_query, False
- return f"{base_query}\n语境: {'; '.join(context_parts)}", True
-
- def _extract_current_message_signature(self, question: str) -> dict[str, str]:
- matched = _CURRENT_MESSAGE_RE.search(str(question or ""))
- if not matched:
- return {}
-
- attrs_text = str(matched.group("attrs") or "")
- attrs: dict[str, str] = {}
- for attr_match in _XML_ATTR_RE.finditer(attrs_text):
- key = str(attr_match.group("key") or "").strip()
- if not key:
- continue
- attrs[key] = html.unescape(str(attr_match.group("value") or "")).strip()
-
- content = html.unescape(str(matched.group("content") or "")).strip()
- return {
- "sender_id": attrs.get("sender_id", ""),
- "timestamp": attrs.get("time", ""),
- "content": content,
- }
-
- def _drop_current_message_if_duplicated(
- self, recent_msgs: list[dict[str, Any]], question: str
- ) -> list[dict[str, Any]]:
- if not recent_msgs:
- return recent_msgs
-
- signature = self._extract_current_message_signature(question)
- if not signature:
- return recent_msgs
-
- last_msg = recent_msgs[-1]
- last_sender_id = str(last_msg.get("user_id", "")).strip()
- last_timestamp = str(last_msg.get("timestamp", "")).strip()
- last_content = str(last_msg.get("message", "")).strip()
-
- sig_sender_id = str(signature.get("sender_id", "")).strip()
- sig_timestamp = str(signature.get("timestamp", "")).strip()
- sig_content = str(signature.get("content", "")).strip()
- if not sig_sender_id or not sig_content:
- return recent_msgs
-
- if last_sender_id != sig_sender_id:
- return recent_msgs
- if last_content != sig_content:
- return recent_msgs
-
- if sig_timestamp and last_timestamp and sig_timestamp != last_timestamp:
- # history 写入时间与事件时间可能存在秒级偏差；若分钟都不同则判定不是同一帧。
- if sig_timestamp[:16] != last_timestamp[:16]:
- return recent_msgs
-
- logger.info(
- "[Prompt] 历史注入剔除当前帧: sender=%s sig_time=%s history_time=%s content_preview=%s",
- sig_sender_id,
- sig_timestamp,
- last_timestamp,
- sig_content[:60],
- )
- return recent_msgs[:-1]
+__all__ = ["PromptBuilder"]
diff --git a/src/Undefined/ai/prompts/cognitive.py b/src/Undefined/ai/prompts/cognitive.py
new file mode 100644
index 00000000..bd799ee0
--- /dev/null
+++ b/src/Undefined/ai/prompts/cognitive.py
@@ -0,0 +1,137 @@
+"""认知记忆检索查询构建辅助。"""
+
+from __future__ import annotations
+
+import html
+import logging
+from typing import Any
+
+from Undefined.ai.prompts.constants import (
+ COGNITIVE_CONTEXT_VALUE_MAX_LEN,
+ COGNITIVE_QUERY_SHORT_THRESHOLD,
+ CURRENT_MESSAGE_RE,
+ XML_ATTR_RE,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def normalize_cognitive_context_value(value: Any) -> str:
+ """压缩过长的上下文字段，避免污染检索 query。"""
+ text = " ".join(str(value or "").split()).strip()
+ if len(text) <= COGNITIVE_CONTEXT_VALUE_MAX_LEN:
+ return text
+ return text[: COGNITIVE_CONTEXT_VALUE_MAX_LEN - 3].rstrip() + "..."
+
+
+def extract_current_message_signature(question: str) -> dict[str, str]:
+ """从当前消息 XML 中提取 sender/time/content 签名。"""
+ matched = CURRENT_MESSAGE_RE.search(str(question or ""))
+ if not matched:
+ return {}
+
+ attrs_text = str(matched.group("attrs") or "")
+ attrs: dict[str, str] = {}
+ for attr_match in XML_ATTR_RE.finditer(attrs_text):
+ key = str(attr_match.group("key") or "").strip()
+ if not key:
+ continue
+ attrs[key] = html.unescape(str(attr_match.group("value") or "")).strip()
+
+ content = html.unescape(str(matched.group("content") or "")).strip()
+ return {
+ "sender_id": attrs.get("sender_id", ""),
+ "timestamp": attrs.get("time", ""),
+ "content": content,
+ }
+
+
+def build_cognitive_query(
+ question: str, extra_context: dict[str, Any] | None = None
+) -> tuple[str, bool]:
+ """构建认知记忆检索 query，短消息时追加少量会话语境。"""
+ question_text = str(question or "").strip()
+ signature = extract_current_message_signature(question_text)
+ current_content = str(signature.get("content", "")).strip()
+ base_query = current_content or question_text
+ if not base_query:
+ return "", False
+
+ if not current_content or len(current_content) > COGNITIVE_QUERY_SHORT_THRESHOLD:
+ return base_query, False
+
+ # 短消息检索质量差，追加轻量会话语境提升向量召回
+ context_parts: list[str] = []
+ if extra_context:
+ if bool(extra_context.get("is_private_chat", False)):
+ context_parts.append("会话:私聊")
+ elif str(extra_context.get("group_id", "")).strip():
+ context_parts.append("会话:群聊")
+ if bool(extra_context.get("is_at_bot", False)):
+ context_parts.append("触发:@机器人")
+
+ sender_name = normalize_cognitive_context_value(
+ extra_context.get("sender_name", "")
+ )
+ if sender_name:
+ context_parts.append(f"发送者:{sender_name}")
+
+ group_name = normalize_cognitive_context_value(
+ extra_context.get("group_name", "")
+ )
+ if group_name:
+ context_parts.append(f"群:{group_name}")
+
+ if not context_parts:
+ return base_query, False
+ return f"{base_query}\n语境: {'; '.join(context_parts)}", True
+
+
+def drop_current_message_if_duplicated(
+ recent_msgs: list[dict[str, Any]], question: str
+) -> list[dict[str, Any]]:
+ """若历史末尾与当前帧重复，则剔除最后一条避免双重注入。"""
+ if not recent_msgs:
+ return recent_msgs
+
+ signature = extract_current_message_signature(question)
+ if not signature:
+ return recent_msgs
+
+ last_msg = recent_msgs[-1]
+ last_sender_id = str(last_msg.get("user_id", "")).strip()
+ last_timestamp = str(last_msg.get("timestamp", "")).strip()
+ last_content = str(last_msg.get("message", "")).strip()
+
+ sig_sender_id = str(signature.get("sender_id", "")).strip()
+ sig_timestamp = str(signature.get("timestamp", "")).strip()
+ sig_content = str(signature.get("content", "")).strip()
+ if not sig_sender_id or not sig_content:
+ return recent_msgs
+
+ if last_sender_id != sig_sender_id:
+ return recent_msgs
+ if last_content != sig_content:
+ return recent_msgs
+
+ if sig_timestamp and last_timestamp and sig_timestamp != last_timestamp:
+ # 秒级时间戳不一致时，比较到分钟粒度，避免格式差异误杀
+ if sig_timestamp[:16] != last_timestamp[:16]:
+ return recent_msgs
+
+ logger.info(
+ "[Prompt] 历史注入剔除当前帧: sender=%s sig_time=%s history_time=%s content_preview=%s",
+ sig_sender_id,
+ sig_timestamp,
+ last_timestamp,
+ sig_content[:60],
+ )
+ return recent_msgs[:-1]
+
+
+__all__ = [
+ "build_cognitive_query",
+ "drop_current_message_if_duplicated",
+ "extract_current_message_signature",
+ "normalize_cognitive_context_value",
+]
diff --git a/src/Undefined/ai/prompts/constants.py b/src/Undefined/ai/prompts/constants.py
new file mode 100644
index 00000000..81f6bd8b
--- /dev/null
+++ b/src/Undefined/ai/prompts/constants.py
@@ -0,0 +1,20 @@
+"""Prompt 构建相关常量与正则。"""
+
+from __future__ import annotations
+
+import re
+
+CURRENT_MESSAGE_RE = re.compile(
+ r"[^>]*)>.*?(?P.*?).*?",
+ re.DOTALL | re.IGNORECASE,
+)
+XML_ATTR_RE = re.compile(r'(?P[a-zA-Z_][a-zA-Z0-9_-]*)="(?P[^"]*)"')
+COGNITIVE_QUERY_SHORT_THRESHOLD = 20 # 低于此长度视为短 query，追加语境
+COGNITIVE_CONTEXT_VALUE_MAX_LEN = 18 # 注入检索 query 的单字段上限
+
+__all__ = [
+ "COGNITIVE_CONTEXT_VALUE_MAX_LEN",
+ "COGNITIVE_QUERY_SHORT_THRESHOLD",
+ "CURRENT_MESSAGE_RE",
+ "XML_ATTR_RE",
+]
diff --git a/src/Undefined/ai/prompts/system_context.py b/src/Undefined/ai/prompts/system_context.py
new file mode 100644
index 00000000..7dbf2840
--- /dev/null
+++ b/src/Undefined/ai/prompts/system_context.py
@@ -0,0 +1,165 @@
+"""系统提示词选择与运行环境配置注入。"""
+
+from __future__ import annotations
+
+from typing import Any
+
+
+def select_system_prompt_path(
+ *,
+ default_path: str,
+ runtime_config_getter: Any | None,
+) -> str:
+ """根据运行时配置选择系统提示词路径。"""
+ if runtime_config_getter is None:
+ return default_path
+
+ runtime_config = None
+ try:
+ runtime_config = runtime_config_getter()
+ except Exception:
+ runtime_config = None
+
+ enabled = bool(getattr(runtime_config, "nagaagent_mode_enabled", False))
+ # NagaAgent 模式切换专用系统提示词模板
+ if enabled:
+ return "res/prompts/undefined_nagaagent.xml"
+ return default_path
+
+
+def build_model_config_info(runtime_config: Any) -> str:
+ """构建模型配置信息，用于注入到 AI 上下文中。
+
+ 只暴露非隐私字段（model_name 等），不暴露 api_key、api_url 等敏感信息。
+ """
+ parts: list[str] = ["【当前运行环境配置】"]
+
+ chat_model = getattr(runtime_config, "chat_model", None)
+ if chat_model:
+ model_name = getattr(chat_model, "model_name", "未知")
+ parts.append(f"- 我使用的模型: {model_name}")
+
+ vision_model = getattr(runtime_config, "vision_model", None)
+ if vision_model:
+ model_name = getattr(vision_model, "model_name", "")
+ if model_name:
+ parts.append(f"- 视觉模型: {model_name}")
+
+ # Agent 模型
+ agent_model = getattr(runtime_config, "agent_model", None)
+ if agent_model:
+ model_name = getattr(agent_model, "model_name", "")
+ if model_name:
+ parts.append(f"- Agent 模型: {model_name}")
+
+ embedding_model = getattr(runtime_config, "embedding_model", None)
+ if embedding_model:
+ model_name = getattr(embedding_model, "model_name", "")
+ if model_name:
+ parts.append(f"- 嵌入模型: {model_name}")
+
+ security_model = getattr(runtime_config, "security_model", None)
+ if security_model:
+ model_name = getattr(security_model, "model_name", "")
+ if model_name:
+ parts.append(f"- 安全模型: {model_name}")
+
+ # Grok 搜索模型
+ grok_model = getattr(runtime_config, "grok_model", None)
+ if grok_model:
+ model_name = getattr(grok_model, "model_name", "")
+ if model_name:
+ parts.append(f"- 搜索模型: {model_name}")
+
+ cognitive = getattr(runtime_config, "cognitive", None)
+ if cognitive:
+ enabled = getattr(cognitive, "enabled", False)
+ parts.append(f"- 认知记忆: {'已启用' if enabled else '未启用'}")
+
+ knowledge_enabled = bool(getattr(runtime_config, "knowledge_enabled", False))
+ parts.append(f"- 知识库: {'已启用' if knowledge_enabled else '未启用'}")
+
+ grok_search_enabled = bool(getattr(runtime_config, "grok_search_enabled", False))
+ parts.append(f"- 联网搜索: {'已启用' if grok_search_enabled else '未启用'}")
+
+ memes = getattr(runtime_config, "memes", None)
+ if memes is not None:
+ memes_enabled = bool(getattr(memes, "enabled", False))
+ if memes_enabled:
+ query_mode = str(
+ getattr(memes, "query_default_mode", "hybrid") or "hybrid"
+ ).strip()
+ allow_gif = bool(getattr(memes, "allow_gif", True))
+ max_source_bytes = int(getattr(memes, "max_source_image_bytes", 0) or 0)
+ max_source_kb = max_source_bytes // 1024 if max_source_bytes > 0 else 0
+ parts.append(
+ f"- 表情包库: 已启用（默认检索={query_mode}，GIF={'允许' if allow_gif else '禁用'}，入库上限={max_source_kb}KB）"
+ )
+ else:
+ parts.append("- 表情包库: 未启用")
+
+ if chat_model:
+ pool = getattr(chat_model, "pool", None)
+ if pool:
+ pool_enabled = getattr(pool, "enabled", False)
+ if pool_enabled:
+ strategy = getattr(pool, "strategy", "default")
+ parts.append(f"- 模型池: 已启用（{strategy}）")
+ else:
+ parts.append("- 模型池: 未启用")
+
+ if chat_model:
+ thinking = getattr(chat_model, "thinking_enabled", False)
+ reasoning = getattr(chat_model, "reasoning_enabled", False)
+ if thinking or reasoning:
+ parts.append("- 思维链: 已启用")
+ else:
+ parts.append("- 思维链: 未启用")
+
+ keyword_reply_enabled = bool(
+ getattr(runtime_config, "keyword_reply_enabled", False)
+ )
+ repeat_enabled = bool(getattr(runtime_config, "repeat_enabled", False))
+ inverted_question_enabled = bool(
+ getattr(runtime_config, "inverted_question_enabled", False)
+ )
+ agent_call_mode = str(
+ getattr(runtime_config, "easter_egg_agent_call_message_mode", "none")
+ )
+ easter_egg_parts: list[str] = []
+ if keyword_reply_enabled:
+ easter_egg_parts.append(
+ '关键词自动回复（触发词"心理委员"等，系统自动发送固定回复）'
+ )
+ if repeat_enabled:
+ threshold = int(getattr(runtime_config, "repeat_threshold", 3))
+ desc = f"复读（群聊连续{threshold}条相同消息时自动复读）"
+ if inverted_question_enabled:
+ desc += "，倒问号（复读触发时若消息为问号则发送¿）"
+ easter_egg_parts.append(desc)
+ elif inverted_question_enabled:
+ easter_egg_parts.append("倒问号（复读未启用，此功能不生效）")
+ if agent_call_mode != "none":
+ mode_desc = {
+ "agent": "Agent调用提示",
+ "tools": "工具调用提示",
+ "clean": "降噪调用提示",
+ "all": "全量调用提示",
+ }.get(agent_call_mode, agent_call_mode)
+ easter_egg_parts.append(f"调用提示模式={mode_desc}")
+ if easter_egg_parts:
+ parts.append("- 彩蛋功能: " + "；".join(easter_egg_parts))
+ else:
+ parts.append("- 彩蛋功能: 未启用")
+
+ parts.append("")
+ parts.append(
+ "重要：以上是你的模型配置信息。\n"
+ "当你需要描述自己是谁、使用什么模型、能力或限制时，\n"
+ "必须以上述配置为准，忽略你训练数据、长期及认知记忆中的任何冲突信息。"
+ )
+
+ return "\n".join(parts)
+
+
+__all__ = ["build_model_config_info", "select_system_prompt_path"]
diff --git a/src/Undefined/ai/tooling.py b/src/Undefined/ai/tooling.py
index 810d7e00..bcc88cce 100644
--- a/src/Undefined/ai/tooling.py
+++ b/src/Undefined/ai/tooling.py
@@ -16,6 +16,13 @@
logger = logging.getLogger(__name__)
+# end 与同轮其它 tool 一并调用时，回填给 end 的 tool 响应（end 本身不执行）
+END_CO_CALL_REJECT_CONTENT = (
+ "错误：end 不得与其他工具同轮调用，本轮未执行 end，对话未结束。"
+ "其它工具已正常执行并返回其结果。"
+ "请根据其它 tool 结果在下一轮单独调用 end；若 send_message 已成功，勿重复发送相同内容。"
+)
+
class ToolManager:
"""工具与智能体（Agent）执行管理器
diff --git a/src/Undefined/ai/transports/openai_transport.py b/src/Undefined/ai/transports/openai_transport.py
index 7f08cf85..33919d6c 100644
--- a/src/Undefined/ai/transports/openai_transport.py
+++ b/src/Undefined/ai/transports/openai_transport.py
@@ -392,7 +392,6 @@ def _copy_responses_output_items(
call_id = str(cloned.get("call_id") or "").strip()
# Some compatibility gateways incorrectly mirror the model's call_id into
# function_call.id. OpenAI accepts id as optional, but when present it must
- # be the item id generated by the model (typically fc_*), not call_*.
if item_id and not item_id.startswith("fc"):
if not call_id and item_id.startswith("call"):
cloned["call_id"] = item_id
diff --git a/src/Undefined/api/routes/memes.py b/src/Undefined/api/routes/memes.py
index dcef98ad..07e75a5e 100644
--- a/src/Undefined/api/routes/memes.py
+++ b/src/Undefined/api/routes/memes.py
@@ -1,4 +1,4 @@
-"""Meme management route handlers."""
+"""Meme API route handlers."""
from __future__ import annotations
@@ -11,10 +11,21 @@
from Undefined.api._helpers import _json_error, _optional_query_param, _to_bool
-async def meme_list_handler(ctx: RuntimeAPIContext, request: web.Request) -> Response:
+def _require_meme_service(ctx: RuntimeAPIContext) -> tuple[Any, Response | None]:
meme_service = ctx.meme_service
if meme_service is None or not meme_service.enabled:
- return _json_error("Meme service disabled", status=400)
+ return None, _json_error("Meme service disabled", status=400)
+ return meme_service, None
+
+
+def _meme_uid(request: web.Request) -> str:
+ return str(request.match_info.get("uid", "")).strip()
+
+
+async def meme_list_handler(ctx: RuntimeAPIContext, request: web.Request) -> Response:
+ meme_service, error = _require_meme_service(ctx)
+ if error is not None:
+ return error
def _parse_optional_bool(name: str) -> bool | None:
raw = request.query.get(name)
@@ -120,17 +131,17 @@ def _parse_optional_bool(name: str) -> bool | None:
async def meme_stats_handler(ctx: RuntimeAPIContext, request: web.Request) -> Response:
_ = request
- meme_service = ctx.meme_service
- if meme_service is None or not meme_service.enabled:
- return _json_error("Meme service disabled", status=400)
+ meme_service, error = _require_meme_service(ctx)
+ if error is not None:
+ return error
return web.json_response(await meme_service.stats())
async def meme_detail_handler(ctx: RuntimeAPIContext, request: web.Request) -> Response:
- meme_service = ctx.meme_service
- if meme_service is None or not meme_service.enabled:
- return _json_error("Meme service disabled", status=400)
- uid = str(request.match_info.get("uid", "")).strip()
+ meme_service, error = _require_meme_service(ctx)
+ if error is not None:
+ return error
+ uid = _meme_uid(request)
detail = await meme_service.get_meme(uid)
if detail is None:
return _json_error("Meme not found", status=404)
@@ -138,10 +149,10 @@ async def meme_detail_handler(ctx: RuntimeAPIContext, request: web.Request) -> R
async def meme_blob_handler(ctx: RuntimeAPIContext, request: web.Request) -> Response:
- meme_service = ctx.meme_service
- if meme_service is None or not meme_service.enabled:
- return _json_error("Meme service disabled", status=400)
- uid = str(request.match_info.get("uid", "")).strip()
+ meme_service, error = _require_meme_service(ctx)
+ if error is not None:
+ return error
+ uid = _meme_uid(request)
path = await meme_service.blob_path_for_uid(uid, preview=False)
if path is None:
return _json_error("Meme blob not found", status=404)
@@ -151,10 +162,10 @@ async def meme_blob_handler(ctx: RuntimeAPIContext, request: web.Request) -> Res
async def meme_preview_handler(
ctx: RuntimeAPIContext, request: web.Request
) -> Response:
- meme_service = ctx.meme_service
- if meme_service is None or not meme_service.enabled:
- return _json_error("Meme service disabled", status=400)
- uid = str(request.match_info.get("uid", "")).strip()
+ meme_service, error = _require_meme_service(ctx)
+ if error is not None:
+ return error
+ uid = _meme_uid(request)
path = await meme_service.blob_path_for_uid(uid, preview=True)
if path is None:
return _json_error("Meme preview not found", status=404)
@@ -162,10 +173,10 @@ async def meme_preview_handler(
async def meme_update_handler(ctx: RuntimeAPIContext, request: web.Request) -> Response:
- meme_service = ctx.meme_service
- if meme_service is None or not meme_service.enabled:
- return _json_error("Meme service disabled", status=400)
- uid = str(request.match_info.get("uid", "")).strip()
+ meme_service, error = _require_meme_service(ctx)
+ if error is not None:
+ return error
+ uid = _meme_uid(request)
try:
payload = await request.json()
except Exception:
@@ -186,10 +197,10 @@ async def meme_update_handler(ctx: RuntimeAPIContext, request: web.Request) -> R
async def meme_delete_handler(ctx: RuntimeAPIContext, request: web.Request) -> Response:
- meme_service = ctx.meme_service
- if meme_service is None or not meme_service.enabled:
- return _json_error("Meme service disabled", status=400)
- uid = str(request.match_info.get("uid", "")).strip()
+ meme_service, error = _require_meme_service(ctx)
+ if error is not None:
+ return error
+ uid = _meme_uid(request)
deleted = await meme_service.delete_meme(uid)
if not deleted:
return _json_error("Meme not found", status=404)
@@ -199,10 +210,10 @@ async def meme_delete_handler(ctx: RuntimeAPIContext, request: web.Request) -> R
async def meme_reanalyze_handler(
ctx: RuntimeAPIContext, request: web.Request
) -> Response:
- meme_service = ctx.meme_service
- if meme_service is None or not meme_service.enabled:
- return _json_error("Meme service disabled", status=400)
- uid = str(request.match_info.get("uid", "")).strip()
+ meme_service, error = _require_meme_service(ctx)
+ if error is not None:
+ return error
+ uid = _meme_uid(request)
job_id = await meme_service.enqueue_reanalyze(uid)
if not job_id:
return _json_error("Meme queue unavailable", status=503)
@@ -212,10 +223,10 @@ async def meme_reanalyze_handler(
async def meme_reindex_handler(
ctx: RuntimeAPIContext, request: web.Request
) -> Response:
- meme_service = ctx.meme_service
- if meme_service is None or not meme_service.enabled:
- return _json_error("Meme service disabled", status=400)
- uid = str(request.match_info.get("uid", "")).strip()
+ meme_service, error = _require_meme_service(ctx)
+ if error is not None:
+ return error
+ uid = _meme_uid(request)
job_id = await meme_service.enqueue_reindex(uid)
if not job_id:
return _json_error("Meme queue unavailable", status=503)
diff --git a/src/Undefined/api/routes/naga/__init__.py b/src/Undefined/api/routes/naga/__init__.py
new file mode 100644
index 00000000..f6642219
--- /dev/null
+++ b/src/Undefined/api/routes/naga/__init__.py
@@ -0,0 +1,21 @@
+"""Naga integration route handlers."""
+
+# 同时 re-export 渲染 helper，供 send 路由生成 HTML/Markdown 卡片。
+from Undefined.render import render_html_to_image, render_markdown_to_html
+from Undefined.api.routes.naga.auth import verify_naga_api_key
+from Undefined.api.routes.naga.bind import naga_bind_callback_handler
+from Undefined.api.routes.naga.send import (
+ naga_messages_send_handler,
+ naga_messages_send_impl,
+)
+from Undefined.api.routes.naga.unbind import naga_unbind_handler
+
+__all__ = [
+ "render_html_to_image",
+ "render_markdown_to_html",
+ "verify_naga_api_key",
+ "naga_bind_callback_handler",
+ "naga_messages_send_handler",
+ "naga_messages_send_impl",
+ "naga_unbind_handler",
+]
diff --git a/src/Undefined/api/routes/naga/auth.py b/src/Undefined/api/routes/naga/auth.py
new file mode 100644
index 00000000..1318fc6c
--- /dev/null
+++ b/src/Undefined/api/routes/naga/auth.py
@@ -0,0 +1,30 @@
+"""Naga API 鉴权辅助。"""
+
+from __future__ import annotations
+
+import logging
+
+from aiohttp import web
+
+from Undefined.api._context import RuntimeAPIContext
+
+logger = logging.getLogger(__name__)
+
+
+# 校验 Naga 共享密钥，返回错误信息或 ``None`` 表示通过
+def verify_naga_api_key(ctx: RuntimeAPIContext, request: web.Request) -> str | None:
+ """校验 Naga 共享密钥，返回错误信息或 ``None`` 表示通过。"""
+ import secrets as _secrets
+
+ cfg = ctx.config_getter()
+ expected = cfg.naga.api_key
+ if not expected:
+ return "naga api_key not configured"
+ auth_header = request.headers.get("Authorization", "")
+ if not auth_header.startswith("Bearer "):
+ return "missing or invalid Authorization header"
+ provided = auth_header[7:]
+ # 常量时间比较，避免时序侧信道泄露密钥。
+ if not _secrets.compare_digest(provided, expected):
+ return "invalid api_key"
+ return None
diff --git a/src/Undefined/api/routes/naga/bind.py b/src/Undefined/api/routes/naga/bind.py
new file mode 100644
index 00000000..f2f3b9a2
--- /dev/null
+++ b/src/Undefined/api/routes/naga/bind.py
@@ -0,0 +1,158 @@
+"""Naga 绑定回调路由。"""
+
+from __future__ import annotations
+
+import logging
+import uuid as _uuid
+
+from aiohttp import web
+from aiohttp.web_response import Response
+
+from Undefined.api._context import RuntimeAPIContext
+from Undefined.api._helpers import (
+ _json_error,
+ _short_text_preview,
+)
+from Undefined.api.routes.naga.auth import verify_naga_api_key
+
+logger = logging.getLogger(__name__)
+
+# ------------------------------------------------------------------
+# POST /api/v1/naga/bind/callback
+# ------------------------------------------------------------------
+
+
+async def naga_bind_callback_handler(
+ ctx: RuntimeAPIContext, request: web.Request
+) -> Response:
+ """POST /api/v1/naga/bind/callback — Naga 绑定回调。"""
+ trace_id = _uuid.uuid4().hex[:8]
+ auth_err = verify_naga_api_key(ctx, request)
+ if auth_err is not None:
+ logger.warning(
+ "[NagaBindCallback] 鉴权失败: trace=%s remote=%s err=%s",
+ trace_id,
+ getattr(request, "remote", None),
+ auth_err,
+ )
+ return _json_error("Unauthorized", status=401)
+
+ try:
+ body = await request.json()
+ except Exception:
+ return _json_error("Invalid JSON", status=400)
+ if not isinstance(body, dict):
+ return _json_error("JSON body must be an object", status=400)
+
+ bind_uuid = str(body.get("bind_uuid", "") or "").strip()
+ naga_id = str(body.get("naga_id", "") or "").strip()
+ status = str(body.get("status", "") or "").strip().lower()
+ delivery_signature = str(body.get("delivery_signature", "") or "").strip()
+ reason = str(body.get("reason", "") or "").strip()
+ if not bind_uuid or not naga_id:
+ return _json_error("bind_uuid and naga_id are required", status=400)
+ if status not in {"approved", "rejected"}:
+ return _json_error("status must be 'approved' or 'rejected'", status=400)
+ logger.info(
+ "[NagaBindCallback] 请求开始: trace=%s remote=%s naga_id=%s bind_uuid=%s status=%s reason=%s signature=%s",
+ trace_id,
+ getattr(request, "remote", None),
+ naga_id,
+ bind_uuid,
+ status,
+ _short_text_preview(reason, limit=60),
+ delivery_signature[:12] + "..." if delivery_signature else "",
+ )
+
+ naga_store = ctx.naga_store
+ if naga_store is None:
+ return _json_error("Naga integration not available", status=503)
+
+ sender = ctx.sender
+ if status == "approved":
+ if not delivery_signature:
+ return _json_error(
+ "delivery_signature is required when approved", status=400
+ )
+ # 激活绑定：写入 delivery_signature 并移出 pending 队列。
+ binding, created, err = await naga_store.activate_binding(
+ bind_uuid=bind_uuid,
+ naga_id=naga_id,
+ delivery_signature=delivery_signature,
+ )
+ if err:
+ logger.warning(
+ "[NagaBindCallback] 激活失败: trace=%s naga_id=%s bind_uuid=%s err=%s",
+ trace_id,
+ naga_id,
+ bind_uuid,
+ err.message,
+ )
+ return _json_error(err.message, status=err.http_status)
+ logger.info(
+ "[NagaBindCallback] 激活完成: trace=%s naga_id=%s bind_uuid=%s created=%s qq=%s",
+ trace_id,
+ naga_id,
+ bind_uuid,
+ created,
+ binding.qq_id if binding is not None else "",
+ )
+ if created and binding is not None and sender is not None:
+ try:
+ await sender.send_private_message(
+ binding.qq_id,
+ f"🎉 你的 Naga 绑定已生效\nnaga_id: {naga_id}",
+ )
+ except Exception as exc:
+ logger.warning("[NagaBindCallback] 通知绑定成功失败: %s", exc)
+ return web.json_response(
+ {
+ "ok": True,
+ "status": "approved",
+ "idempotent": not created,
+ "naga_id": naga_id,
+ "bind_uuid": bind_uuid,
+ }
+ )
+
+ # --- rejected ---
+ pending, removed, err = await naga_store.reject_binding(
+ bind_uuid=bind_uuid,
+ naga_id=naga_id,
+ reason=reason,
+ )
+ if err:
+ logger.warning(
+ "[NagaBindCallback] 拒绝失败: trace=%s naga_id=%s bind_uuid=%s err=%s",
+ trace_id,
+ naga_id,
+ bind_uuid,
+ err.message,
+ )
+ return _json_error(err.message, status=err.http_status)
+ logger.info(
+ "[NagaBindCallback] 拒绝完成: trace=%s naga_id=%s bind_uuid=%s removed=%s qq=%s",
+ trace_id,
+ naga_id,
+ bind_uuid,
+ removed,
+ pending.qq_id if pending is not None else "",
+ )
+ if removed and pending is not None and sender is not None:
+ try:
+ detail = f"\n原因: {reason}" if reason else ""
+ await sender.send_private_message(
+ pending.qq_id,
+ f"❌ 你的 Naga 绑定被远端拒绝\nnaga_id: {naga_id}{detail}",
+ )
+ except Exception as exc:
+ logger.warning("[NagaBindCallback] 通知绑定拒绝失败: %s", exc)
+ return web.json_response(
+ {
+ "ok": True,
+ "status": "rejected",
+ "idempotent": not removed,
+ "naga_id": naga_id,
+ "bind_uuid": bind_uuid,
+ }
+ )
diff --git a/src/Undefined/api/routes/naga.py b/src/Undefined/api/routes/naga/send.py
similarity index 73%
rename from src/Undefined/api/routes/naga.py
rename to src/Undefined/api/routes/naga/send.py
index b76375de..0e2c3280 100644
--- a/src/Undefined/api/routes/naga.py
+++ b/src/Undefined/api/routes/naga/send.py
@@ -1,8 +1,4 @@
-"""Naga integration route handlers.
-
-Extracted from ``RuntimeAPI`` methods into free functions so they can be
-registered declaratively in the route table.
-"""
+"""Naga 消息发送路由与实现。"""
from __future__ import annotations
@@ -24,170 +20,10 @@
_short_text_preview,
)
from Undefined.api._naga_state import NagaState
-from Undefined.render import render_html_to_image, render_markdown_to_html
-
-logger = logging.getLogger(__name__)
-
-
-# ------------------------------------------------------------------
-# Auth helper
-# ------------------------------------------------------------------
-
-
-def verify_naga_api_key(ctx: RuntimeAPIContext, request: web.Request) -> str | None:
- """校验 Naga 共享密钥，返回错误信息或 ``None`` 表示通过。"""
- import secrets as _secrets
-
- cfg = ctx.config_getter()
- expected = cfg.naga.api_key
- if not expected:
- return "naga api_key not configured"
- auth_header = request.headers.get("Authorization", "")
- if not auth_header.startswith("Bearer "):
- return "missing or invalid Authorization header"
- provided = auth_header[7:]
- if not _secrets.compare_digest(provided, expected):
- return "invalid api_key"
- return None
+from Undefined.api.routes.naga.auth import verify_naga_api_key
-# ------------------------------------------------------------------
-# POST /api/v1/naga/bind/callback
-# ------------------------------------------------------------------
-
-
-async def naga_bind_callback_handler(
- ctx: RuntimeAPIContext, request: web.Request
-) -> Response:
- """POST /api/v1/naga/bind/callback — Naga 绑定回调。"""
- trace_id = _uuid.uuid4().hex[:8]
- auth_err = verify_naga_api_key(ctx, request)
- if auth_err is not None:
- logger.warning(
- "[NagaBindCallback] 鉴权失败: trace=%s remote=%s err=%s",
- trace_id,
- getattr(request, "remote", None),
- auth_err,
- )
- return _json_error("Unauthorized", status=401)
-
- try:
- body = await request.json()
- except Exception:
- return _json_error("Invalid JSON", status=400)
-
- bind_uuid = str(body.get("bind_uuid", "") or "").strip()
- naga_id = str(body.get("naga_id", "") or "").strip()
- status = str(body.get("status", "") or "").strip().lower()
- delivery_signature = str(body.get("delivery_signature", "") or "").strip()
- reason = str(body.get("reason", "") or "").strip()
- if not bind_uuid or not naga_id:
- return _json_error("bind_uuid and naga_id are required", status=400)
- if status not in {"approved", "rejected"}:
- return _json_error("status must be 'approved' or 'rejected'", status=400)
- logger.info(
- "[NagaBindCallback] 请求开始: trace=%s remote=%s naga_id=%s bind_uuid=%s status=%s reason=%s signature=%s",
- trace_id,
- getattr(request, "remote", None),
- naga_id,
- bind_uuid,
- status,
- _short_text_preview(reason, limit=60),
- delivery_signature[:12] + "..." if delivery_signature else "",
- )
-
- naga_store = ctx.naga_store
- if naga_store is None:
- return _json_error("Naga integration not available", status=503)
-
- sender = ctx.sender
- if status == "approved":
- if not delivery_signature:
- return _json_error(
- "delivery_signature is required when approved", status=400
- )
- binding, created, err = await naga_store.activate_binding(
- bind_uuid=bind_uuid,
- naga_id=naga_id,
- delivery_signature=delivery_signature,
- )
- if err:
- logger.warning(
- "[NagaBindCallback] 激活失败: trace=%s naga_id=%s bind_uuid=%s err=%s",
- trace_id,
- naga_id,
- bind_uuid,
- err.message,
- )
- return _json_error(err.message, status=err.http_status)
- logger.info(
- "[NagaBindCallback] 激活完成: trace=%s naga_id=%s bind_uuid=%s created=%s qq=%s",
- trace_id,
- naga_id,
- bind_uuid,
- created,
- binding.qq_id if binding is not None else "",
- )
- if created and binding is not None and sender is not None:
- try:
- await sender.send_private_message(
- binding.qq_id,
- f"🎉 你的 Naga 绑定已生效\nnaga_id: {naga_id}",
- )
- except Exception as exc:
- logger.warning("[NagaBindCallback] 通知绑定成功失败: %s", exc)
- return web.json_response(
- {
- "ok": True,
- "status": "approved",
- "idempotent": not created,
- "naga_id": naga_id,
- "bind_uuid": bind_uuid,
- }
- )
-
- # --- rejected ---
- pending, removed, err = await naga_store.reject_binding(
- bind_uuid=bind_uuid,
- naga_id=naga_id,
- reason=reason,
- )
- if err:
- logger.warning(
- "[NagaBindCallback] 拒绝失败: trace=%s naga_id=%s bind_uuid=%s err=%s",
- trace_id,
- naga_id,
- bind_uuid,
- err.message,
- )
- return _json_error(err.message, status=err.http_status)
- logger.info(
- "[NagaBindCallback] 拒绝完成: trace=%s naga_id=%s bind_uuid=%s removed=%s qq=%s",
- trace_id,
- naga_id,
- bind_uuid,
- removed,
- pending.qq_id if pending is not None else "",
- )
- if removed and pending is not None and sender is not None:
- try:
- detail = f"\n原因: {reason}" if reason else ""
- await sender.send_private_message(
- pending.qq_id,
- f"❌ 你的 Naga 绑定被远端拒绝\nnaga_id: {naga_id}{detail}",
- )
- except Exception as exc:
- logger.warning("[NagaBindCallback] 通知绑定拒绝失败: %s", exc)
- return web.json_response(
- {
- "ok": True,
- "status": "rejected",
- "idempotent": not removed,
- "naga_id": naga_id,
- "bind_uuid": bind_uuid,
- }
- )
-
+logger = logging.getLogger(__name__)
# ------------------------------------------------------------------
# POST /api/v1/naga/messages/send
@@ -243,7 +79,9 @@ async def naga_messages_send_handler(
mode = str(target.get("mode", "") or "").strip().lower()
if mode not in {"private", "group", "both"}:
return _json_error(
- "target.mode must be 'private', 'group', or 'both'", status=400
+ # "target.mode must be 'private', 'group', or 'both'", status=...
+ "target.mode must be 'private', 'group', or 'both'",
+ status=400,
)
fmt = str(message.get("format", "text") or "text").strip().lower()
@@ -264,6 +102,7 @@ async def naga_messages_send_handler(
message_format=fmt,
content=content,
)
+ # message_key 用于并发计数与 request_uuid 幂等，相同 payload 共享同一键。
logger.info(
"[NagaSend] 请求开始: trace=%s remote=%s naga_id=%s bind_uuid=%s request_uuid=%s mode=%s fmt=%s qq=%s group=%s key=%s content_len=%s preview=%s signature=%s",
trace_id,
@@ -302,6 +141,7 @@ async def naga_messages_send_handler(
)
try:
if request_uuid:
+ # 可选 uuid 启用幂等：冲突/缓存/等待/owner 四态由 NagaState 协调。
dedupe_action, dedupe_value = await naga_state.register_request_uuid(
request_uuid, message_key
)
@@ -384,7 +224,7 @@ async def naga_messages_send_handler(
# ------------------------------------------------------------------
-# Core send implementation (no NagaState dependency)
+# Core send implementation
# ------------------------------------------------------------------
@@ -568,13 +408,15 @@ async def naga_messages_send_impl(
if message_format in {"markdown", "html"}:
import tempfile
+ from Undefined.api.routes import naga as naga_routes
+
try:
html_str = content
if message_format == "markdown":
- html_str = await render_markdown_to_html(content)
+ html_str = await naga_routes.render_markdown_to_html(content)
fd, tmp_path = tempfile.mkstemp(suffix=".png", prefix="naga_send_")
os.close(fd)
- await render_html_to_image(html_str, tmp_path)
+ await naga_routes.render_html_to_image(html_str, tmp_path)
image_path = tmp_path
rendered = True
logger.info(
@@ -815,83 +657,3 @@ async def _ensure_delivery_active() -> tuple[Any, Response | None]:
)
finally:
await naga_store.release_delivery(bind_uuid=bind_uuid)
-
-
-# ------------------------------------------------------------------
-# POST /api/v1/naga/unbind
-# ------------------------------------------------------------------
-
-
-async def naga_unbind_handler(ctx: RuntimeAPIContext, request: web.Request) -> Response:
- """POST /api/v1/naga/unbind — 远端主动解绑。"""
- trace_id = _uuid.uuid4().hex[:8]
- auth_err = verify_naga_api_key(ctx, request)
- if auth_err is not None:
- logger.warning(
- "[NagaUnbind] 鉴权失败: trace=%s remote=%s err=%s",
- trace_id,
- getattr(request, "remote", None),
- auth_err,
- )
- return _json_error("Unauthorized", status=401)
-
- try:
- body = await request.json()
- except Exception:
- return _json_error("Invalid JSON", status=400)
-
- bind_uuid = str(body.get("bind_uuid", "") or "").strip()
- naga_id = str(body.get("naga_id", "") or "").strip()
- delivery_signature = str(body.get("delivery_signature", "") or "").strip()
- if not bind_uuid or not naga_id or not delivery_signature:
- return _json_error(
- "bind_uuid, naga_id and delivery_signature are required",
- status=400,
- )
- logger.info(
- "[NagaUnbind] 请求开始: trace=%s remote=%s naga_id=%s bind_uuid=%s signature=%s",
- trace_id,
- getattr(request, "remote", None),
- naga_id,
- bind_uuid,
- delivery_signature[:12] + "...",
- )
-
- naga_store = ctx.naga_store
- if naga_store is None:
- return _json_error("Naga integration not available", status=503)
-
- binding, changed, err = await naga_store.revoke_binding(
- naga_id,
- expected_bind_uuid=bind_uuid,
- delivery_signature=delivery_signature,
- )
- if binding is None:
- logger.warning(
- "[NagaUnbind] 吊销失败: trace=%s naga_id=%s bind_uuid=%s err=%s",
- trace_id,
- naga_id,
- bind_uuid,
- err.message if err is not None else "binding not found",
- )
- return _json_error(
- err.message if err is not None else "binding not found",
- status=err.http_status if err is not None else 404,
- )
- logger.info(
- "[NagaUnbind] 吊销完成: trace=%s naga_id=%s bind_uuid=%s changed=%s qq=%s group=%s",
- trace_id,
- naga_id,
- bind_uuid,
- changed,
- binding.qq_id,
- binding.group_id,
- )
- return web.json_response(
- {
- "ok": True,
- "idempotent": not changed,
- "naga_id": naga_id,
- "bind_uuid": bind_uuid,
- }
- )
diff --git a/src/Undefined/api/routes/naga/unbind.py b/src/Undefined/api/routes/naga/unbind.py
new file mode 100644
index 00000000..d6b18d13
--- /dev/null
+++ b/src/Undefined/api/routes/naga/unbind.py
@@ -0,0 +1,99 @@
+"""Naga 解绑路由。"""
+
+from __future__ import annotations
+
+import logging
+import uuid as _uuid
+
+from aiohttp import web
+from aiohttp.web_response import Response
+
+from Undefined.api._context import RuntimeAPIContext
+from Undefined.api._helpers import (
+ _json_error,
+)
+from Undefined.api.routes.naga.auth import verify_naga_api_key
+
+logger = logging.getLogger(__name__)
+
+# ------------------------------------------------------------------
+# POST /api/v1/naga/unbind
+# ------------------------------------------------------------------
+
+
+async def naga_unbind_handler(ctx: RuntimeAPIContext, request: web.Request) -> Response:
+ """POST /api/v1/naga/unbind — 远端主动解绑。"""
+ trace_id = _uuid.uuid4().hex[:8]
+ auth_err = verify_naga_api_key(ctx, request)
+ if auth_err is not None:
+ logger.warning(
+ "[NagaUnbind] 鉴权失败: trace=%s remote=%s err=%s",
+ trace_id,
+ getattr(request, "remote", None),
+ auth_err,
+ )
+ return _json_error("Unauthorized", status=401)
+
+ try:
+ body = await request.json()
+ except Exception:
+ return _json_error("Invalid JSON", status=400)
+ if not isinstance(body, dict):
+ return _json_error("JSON body must be an object", status=400)
+
+ bind_uuid = str(body.get("bind_uuid", "") or "").strip()
+ naga_id = str(body.get("naga_id", "") or "").strip()
+ delivery_signature = str(body.get("delivery_signature", "") or "").strip()
+ if not bind_uuid or not naga_id or not delivery_signature:
+ return _json_error(
+ "bind_uuid, naga_id and delivery_signature are required",
+ status=400,
+ )
+ logger.info(
+ "[NagaUnbind] 请求开始: trace=%s remote=%s naga_id=%s bind_uuid=%s signature=%s",
+ trace_id,
+ getattr(request, "remote", None),
+ naga_id,
+ bind_uuid,
+ delivery_signature[:12] + "...",
+ )
+
+ naga_store = ctx.naga_store
+ if naga_store is None:
+ return _json_error("Naga integration not available", status=503)
+
+ # 解绑时等待在途投递完成，避免消息发到已吊销绑定。
+ binding, changed, err = await naga_store.revoke_binding(
+ naga_id,
+ expected_bind_uuid=bind_uuid,
+ delivery_signature=delivery_signature,
+ )
+ if binding is None:
+ logger.warning(
+ "[NagaUnbind] 吊销失败: trace=%s naga_id=%s bind_uuid=%s err=%s",
+ trace_id,
+ naga_id,
+ bind_uuid,
+ err.message if err is not None else "binding not found",
+ )
+ return _json_error(
+ err.message if err is not None else "binding not found",
+ status=err.http_status if err is not None else 404,
+ )
+ logger.info(
+ "[NagaUnbind] 吊销完成: trace=%s naga_id=%s bind_uuid=%s changed=%s qq=%s group=%s",
+ trace_id,
+ naga_id,
+ bind_uuid,
+ changed,
+ binding.qq_id,
+ binding.group_id,
+ )
+ return web.json_response(
+ {
+ "ok": True,
+ "idempotent": not changed,
+ "naga_id": naga_id,
+ "bind_uuid": bind_uuid,
+ }
+ )
diff --git a/src/Undefined/attachments/__init__.py b/src/Undefined/attachments/__init__.py
new file mode 100644
index 00000000..ad4d1a56
--- /dev/null
+++ b/src/Undefined/attachments/__init__.py
@@ -0,0 +1,43 @@
+"""附件注册表与富媒体消息辅助工具包。
+
+聚合 models、segments、registry、render 子模块的公开 API；
+下游可 ``from Undefined.attachments import AttachmentRegistry`` 等。
+"""
+
+from Undefined.attachments.models import (
+ AttachmentRecord,
+ AttachmentRenderError,
+ RegisteredMessageAttachments,
+ RenderedRichMessage,
+)
+from Undefined.attachments.registry import AttachmentRegistry
+from Undefined.attachments.render import (
+ dispatch_pending_file_sends,
+ render_message_with_attachments,
+ render_message_with_pic_placeholders,
+)
+from Undefined.attachments.segments import (
+ append_attachment_text,
+ attachment_refs_to_text,
+ attachment_refs_to_xml,
+ build_attachment_scope,
+ register_message_attachments,
+ scope_from_context,
+)
+
+__all__ = [
+ "AttachmentRecord",
+ "AttachmentRegistry",
+ "AttachmentRenderError",
+ "RegisteredMessageAttachments",
+ "RenderedRichMessage",
+ "append_attachment_text",
+ "attachment_refs_to_text",
+ "attachment_refs_to_xml",
+ "build_attachment_scope",
+ "dispatch_pending_file_sends",
+ "register_message_attachments",
+ "render_message_with_attachments",
+ "render_message_with_pic_placeholders",
+ "scope_from_context",
+]
diff --git a/src/Undefined/attachments/models.py b/src/Undefined/attachments/models.py
new file mode 100644
index 00000000..fd7c58d3
--- /dev/null
+++ b/src/Undefined/attachments/models.py
@@ -0,0 +1,93 @@
+"""附件领域模型与渲染异常类型。
+
+定义 ``AttachmentRecord`` 等不可变数据类及 ``AttachmentRenderError``；
+不含注册、解析或 CQ 渲染逻辑。
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+
+
+@dataclass(frozen=True)
+class AttachmentRecord:
+ """单条附件的持久化记录。
+
+ 由 ``AttachmentRegistry`` 写入磁盘并在消息渲染时按 UID 解析；
+ ``prompt_ref()`` 供 LLM 上下文引用本地可用或远程 URL 附件。
+ """
+
+ uid: str
+ scope_key: str
+ kind: str
+ media_type: str
+ display_name: str
+ source_kind: str
+ source_ref: str
+ local_path: str | None
+ mime_type: str
+ sha256: str
+ created_at: str
+ segment_data: dict[str, str]
+ semantic_kind: str = ""
+ description: str = ""
+
+ def prompt_ref(self) -> dict[str, str]:
+ """构建供提示词/历史引用的精简附件字典。
+
+ Returns:
+ 含 ``uid``、``kind``、``media_type`` 等字段的字典；
+ 本地文件不可用时回退 ``source_ref``。
+ """
+ local_available = False
+ if self.local_path is not None:
+ try:
+ local_available = Path(self.local_path).is_file()
+ except OSError:
+ local_available = False
+ ref: dict[str, str] = {
+ "uid": self.uid,
+ "kind": self.kind,
+ "media_type": self.media_type,
+ "display_name": self.display_name,
+ }
+ if self.source_kind.strip():
+ ref["source_kind"] = self.source_kind.strip()
+ # 本地文件缺失时回退 source_ref，供 LLM 引用远程 URL
+ if not local_available and self.source_ref.strip():
+ ref["source_ref"] = self.source_ref.strip()
+ if self.semantic_kind.strip():
+ ref["semantic_kind"] = self.semantic_kind.strip()
+ if self.description.strip():
+ ref["description"] = self.description.strip()
+ return ref
+
+
+@dataclass(frozen=True)
+class RegisteredMessageAttachments:
+ """OneBot 消息段注册附件后的归一化结果。"""
+
+ attachments: list[dict[str, str]]
+ normalized_text: str
+
+
+@dataclass(frozen=True)
+class RenderedRichMessage:
+ """富媒体标签渲染后的投递与历史文本。"""
+
+ delivery_text: str
+ history_text: str
+ attachments: list[dict[str, str]]
+ pending_file_sends: tuple[AttachmentRecord, ...] = ()
+
+
+class AttachmentRenderError(RuntimeError):
+ """附件标签无法渲染时抛出（``strict=True`` 场景）。"""
+
+
+class _RemoteAttachmentTooLarge(Exception):
+ """远程下载超过字节上限时由 registry 内部捕获。"""
+
+ def __init__(self, mime_type: str = "") -> None:
+ self.mime_type = mime_type
diff --git a/src/Undefined/attachments.py b/src/Undefined/attachments/registry.py
similarity index 50%
rename from src/Undefined/attachments.py
rename to src/Undefined/attachments/registry.py
index daa87245..1e958f90 100644
--- a/src/Undefined/attachments.py
+++ b/src/Undefined/attachments/registry.py
@@ -1,55 +1,40 @@
-"""Attachment registry and rich-media helpers."""
+"""附件持久化注册表。
+
+负责本地缓存、远程下载、去重与 scope 隔离；由 handlers 与 AI 协调器持有进程级单例。
+"""
from __future__ import annotations
import asyncio
import base64
-import binascii
-from dataclasses import asdict, dataclass, replace
-from datetime import datetime
import hashlib
import logging
import mimetypes
+from dataclasses import asdict, replace
+from datetime import datetime
from pathlib import Path
-import re
import time
-from typing import Any, Awaitable, Callable, Mapping, Sequence
-from urllib.parse import unquote, urlsplit
+from typing import Any, Awaitable, Callable, Mapping
+from uuid import uuid4
import httpx
+from Undefined.attachments.models import AttachmentRecord, _RemoteAttachmentTooLarge
+from Undefined.attachments.segments import (
+ display_name_from_source,
+ is_http_url,
+ media_kind_from_value,
+ scope_from_context,
+)
from Undefined.utils import io
from Undefined.utils.paths import (
ATTACHMENT_CACHE_DIR,
ATTACHMENT_REGISTRY_FILE,
- WEBUI_FILE_CACHE_DIR,
ensure_dir,
)
-from Undefined.utils.xml import escape_xml_attr
logger = logging.getLogger(__name__)
-_PIC_TAG_PATTERN = re.compile(
- r"[\"'])(?P[^\"']+)(?P=quote)\s*/?>",
- re.IGNORECASE,
-)
-_ATTACHMENT_TAG_PATTERN = re.compile(
- r"[\"'])(?P[^\"']+)(?P=quote)\s*/?>",
- re.IGNORECASE,
-)
-_UNIFIED_TAG_PATTERN = re.compile(
- r"<(?Ppic|attachment)\s+uid=(?P[\"'])(?P[^\"']+)(?P=quote)\s*/?>",
- re.IGNORECASE,
-)
-_MEDIA_LABELS = {
- "image": "图片",
- "file": "文件",
- "audio": "音频",
- "video": "视频",
- "record": "语音",
- "pic": "图片",
-}
-_WINDOWS_ABS_PATH_RE = re.compile(r"^[A-Za-z]:[\\/]")
_DEFAULT_REMOTE_TIMEOUT_SECONDS = 120.0
_IMAGE_SUFFIX_TO_MIME = {
".png": "image/png",
@@ -67,7 +52,6 @@
(b"GIF89a", ".gif"),
(b"BM", ".bmp"),
)
-_FORWARD_ATTACHMENT_MAX_DEPTH = 3
_ATTACHMENT_CACHE_MAX_AGE_SECONDS = 7 * 24 * 60 * 60
_ATTACHMENT_REGISTRY_MAX_RECORDS = 2000
_ATTACHMENT_CACHE_MAX_BYTES = 0
@@ -76,219 +60,10 @@
_DEFAULT_REMOTE_DOWNLOAD_MAX_BYTES = 25 * 1024 * 1024
-@dataclass(frozen=True)
-class AttachmentRecord:
- uid: str
- scope_key: str
- kind: str
- media_type: str
- display_name: str
- source_kind: str
- source_ref: str
- local_path: str | None
- mime_type: str
- sha256: str
- created_at: str
- segment_data: dict[str, str]
- semantic_kind: str = ""
- description: str = ""
-
- def prompt_ref(self) -> dict[str, str]:
- local_available = False
- if self.local_path is not None:
- try:
- local_available = Path(self.local_path).is_file()
- except OSError:
- local_available = False
- ref: dict[str, str] = {
- "uid": self.uid,
- "kind": self.kind,
- "media_type": self.media_type,
- "display_name": self.display_name,
- }
- if self.source_kind.strip():
- ref["source_kind"] = self.source_kind.strip()
- if not local_available and self.source_ref.strip():
- ref["source_ref"] = self.source_ref.strip()
- if self.semantic_kind.strip():
- ref["semantic_kind"] = self.semantic_kind.strip()
- if self.description.strip():
- ref["description"] = self.description.strip()
- return ref
-
-
-@dataclass(frozen=True)
-class RegisteredMessageAttachments:
- attachments: list[dict[str, str]]
- normalized_text: str
-
-
-@dataclass(frozen=True)
-class RenderedRichMessage:
- delivery_text: str
- history_text: str
- attachments: list[dict[str, str]]
- pending_file_sends: tuple[AttachmentRecord, ...] = ()
-
-
-class AttachmentRenderError(RuntimeError):
- """Raised when an attachment tag cannot be rendered."""
-
-
-class _RemoteAttachmentTooLarge(Exception):
- def __init__(self, mime_type: str = "") -> None:
- self.mime_type = mime_type
-
-
def _now_iso() -> str:
return datetime.now().isoformat(timespec="seconds")
-def _escape_cq_component(value: str) -> str:
- return (
- value.replace("&", "&")
- .replace("[", "[")
- .replace("]", "]")
- .replace(",", ",")
- )
-
-
-def _coerce_positive_int(value: Any) -> int | None:
- if isinstance(value, bool):
- return None
- if isinstance(value, int):
- return value if value > 0 else None
- if isinstance(value, str):
- text = value.strip()
- if not text:
- return None
- try:
- parsed = int(text)
- except ValueError:
- return None
- return parsed if parsed > 0 else None
- return None
-
-
-def build_attachment_scope(
- *,
- group_id: Any = None,
- user_id: Any = None,
- request_type: str | None = None,
- webui_session: bool = False,
-) -> str | None:
- """Build a scope key for attachment visibility."""
- if webui_session:
- return "webui"
-
- group = _coerce_positive_int(group_id)
- if group is not None:
- return f"group:{group}"
-
- user = _coerce_positive_int(user_id)
- request_type_text = str(request_type or "").strip().lower()
- if request_type_text == "private" and user is not None:
- return f"private:{user}"
- if user is not None:
- return f"private:{user}"
- return None
-
-
-def scope_from_context(context: Mapping[str, Any] | None) -> str | None:
- if not context:
- return None
- return build_attachment_scope(
- group_id=context.get("group_id"),
- user_id=context.get("user_id"),
- request_type=str(context.get("request_type", "") or ""),
- webui_session=bool(context.get("webui_session", False)),
- )
-
-
-def attachment_refs_to_text(attachments: Sequence[Mapping[str, str]]) -> str:
- if not attachments:
- return ""
- parts: list[str] = []
- for item in attachments:
- uid = str(item.get("uid", "") or "").strip()
- if not uid:
- continue
- media_type = str(item.get("media_type") or item.get("kind") or "file").strip()
- label = _MEDIA_LABELS.get(media_type, "附件")
- name = str(item.get("display_name", "") or "").strip()
- if name:
- parts.append(f"[{label} uid={uid} name={name}]")
- else:
- parts.append(f"[{label} uid={uid}]")
- return " ".join(parts)
-
-
-def attachment_refs_to_xml(
- attachments: Sequence[Mapping[str, str]],
- *,
- indent: str = " ",
-) -> str:
- if not attachments:
- return ""
- lines = [f"{indent}"]
- for item in attachments:
- uid = str(item.get("uid", "") or "").strip()
- if not uid:
- continue
- kind = str(item.get("kind", "") or item.get("media_type", "") or "file").strip()
- media_type = str(item.get("media_type", "") or kind or "file").strip()
- name = str(item.get("display_name", "") or "").strip()
- attrs = [
- f'uid="{escape_xml_attr(uid)}"',
- f'type="{escape_xml_attr(kind or media_type)}"',
- f'media_type="{escape_xml_attr(media_type)}"',
- ]
- if name:
- attrs.append(f'name="{escape_xml_attr(name)}"')
- source_kind = str(item.get("source_kind", "") or "").strip()
- if source_kind:
- attrs.append(f'source_kind="{escape_xml_attr(source_kind)}"')
- source_ref = str(item.get("source_ref", "") or "").strip()
- if source_ref:
- attrs.append(f'source_ref="{escape_xml_attr(source_ref)}"')
- semantic_kind = str(item.get("semantic_kind", "") or "").strip()
- if semantic_kind:
- attrs.append(f'semantic_kind="{escape_xml_attr(semantic_kind)}"')
- description = str(item.get("description", "") or "").strip()
- if description:
- attrs.append(f'description="{escape_xml_attr(description)}"')
- lines.append(f"{indent} ")
- lines.append(f"{indent}")
- return "\n".join(lines)
-
-
-def append_attachment_text(
- base_text: str, attachments: Sequence[Mapping[str, str]]
-) -> str:
- attachment_text = attachment_refs_to_text(attachments)
- if not attachment_text:
- return base_text
- if not base_text.strip():
- return attachment_text
- return f"{base_text}\n附件: {attachment_text}"
-
-
-def _is_http_url(value: str) -> bool:
- return value.startswith("http://") or value.startswith("https://")
-
-
-def _is_data_url(value: str) -> bool:
- return value.startswith("data:")
-
-
-def _is_localish_path(value: str) -> bool:
- return (
- value.startswith("/")
- or value.startswith("file://")
- or bool(_WINDOWS_ABS_PATH_RE.match(value))
- )
-
-
def _decode_data_url(data_url: str) -> tuple[bytes, str]:
header, _, payload = data_url.partition(",")
if ";base64" not in header.lower():
@@ -326,22 +101,6 @@ def _guess_mime_type(name: str, content: bytes) -> str:
return _IMAGE_SUFFIX_TO_MIME.get(suffix, "application/octet-stream")
-def _display_name_from_source(raw_source: str, fallback: str) -> str:
- if not raw_source:
- return fallback
- if raw_source.startswith("file://"):
- raw_source = raw_source[7:]
- name = Path(unquote(urlsplit(raw_source).path)).name
- return name or fallback
-
-
-def _media_kind_from_value(value: str) -> str:
- text = str(value or "").strip().lower()
- if text in {"image", "file", "audio", "video", "record"}:
- return text
- return "file"
-
-
def _remote_reference_source_kind(source_kind: str) -> str:
cleaned = str(source_kind or "").strip()
if not cleaned:
@@ -351,114 +110,11 @@ def _remote_reference_source_kind(source_kind: str) -> str:
return f"{cleaned}_reference"
-def _segment_text(
- type_: str, data: Mapping[str, Any], ref: Mapping[str, str] | None
-) -> str:
- if type_ == "text":
- return str(data.get("text", "") or "")
- if type_ == "at":
- qq = str(data.get("qq", "") or "").strip()
- name = str(data.get("name") or data.get("nickname") or "").strip()
- if qq and name:
- return f"[@{qq}({name})]"
- if qq:
- return f"[@{qq}]"
- return "[@]"
- if type_ == "face":
- return "[表情]"
- if type_ == "reply":
- reply_id = str(data.get("id") or data.get("message_id") or "").strip()
- return f"[引用: {reply_id}]" if reply_id else "[引用]"
- if type_ == "forward":
- forward_id = str(data.get("id") or data.get("resid") or "").strip()
- return f"[合并转发: {forward_id}]" if forward_id else "[合并转发]"
- if ref is not None:
- label = _MEDIA_LABELS.get(
- str(ref.get("media_type") or ref.get("kind") or type_).strip(), "附件"
- )
- uid = str(ref.get("uid", "") or "").strip()
- name = str(ref.get("display_name", "") or "").strip()
- if uid and name:
- return f"[{label} uid={uid} name={name}]"
- if uid:
- return f"[{label} uid={uid}]"
- label = _MEDIA_LABELS.get(type_, "附件")
- raw = str(data.get("file") or data.get("url") or data.get("id") or "").strip()
- return f"[{label}: {raw}]" if raw else f"[{label}]"
-
-
-def _resolve_webui_file_id(file_id: str) -> Path | None:
- if not file_id or not file_id.isalnum():
- return None
- file_dir = (Path.cwd() / WEBUI_FILE_CACHE_DIR / file_id).resolve()
- cache_root = (Path.cwd() / WEBUI_FILE_CACHE_DIR).resolve()
- if cache_root not in file_dir.parents and file_dir != cache_root:
- return None
- if not file_dir.is_dir():
- return None
- try:
- files = list(file_dir.iterdir())
- except OSError:
- return None
- for candidate in files:
- if candidate.is_file():
- return candidate
- return None
-
-
-def _extract_forward_id(data: Mapping[str, Any]) -> str:
- forward_id = data.get("id") or data.get("resid") or data.get("message_id")
- return str(forward_id).strip() if forward_id is not None else ""
-
-
-def _segment_data_from_onebot_data(
- data: Mapping[str, Any],
- *,
- exclude_keys: set[str] | None = None,
-) -> dict[str, str]:
- excluded = {key.strip().lower() for key in (exclude_keys or set()) if key.strip()}
- normalized: dict[str, str] = {}
- for raw_key, raw_value in data.items():
- key = str(raw_key or "").strip()
- if not key:
- continue
- if key.lower() in excluded:
- continue
- text = str(raw_value or "").strip()
- if not text:
- continue
- normalized[key] = text
- return normalized
-
-
-def _normalize_message_segments(message: Any) -> list[Mapping[str, Any]]:
- if isinstance(message, list):
- normalized: list[Mapping[str, Any]] = []
- for item in message:
- if isinstance(item, Mapping):
- normalized.append(item)
- elif isinstance(item, str):
- normalized.append({"type": "text", "data": {"text": item}})
- return normalized
- if isinstance(message, Mapping):
- return [message]
- if isinstance(message, str):
- return [{"type": "text", "data": {"text": message}}]
- return []
-
-
-def _normalize_forward_nodes(raw_nodes: Any) -> list[Mapping[str, Any]]:
- if isinstance(raw_nodes, list):
- return [node for node in raw_nodes if isinstance(node, Mapping)]
- if isinstance(raw_nodes, Mapping):
- messages = raw_nodes.get("messages")
- if isinstance(messages, list):
- return [node for node in messages if isinstance(node, Mapping)]
- return []
-
-
class AttachmentRegistry:
- """Persistent attachment registry scoped by conversation."""
+ """按会话作用域持久化的附件注册表。
+
+ 写入 JSON 注册表与本地缓存目录，支持远程 URL 引用与按需回源下载。
+ """
def __init__(
self,
@@ -494,6 +150,7 @@ def __init__(
) = None
def set_remote_download_max_bytes(self, value: int) -> None:
+ """设置单次远程下载字节上限。"""
self._remote_download_max_bytes = max(0, int(value))
def set_limits(
@@ -506,6 +163,7 @@ def set_limits(
url_reference_max_records: int | None = None,
url_max_length: int | None = None,
) -> None:
+ """批量更新注册表容量与 TTL 限制。"""
if remote_download_max_bytes is not None:
self._remote_download_max_bytes = max(0, int(remote_download_max_bytes))
if max_cache_bytes is not None:
@@ -523,12 +181,14 @@ def set_global_image_resolver(
self,
resolver: Callable[[str], AttachmentRecord | None] | None,
) -> None:
+ """注册同步全局图片 UID 回退解析器。"""
self._global_image_resolver = resolver
def set_global_image_resolver_async(
self,
resolver: Callable[[str], Awaitable[AttachmentRecord | None]] | None,
) -> None:
+ """注册异步全局图片 UID 回退解析器。"""
self._global_image_resolver_async = resolver
def _resolve_managed_cache_path(self, raw_path: str | None) -> Path | None:
@@ -546,7 +206,7 @@ def _resolve_managed_cache_path(self, raw_path: str | None) -> Path | None:
def _normalized_url_ref(self, value: str) -> str:
text = str(value or "").strip()
- if not _is_http_url(text):
+ if not is_http_url(text):
return ""
if self._url_max_length > 0 and len(text) > self._url_max_length:
return ""
@@ -559,7 +219,7 @@ def _record_with_local_path(
record,
local_path=local_path,
source_kind=_remote_reference_source_kind(record.source_kind)
- if local_path is None and _is_http_url(record.source_ref)
+ if local_path is None and is_http_url(record.source_ref)
else record.source_kind,
)
@@ -588,7 +248,7 @@ def _prune_records(self) -> bool:
cache_path = self._resolve_managed_cache_path(record.local_path)
if record.local_path is None:
has_url_ref = bool(self._normalized_url_ref(record.source_ref))
- if _is_http_url(record.source_ref) and not has_url_ref:
+ if is_http_url(record.source_ref) and not has_url_ref:
dirty = True
continue
try:
@@ -641,6 +301,7 @@ def _prune_records(self) -> bool:
retained.append((uid, record, cache_path, mtime, size))
if self._max_records > 0 and len(retained) > self._max_records:
+ # 超出记录上限时按 mtime 淘汰最旧条目
retained.sort(key=lambda item: item[3])
overflow = len(retained) - self._max_records
for _uid, _record, cache_path, _mtime, _size in retained[:overflow]:
@@ -678,9 +339,10 @@ def _prune_records(self) -> bool:
url_refs = [
item
for item in retained
- if item[2] is None and _is_http_url(item[1].source_ref)
+ if item[2] is None and is_http_url(item[1].source_ref)
]
if len(url_refs) > self._url_reference_max_records:
+ # 仅 URL 引用（未下载）单独计数上限
url_ref_ids = {
uid
for uid, _record, _path, _mtime, _size in sorted(
@@ -743,8 +405,8 @@ def _load_records_from_payload(self, raw: Any) -> dict[str, AttachmentRecord]:
loaded[str(uid)] = AttachmentRecord(
uid=str(item.get("uid") or uid),
scope_key=str(item.get("scope_key", "") or ""),
- kind=_media_kind_from_value(item.get("kind", "file")),
- media_type=_media_kind_from_value(
+ kind=media_kind_from_value(item.get("kind", "file")),
+ media_type=media_kind_from_value(
item.get("media_type") or item.get("kind") or "file"
),
display_name=str(item.get("display_name", "") or ""),
@@ -800,11 +462,14 @@ async def flush(self) -> None:
await self._persist()
def get(self, uid: str) -> AttachmentRecord | None:
+ """按 UID 读取内存中的附件记录（不触发磁盘加载）。"""
return self._records.get(str(uid).strip())
def resolve(self, uid: str, scope_key: str | None) -> AttachmentRecord | None:
+ """同步解析 UID，含 scope 校验与全局图片回退。"""
record = self.get(uid)
if record is not None:
+ # scope 不匹配时拒绝跨会话引用
if record.scope_key and scope_key and record.scope_key != scope_key:
return None
return record
@@ -825,6 +490,7 @@ def resolve(self, uid: str, scope_key: str | None) -> AttachmentRecord | None:
async def resolve_async(
self, uid: str, scope_key: str | None
) -> AttachmentRecord | None:
+ """异步解析 UID，优先异步全局回退解析器。"""
record = self.get(uid)
if record is not None:
if record.scope_key and scope_key and record.scope_key != scope_key:
@@ -860,6 +526,7 @@ def resolve_for_context(
uid: str,
context: Mapping[str, Any] | None,
) -> AttachmentRecord | None:
+ """从请求上下文推断 scope 后解析 UID。"""
return self.resolve(uid, scope_from_context(context))
async def get_url_by_uid(self, uid: str) -> str | None:
@@ -882,8 +549,6 @@ async def get_uid_by_url(self, url: str) -> str | None:
return None
def _build_uid(self, prefix: str) -> str:
- from uuid import uuid4
-
while True:
uid = f"{prefix}_{uuid4().hex[:8]}"
if uid not in self._records:
@@ -920,8 +585,9 @@ async def register_bytes(
mime_type: str | None = None,
segment_data: Mapping[str, str] | None = None,
) -> AttachmentRecord:
+ """将字节内容写入缓存并注册新附件（含 SHA-256 去重）。"""
await self.load()
- normalized_kind = _media_kind_from_value(kind)
+ normalized_kind = media_kind_from_value(kind)
normalized_media_type = (
"image" if normalized_kind == "image" else normalized_kind
)
@@ -935,6 +601,7 @@ async def register_bytes(
existing = self._find_by_sha256(scope_key, digest_hex, normalized_kind)
if existing is not None:
+ # 同 scope+SHA256 去重，复用已有 UID
return existing
uid = self._build_uid(prefix)
@@ -976,6 +643,7 @@ async def register_local_file(
source_ref: str = "",
segment_data: Mapping[str, str] | None = None,
) -> AttachmentRecord:
+ """读取本地文件并注册为附件。"""
path = Path(str(local_path)).expanduser()
if not path.is_absolute():
path = (Path.cwd() / path).resolve()
@@ -1010,6 +678,7 @@ async def register_data_url(
source_ref: str = "",
segment_data: Mapping[str, str] | None = None,
) -> AttachmentRecord:
+ """解码 ``data:`` URL 并注册附件。"""
content, mime_type = _decode_data_url(data_url)
return await self.register_bytes(
scope_key,
@@ -1033,7 +702,8 @@ async def register_remote_url(
source_ref: str = "",
segment_data: Mapping[str, str] | None = None,
) -> AttachmentRecord:
- name = display_name or _display_name_from_source(url, "attachment.bin")
+ """下载远程 URL 或在上限时降级为 URL 引用。"""
+ name = display_name or display_name_from_source(url, "attachment.bin")
return await self._register_remote_url_or_reference(
scope_key,
url,
@@ -1057,10 +727,11 @@ async def register_remote_reference(
segment_data: Mapping[str, str] | None = None,
description: str = "",
) -> AttachmentRecord:
+ """仅登记远程 URL 引用，不下载内容。"""
await self.load()
if not self._normalized_url_ref(url):
raise ValueError("远程附件 URL 为空或超过长度上限")
- normalized_kind = _media_kind_from_value(kind)
+ normalized_kind = media_kind_from_value(kind)
normalized_media_type = (
"image" if normalized_kind == "image" else normalized_kind
)
@@ -1069,7 +740,7 @@ async def register_remote_reference(
normalized_segment_data = dict(segment_data or {})
if source_ref and source_ref != url:
normalized_segment_data.setdefault("original_source_ref", source_ref)
- name = display_name or _display_name_from_source(url, "attachment.bin")
+ name = display_name or display_name_from_source(url, "attachment.bin")
digest_hex = hashlib.sha256(ref.encode("utf-8")).hexdigest()
async with self._lock:
@@ -1126,6 +797,7 @@ async def _register_remote_url_or_reference(
if source_ref and source_ref != url:
reference_segment_data.setdefault("original_source_ref", source_ref)
if max_bytes <= 0:
+ # 配置为 0 时一律只登记 URL 引用，不下载
return await self.register_remote_reference(
scope_key,
url,
@@ -1153,6 +825,7 @@ async def _stream(client: httpx.AsyncClient) -> tuple[bytes, str]:
total = 0
async for chunk in response.aiter_bytes():
total += len(chunk)
+ # 流式累计超限则降级为 URL 引用
if total > max_bytes:
raise _RemoteAttachmentTooLarge(mime_type)
chunks.append(chunk)
@@ -1191,6 +864,7 @@ async def _stream(client: httpx.AsyncClient) -> tuple[bytes, str]:
)
async def ensure_local_file(self, record: AttachmentRecord) -> AttachmentRecord:
+ """若记录仅有 URL 引用则尝试回源下载到本地缓存。"""
await self.load()
if record.local_path and Path(record.local_path).is_file():
return record
@@ -1227,454 +901,3 @@ async def ensure_local_file(self, record: AttachmentRecord) -> AttachmentRecord:
self._prune_records()
await self._persist()
return self._records.get(record.uid, updated)
-
-
-async def register_message_attachments(
- *,
- registry: AttachmentRegistry | None,
- segments: Sequence[Mapping[str, Any]],
- scope_key: str | None,
- resolve_image_url: Callable[[str], Awaitable[str | None]] | None = None,
- get_forward_messages: Callable[[str], Awaitable[list[dict[str, Any]]]]
- | None = None,
-) -> RegisteredMessageAttachments:
- attachments: list[dict[str, str]] = []
- normalized_parts: list[str] = []
- if registry is None or not scope_key:
- for segment in segments:
- type_ = str(segment.get("type", "") or "")
- raw_data = segment.get("data", {})
- data = raw_data if isinstance(raw_data, Mapping) else {}
- normalized_parts.append(_segment_text(type_, data, None))
- return RegisteredMessageAttachments(
- attachments=[],
- normalized_text="".join(normalized_parts).strip(),
- )
-
- visited_forward_ids: set[str] = set()
-
- async def _collect_from_segments(
- current_segments: Sequence[Mapping[str, Any]],
- *,
- depth: int,
- prefix: str,
- ) -> None:
- for index, segment in enumerate(current_segments):
- type_ = str(segment.get("type", "") or "").strip().lower()
- raw_data = segment.get("data", {})
- data = raw_data if isinstance(raw_data, Mapping) else {}
- ref: dict[str, str] | None = None
-
- try:
- if type_ == "image":
- raw_source = str(data.get("file") or data.get("url") or "").strip()
- display_name = _display_name_from_source(
- raw_source,
- f"image_{index + 1}.png",
- )
- if raw_source.startswith("base64://"):
- payload = raw_source[len("base64://") :].strip()
- content = base64.b64decode(payload)
- record = await registry.register_bytes(
- scope_key,
- content,
- kind="image",
- display_name=display_name,
- source_kind="base64_image",
- source_ref=f"{prefix}segment:{index}",
- segment_data=_segment_data_from_onebot_data(
- data,
- exclude_keys={"file", "url"},
- ),
- )
- ref = record.prompt_ref()
- elif _is_data_url(raw_source):
- record = await registry.register_data_url(
- scope_key,
- raw_source,
- kind="image",
- display_name=display_name,
- source_kind="data_url_image",
- source_ref=f"{prefix}segment:{index}",
- segment_data=_segment_data_from_onebot_data(
- data,
- exclude_keys={"file", "url"},
- ),
- )
- ref = record.prompt_ref()
- else:
- resolved_source = raw_source
- if raw_source and resolve_image_url is not None:
- try:
- resolved = await resolve_image_url(raw_source)
- except Exception as exc:
- logger.debug(
- "[AttachmentRegistry] image resolver failed: file=%s err=%s",
- raw_source,
- exc,
- )
- resolved = None
- if resolved:
- resolved_source = str(resolved)
-
- if _is_http_url(resolved_source):
- record = await registry.register_remote_url(
- scope_key,
- resolved_source,
- kind="image",
- display_name=display_name,
- source_kind="remote_image",
- source_ref=raw_source or resolved_source,
- segment_data=_segment_data_from_onebot_data(
- data,
- exclude_keys={"file", "url"},
- ),
- )
- ref = record.prompt_ref()
- elif _is_localish_path(resolved_source):
- local_path = (
- resolved_source[7:]
- if resolved_source.startswith("file://")
- else resolved_source
- )
- record = await registry.register_local_file(
- scope_key,
- local_path,
- kind="image",
- display_name=display_name,
- source_kind="local_image",
- source_ref=raw_source or resolved_source,
- segment_data=_segment_data_from_onebot_data(
- data,
- exclude_keys={"file", "url"},
- ),
- )
- ref = record.prompt_ref()
-
- elif type_ == "file":
- file_id = str(data.get("id", "") or "").strip()
- raw_source = str(data.get("file") or data.get("url") or "").strip()
- local_file_path: Path | None = None
- if file_id:
- local_file_path = _resolve_webui_file_id(file_id)
- elif _is_localish_path(raw_source):
- local_file_path = Path(
- raw_source[7:]
- if raw_source.startswith("file://")
- else raw_source
- )
- display_name = (
- str(data.get("name", "") or "").strip()
- or (local_file_path.name if local_file_path is not None else "")
- or _display_name_from_source(
- raw_source, f"file_{index + 1}.bin"
- )
- )
- if local_file_path is not None and local_file_path.is_file():
- record = await registry.register_local_file(
- scope_key,
- local_file_path,
- kind="file",
- display_name=display_name,
- source_kind="webui_file" if file_id else "local_file",
- source_ref=file_id or raw_source or str(local_file_path),
- segment_data=_segment_data_from_onebot_data(
- data,
- exclude_keys={"file", "url"},
- ),
- )
- ref = record.prompt_ref()
- elif _is_http_url(raw_source):
- record = await registry.register_remote_url(
- scope_key,
- raw_source,
- kind="file",
- display_name=display_name,
- source_kind="remote_file",
- source_ref=file_id or raw_source,
- segment_data=_segment_data_from_onebot_data(
- data,
- exclude_keys={"file", "url"},
- ),
- )
- ref = record.prompt_ref()
-
- elif (
- type_ == "forward"
- and get_forward_messages is not None
- and depth < _FORWARD_ATTACHMENT_MAX_DEPTH
- ):
- forward_id = _extract_forward_id(data)
- if forward_id and forward_id not in visited_forward_ids:
- visited_forward_ids.add(forward_id)
- try:
- nodes = _normalize_forward_nodes(
- await get_forward_messages(forward_id)
- )
- except Exception as exc:
- logger.debug(
- "[AttachmentRegistry] forward resolver failed: id=%s err=%s",
- forward_id,
- exc,
- )
- nodes = []
- for node_index, node in enumerate(nodes):
- raw_message = (
- node.get("content")
- or node.get("message")
- or node.get("raw_message")
- )
- nested_segments = _normalize_message_segments(raw_message)
- if not nested_segments:
- continue
- await _collect_from_segments(
- nested_segments,
- depth=depth + 1,
- prefix=f"{prefix}forward:{forward_id}:{node_index}:",
- )
- except (
- binascii.Error,
- ValueError,
- FileNotFoundError,
- httpx.HTTPError,
- ) as exc:
- logger.warning(
- "[AttachmentRegistry] segment registration skipped: type=%s index=%s err=%s",
- type_,
- index,
- exc,
- )
- except Exception as exc:
- logger.exception(
- "[AttachmentRegistry] unexpected segment registration failure: type=%s index=%s err=%s",
- type_,
- index,
- exc,
- )
-
- if ref is not None:
- attachments.append(ref)
- if depth == 0:
- normalized_parts.append(_segment_text(type_, data, ref))
-
- await _collect_from_segments(segments, depth=0, prefix="")
-
- return RegisteredMessageAttachments(
- attachments=attachments,
- normalized_text="".join(normalized_parts).strip(),
- )
-
-
-async def render_message_with_attachments(
- message: str,
- *,
- registry: AttachmentRegistry | None,
- scope_key: str | None,
- strict: bool,
-) -> RenderedRichMessage:
- """Render ```` and ```` tags into delivery/history text.
-
- * ```` — backward-compatible, image-only.
- * ```` — unified tag for any media type.
- Images (``pic_*``) are inlined as CQ images; files (``file_*``)
- are collected into *pending_file_sends* for later dispatch.
- """
- has_tags = message and (
- " tag: strictly image-only
- if tag_name == "pic" and record.media_type != "image":
- replacement = f"[图片 uid={uid} 类型错误]"
- if strict:
- raise AttachmentRenderError(f"UID 不是图片，不能用于 ：{uid}")
- delivery_parts.append(replacement)
- history_parts.append(replacement)
- continue
-
- # Route by media type
- if record.media_type == "image":
- ok = _render_image_tag(record, uid, strict, delivery_parts, history_parts)
- else:
- ok = _render_file_tag(
- record,
- uid,
- strict,
- delivery_parts,
- history_parts,
- pending_files,
- )
-
- if ok:
- attachments.append(record.prompt_ref())
-
- delivery_parts.append(message[last_index:])
- history_parts.append(message[last_index:])
- return RenderedRichMessage(
- delivery_text="".join(delivery_parts),
- history_text="".join(history_parts),
- attachments=attachments,
- pending_file_sends=tuple(pending_files),
- )
-
-
-def _render_image_tag(
- record: AttachmentRecord,
- uid: str,
- strict: bool,
- delivery_parts: list[str],
- history_parts: list[str],
-) -> bool:
- """Render an image attachment as an inline CQ:image. Returns True on success."""
- image_source = record.source_ref
- if record.local_path:
- image_source = Path(record.local_path).resolve().as_uri()
- elif not image_source:
- replacement = f"[图片 uid={uid} 缺少文件]"
- if strict:
- raise AttachmentRenderError(f"图片 UID 缺少可发送的文件：{uid}")
- delivery_parts.append(replacement)
- history_parts.append(replacement)
- return False
-
- cq_args = [f"file={image_source}"]
- for key, value in dict(getattr(record, "segment_data", {}) or {}).items():
- cleaned_key = str(key or "").strip()
- cleaned_value = str(value or "").strip()
- if (
- not cleaned_key
- or not cleaned_value
- or cleaned_key in {"file", "original_source_ref"}
- ):
- continue
- cq_args.append(
- f"{_escape_cq_component(cleaned_key)}={_escape_cq_component(cleaned_value)}"
- )
- delivery_parts.append(f"[CQ:image,{','.join(cq_args)}]")
- if record.display_name:
- history_parts.append(f"[图片 uid={uid} name={record.display_name}]")
- else:
- history_parts.append(f"[图片 uid={uid}]")
- return True
-
-
-def _render_file_tag(
- record: AttachmentRecord,
- uid: str,
- strict: bool,
- delivery_parts: list[str],
- history_parts: list[str],
- pending_files: list[AttachmentRecord],
-) -> bool:
- """Render a non-image attachment as a pending file send. Returns True on success."""
- if not record.local_path or not Path(record.local_path).is_file():
- if _is_http_url(record.source_ref):
- name_part = f" name={record.display_name}" if record.display_name else ""
- history_parts.append(f"[文件 uid={uid}{name_part}]")
- pending_files.append(record)
- return True
- replacement = f"[文件 uid={uid} 缺少本地文件]"
- if strict:
- raise AttachmentRenderError(f"文件 UID 缺少本地文件，无法发送：{uid}")
- delivery_parts.append(replacement)
- history_parts.append(replacement)
- return False
-
- # Remove from delivery text (file sent separately)
- # Keep a readable placeholder in history
- name_part = f" name={record.display_name}" if record.display_name else ""
- history_parts.append(f"[文件 uid={uid}{name_part}]")
- pending_files.append(record)
- return True
-
-
-# Backward-compatible alias
-render_message_with_pic_placeholders = render_message_with_attachments
-
-
-async def dispatch_pending_file_sends(
- rendered: RenderedRichMessage,
- *,
- sender: Any,
- target_type: str,
- target_id: int,
- registry: AttachmentRegistry | None = None,
-) -> None:
- """Send pending file attachments collected by *render_message_with_attachments*.
-
- This is best-effort: each file send failure is logged but does not interrupt
- the remaining sends or the caller.
- """
- if not rendered.pending_file_sends or sender is None:
- return
- for record in rendered.pending_file_sends:
- send_record = record
- if (
- not send_record.local_path or not Path(send_record.local_path).is_file()
- ) and registry is not None:
- try:
- send_record = await registry.ensure_local_file(send_record)
- except Exception:
- logger.warning(
- "[文件发送] 回源下载失败 uid=%s source=%s",
- send_record.uid,
- send_record.source_ref,
- exc_info=True,
- )
- if not send_record.local_path or not Path(send_record.local_path).is_file():
- logger.warning(
- "[文件发送] 跳过：本地文件缺失 uid=%s path=%s",
- send_record.uid,
- send_record.local_path,
- )
- continue
- try:
- if target_type == "group":
- await sender.send_group_file(
- target_id,
- send_record.local_path,
- name=send_record.display_name or None,
- )
- else:
- await sender.send_private_file(
- target_id,
- send_record.local_path,
- name=send_record.display_name or None,
- )
- except Exception:
- logger.warning(
- "[文件发送] 发送失败（最佳努力） uid=%s target=%s:%s",
- send_record.uid,
- target_type,
- target_id,
- exc_info=True,
- )
diff --git a/src/Undefined/attachments/render.py b/src/Undefined/attachments/render.py
new file mode 100644
index 00000000..6f3433c6
--- /dev/null
+++ b/src/Undefined/attachments/render.py
@@ -0,0 +1,287 @@
+"""富媒体标签渲染与待发送文件派发。
+
+将 ```` / ```` 占位符转为 CQ 段或历史可读文本；
+不修改注册表结构。
+"""
+
+from __future__ import annotations
+
+import logging
+import re
+from pathlib import Path
+from typing import TYPE_CHECKING, Any
+
+from Undefined.attachments.models import (
+ AttachmentRecord,
+ AttachmentRenderError,
+ RenderedRichMessage,
+)
+from Undefined.attachments.segments import _MEDIA_LABELS, is_http_url
+
+if TYPE_CHECKING:
+ from Undefined.attachments.registry import AttachmentRegistry
+
+logger = logging.getLogger(__name__)
+
+_PIC_TAG_PATTERN = re.compile(
+ r"[\"'])(?P[^\"']+)(?P=quote)\s*/?>",
+ re.IGNORECASE,
+)
+_ATTACHMENT_TAG_PATTERN = re.compile(
+ r"[\"'])(?P[^\"']+)(?P=quote)\s*/?>",
+ re.IGNORECASE,
+)
+_UNIFIED_TAG_PATTERN = re.compile(
+ r"<(?Ppic|attachment)\s+uid=(?P[\"'])(?P[^\"']+)(?P=quote)\s*/?>",
+ re.IGNORECASE,
+)
+
+
+def _escape_cq_component(value: str) -> str:
+ return (
+ value.replace("&", "&")
+ .replace("[", "[")
+ .replace("]", "]")
+ .replace(",", ",")
+ )
+
+
+async def render_message_with_attachments(
+ message: str,
+ *,
+ registry: AttachmentRegistry | None,
+ scope_key: str | None,
+ strict: bool,
+) -> RenderedRichMessage:
+ """Render ```` and ```` tags into delivery/history text.
+
+ * ```` — backward-compatible, image-only.
+ * ```` — unified tag for any media type.
+ Images (``pic_*``) are inlined as CQ images; files (``file_*``)
+ are collected into *pending_file_sends* for later dispatch.
+
+ Args:
+ message: 含占位标签的原始消息文本。
+ registry: 附件注册表。
+ scope_key: 当前会话作用域键。
+ strict: 为 True 时 UID 不可用或类型不匹配则抛出 ``AttachmentRenderError``。
+
+ Returns:
+ 投递文本、历史文本、附件引用及待发送文件列表。
+
+ Raises:
+ AttachmentRenderError: ``strict=True`` 且标签无法解析时。
+ """
+ has_tags = message and (
+ " tag: strictly image-only
+ if tag_name == "pic" and record.media_type != "image":
+ replacement = f"[图片 uid={uid} 类型错误]"
+ if strict:
+ raise AttachmentRenderError(f"UID 不是图片，不能用于 ：{uid}")
+ delivery_parts.append(replacement)
+ history_parts.append(replacement)
+ continue
+
+ # 仅允许图片； 按 media_type 分流
+ if record.media_type == "image":
+ ok = _render_image_tag(record, uid, strict, delivery_parts, history_parts)
+ else:
+ ok = _render_file_tag(
+ record,
+ uid,
+ strict,
+ delivery_parts,
+ history_parts,
+ pending_files,
+ )
+
+ if ok:
+ attachments.append(record.prompt_ref())
+
+ delivery_parts.append(message[last_index:])
+ history_parts.append(message[last_index:])
+ return RenderedRichMessage(
+ delivery_text="".join(delivery_parts),
+ history_text="".join(history_parts),
+ attachments=attachments,
+ pending_file_sends=tuple(pending_files),
+ )
+
+
+def _render_image_tag(
+ record: AttachmentRecord,
+ uid: str,
+ strict: bool,
+ delivery_parts: list[str],
+ history_parts: list[str],
+) -> bool:
+ """Render an image attachment as an inline CQ:image. Returns True on success."""
+ image_source = record.source_ref
+ if record.local_path and Path(record.local_path).is_file():
+ image_source = Path(record.local_path).resolve().as_uri()
+ elif not image_source:
+ replacement = f"[图片 uid={uid} 缺少文件]"
+ if strict:
+ raise AttachmentRenderError(f"图片 UID 缺少可发送的文件：{uid}")
+ delivery_parts.append(replacement)
+ history_parts.append(replacement)
+ return False
+
+ cq_args = [f"file={image_source}"]
+ for key, value in dict(getattr(record, "segment_data", {}) or {}).items():
+ cleaned_key = str(key or "").strip()
+ cleaned_value = str(value or "").strip()
+ if (
+ not cleaned_key
+ or not cleaned_value
+ or cleaned_key in {"file", "original_source_ref"}
+ ):
+ continue
+ cq_args.append(
+ f"{_escape_cq_component(cleaned_key)}={_escape_cq_component(cleaned_value)}"
+ )
+ delivery_parts.append(f"[CQ:image,{','.join(cq_args)}]")
+ if record.display_name:
+ history_parts.append(f"[图片 uid={uid} name={record.display_name}]")
+ else:
+ history_parts.append(f"[图片 uid={uid}]")
+ return True
+
+
+def _render_file_tag(
+ record: AttachmentRecord,
+ uid: str,
+ strict: bool,
+ delivery_parts: list[str],
+ history_parts: list[str],
+ pending_files: list[AttachmentRecord],
+) -> bool:
+ """Render a non-image attachment as a pending file send. Returns True on success."""
+ if not record.local_path or not Path(record.local_path).is_file():
+ if is_http_url(record.source_ref):
+ # 仅有远程 URL 时先入 pending，发送前尝试回源下载
+ name_part = f" name={record.display_name}" if record.display_name else ""
+ history_parts.append(f"[文件 uid={uid}{name_part}]")
+ pending_files.append(record)
+ return True
+ replacement = f"[文件 uid={uid} 缺少本地文件]"
+ if strict:
+ raise AttachmentRenderError(f"文件 UID 缺少本地文件，无法发送：{uid}")
+ delivery_parts.append(replacement)
+ history_parts.append(replacement)
+ return False
+
+ # 文件不在 CQ 文本中内联，单独走 send_group/private_file
+ # Keep a readable placeholder in history
+ name_part = f" name={record.display_name}" if record.display_name else ""
+ history_parts.append(f"[文件 uid={uid}{name_part}]")
+ pending_files.append(record)
+ return True
+
+
+render_message_with_pic_placeholders = render_message_with_attachments
+
+
+async def dispatch_pending_file_sends(
+ rendered: RenderedRichMessage,
+ *,
+ sender: Any,
+ target_type: str,
+ target_id: int,
+ registry: AttachmentRegistry | None = None,
+) -> None:
+ """Send pending file attachments collected by *render_message_with_attachments*.
+
+ This is best-effort: each file send failure is logged but does not interrupt
+ the remaining sends or the caller.
+
+ Args:
+ rendered: ``render_message_with_attachments`` 的返回值。
+ sender: 实现群/私聊文件发送的 OneBot 客户端。
+ target_type: ``group`` 或 ``private``。
+ target_id: 目标群号或 QQ 号。
+ registry: 可选，用于发送前回源下载仅有 URL 的附件。
+ """
+ if not rendered.pending_file_sends or sender is None:
+ return
+ for record in rendered.pending_file_sends:
+ send_record = record
+ if (
+ not send_record.local_path or not Path(send_record.local_path).is_file()
+ ) and registry is not None:
+ try:
+ send_record = await registry.ensure_local_file(send_record)
+ except Exception:
+ logger.warning(
+ "[文件发送] 回源下载失败 uid=%s source=%s",
+ send_record.uid,
+ send_record.source_ref,
+ exc_info=True,
+ )
+ if not send_record.local_path or not Path(send_record.local_path).is_file():
+ logger.warning(
+ "[文件发送] 跳过：本地文件缺失 uid=%s path=%s",
+ send_record.uid,
+ send_record.local_path,
+ )
+ continue
+ try:
+ if target_type == "group":
+ await sender.send_group_file(
+ target_id,
+ send_record.local_path,
+ name=send_record.display_name or None,
+ )
+ elif target_type == "private":
+ await sender.send_private_file(
+ target_id,
+ send_record.local_path,
+ name=send_record.display_name or None,
+ )
+ else:
+ logger.warning(
+ "[文件发送] 跳过：不支持的 target_type=%s uid=%s",
+ target_type,
+ send_record.uid,
+ )
+ continue
+ except Exception:
+ logger.warning(
+ "[文件发送] 发送失败（最佳努力） uid=%s target=%s:%s",
+ send_record.uid,
+ target_type,
+ target_id,
+ exc_info=True,
+ )
diff --git a/src/Undefined/attachments/segments.py b/src/Undefined/attachments/segments.py
new file mode 100644
index 00000000..d78b458f
--- /dev/null
+++ b/src/Undefined/attachments/segments.py
@@ -0,0 +1,566 @@
+"""OneBot 消息段解析与会话作用域辅助。
+
+负责 scope 键构建、附件引用文本/XML 序列化，以及从消息段批量注册附件；
+不处理磁盘持久化或 CQ 标签渲染。
+"""
+
+from __future__ import annotations
+
+import base64
+import binascii
+import logging
+import re
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Awaitable, Callable, Mapping, Sequence
+from urllib.parse import unquote, urlsplit
+
+import httpx
+
+from Undefined.attachments.models import RegisteredMessageAttachments
+from Undefined.utils.paths import WEBUI_FILE_CACHE_DIR
+from Undefined.utils.xml import escape_xml_attr
+
+if TYPE_CHECKING:
+ from Undefined.attachments.registry import AttachmentRegistry
+
+logger = logging.getLogger(__name__)
+
+_MEDIA_LABELS = {
+ "image": "图片",
+ "file": "文件",
+ "audio": "音频",
+ "video": "视频",
+ "record": "语音",
+ "pic": "图片",
+}
+_WINDOWS_ABS_PATH_RE = re.compile(r"^[A-Za-z]:[\\/]")
+_FORWARD_ATTACHMENT_MAX_DEPTH = 3
+
+
+def _coerce_positive_int(value: Any) -> int | None:
+ if isinstance(value, bool):
+ return None
+ if isinstance(value, int):
+ return value if value > 0 else None
+ if isinstance(value, str):
+ text = value.strip()
+ if not text:
+ return None
+ try:
+ parsed = int(text)
+ except ValueError:
+ return None
+ return parsed if parsed > 0 else None
+ return None
+
+
+def build_attachment_scope(
+ *,
+ group_id: Any = None,
+ user_id: Any = None,
+ request_type: str | None = None,
+ webui_session: bool = False,
+) -> str | None:
+ """构建附件可见性作用域键。
+
+ Args:
+ group_id: 群号；优先于私聊作用域。
+ user_id: 用户 QQ 号。
+ request_type: 请求类型（``private`` 等）。
+ webui_session: WebUI 会话时使用固定 ``webui`` 作用域。
+
+ Returns:
+ 形如 ``group:123`` / ``private:456`` / ``webui`` 的键，无法推断时返回 ``None``。
+ """
+ if webui_session:
+ return "webui"
+
+ group = _coerce_positive_int(group_id)
+ if group is not None:
+ # 群聊作用域优先于私聊，避免同会话附件串读
+ return f"group:{group}"
+
+ user = _coerce_positive_int(user_id)
+ request_type_text = str(request_type or "").strip().lower()
+ if request_type_text == "private" and user is not None:
+ return f"private:{user}"
+ if user is not None:
+ return f"private:{user}"
+ return None
+
+
+def scope_from_context(context: Mapping[str, Any] | None) -> str | None:
+ """从请求上下文字典提取附件作用域键。"""
+ if not context:
+ return None
+ return build_attachment_scope(
+ group_id=context.get("group_id"),
+ user_id=context.get("user_id"),
+ request_type=str(context.get("request_type", "") or ""),
+ webui_session=bool(context.get("webui_session", False)),
+ )
+
+
+def attachment_refs_to_text(attachments: Sequence[Mapping[str, str]]) -> str:
+ """将附件引用列表转为可读占位文本。"""
+ if not attachments:
+ return ""
+ parts: list[str] = []
+ for item in attachments:
+ uid = str(item.get("uid", "") or "").strip()
+ if not uid:
+ continue
+ media_type = str(item.get("media_type") or item.get("kind") or "file").strip()
+ label = _MEDIA_LABELS.get(media_type, "附件")
+ name = str(item.get("display_name", "") or "").strip()
+ if name:
+ parts.append(f"[{label} uid={uid} name={name}]")
+ else:
+ parts.append(f"[{label} uid={uid}]")
+ return " ".join(parts)
+
+
+def attachment_refs_to_xml(
+ attachments: Sequence[Mapping[str, str]],
+ *,
+ indent: str = " ",
+) -> str:
+ """将附件引用列表序列化为 XML ```` 片段。"""
+ if not attachments:
+ return ""
+ lines = [f"{indent}"]
+ for item in attachments:
+ uid = str(item.get("uid", "") or "").strip()
+ if not uid:
+ continue
+ kind = str(item.get("kind", "") or item.get("media_type", "") or "file").strip()
+ media_type = str(item.get("media_type", "") or kind or "file").strip()
+ name = str(item.get("display_name", "") or "").strip()
+ attrs = [
+ f'uid="{escape_xml_attr(uid)}"',
+ f'type="{escape_xml_attr(kind or media_type)}"',
+ f'media_type="{escape_xml_attr(media_type)}"',
+ ]
+ if name:
+ attrs.append(f'name="{escape_xml_attr(name)}"')
+ source_kind = str(item.get("source_kind", "") or "").strip()
+ if source_kind:
+ attrs.append(f'source_kind="{escape_xml_attr(source_kind)}"')
+ source_ref = str(item.get("source_ref", "") or "").strip()
+ if source_ref:
+ attrs.append(f'source_ref="{escape_xml_attr(source_ref)}"')
+ semantic_kind = str(item.get("semantic_kind", "") or "").strip()
+ if semantic_kind:
+ attrs.append(f'semantic_kind="{escape_xml_attr(semantic_kind)}"')
+ description = str(item.get("description", "") or "").strip()
+ if description:
+ attrs.append(f'description="{escape_xml_attr(description)}"')
+ lines.append(f"{indent} ")
+ lines.append(f"{indent}")
+ return "\n".join(lines)
+
+
+def append_attachment_text(
+ base_text: str, attachments: Sequence[Mapping[str, str]]
+) -> str:
+ """在基础文本后追加附件占位摘要行。"""
+ attachment_text = attachment_refs_to_text(attachments)
+ if not attachment_text:
+ return base_text
+ if not base_text.strip():
+ return attachment_text
+ return f"{base_text}\n附件: {attachment_text}"
+
+
+def is_http_url(value: str) -> bool:
+ """判断字符串是否为 HTTP(S) URL。"""
+ return value.startswith("http://") or value.startswith("https://")
+
+
+def is_data_url(value: str) -> bool:
+ """判断字符串是否为 ``data:`` URL。"""
+ return value.startswith("data:")
+
+
+def is_localish_path(value: str) -> bool:
+ """判断字符串是否像本地绝对路径或 ``file://`` URI。"""
+ return (
+ value.startswith("/")
+ or value.startswith("file://")
+ or bool(_WINDOWS_ABS_PATH_RE.match(value))
+ )
+
+
+def display_name_from_source(raw_source: str, fallback: str) -> str:
+ """从 URL 或路径推断展示文件名。"""
+ if not raw_source:
+ return fallback
+ if raw_source.startswith("file://"):
+ raw_source = raw_source[7:]
+ name = Path(unquote(urlsplit(raw_source).path)).name
+ return name or fallback
+
+
+def media_kind_from_value(value: str) -> str:
+ """将任意媒体类型字符串规范为 registry 支持的 kind。"""
+ text = str(value or "").strip().lower()
+ if text in {"image", "file", "audio", "video", "record"}:
+ return text
+ return "file"
+
+
+def segment_text(
+ type_: str, data: Mapping[str, Any], ref: Mapping[str, str] | None
+) -> str:
+ """将单条 OneBot 消息段转为可读占位文本。"""
+ if type_ == "text":
+ return str(data.get("text", "") or "")
+ if type_ == "at":
+ qq = str(data.get("qq", "") or "").strip()
+ name = str(data.get("name") or data.get("nickname") or "").strip()
+ if qq and name:
+ return f"[@{qq}({name})]"
+ if qq:
+ return f"[@{qq}]"
+ return "[@]"
+ if type_ == "face":
+ return "[表情]"
+ if type_ == "reply":
+ reply_id = str(data.get("id") or data.get("message_id") or "").strip()
+ return f"[引用: {reply_id}]" if reply_id else "[引用]"
+ if type_ == "forward":
+ forward_id = str(data.get("id") or data.get("resid") or "").strip()
+ return f"[合并转发: {forward_id}]" if forward_id else "[合并转发]"
+ if ref is not None:
+ label = _MEDIA_LABELS.get(
+ str(ref.get("media_type") or ref.get("kind") or type_).strip(), "附件"
+ )
+ uid = str(ref.get("uid", "") or "").strip()
+ name = str(ref.get("display_name", "") or "").strip()
+ if uid and name:
+ return f"[{label} uid={uid} name={name}]"
+ if uid:
+ return f"[{label} uid={uid}]"
+ label = _MEDIA_LABELS.get(type_, "附件")
+ raw = str(data.get("file") or data.get("url") or data.get("id") or "").strip()
+ return f"[{label}: {raw}]" if raw else f"[{label}]"
+
+
+def _resolve_webui_file_id(file_id: str) -> Path | None:
+ if not file_id or not file_id.isalnum():
+ return None
+ file_dir = (Path.cwd() / WEBUI_FILE_CACHE_DIR / file_id).resolve()
+ cache_root = (Path.cwd() / WEBUI_FILE_CACHE_DIR).resolve()
+ if cache_root not in file_dir.parents and file_dir != cache_root:
+ return None
+ if not file_dir.is_dir():
+ return None
+ try:
+ files = list(file_dir.iterdir())
+ except OSError:
+ return None
+ for candidate in files:
+ if candidate.is_file():
+ return candidate
+ return None
+
+
+def _extract_forward_id(data: Mapping[str, Any]) -> str:
+ forward_id = data.get("id") or data.get("resid") or data.get("message_id")
+ return str(forward_id).strip() if forward_id is not None else ""
+
+
+def segment_data_from_onebot_data(
+ data: Mapping[str, Any],
+ *,
+ exclude_keys: set[str] | None = None,
+) -> dict[str, str]:
+ """提取 OneBot 段 ``data`` 中需保留的字符串键值。"""
+ excluded = {key.strip().lower() for key in (exclude_keys or set()) if key.strip()}
+ normalized: dict[str, str] = {}
+ for raw_key, raw_value in data.items():
+ key = str(raw_key or "").strip()
+ if not key:
+ continue
+ if key.lower() in excluded:
+ continue
+ text = str(raw_value or "").strip()
+ if not text:
+ continue
+ normalized[key] = text
+ return normalized
+
+
+def normalize_message_segments(message: Any) -> list[Mapping[str, Any]]:
+ """将多种消息表示统一为 OneBot 段列表。"""
+ if isinstance(message, list):
+ normalized: list[Mapping[str, Any]] = []
+ for item in message:
+ if isinstance(item, Mapping):
+ normalized.append(item)
+ elif isinstance(item, str):
+ normalized.append({"type": "text", "data": {"text": item}})
+ return normalized
+ if isinstance(message, Mapping):
+ return [message]
+ if isinstance(message, str):
+ return [{"type": "text", "data": {"text": message}}]
+ return []
+
+
+def _normalize_forward_nodes(raw_nodes: Any) -> list[Mapping[str, Any]]:
+ if isinstance(raw_nodes, list):
+ return [node for node in raw_nodes if isinstance(node, Mapping)]
+ if isinstance(raw_nodes, Mapping):
+ messages = raw_nodes.get("messages")
+ if isinstance(messages, list):
+ return [node for node in messages if isinstance(node, Mapping)]
+ return []
+
+
+async def register_message_attachments(
+ *,
+ registry: AttachmentRegistry | None,
+ segments: Sequence[Mapping[str, Any]],
+ scope_key: str | None,
+ resolve_image_url: Callable[[str], Awaitable[str | None]] | None = None,
+ get_forward_messages: Callable[[str], Awaitable[list[dict[str, Any]]]]
+ | None = None,
+) -> RegisteredMessageAttachments:
+ """扫描消息段并将图片/文件注册到 ``AttachmentRegistry``。
+
+ Args:
+ registry: 附件注册表；为 ``None`` 时仅归一化文本。
+ segments: OneBot 消息段序列。
+ scope_key: 会话作用域键。
+ resolve_image_url: 可选，将 ``file`` 字段解析为可下载 URL。
+ get_forward_messages: 可选，拉取合并转发子消息。
+
+ Returns:
+ 已注册附件引用与归一化纯文本。
+ """
+ attachments: list[dict[str, str]] = []
+ normalized_parts: list[str] = []
+ if registry is None or not scope_key:
+ for segment in segments:
+ type_ = str(segment.get("type", "") or "")
+ raw_data = segment.get("data", {})
+ data = raw_data if isinstance(raw_data, Mapping) else {}
+ normalized_parts.append(segment_text(type_, data, None))
+ return RegisteredMessageAttachments(
+ attachments=[],
+ normalized_text="".join(normalized_parts).strip(),
+ )
+
+ visited_forward_ids: set[str] = set()
+
+ async def _collect_from_segments(
+ current_segments: Sequence[Mapping[str, Any]],
+ *,
+ depth: int,
+ prefix: str,
+ ) -> None:
+ for index, segment in enumerate(current_segments):
+ type_ = str(segment.get("type", "") or "").strip().lower()
+ raw_data = segment.get("data", {})
+ data = raw_data if isinstance(raw_data, Mapping) else {}
+ ref: dict[str, str] | None = None
+
+ try:
+ if type_ == "image":
+ raw_source = str(data.get("file") or data.get("url") or "").strip()
+ display_name = display_name_from_source(
+ raw_source,
+ f"image_{index + 1}.png",
+ )
+ if raw_source.startswith("base64://"):
+ payload = raw_source[len("base64://") :].strip()
+ content = base64.b64decode(payload)
+ record = await registry.register_bytes(
+ scope_key,
+ content,
+ kind="image",
+ display_name=display_name,
+ source_kind="base64_image",
+ source_ref=f"{prefix}segment:{index}",
+ segment_data=segment_data_from_onebot_data(
+ data,
+ exclude_keys={"file", "url"},
+ ),
+ )
+ ref = record.prompt_ref()
+ elif is_data_url(raw_source):
+ record = await registry.register_data_url(
+ scope_key,
+ raw_source,
+ kind="image",
+ display_name=display_name,
+ source_kind="data_url_image",
+ source_ref=f"{prefix}segment:{index}",
+ segment_data=segment_data_from_onebot_data(
+ data,
+ exclude_keys={"file", "url"},
+ ),
+ )
+ ref = record.prompt_ref()
+ else:
+ resolved_source = raw_source
+ if raw_source and resolve_image_url is not None:
+ try:
+ # NapCat file id 需经 get_image 解析为可下载 URL
+ resolved = await resolve_image_url(raw_source)
+ except Exception as exc:
+ logger.debug(
+ "[AttachmentRegistry] image resolver failed: file=%s err=%s",
+ raw_source,
+ exc,
+ )
+ resolved = None
+ if resolved:
+ resolved_source = str(resolved)
+
+ if is_http_url(resolved_source):
+ record = await registry.register_remote_url(
+ scope_key,
+ resolved_source,
+ kind="image",
+ display_name=display_name,
+ source_kind="remote_image",
+ source_ref=raw_source or resolved_source,
+ segment_data=segment_data_from_onebot_data(
+ data,
+ exclude_keys={"file", "url"},
+ ),
+ )
+ ref = record.prompt_ref()
+ elif is_localish_path(resolved_source):
+ local_path = (
+ resolved_source[7:]
+ if resolved_source.startswith("file://")
+ else resolved_source
+ )
+ record = await registry.register_local_file(
+ scope_key,
+ local_path,
+ kind="image",
+ display_name=display_name,
+ source_kind="local_image",
+ source_ref=raw_source or resolved_source,
+ segment_data=segment_data_from_onebot_data(
+ data,
+ exclude_keys={"file", "url"},
+ ),
+ )
+ ref = record.prompt_ref()
+
+ elif type_ == "file":
+ file_id = str(data.get("id", "") or "").strip()
+ raw_source = str(data.get("file") or data.get("url") or "").strip()
+ local_file_path: Path | None = None
+ if file_id:
+ local_file_path = _resolve_webui_file_id(file_id)
+ elif is_localish_path(raw_source):
+ local_file_path = Path(
+ raw_source[7:]
+ if raw_source.startswith("file://")
+ else raw_source
+ )
+ display_name = (
+ str(data.get("name", "") or "").strip()
+ or (local_file_path.name if local_file_path is not None else "")
+ or display_name_from_source(raw_source, f"file_{index + 1}.bin")
+ )
+ if local_file_path is not None and local_file_path.is_file():
+ record = await registry.register_local_file(
+ scope_key,
+ local_file_path,
+ kind="file",
+ display_name=display_name,
+ source_kind="webui_file" if file_id else "local_file",
+ source_ref=file_id or raw_source or str(local_file_path),
+ segment_data=segment_data_from_onebot_data(
+ data,
+ exclude_keys={"file", "url"},
+ ),
+ )
+ ref = record.prompt_ref()
+ elif is_http_url(raw_source):
+ record = await registry.register_remote_url(
+ scope_key,
+ raw_source,
+ kind="file",
+ display_name=display_name,
+ source_kind="remote_file",
+ source_ref=file_id or raw_source,
+ segment_data=segment_data_from_onebot_data(
+ data,
+ exclude_keys={"file", "url"},
+ ),
+ )
+ ref = record.prompt_ref()
+
+ elif (
+ type_ == "forward"
+ and get_forward_messages is not None
+ and depth < _FORWARD_ATTACHMENT_MAX_DEPTH
+ ):
+ # 合并转发递归展开，深度上限防止无限嵌套
+ forward_id = _extract_forward_id(data)
+ if forward_id and forward_id not in visited_forward_ids:
+ visited_forward_ids.add(forward_id)
+ try:
+ nodes = _normalize_forward_nodes(
+ await get_forward_messages(forward_id)
+ )
+ except Exception as exc:
+ logger.debug(
+ "[AttachmentRegistry] forward resolver failed: id=%s err=%s",
+ forward_id,
+ exc,
+ )
+ nodes = []
+ for node_index, node in enumerate(nodes):
+ raw_message = (
+ node.get("content")
+ or node.get("message")
+ or node.get("raw_message")
+ )
+ nested_segments = normalize_message_segments(raw_message)
+ if not nested_segments:
+ continue
+ await _collect_from_segments(
+ nested_segments,
+ depth=depth + 1,
+ prefix=f"{prefix}forward:{forward_id}:{node_index}:",
+ )
+ except (
+ binascii.Error,
+ ValueError,
+ FileNotFoundError,
+ httpx.HTTPError,
+ ) as exc:
+ logger.warning(
+ "[AttachmentRegistry] segment registration skipped: type=%s index=%s err=%s",
+ type_,
+ index,
+ exc,
+ )
+ except Exception as exc:
+ logger.exception(
+ "[AttachmentRegistry] unexpected segment registration failure: type=%s index=%s err=%s",
+ type_,
+ index,
+ exc,
+ )
+
+ if ref is not None:
+ attachments.append(ref)
+ if depth == 0:
+ normalized_parts.append(segment_text(type_, data, ref))
+
+ await _collect_from_segments(segments, depth=0, prefix="")
+
+ return RegisteredMessageAttachments(
+ attachments=attachments,
+ normalized_text="".join(normalized_parts).strip(),
+ )
diff --git a/src/Undefined/bilibili/wbi.py b/src/Undefined/bilibili/wbi.py
index f898af05..f3fdb97b 100644
--- a/src/Undefined/bilibili/wbi.py
+++ b/src/Undefined/bilibili/wbi.py
@@ -152,11 +152,7 @@ def _compute_mixin_key(img_key: str, sub_key: str) -> str:
return mixed[:32]
-async def _refresh_mixin_key(client: httpx.AsyncClient) -> str:
- resp = await client.get(_BILIBILI_API_NAV)
- resp.raise_for_status()
- payload = resp.json()
-
+def _mixin_key_from_nav_payload(payload: Any) -> str:
if not isinstance(payload, dict):
raise ValueError("nav 接口返回格式异常")
@@ -186,6 +182,12 @@ async def _refresh_mixin_key(client: httpx.AsyncClient) -> str:
return _compute_mixin_key(img_key, sub_key)
+async def _refresh_mixin_key(client: httpx.AsyncClient) -> str:
+ resp = await client.get(_BILIBILI_API_NAV)
+ resp.raise_for_status()
+ return _mixin_key_from_nav_payload(resp.json())
+
+
async def get_mixin_key(
client: httpx.AsyncClient,
*,
@@ -258,35 +260,7 @@ async def build_signed_params(
def _refresh_mixin_key_sync(client: httpx.Client) -> str:
resp = client.get(_BILIBILI_API_NAV)
resp.raise_for_status()
- payload = resp.json()
-
- if not isinstance(payload, dict):
- raise ValueError("nav 接口返回格式异常")
-
- code = int(payload.get("code", -1))
- if code not in (0, -101):
- message = payload.get("message", "未知错误")
- raise ValueError(f"获取 wbi key 失败: {message} (code={code})")
-
- data = payload.get("data")
- if not isinstance(data, dict):
- raise ValueError("nav 接口 data 字段异常")
-
- wbi_img = data.get("wbi_img")
- if not isinstance(wbi_img, dict):
- raise ValueError("nav 接口 wbi_img 字段缺失")
-
- img_url = str(wbi_img.get("img_url", "")).strip()
- sub_url = str(wbi_img.get("sub_url", "")).strip()
- if not img_url or not sub_url:
- raise ValueError("nav 接口未返回有效的 img_url/sub_url")
-
- img_key = _extract_key_from_url(img_url)
- sub_key = _extract_key_from_url(sub_url)
- if not img_key or not sub_key:
- raise ValueError("无法提取有效的 img_key/sub_key")
-
- return _compute_mixin_key(img_key, sub_key)
+ return _mixin_key_from_nav_payload(resp.json())
def get_mixin_key_sync(
diff --git a/src/Undefined/cognitive/historian/__init__.py b/src/Undefined/cognitive/historian/__init__.py
new file mode 100644
index 00000000..0e1fce98
--- /dev/null
+++ b/src/Undefined/cognitive/historian/__init__.py
@@ -0,0 +1,5 @@
+"""认知史官 Worker 包。"""
+
+from Undefined.cognitive.historian.worker import HistorianWorker
+
+__all__ = ["HistorianWorker"]
diff --git a/src/Undefined/cognitive/historian/helpers.py b/src/Undefined/cognitive/historian/helpers.py
new file mode 100644
index 00000000..e1bda7da
--- /dev/null
+++ b/src/Undefined/cognitive/historian/helpers.py
@@ -0,0 +1,85 @@
+"""Historian 辅助函数。"""
+
+from __future__ import annotations
+
+import logging
+import re
+from datetime import datetime, timezone
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+_MAX_LOG_PREVIEW_LEN = 200
+
+
+def _preview_text(text: str, max_len: int = _MAX_LOG_PREVIEW_LEN) -> str:
+ compact = re.sub(r"\s+", " ", str(text or "")).strip()
+ if len(compact) <= max_len:
+ return compact
+ return f"{compact[:max_len]}..."
+
+
+def _extract_frontmatter_name(markdown: str) -> str:
+ text = str(markdown or "")
+ if not text.startswith("---"):
+ return ""
+ try:
+ import yaml
+
+ parts = text[3:].split("---", 1)
+ if len(parts) != 2:
+ return ""
+ frontmatter = yaml.safe_load(parts[0])
+ if not isinstance(frontmatter, dict):
+ return ""
+ value = frontmatter.get("name")
+ return str(value).strip() if value is not None else ""
+ except Exception:
+ return ""
+
+
+def _escape_braces(text: str) -> str:
+ value = str(text or "")
+ return value.replace("{", "{{").replace("}", "}}")
+
+
+def _resolve_timestamp_epoch(job: dict[str, Any]) -> int:
+ raw_epoch = job.get("timestamp_epoch")
+ if isinstance(raw_epoch, (int, float)):
+ return int(raw_epoch)
+ if isinstance(raw_epoch, str):
+ try:
+ return int(float(raw_epoch.strip()))
+ except Exception:
+ pass
+
+ for key in ("timestamp_utc", "timestamp_local"):
+ raw_value = job.get(key)
+ if not isinstance(raw_value, str):
+ continue
+ text = raw_value.strip()
+ if not text:
+ continue
+ try:
+ parsed = datetime.fromisoformat(text.replace("Z", "+00:00"))
+ if parsed.tzinfo is None:
+ parsed = parsed.replace(tzinfo=timezone.utc)
+ return int(parsed.timestamp())
+ except Exception:
+ continue
+
+ return int(datetime.now(timezone.utc).timestamp())
+
+
+def _coerce_bool(value: Any) -> bool:
+ if isinstance(value, bool):
+ return value
+ if isinstance(value, (int, float)):
+ return bool(value)
+ if isinstance(value, str):
+ normalized = value.strip().lower()
+ if normalized in {"1", "true", "yes", "y", "on"}:
+ return True
+ if normalized in {"0", "false", "no", "n", "off", ""}:
+ return False
+ return False
diff --git a/src/Undefined/cognitive/historian/tools.py b/src/Undefined/cognitive/historian/tools.py
new file mode 100644
index 00000000..83cc3018
--- /dev/null
+++ b/src/Undefined/cognitive/historian/tools.py
@@ -0,0 +1,78 @@
+"""Historian LLM 工具定义。"""
+
+from __future__ import annotations
+
+_REWRITE_TOOL = {
+ "type": "function",
+ "function": {
+ "name": "submit_rewrite",
+ "description": "提交绝对化改写后的事件文本",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "text": {"type": "string", "description": "改写后的纯文本"},
+ },
+ "required": ["text"],
+ },
+ },
+}
+_READ_PROFILE_TOOL = {
+ "type": "function",
+ "function": {
+ "name": "read_profile",
+ "description": "读取指定实体的当前侧写内容",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "entity_type": {
+ "type": "string",
+ "enum": ["user", "group"],
+ "description": "实体类型：user 或 group",
+ },
+ "entity_id": {
+ "type": "string",
+ "description": "实体 ID（用户 QQ 号或群号）",
+ },
+ },
+ "required": ["entity_type", "entity_id"],
+ },
+ },
+}
+_PROFILE_TOOL = {
+ "type": "function",
+ "function": {
+ "name": "update_profile",
+ "description": "更新用户/群侧写。调用前必须先用 read_profile 查看当前内容",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "entity_type": {
+ "type": "string",
+ "enum": ["user", "group"],
+ "description": "实体类型：user 或 group",
+ },
+ "entity_id": {
+ "type": "string",
+ "description": "实体 ID（用户 QQ 号或群号）",
+ },
+ "skip": {
+ "type": "boolean",
+ "description": "是否跳过更新；当新信息不稳定/不足时为 true",
+ },
+ "skip_reason": {
+ "type": "string",
+ "description": "跳过原因",
+ },
+ "name": {"type": "string", "description": "用户/群名称"},
+ "tags": {
+ "type": "array",
+ "items": {"type": "string"},
+ "maxItems": 10,
+ "description": "身份级标签（角色/核心领域），最多 10 个，不写话题",
+ },
+ "summary": {"type": "string", "description": "侧写正文（Markdown）"},
+ },
+ "required": ["entity_type", "entity_id", "skip", "name", "tags", "summary"],
+ },
+ },
+}
diff --git a/src/Undefined/cognitive/historian.py b/src/Undefined/cognitive/historian/worker.py
similarity index 86%
rename from src/Undefined/cognitive/historian.py
rename to src/Undefined/cognitive/historian/worker.py
index fda333c3..30ecf43c 100644
--- a/src/Undefined/cognitive/historian.py
+++ b/src/Undefined/cognitive/historian/worker.py
@@ -1,170 +1,30 @@
-"""后台史官 Worker，轮询队列处理任务。"""
+"""HistorianWorker 实现。"""
from __future__ import annotations
import asyncio
import json
import logging
-import re
-from datetime import datetime, timezone
+from datetime import datetime
from typing import Any, Callable
from Undefined.ai.transports.openai_transport import RESPONSES_OUTPUT_ITEMS_KEY
from Undefined.utils.tool_calls import extract_required_tool_call_arguments
-logger = logging.getLogger(__name__)
-
-_MAX_LOG_PREVIEW_LEN = 200
-
-_REWRITE_TOOL = {
- "type": "function",
- "function": {
- "name": "submit_rewrite",
- "description": "提交绝对化改写后的事件文本",
- "parameters": {
- "type": "object",
- "properties": {
- "text": {"type": "string", "description": "改写后的纯文本"},
- },
- "required": ["text"],
- },
- },
-}
-
-_READ_PROFILE_TOOL = {
- "type": "function",
- "function": {
- "name": "read_profile",
- "description": "读取指定实体的当前侧写内容",
- "parameters": {
- "type": "object",
- "properties": {
- "entity_type": {
- "type": "string",
- "enum": ["user", "group"],
- "description": "实体类型：user 或 group",
- },
- "entity_id": {
- "type": "string",
- "description": "实体 ID（用户 QQ 号或群号）",
- },
- },
- "required": ["entity_type", "entity_id"],
- },
- },
-}
-
-_PROFILE_TOOL = {
- "type": "function",
- "function": {
- "name": "update_profile",
- "description": "更新用户/群侧写。调用前必须先用 read_profile 查看当前内容",
- "parameters": {
- "type": "object",
- "properties": {
- "entity_type": {
- "type": "string",
- "enum": ["user", "group"],
- "description": "实体类型：user 或 group",
- },
- "entity_id": {
- "type": "string",
- "description": "实体 ID（用户 QQ 号或群号）",
- },
- "skip": {
- "type": "boolean",
- "description": "是否跳过更新；当新信息不稳定/不足时为 true",
- },
- "skip_reason": {
- "type": "string",
- "description": "跳过原因",
- },
- "name": {"type": "string", "description": "用户/群名称"},
- "tags": {
- "type": "array",
- "items": {"type": "string"},
- "maxItems": 10,
- "description": "身份级标签（角色/核心领域），最多 10 个，不写话题",
- },
- "summary": {"type": "string", "description": "侧写正文（Markdown）"},
- },
- "required": ["entity_type", "entity_id", "skip", "name", "tags", "summary"],
- },
- },
-}
-
-
-def _preview_text(text: str, max_len: int = _MAX_LOG_PREVIEW_LEN) -> str:
- compact = re.sub(r"\s+", " ", str(text or "")).strip()
- if len(compact) <= max_len:
- return compact
- return f"{compact[:max_len]}..."
-
-
-def _extract_frontmatter_name(markdown: str) -> str:
- text = str(markdown or "")
- if not text.startswith("---"):
- return ""
- try:
- import yaml
-
- parts = text[3:].split("---", 1)
- if len(parts) != 2:
- return ""
- frontmatter = yaml.safe_load(parts[0])
- if not isinstance(frontmatter, dict):
- return ""
- value = frontmatter.get("name")
- return str(value).strip() if value is not None else ""
- except Exception:
- return ""
-
-
-def _escape_braces(text: str) -> str:
- value = str(text or "")
- return value.replace("{", "{{").replace("}", "}}")
+from Undefined.cognitive.historian.helpers import (
+ _coerce_bool,
+ _escape_braces,
+ _extract_frontmatter_name,
+ _preview_text,
+ _resolve_timestamp_epoch,
+)
+from Undefined.cognitive.historian.tools import (
+ _PROFILE_TOOL,
+ _READ_PROFILE_TOOL,
+ _REWRITE_TOOL,
+)
-
-def _resolve_timestamp_epoch(job: dict[str, Any]) -> int:
- raw_epoch = job.get("timestamp_epoch")
- if isinstance(raw_epoch, (int, float)):
- return int(raw_epoch)
- if isinstance(raw_epoch, str):
- try:
- return int(float(raw_epoch.strip()))
- except Exception:
- pass
-
- for key in ("timestamp_utc", "timestamp_local"):
- raw_value = job.get(key)
- if not isinstance(raw_value, str):
- continue
- text = raw_value.strip()
- if not text:
- continue
- try:
- parsed = datetime.fromisoformat(text.replace("Z", "+00:00"))
- if parsed.tzinfo is None:
- parsed = parsed.replace(tzinfo=timezone.utc)
- return int(parsed.timestamp())
- except Exception:
- continue
-
- return int(datetime.now(timezone.utc).timestamp())
-
-
-def _coerce_bool(value: Any) -> bool:
- if isinstance(value, bool):
- return value
- if isinstance(value, (int, float)):
- return bool(value)
- if isinstance(value, str):
- normalized = value.strip().lower()
- if normalized in {"1", "true", "yes", "y", "on"}:
- return True
- if normalized in {"0", "false", "no", "n", "off", ""}:
- return False
- return False
+logger = logging.getLogger(__name__)
class HistorianWorker:
@@ -307,7 +167,6 @@ async def _process_job(self, job_id: str, job: dict[str, Any]) -> None:
len(job.get("profile_targets", []) or []),
)
- # 兼容旧版：优先 observations，fallback new_info
raw_observations = (
job.get("observations")
if "observations" in job
@@ -363,7 +222,6 @@ async def _process_job(self, job_id: str, job: dict[str, Any]) -> None:
len(canonical),
)
- # 侧写合并：传入所有 canonical 文本
has_obs = (
job.get("has_observations")
if "has_observations" in job
@@ -413,9 +271,7 @@ async def _rewrite(
) -> str:
from Undefined.utils.resources import read_text_resource
- # 向后兼容：优先 memo，fallback action_summary
memo = str(job.get("memo") if "memo" in job else job.get("action_summary", ""))
- # 向后兼容：优先 observations，fallback new_info
observations = str(
job.get("observations")
if "observations" in job
@@ -537,7 +393,6 @@ def _resolve_profile_targets(self, job: dict[str, Any]) -> list[dict[str, str]]:
if targets:
return targets
- # 向后兼容旧任务：沿用单目标策略。
entity_type = "group" if str(job.get("group_id", "")).strip() else "user"
entity_id = str(
job.get("group_id") or job.get("user_id") or job.get("sender_id", "")
@@ -768,7 +623,6 @@ async def _merge_profile_target(
preferred_name = str(target.get("preferred_name", "")).strip()
- # 检索该实体的历史事件作为 merge 参考
observations_raw = job.get("observations", job.get("new_info", []))
observations_text = (
"\n".join(observations_raw)
@@ -884,7 +738,6 @@ async def _merge_profile_target(
)
break
- # 追加 assistant 轮次
assistant_msg: dict[str, Any] = {
"role": "assistant",
"tool_calls": tool_calls,
diff --git a/src/Undefined/cognitive/job_queue.py b/src/Undefined/cognitive/job_queue.py
index 7c5eba61..e1b036d2 100644
--- a/src/Undefined/cognitive/job_queue.py
+++ b/src/Undefined/cognitive/job_queue.py
@@ -23,7 +23,6 @@ def __init__(self, base_path: str | Path) -> None:
self._failed_dir = base / "failed"
for d in (self._pending_dir, self._processing_dir, self._failed_dir):
d.mkdir(parents=True, exist_ok=True)
- # 启动时清理所有遗留的 lock 文件
stale_lock_count = 0
for d in (self._pending_dir, self._processing_dir, self._failed_dir):
for lock_file in d.glob("*.lock"):
@@ -65,7 +64,6 @@ def _pick() -> tuple[str, dict[str, Any]] | None:
with open(dst, "r", encoding="utf-8") as fh:
data = json.load(fh)
- # 清理遗留的 lock 文件
lock_file = f.with_name(f"{f.name}.lock")
lock_file.unlink(missing_ok=True)
return f.stem, data
@@ -123,7 +121,6 @@ async def requeue(self, job_id: str, error: str) -> None:
data = await read_json(src) or {}
data["_retry_count"] = data.get("_retry_count", 0) + 1
data["_last_error"] = error
- # 先原子更新 processing 内容，再原子移动到 pending
await write_json(src, data)
await asyncio.to_thread(lambda: os.replace(src, dst))
logger.info(
diff --git a/src/Undefined/cognitive/profile_storage.py b/src/Undefined/cognitive/profile_storage.py
index 90585fae..d1f61215 100644
--- a/src/Undefined/cognitive/profile_storage.py
+++ b/src/Undefined/cognitive/profile_storage.py
@@ -74,7 +74,6 @@ def _write() -> None:
p.parent.mkdir(parents=True, exist_ok=True)
hist_dir.mkdir(parents=True, exist_ok=True)
- # 备份现有版本
if p.exists():
ts = datetime.now().strftime("%Y%m%d%H%M%S%f")
(hist_dir / f"{ts}.md").write_text(
@@ -87,7 +86,6 @@ def _write() -> None:
ts,
)
- # 原子写入
fd, tmp = tempfile.mkstemp(
prefix=f".{p.name}.", suffix=".tmp", dir=str(p.parent)
)
@@ -102,7 +100,6 @@ def _write() -> None:
pass
raise
- # 清理旧快照
snapshots = sorted(hist_dir.glob("*.md"))
for old in snapshots[: max(0, len(snapshots) - self._revision_keep)]:
try:
@@ -140,7 +137,6 @@ def _list() -> list[str]:
def _sanitize_profile(content: str, entity_type: str, entity_id: str) -> str:
import yaml
- # 剥离 ```markdown / ``` 包裹
stripped = content.strip()
if stripped.startswith("```"):
lines = stripped.splitlines()
@@ -151,7 +147,6 @@ def _sanitize_profile(content: str, entity_type: str, entity_id: str) -> str:
if end:
stripped = "\n".join(lines[1:end])
- # 解析 frontmatter
if stripped.startswith("---"):
parts = stripped[3:].split("---", 1)
if len(parts) == 2:
diff --git a/src/Undefined/cognitive/service/__init__.py b/src/Undefined/cognitive/service/__init__.py
new file mode 100644
index 00000000..45878197
--- /dev/null
+++ b/src/Undefined/cognitive/service/__init__.py
@@ -0,0 +1,5 @@
+"""认知记忆服务包。"""
+
+from Undefined.cognitive.service.service import CognitiveService
+
+__all__ = ["CognitiveService"]
diff --git a/src/Undefined/cognitive/service/helpers.py b/src/Undefined/cognitive/service/helpers.py
new file mode 100644
index 00000000..74752c48
--- /dev/null
+++ b/src/Undefined/cognitive/service/helpers.py
@@ -0,0 +1,169 @@
+"""认知服务辅助函数。"""
+
+from __future__ import annotations
+
+from datetime import datetime, timezone
+from typing import Any
+
+from Undefined.utils.coerce import safe_float
+
+
+def _parse_iso_to_epoch_seconds(value: Any) -> int | None:
+ if not isinstance(value, str):
+ return None
+ text = value.strip()
+ if not text:
+ return None
+ try:
+ parsed = datetime.fromisoformat(text.replace("Z", "+00:00"))
+ except Exception:
+ return None
+ if parsed.tzinfo is None:
+ parsed = parsed.replace(tzinfo=timezone.utc)
+ return int(parsed.timestamp())
+
+
+def _compose_where(clauses: list[dict[str, Any]]) -> dict[str, Any] | None:
+ if not clauses:
+ return None
+ if len(clauses) == 1:
+ return clauses[0]
+ return {"$and": clauses}
+
+
+def _event_base_score(item: dict[str, Any]) -> float:
+ # 优先 rerank 分，否则用 1-distance 作为相似度
+ rerank_score = item.get("rerank_score")
+ if isinstance(rerank_score, (int, float)):
+ return max(0.0, float(rerank_score))
+ if isinstance(rerank_score, str):
+ try:
+ return max(0.0, float(rerank_score.strip()))
+ except Exception:
+ pass
+ similarity = 1.0 - safe_float(item.get("distance"), default=1.0)
+ if similarity < 0.0:
+ return 0.0
+ if similarity > 1.0:
+ return 1.0
+ return similarity
+
+
+def _event_timestamp_epoch(metadata: Any) -> float:
+ if not isinstance(metadata, dict):
+ return float("-inf")
+ raw_epoch = metadata.get("timestamp_epoch")
+ if isinstance(raw_epoch, (int, float)):
+ return float(raw_epoch)
+ if isinstance(raw_epoch, str):
+ try:
+ return float(raw_epoch.strip())
+ except Exception:
+ pass
+ for key in ("timestamp_utc", "timestamp_local"):
+ parsed = _parse_iso_to_epoch_seconds(metadata.get(key))
+ if parsed is not None:
+ return float(parsed)
+ return float("-inf")
+
+
+def _event_dedupe_key(item: dict[str, Any]) -> tuple[str, str, str, str, str, str]:
+ metadata = item.get("metadata")
+ if not isinstance(metadata, dict):
+ metadata = {}
+ return (
+ str(item.get("document", "")).strip(),
+ str(metadata.get("timestamp_epoch", "")).strip(),
+ str(metadata.get("timestamp_local", "")).strip(),
+ str(metadata.get("group_id", "")).strip(),
+ str(metadata.get("sender_id", "")).strip(),
+ str(metadata.get("user_id", "")).strip(),
+ )
+
+
+def _resolve_auto_request_type(
+ *,
+ request_type: str | None,
+ group_id: str,
+ user_id: str,
+ sender_id: str,
+) -> str:
+ normalized = str(request_type or "").strip().lower()
+ if normalized in {"group", "private"}:
+ return normalized
+ if group_id:
+ return "group"
+ if sender_id or user_id:
+ return "private"
+ return ""
+
+
+def _parse_profile_markdown(markdown: str) -> tuple[dict[str, Any], str] | None:
+ text = str(markdown or "")
+ if not text.startswith("---"):
+ return None
+ try:
+ import yaml
+
+ parts = text[3:].split("---", 1)
+ if len(parts) != 2:
+ return None
+ frontmatter = yaml.safe_load(parts[0])
+ if not isinstance(frontmatter, dict):
+ return None
+ body = parts[1].lstrip("\n")
+ return frontmatter, body
+ except Exception:
+ return None
+
+
+def _serialize_profile_markdown(frontmatter: dict[str, Any], body: str) -> str:
+ import yaml
+
+ return f"---\n{yaml.dump(frontmatter, allow_unicode=True)}---\n{body}"
+
+
+def _normalize_profile_tags(value: Any) -> list[str]:
+ if not isinstance(value, list):
+ return []
+ return [str(item).strip() for item in value if str(item).strip()]
+
+
+def _current_profile_name(entity_type: str, frontmatter: dict[str, Any]) -> str:
+ if entity_type == "user":
+ return str(frontmatter.get("nickname") or frontmatter.get("name") or "").strip()
+ return str(frontmatter.get("group_name") or frontmatter.get("name") or "").strip()
+
+
+def _build_profile_vector_payload(
+ *,
+ entity_type: str,
+ entity_id: str,
+ effective_name: str,
+ tags: list[str],
+ summary: str,
+) -> tuple[str, dict[str, Any]]:
+ profile_doc_lines: list[str] = []
+ if entity_type == "user":
+ profile_doc_lines.append(f"昵称: {effective_name}")
+ profile_doc_lines.append(f"QQ号: {entity_id}")
+ else:
+ profile_doc_lines.append(f"群名: {effective_name}")
+ profile_doc_lines.append(f"群号: {entity_id}")
+ if tags:
+ profile_doc_lines.append(f"标签: {', '.join(tags)}")
+ profile_doc_lines.append(summary)
+ profile_doc = "\n".join(line for line in profile_doc_lines if line.strip())
+
+ metadata: dict[str, Any] = {
+ "entity_type": entity_type,
+ "entity_id": entity_id,
+ "name": effective_name,
+ }
+ if entity_type == "user":
+ metadata["nickname"] = effective_name
+ metadata["qq"] = entity_id
+ else:
+ metadata["group_name"] = effective_name
+ metadata["group_id"] = entity_id
+ return profile_doc, metadata
diff --git a/src/Undefined/cognitive/service.py b/src/Undefined/cognitive/service/service.py
similarity index 84%
rename from src/Undefined/cognitive/service.py
rename to src/Undefined/cognitive/service/service.py
index 13cf3f1c..c7a69e9d 100644
--- a/src/Undefined/cognitive/service.py
+++ b/src/Undefined/cognitive/service/service.py
@@ -1,4 +1,4 @@
-"""认知记忆服务门面。"""
+"""认知记忆服务实现。"""
from __future__ import annotations
@@ -10,171 +10,24 @@
from Undefined.context import RequestContext
from Undefined.utils.coerce import safe_float
-
-logger = logging.getLogger(__name__)
+from Undefined.cognitive.service.helpers import (
+ _build_profile_vector_payload,
+ _compose_where,
+ _current_profile_name,
+ _event_base_score,
+ _event_dedupe_key,
+ _event_timestamp_epoch,
+ _normalize_profile_tags,
+ _parse_iso_to_epoch_seconds,
+ _parse_profile_markdown,
+ _resolve_auto_request_type,
+ _serialize_profile_markdown,
+)
if TYPE_CHECKING:
from Undefined.knowledge.runtime import RetrievalRuntime
-
-def _parse_iso_to_epoch_seconds(value: Any) -> int | None:
- if not isinstance(value, str):
- return None
- text = value.strip()
- if not text:
- return None
- try:
- parsed = datetime.fromisoformat(text.replace("Z", "+00:00"))
- except Exception:
- return None
- if parsed.tzinfo is None:
- parsed = parsed.replace(tzinfo=timezone.utc)
- return int(parsed.timestamp())
-
-
-def _compose_where(clauses: list[dict[str, Any]]) -> dict[str, Any] | None:
- if not clauses:
- return None
- if len(clauses) == 1:
- return clauses[0]
- return {"$and": clauses}
-
-
-def _event_base_score(item: dict[str, Any]) -> float:
- rerank_score = item.get("rerank_score")
- if isinstance(rerank_score, (int, float)):
- return max(0.0, float(rerank_score))
- if isinstance(rerank_score, str):
- try:
- return max(0.0, float(rerank_score.strip()))
- except Exception:
- pass
- similarity = 1.0 - safe_float(item.get("distance"), default=1.0)
- if similarity < 0.0:
- return 0.0
- if similarity > 1.0:
- return 1.0
- return similarity
-
-
-def _event_timestamp_epoch(metadata: Any) -> float:
- if not isinstance(metadata, dict):
- return float("-inf")
- raw_epoch = metadata.get("timestamp_epoch")
- if isinstance(raw_epoch, (int, float)):
- return float(raw_epoch)
- if isinstance(raw_epoch, str):
- try:
- return float(raw_epoch.strip())
- except Exception:
- pass
- for key in ("timestamp_utc", "timestamp_local"):
- parsed = _parse_iso_to_epoch_seconds(metadata.get(key))
- if parsed is not None:
- return float(parsed)
- return float("-inf")
-
-
-def _event_dedupe_key(item: dict[str, Any]) -> tuple[str, str, str, str, str, str]:
- metadata = item.get("metadata")
- if not isinstance(metadata, dict):
- metadata = {}
- return (
- str(item.get("document", "")).strip(),
- str(metadata.get("timestamp_epoch", "")).strip(),
- str(metadata.get("timestamp_local", "")).strip(),
- str(metadata.get("group_id", "")).strip(),
- str(metadata.get("sender_id", "")).strip(),
- str(metadata.get("user_id", "")).strip(),
- )
-
-
-def _resolve_auto_request_type(
- *,
- request_type: str | None,
- group_id: str,
- user_id: str,
- sender_id: str,
-) -> str:
- normalized = str(request_type or "").strip().lower()
- if normalized in {"group", "private"}:
- return normalized
- if group_id:
- return "group"
- if sender_id or user_id:
- return "private"
- return ""
-
-
-def _parse_profile_markdown(markdown: str) -> tuple[dict[str, Any], str] | None:
- text = str(markdown or "")
- if not text.startswith("---"):
- return None
- try:
- import yaml
-
- parts = text[3:].split("---", 1)
- if len(parts) != 2:
- return None
- frontmatter = yaml.safe_load(parts[0])
- if not isinstance(frontmatter, dict):
- return None
- body = parts[1].lstrip("\n")
- return frontmatter, body
- except Exception:
- return None
-
-
-def _serialize_profile_markdown(frontmatter: dict[str, Any], body: str) -> str:
- import yaml
-
- return f"---\n{yaml.dump(frontmatter, allow_unicode=True)}---\n{body}"
-
-
-def _normalize_profile_tags(value: Any) -> list[str]:
- if not isinstance(value, list):
- return []
- return [str(item).strip() for item in value if str(item).strip()]
-
-
-def _current_profile_name(entity_type: str, frontmatter: dict[str, Any]) -> str:
- if entity_type == "user":
- return str(frontmatter.get("nickname") or frontmatter.get("name") or "").strip()
- return str(frontmatter.get("group_name") or frontmatter.get("name") or "").strip()
-
-
-def _build_profile_vector_payload(
- *,
- entity_type: str,
- entity_id: str,
- effective_name: str,
- tags: list[str],
- summary: str,
-) -> tuple[str, dict[str, Any]]:
- profile_doc_lines: list[str] = []
- if entity_type == "user":
- profile_doc_lines.append(f"昵称: {effective_name}")
- profile_doc_lines.append(f"QQ号: {entity_id}")
- else:
- profile_doc_lines.append(f"群名: {effective_name}")
- profile_doc_lines.append(f"群号: {entity_id}")
- if tags:
- profile_doc_lines.append(f"标签: {', '.join(tags)}")
- profile_doc_lines.append(summary)
- profile_doc = "\n".join(line for line in profile_doc_lines if line.strip())
-
- metadata: dict[str, Any] = {
- "entity_type": entity_type,
- "entity_id": entity_id,
- "name": effective_name,
- }
- if entity_type == "user":
- metadata["nickname"] = effective_name
- metadata["qq"] = entity_id
- else:
- metadata["group_name"] = effective_name
- metadata["group_id"] = entity_id
- return profile_doc, metadata
+logger = logging.getLogger(__name__)
class CognitiveService:
@@ -320,7 +173,6 @@ def _merge_weighted_events(
safe_group_boost = max(0.0, float(current_group_boost))
seen_keys: set[tuple[str, str, str, str, str, str]] = set()
# 排序主键优先使用“作用域内原始排名”（已含 time_decay/mmr/rerank 效果），
- # 再使用相似度分值兜底，避免跨 scope 合并时打乱衰减后的顺序。
scored_items: list[
tuple[float, float, float, float, float, int, dict[str, Any]]
] = []
@@ -547,7 +399,6 @@ async def enqueue_job(
else str(context.get("request_id", "")).strip()
)
if not safe_request_id:
- # 最终兜底由 JobQueue 生成 request_id。
safe_request_id = ""
end_seq_raw = context.get("_end_seq", 0)
@@ -699,7 +550,6 @@ async def build_context(
getattr(config, "auto_top_k", 5),
)
- # 用户侧写（优先 sender_id，与 enqueue_job 写入侧一致）
uid = safe_sender_id or safe_user_id
if uid:
profile = await self._profile_storage.read_profile("user", uid)
@@ -707,7 +557,6 @@ async def build_context(
label = f"{sender_name}（UID: {uid}）" if sender_name else f"UID: {uid}"
parts.append(f"## 用户侧写 — {label}\n{profile}")
- # 群聊侧写
if safe_group_id:
gprofile = await self._profile_storage.read_profile("group", safe_group_id)
if gprofile:
diff --git a/src/Undefined/cognitive/vector_store.py b/src/Undefined/cognitive/vector_store.py
index 48b80eb1..076b8a9f 100644
--- a/src/Undefined/cognitive/vector_store.py
+++ b/src/Undefined/cognitive/vector_store.py
@@ -129,7 +129,6 @@ def _mmr_select(
if n <= top_k:
return np.arange(n)
- # 预计算 query-doc 相关性（cosine similarity）
query_norm = np.sqrt(np.sum(query_embedding * query_embedding))
relevance = np.empty(n, dtype=np.float64)
norms = np.empty(n, dtype=np.float64)
@@ -161,7 +160,6 @@ def _mmr_select(
return selected[:step]
selected[step] = best_idx
chosen[best_idx] = True
- # 更新 max_sim_to_selected：新选中项与所有未选中项的相似度
if norms[best_idx] > 0.0:
for j in range(n):
if not chosen[j] and norms[j] > 0.0:
@@ -446,7 +444,6 @@ async def _query(
query_embedding=query_embedding,
)
embed_duration = time.perf_counter() - embed_started
- # 重排要求候选数 > 最终返回数，否则重排无意义
use_reranker = bool(reranker) and safe_multiplier >= 2
if reranker and safe_multiplier < 2:
logger.warning(
diff --git a/src/Undefined/config/__init__.py b/src/Undefined/config/__init__.py
index 7242f3cf..4fe960fb 100644
--- a/src/Undefined/config/__init__.py
+++ b/src/Undefined/config/__init__.py
@@ -2,7 +2,7 @@
from typing import Optional
-from .loader import Config, WebUISettings, load_webui_settings
+from .config_class import Config, ConfigBuilder
from .manager import ConfigManager
from .models import (
APIConfig,
@@ -19,9 +19,11 @@
SecurityModelConfig,
VisionModelConfig,
)
+from .webui_settings import WebUISettings, load_webui_settings
__all__ = [
"Config",
+ "ConfigBuilder",
"ChatModelConfig",
"VisionModelConfig",
"SecurityModelConfig",
@@ -37,11 +39,11 @@
"RenderCacheConfig",
"get_config",
"get_config_manager",
+ "set_config",
"load_webui_settings",
"WebUISettings",
]
-# 全局配置实例
_config: Optional[Config] = None
_config_manager: Optional[ConfigManager] = None
@@ -60,3 +62,10 @@ def get_config(strict: bool = True) -> Config:
if _config is None:
_config = get_config_manager().load(strict=strict)
return _config
+
+
+def set_config(config: Config) -> None:
+ """注入 Config 单例（库嵌入 opt-in；CLI / WebUI 启动链不得调用）。"""
+ global _config
+ _config = config
+ get_config_manager().replace(config)
diff --git a/src/Undefined/config/build_config.py b/src/Undefined/config/build_config.py
new file mode 100644
index 00000000..72021e21
--- /dev/null
+++ b/src/Undefined/config/build_config.py
@@ -0,0 +1,57 @@
+"""Build Config from parsed TOML mapping."""
+
+from __future__ import annotations
+
+
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Optional
+
+if TYPE_CHECKING:
+ from .config_class import Config
+
+from .load_sections import (
+ load_access,
+ load_core,
+ load_domains,
+ load_finalize,
+ load_history_skills,
+ load_integrations,
+ load_knowledge,
+ load_logging_tools,
+ load_models,
+ load_network,
+)
+
+
+# 从中间态构建最终对象
+def build_config(
+ data: dict[str, Any],
+ *,
+ strict: bool = True,
+ config_path: Optional[Path] = None,
+) -> "Config":
+ """从已解析的 TOML mapping 构建 Config。"""
+ from .config_class import Config
+
+ # 按依赖顺序分阶段加载：core/knowledge/models 在前，access 等依赖 admin 合并
+ ctx: dict[str, Any] = {}
+ ctx.update(load_core(data))
+ ctx.update(load_knowledge(data))
+ ctx.update(load_models(data))
+ from .model_parsers import _merge_admins
+
+ # 合并 config.toml 与本地 admins.json，超管始终纳入 admin 列表
+ superadmin_qq, admin_qqs = _merge_admins(
+ superadmin_qq=ctx["superadmin_qq"], admin_qqs=ctx["admin_qqs"]
+ )
+ ctx["superadmin_qq"] = superadmin_qq
+ ctx["admin_qqs"] = admin_qqs
+ ctx.update(load_access(data))
+ ctx.update(load_logging_tools(data))
+ ctx.update(load_history_skills(data))
+ ctx.update(load_network(data))
+ ctx.update(load_integrations(data))
+ # domains 含 WebUI/API/认知/合并器等子域，放最后以便前面模型段已就绪
+ ctx.update(load_domains(data, config_path=config_path))
+ load_finalize(ctx, strict=strict) # strict 时校验必填项并打 debug 摘要
+ return Config(**ctx)
diff --git a/src/Undefined/config/config_class.py b/src/Undefined/config/config_class.py
new file mode 100644
index 00000000..b29a786e
--- /dev/null
+++ b/src/Undefined/config/config_class.py
@@ -0,0 +1,583 @@
+"""Config dataclass and instance methods."""
+
+from __future__ import annotations
+
+
+from dataclasses import dataclass, field as dataclass_field, fields
+from pathlib import Path
+from typing import Any, Optional
+
+from .admin import load_local_admins, save_local_admins
+from .domain_parsers import _update_dataclass
+from .models import (
+ AgentModelConfig,
+ APIConfig,
+ ChatModelConfig,
+ CognitiveConfig,
+ EmbeddingModelConfig,
+ GrokModelConfig,
+ ImageGenConfig,
+ ImageGenModelConfig,
+ MemeConfig,
+ MessageBatcherConfig,
+ NagaConfig,
+ RenderCacheConfig,
+ RerankModelConfig,
+ SecurityModelConfig,
+ VisionModelConfig,
+)
+from .toml_io import _load_env, load_toml_data
+
+
+@dataclass
+class Config:
+ """应用配置"""
+
+ bot_qq: int
+ superadmin_qq: int
+ admin_qqs: list[int]
+ # 访问控制模式：off / blacklist / allowlist
+ access_mode: str
+ # 访问控制（会话白名单 + 黑名单）
+ allowed_group_ids: list[int]
+ blocked_group_ids: list[int]
+ allowed_private_ids: list[int]
+ blocked_private_ids: list[int]
+ # 是否允许超级管理员在私聊中绕过 allowed_private_ids（仅私聊收发）
+ superadmin_bypass_allowlist: bool
+ # 是否允许超级管理员在私聊中绕过 blocked_private_ids（仅私聊收发）
+ superadmin_bypass_private_blacklist: bool
+ forward_proxy_qq: int | None
+ process_every_message: bool
+ process_private_message: bool
+ process_poke_message: bool
+ keyword_reply_enabled: bool
+ repeat_enabled: bool
+ repeat_threshold: int
+ repeat_cooldown_minutes: int
+ inverted_question_enabled: bool
+ context_recent_messages_limit: int
+ ai_request_max_retries: int
+ missing_tool_call_retries: int
+ nagaagent_mode_enabled: bool
+ onebot_ws_url: str
+ onebot_token: str
+ chat_model: ChatModelConfig
+ vision_model: VisionModelConfig
+ security_model_enabled: bool
+ security_model: SecurityModelConfig
+ naga_model: SecurityModelConfig
+ agent_model: AgentModelConfig
+ historian_model: AgentModelConfig
+ summary_model: AgentModelConfig
+ summary_model_configured: bool
+ grok_model: GrokModelConfig
+ model_pool_enabled: bool
+ log_level: str
+ log_file_path: str
+ log_max_size: int
+ log_backup_count: int
+ log_tty_enabled: bool
+ log_thinking: bool
+ tools_dot_delimiter: str
+ tools_description_truncate_enabled: bool
+ tools_description_max_len: int
+ tools_sanitize_verbose: bool
+ tools_description_preview_len: int
+ easter_egg_agent_call_message_mode: str
+ token_usage_max_size_mb: int
+ token_usage_max_archives: int
+ token_usage_max_total_mb: int
+ token_usage_archive_prune_mode: str
+ history_max_records: int
+ history_filtered_result_limit: int
+ history_search_scan_limit: int
+ history_summary_fetch_limit: int
+ history_summary_time_fetch_limit: int
+ history_onebot_fetch_limit: int
+ history_group_analysis_limit: int
+ attachment_remote_download_max_size_mb: int
+ attachment_cache_max_total_size_mb: int
+ attachment_cache_max_records: int
+ attachment_cache_max_age_days: int
+ attachment_url_reference_max_records: int
+ attachment_url_max_length: int
+ skills_hot_reload: bool
+ skills_hot_reload_interval: float
+ skills_hot_reload_debounce: float
+ agent_intro_autogen_enabled: bool
+ agent_intro_autogen_queue_interval: float
+ agent_intro_autogen_max_tokens: int
+ agent_intro_hash_path: str
+ searxng_url: str
+ grok_search_enabled: bool
+ use_proxy: bool
+ http_proxy: str
+ https_proxy: str
+ network_request_timeout: float
+ network_request_retries: int
+ render_browser_max_concurrency: int
+ api_xxapi_base_url: str
+ api_xingzhige_base_url: str
+ api_jkyai_base_url: str
+ api_seniverse_base_url: str
+ weather_api_key: str
+ xxapi_api_token: str
+ mcp_config_path: str
+ prefetch_tools: list[str]
+ prefetch_tools_hide: bool
+ webui_url: str
+ webui_port: int
+ webui_password: str
+ api: APIConfig
+ # Code Delivery Agent
+ code_delivery_enabled: bool
+ code_delivery_task_root: str
+ code_delivery_docker_image: str
+ code_delivery_container_name_prefix: str
+ code_delivery_container_name_suffix: str
+ code_delivery_command_timeout: int
+ code_delivery_max_command_output: int
+ code_delivery_default_archive_format: str
+ code_delivery_max_archive_size_mb: int
+ code_delivery_cleanup_on_finish: bool
+ code_delivery_cleanup_on_start: bool
+ code_delivery_llm_max_retries: int
+ code_delivery_notify_on_llm_failure: bool
+ code_delivery_container_memory_limit: str
+ code_delivery_container_cpu_limit: str
+ code_delivery_command_blacklist: list[str]
+ # messages 工具集
+ messages_send_text_file_max_size_kb: int
+ messages_send_url_file_max_size_mb: int
+ # 嵌入模型
+ embedding_model: EmbeddingModelConfig
+ rerank_model: RerankModelConfig
+ # 知识库
+ knowledge_enabled: bool
+ knowledge_base_dir: str
+ knowledge_auto_scan: bool
+ knowledge_auto_embed: bool
+ knowledge_scan_interval: float
+ knowledge_embed_batch_size: int
+ knowledge_chunk_size: int
+ knowledge_chunk_overlap: int
+ knowledge_default_top_k: int
+ knowledge_enable_rerank: bool
+ knowledge_rerank_top_k: int
+ # Bilibili 视频提取
+ bilibili_auto_extract_enabled: bool
+ bilibili_cookie: str
+ bilibili_prefer_quality: int
+ bilibili_max_duration: int
+ bilibili_max_file_size: int
+ bilibili_oversize_strategy: str
+ bilibili_danmaku_enabled: bool
+ bilibili_danmaku_batch_size: int
+ bilibili_danmaku_max_count: int
+ bilibili_auto_extract_group_ids: list[int]
+ bilibili_auto_extract_private_ids: list[int]
+ # arXiv 论文提取
+ arxiv_auto_extract_enabled: bool
+ arxiv_max_file_size: int
+ arxiv_auto_extract_group_ids: list[int]
+ arxiv_auto_extract_private_ids: list[int]
+ arxiv_auto_extract_max_items: int
+ arxiv_author_preview_limit: int
+ arxiv_summary_preview_chars: int
+ # GitHub 仓库自动提取
+ github_auto_extract_enabled: bool
+ github_request_timeout_seconds: float
+ github_auto_extract_group_ids: list[int]
+ github_auto_extract_private_ids: list[int]
+ github_auto_extract_max_items: int
+ # 认知记忆
+ cognitive: CognitiveConfig
+ # 表情包库
+ memes: MemeConfig
+ # 同 sender 短时多消息合并器
+ message_batcher: MessageBatcherConfig
+ # HTML 渲染结果缓存
+ render_cache: RenderCacheConfig
+ # Naga 集成
+ naga: NagaConfig
+ # 生图工具配置
+ image_gen: ImageGenConfig
+ models_image_gen: ImageGenModelConfig
+ models_image_edit: ImageGenModelConfig
+ _allowed_group_ids_set: set[int] = dataclass_field(
+ default_factory=set,
+ init=False,
+ repr=False,
+ )
+ _blocked_group_ids_set: set[int] = dataclass_field(
+ default_factory=set,
+ init=False,
+ repr=False,
+ )
+ _allowed_private_ids_set: set[int] = dataclass_field(
+ default_factory=set,
+ init=False,
+ repr=False,
+ )
+ _blocked_private_ids_set: set[int] = dataclass_field(
+ default_factory=set,
+ init=False,
+ repr=False,
+ )
+ _bilibili_group_ids_set: set[int] = dataclass_field(
+ default_factory=set,
+ init=False,
+ repr=False,
+ )
+ _bilibili_private_ids_set: set[int] = dataclass_field(
+ default_factory=set,
+ init=False,
+ repr=False,
+ )
+ _arxiv_group_ids_set: set[int] = dataclass_field(
+ default_factory=set,
+ init=False,
+ repr=False,
+ )
+ _arxiv_private_ids_set: set[int] = dataclass_field(
+ default_factory=set,
+ init=False,
+ repr=False,
+ )
+ _github_group_ids_set: set[int] = dataclass_field(
+ default_factory=set,
+ init=False,
+ repr=False,
+ )
+ _github_private_ids_set: set[int] = dataclass_field(
+ default_factory=set,
+ init=False,
+ repr=False,
+ )
+
+ def __post_init__(self) -> None:
+ # 访问控制属于高频热路径，启动后缓存为 set 降低重复构建开销。
+ normalized_mode = str(self.access_mode).strip().lower()
+ if normalized_mode not in {"off", "blacklist", "allowlist", "legacy"}:
+ normalized_mode = "off"
+ self.access_mode = normalized_mode
+ self._allowed_group_ids_set = {int(item) for item in self.allowed_group_ids}
+ self._blocked_group_ids_set = {int(item) for item in self.blocked_group_ids}
+ self._allowed_private_ids_set = {int(item) for item in self.allowed_private_ids}
+ self._blocked_private_ids_set = {int(item) for item in self.blocked_private_ids}
+ self._bilibili_group_ids_set = {
+ int(item) for item in self.bilibili_auto_extract_group_ids
+ }
+ self._bilibili_private_ids_set = {
+ int(item) for item in self.bilibili_auto_extract_private_ids
+ }
+ self._arxiv_group_ids_set = {
+ int(item) for item in self.arxiv_auto_extract_group_ids
+ }
+ self._arxiv_private_ids_set = {
+ int(item) for item in self.arxiv_auto_extract_private_ids
+ }
+ self._github_group_ids_set = {
+ int(item) for item in self.github_auto_extract_group_ids
+ }
+ self._github_private_ids_set = {
+ int(item) for item in self.github_auto_extract_private_ids
+ }
+
+ @classmethod
+ def load(cls, config_path: Optional[Path] = None, strict: bool = True) -> "Config":
+ """从 config.toml 和本地配置加载配置"""
+ from .build_config import build_config
+
+ _load_env() # 先加载 .env，供 _get_value 环境变量回退
+ data = load_toml_data(config_path, strict=strict)
+ return build_config(data, strict=strict, config_path=config_path)
+
+ @classmethod
+ def from_mapping(cls, data: dict[str, Any], *, strict: bool = True) -> "Config":
+ """从内存 mapping 构建配置（无 TOML 文件）。"""
+ from .build_config import build_config
+
+ return build_config(data, strict=strict, config_path=None)
+
+ @classmethod
+ def builder(cls) -> "ConfigBuilder":
+ """返回可链式覆盖字段的配置构建器。"""
+ return ConfigBuilder()
+
+ @property
+ def bilibili_sessdata(self) -> str:
+ """兼容旧字段名，等价于 bilibili_cookie。"""
+ return self.bilibili_cookie
+
+ def allowlist_mode_enabled(self) -> bool:
+ """是否启用白名单限制模式。"""
+
+ return self.access_mode in {"allowlist", "legacy"} and (
+ bool(self.allowed_group_ids) or bool(self.allowed_private_ids)
+ )
+
+ def group_allowlist_enabled(self) -> bool:
+ """群聊白名单是否生效（显式 allowlist 模式按维度独立控制）。"""
+
+ return bool(self.allowed_group_ids)
+
+ def private_allowlist_enabled(self) -> bool:
+ """私聊白名单是否生效（显式 allowlist 模式按维度独立控制）。"""
+
+ return bool(self.allowed_private_ids)
+
+ def blacklist_mode_enabled(self) -> bool:
+ """是否启用黑名单限制模式。"""
+
+ return self.access_mode in {"blacklist", "legacy"} and (
+ bool(self.blocked_group_ids) or bool(self.blocked_private_ids)
+ )
+
+ def access_control_enabled(self) -> bool:
+ """是否启用访问控制。"""
+
+ return self.allowlist_mode_enabled() or self.blacklist_mode_enabled()
+
+ def group_access_denied_reason(self, group_id: int) -> str | None:
+ """群聊访问被拒绝原因。
+
+ 返回:
+ - "blacklist": 命中 access.blocked_group_ids
+ - "allowlist": allowlist 模式下不在 access.allowed_group_ids
+ - None: 允许访问
+ """
+
+ normalized_group_id = int(group_id)
+ if self.access_mode == "off":
+ return None
+ if self.access_mode == "blacklist":
+ if normalized_group_id in self._blocked_group_ids_set:
+ return "blacklist"
+ return None
+ if self.access_mode == "legacy":
+ if normalized_group_id in self._blocked_group_ids_set:
+ return "blacklist"
+ if not self.allowlist_mode_enabled():
+ return None
+ if normalized_group_id not in self._allowed_group_ids_set:
+ return "allowlist"
+ return None
+ if not self.group_allowlist_enabled():
+ return None
+ if normalized_group_id not in self._allowed_group_ids_set:
+ return "allowlist"
+ return None
+
+ def is_group_allowed(self, group_id: int) -> bool:
+ """群聊是否允许收发消息。"""
+
+ return self.group_access_denied_reason(group_id) is None
+
+ def private_access_denied_reason(self, user_id: int) -> str | None:
+ """私聊访问被拒绝原因。"""
+
+ normalized_user_id = int(user_id)
+ if self.access_mode == "off":
+ return None
+ if self.access_mode == "blacklist":
+ if normalized_user_id not in self._blocked_private_ids_set:
+ return None
+ if (
+ self.superadmin_bypass_private_blacklist
+ and normalized_user_id == int(self.superadmin_qq)
+ and self.superadmin_qq > 0
+ ):
+ return None
+ return "blacklist"
+ if self.access_mode == "legacy":
+ if normalized_user_id in self._blocked_private_ids_set:
+ if (
+ self.superadmin_bypass_private_blacklist
+ and normalized_user_id == int(self.superadmin_qq)
+ and self.superadmin_qq > 0
+ ):
+ return None
+ return "blacklist"
+ if not self.allowlist_mode_enabled():
+ return None
+ if (
+ self.superadmin_bypass_allowlist
+ and normalized_user_id == int(self.superadmin_qq)
+ and self.superadmin_qq > 0
+ ):
+ return None
+ if normalized_user_id not in self._allowed_private_ids_set:
+ return "allowlist"
+ return None
+ if not self.private_allowlist_enabled():
+ return None
+ if (
+ self.superadmin_bypass_allowlist
+ and normalized_user_id == int(self.superadmin_qq)
+ and self.superadmin_qq > 0
+ ):
+ return None
+ if normalized_user_id not in self._allowed_private_ids_set:
+ return "allowlist"
+ return None
+
+ def is_private_allowed(self, user_id: int) -> bool:
+ """私聊是否允许收发消息。"""
+
+ return self.private_access_denied_reason(user_id) is None
+
+ def is_bilibili_auto_extract_allowed_group(self, group_id: int) -> bool:
+ """群聊是否允许 bilibili 自动提取。"""
+ if self._bilibili_group_ids_set:
+ return int(group_id) in self._bilibili_group_ids_set
+ # 功能白名单为空时跟随全局 access 控制
+ return self.is_group_allowed(group_id)
+
+ def is_bilibili_auto_extract_allowed_private(self, user_id: int) -> bool:
+ """私聊是否允许 bilibili 自动提取。"""
+ if self._bilibili_private_ids_set:
+ return int(user_id) in self._bilibili_private_ids_set
+ # 功能白名单为空时跟随全局 access 控制
+ return self.is_private_allowed(user_id)
+
+ def is_arxiv_auto_extract_allowed_group(self, group_id: int) -> bool:
+ """群聊是否允许 arXiv 自动提取。"""
+ if self._arxiv_group_ids_set:
+ return int(group_id) in self._arxiv_group_ids_set
+ return self.is_group_allowed(group_id)
+
+ def is_arxiv_auto_extract_allowed_private(self, user_id: int) -> bool:
+ """私聊是否允许 arXiv 自动提取。"""
+ if self._arxiv_private_ids_set:
+ return int(user_id) in self._arxiv_private_ids_set
+ return self.is_private_allowed(user_id)
+
+ def is_github_auto_extract_allowed_group(self, group_id: int) -> bool:
+ """群聊是否允许 GitHub 仓库自动提取。"""
+ if self._github_group_ids_set:
+ return int(group_id) in self._github_group_ids_set
+ return self.is_group_allowed(group_id)
+
+ def is_github_auto_extract_allowed_private(self, user_id: int) -> bool:
+ """私聊是否允许 GitHub 仓库自动提取。"""
+ if self._github_private_ids_set:
+ return int(user_id) in self._github_private_ids_set
+ return self.is_private_allowed(user_id)
+
+ def should_process_group_message(self, is_at_bot: bool) -> bool:
+ """是否处理该条群消息。"""
+
+ if self.process_every_message:
+ return True
+ return bool(is_at_bot)
+
+ def should_process_private_message(self) -> bool:
+ """是否处理私聊消息回复。"""
+
+ return bool(self.process_private_message)
+
+ def should_process_poke_message(self) -> bool:
+ """是否处理拍一拍触发。"""
+
+ return bool(self.process_poke_message)
+
+ def get_context_recent_messages_limit(self) -> int:
+ """获取上下文最近历史消息条数上限。"""
+
+ limit = int(self.context_recent_messages_limit)
+ if limit < 0:
+ return 0
+ return limit
+
+ def security_check_enabled(self) -> bool:
+ """是否启用安全模型检查。"""
+ # 热更新运行时参数
+
+ return bool(self.security_model_enabled)
+
+ # 热更新运行时参数
+ def update_from(self, new_config: "Config") -> dict[str, tuple[Any, Any]]:
+ # 逐字段 diff；嵌套模型配置用 _update_dataclass 展开为 chat_model.api_url 等键
+ changes: dict[str, tuple[Any, Any]] = {}
+ for field in fields(self):
+ name = field.name
+ old_value = getattr(self, name)
+ new_value = getattr(new_config, name)
+ if isinstance(
+ old_value,
+ (
+ ChatModelConfig,
+ VisionModelConfig,
+ SecurityModelConfig,
+ AgentModelConfig,
+ GrokModelConfig,
+ ),
+ ):
+ changes.update(_update_dataclass(old_value, new_value, prefix=name))
+ continue
+ if old_value != new_value:
+ setattr(self, name, new_value)
+ changes[name] = (old_value, new_value)
+ return changes
+
+ def reload(self, strict: bool = False) -> dict[str, tuple[Any, Any]]:
+ # 对外入队 API
+ new_config = Config.load(strict=strict)
+ return self.update_from(new_config)
+
+ # 对外入队 API
+ def add_admin(self, qq: int) -> bool:
+ if qq in self.admin_qqs:
+ return False
+ self.admin_qqs.append(qq)
+ local_admins = load_local_admins()
+ if qq not in local_admins:
+ local_admins.append(qq)
+ save_local_admins(local_admins)
+ return True
+
+ def remove_admin(self, qq: int) -> bool:
+ if qq == self.superadmin_qq or qq not in self.admin_qqs:
+ return False
+ self.admin_qqs.remove(qq)
+ local_admins = load_local_admins()
+ if qq in local_admins:
+ local_admins.remove(qq)
+ save_local_admins(local_admins)
+ return True
+
+ def is_superadmin(self, qq: int) -> bool:
+ return qq == self.superadmin_qq
+
+ def is_admin(self, qq: int) -> bool:
+ return qq in self.admin_qqs
+
+
+class ConfigBuilder:
+ """链式构建 Config；未设置的字段使用 build_config 默认值。"""
+
+ def __init__(self) -> None:
+ self._overrides: dict[str, Any] = {}
+
+ def with_mapping(self, data: dict[str, Any]) -> "ConfigBuilder":
+ self._overrides["_base_mapping"] = data
+ return self
+
+ def override(self, **kwargs: Any) -> "ConfigBuilder":
+ self._overrides.update(kwargs)
+ return self
+
+ # 从中间态构建最终对象
+ def build(self, *, strict: bool = True) -> Config:
+ from .build_config import build_config
+
+ base = self._overrides.pop("_base_mapping", {})
+ if not isinstance(base, dict):
+ base = {}
+ data = dict(base)
+ # TOML-style nested overrides for simple top-level keys only
+ for key, value in self._overrides.items():
+ data[key] = value
+ return build_config(data, strict=strict, config_path=None)
diff --git a/src/Undefined/config/domain_parsers.py b/src/Undefined/config/domain_parsers.py
index 986c5cb1..c0de5fa0 100644
--- a/src/Undefined/config/domain_parsers.py
+++ b/src/Undefined/config/domain_parsers.py
@@ -191,7 +191,6 @@ def _parse_message_batcher_config(data: dict[str, Any]) -> MessageBatcherConfig:
window_seconds = _coerce_float(section.get("window_seconds"), 5.0)
if window_seconds < 0:
window_seconds = 0.0
- # max_window_seconds <= 0 视为不限制（仅靠 window_seconds + max_messages_per_batch 触发）
max_window_seconds = _coerce_float(section.get("max_window_seconds"), 30.0)
if max_window_seconds < 0:
max_window_seconds = 0.0
diff --git a/src/Undefined/config/env_registry.py b/src/Undefined/config/env_registry.py
new file mode 100644
index 00000000..99d10ea5
--- /dev/null
+++ b/src/Undefined/config/env_registry.py
@@ -0,0 +1,192 @@
+"""TOML path to environment variable mapping for configuration."""
+
+from __future__ import annotations
+
+
+from typing import Final
+
+# TOML 路径 → 环境变量名；供 _get_value 在 TOML 缺省时回退，以及文档/工具生成
+ENV_REGISTRY: Final[dict[tuple[str, ...], str]] = {
+ ("access", "allowed_group_ids"): "ALLOWED_GROUP_IDS",
+ ("access", "allowed_private_ids"): "ALLOWED_PRIVATE_IDS",
+ ("access", "blocked_group_ids"): "BLOCKED_GROUP_IDS",
+ ("access", "blocked_private_ids"): "BLOCKED_PRIVATE_IDS",
+ ("access", "mode"): "ACCESS_MODE",
+ ("api_endpoints", "jkyai_base_url"): "JKYAI_BASE_URL",
+ ("api_endpoints", "xxapi_base_url"): "XXAPI_BASE_URL",
+ ("core", "admin_qq"): "ADMIN_QQ",
+ ("core", "bot_qq"): "BOT_QQ",
+ ("core", "forward_proxy_qq"): "FORWARD_PROXY_QQ",
+ ("core", "superadmin_qq"): "SUPERADMIN_QQ",
+ ("features", "pool_enabled"): "MODEL_POOL_ENABLED",
+ ("history", "max_records"): "HISTORY_MAX_RECORDS",
+ ("image_gen", "provider"): "IMAGE_GEN_PROVIDER",
+ ("logging", "backup_count"): "LOG_BACKUP_COUNT",
+ ("logging", "file_path"): "LOG_FILE_PATH",
+ ("logging", "level"): "LOG_LEVEL",
+ ("logging", "log_thinking"): "LOG_THINKING",
+ ("logging", "max_size_mb"): "LOG_MAX_SIZE_MB",
+ ("logging", "tty_enabled"): "LOG_TTY_ENABLED",
+ ("mcp", "config_path"): "MCP_CONFIG_PATH",
+ ("models", "agent", "api_key"): "AGENT_MODEL_API_KEY",
+ ("models", "agent", "api_mode"): "AGENT_MODEL_API_MODE",
+ ("models", "agent", "api_url"): "AGENT_MODEL_API_URL",
+ ("models", "agent", "context_window_tokens"): "AGENT_MODEL_CONTEXT_WINDOW_TOKENS",
+ ("models", "agent", "model_name"): "AGENT_MODEL_NAME",
+ (
+ "models",
+ "agent",
+ "reasoning_content_replay",
+ ): "AGENT_MODEL_REASONING_CONTENT_REPLAY",
+ (
+ "models",
+ "agent",
+ "responses_force_stateless_replay",
+ ): "AGENT_MODEL_RESPONSES_FORCE_STATELESS_REPLAY",
+ (
+ "models",
+ "agent",
+ "responses_tool_choice_compat",
+ ): "AGENT_MODEL_RESPONSES_TOOL_CHOICE_COMPAT",
+ ("models", "agent", "system_prompt_as_user"): "AGENT_MODEL_SYSTEM_PROMPT_AS_USER",
+ ("models", "chat", "api_key"): "CHAT_MODEL_API_KEY",
+ ("models", "chat", "api_mode"): "CHAT_MODEL_API_MODE",
+ ("models", "chat", "api_url"): "CHAT_MODEL_API_URL",
+ ("models", "chat", "context_window_tokens"): "CHAT_MODEL_CONTEXT_WINDOW_TOKENS",
+ ("models", "chat", "max_tokens"): "CHAT_MODEL_MAX_TOKENS",
+ ("models", "chat", "model_name"): "CHAT_MODEL_NAME",
+ (
+ "models",
+ "chat",
+ "reasoning_content_replay",
+ ): "CHAT_MODEL_REASONING_CONTENT_REPLAY",
+ (
+ "models",
+ "chat",
+ "responses_force_stateless_replay",
+ ): "CHAT_MODEL_RESPONSES_FORCE_STATELESS_REPLAY",
+ (
+ "models",
+ "chat",
+ "responses_tool_choice_compat",
+ ): "CHAT_MODEL_RESPONSES_TOOL_CHOICE_COMPAT",
+ ("models", "chat", "system_prompt_as_user"): "CHAT_MODEL_SYSTEM_PROMPT_AS_USER",
+ (
+ "models",
+ "embedding",
+ "context_window_tokens",
+ ): "EMBEDDING_MODEL_CONTEXT_WINDOW_TOKENS",
+ ("models", "grok", "api_key"): "GROK_MODEL_API_KEY",
+ ("models", "grok", "api_url"): "GROK_MODEL_API_URL",
+ ("models", "grok", "context_window_tokens"): "GROK_MODEL_CONTEXT_WINDOW_TOKENS",
+ ("models", "grok", "max_tokens"): "GROK_MODEL_MAX_TOKENS",
+ ("models", "grok", "model_name"): "GROK_MODEL_NAME",
+ ("models", "naga", "api_key"): "NAGA_MODEL_API_KEY",
+ ("models", "naga", "api_mode"): "NAGA_MODEL_API_MODE",
+ ("models", "naga", "api_url"): "NAGA_MODEL_API_URL",
+ ("models", "naga", "context_window_tokens"): "NAGA_MODEL_CONTEXT_WINDOW_TOKENS",
+ ("models", "naga", "model_name"): "NAGA_MODEL_NAME",
+ (
+ "models",
+ "naga",
+ "reasoning_content_replay",
+ ): "NAGA_MODEL_REASONING_CONTENT_REPLAY",
+ (
+ "models",
+ "naga",
+ "responses_force_stateless_replay",
+ ): "NAGA_MODEL_RESPONSES_FORCE_STATELESS_REPLAY",
+ (
+ "models",
+ "naga",
+ "responses_tool_choice_compat",
+ ): "NAGA_MODEL_RESPONSES_TOOL_CHOICE_COMPAT",
+ ("models", "naga", "system_prompt_as_user"): "NAGA_MODEL_SYSTEM_PROMPT_AS_USER",
+ ("models", "rerank", "api_key"): "RERANK_MODEL_API_KEY",
+ ("models", "rerank", "api_url"): "RERANK_MODEL_API_URL",
+ ("models", "rerank", "context_window_tokens"): "RERANK_MODEL_CONTEXT_WINDOW_TOKENS",
+ ("models", "rerank", "model_name"): "RERANK_MODEL_NAME",
+ ("models", "security", "api_key"): "SECURITY_MODEL_API_KEY",
+ ("models", "security", "api_mode"): "SECURITY_MODEL_API_MODE",
+ ("models", "security", "api_url"): "SECURITY_MODEL_API_URL",
+ (
+ "models",
+ "security",
+ "context_window_tokens",
+ ): "SECURITY_MODEL_CONTEXT_WINDOW_TOKENS",
+ ("models", "security", "model_name"): "SECURITY_MODEL_NAME",
+ (
+ "models",
+ "security",
+ "reasoning_content_replay",
+ ): "SECURITY_MODEL_REASONING_CONTENT_REPLAY",
+ (
+ "models",
+ "security",
+ "responses_force_stateless_replay",
+ ): "SECURITY_MODEL_RESPONSES_FORCE_STATELESS_REPLAY",
+ (
+ "models",
+ "security",
+ "responses_tool_choice_compat",
+ ): "SECURITY_MODEL_RESPONSES_TOOL_CHOICE_COMPAT",
+ (
+ "models",
+ "security",
+ "system_prompt_as_user",
+ ): "SECURITY_MODEL_SYSTEM_PROMPT_AS_USER",
+ ("models", "vision", "api_key"): "VISION_MODEL_API_KEY",
+ ("models", "vision", "api_mode"): "VISION_MODEL_API_MODE",
+ ("models", "vision", "api_url"): "VISION_MODEL_API_URL",
+ ("models", "vision", "context_window_tokens"): "VISION_MODEL_CONTEXT_WINDOW_TOKENS",
+ ("models", "vision", "model_name"): "VISION_MODEL_NAME",
+ (
+ "models",
+ "vision",
+ "reasoning_content_replay",
+ ): "VISION_MODEL_REASONING_CONTENT_REPLAY",
+ (
+ "models",
+ "vision",
+ "responses_force_stateless_replay",
+ ): "VISION_MODEL_RESPONSES_FORCE_STATELESS_REPLAY",
+ (
+ "models",
+ "vision",
+ "responses_tool_choice_compat",
+ ): "VISION_MODEL_RESPONSES_TOOL_CHOICE_COMPAT",
+ ("models", "vision", "system_prompt_as_user"): "VISION_MODEL_SYSTEM_PROMPT_AS_USER",
+ ("onebot", "token"): "ONEBOT_TOKEN",
+ ("onebot", "ws_url"): "ONEBOT_WS_URL",
+ ("proxy", "use_proxy"): "USE_PROXY",
+ ("search", "searxng_url"): "SEARXNG_URL",
+ ("skills", "hot_reload"): "SKILLS_HOT_RELOAD",
+ ("skills", "intro_hash_path"): "AGENT_INTRO_HASH_PATH",
+ ("skills", "prefetch_tools_hide"): "PREFETCH_TOOLS_HIDE",
+ ("token_usage", "max_archives"): "TOKEN_USAGE_MAX_ARCHIVES",
+ ("token_usage", "max_size_mb"): "TOKEN_USAGE_MAX_SIZE_MB",
+ ("token_usage", "max_total_mb"): "TOKEN_USAGE_MAX_TOTAL_MB",
+ ("tools", "description_max_len"): "TOOLS_DESCRIPTION_MAX_LEN",
+ ("tools", "dot_delimiter"): "TOOLS_DOT_DELIMITER",
+ ("tools", "sanitize_verbose"): "TOOLS_SANITIZE_VERBOSE",
+ ("weather", "api_key"): "WEATHER_API_KEY",
+ ("xxapi", "api_token"): "XXAPI_API_TOKEN",
+}
+
+# 历史/别名环境变量：不经过 _get_value 统一路径，单独在 domain_parsers 等处读取
+ENV_ALTERNATES: Final[dict[str, tuple[str, ...]]] = {
+ "EASTER_EGG_AGENT_CALL_MESSAGE_MODE": ("easter_egg", "agent_call_message_enabled"),
+ "EASTER_EGG_CALL_MESSAGE_MODE": ("easter_egg", "agent_call_message_enabled"),
+ "HTTP_PROXY": ("proxy", "http_proxy"),
+ "HTTPS_PROXY": ("proxy", "https_proxy"),
+}
+
+
+def env_key_for_path(path: tuple[str, ...]) -> str | None:
+ """Return primary env var for a TOML path, if registered."""
+ return ENV_REGISTRY.get(path)
+
+
+def all_env_mappings() -> dict[tuple[str, ...], str]:
+ """Return a copy of the primary env registry."""
+ return dict(ENV_REGISTRY)
diff --git a/src/Undefined/config/load_sections/__init__.py b/src/Undefined/config/load_sections/__init__.py
new file mode 100644
index 00000000..c195d38c
--- /dev/null
+++ b/src/Undefined/config/load_sections/__init__.py
@@ -0,0 +1,26 @@
+"""Config load section parsers."""
+
+# 配置分段加载：按 table 解析 TOML → ctx 字段 dict
+from .access import load_access
+from .core import load_core
+from .domains import load_domains
+from .finalize import load_finalize
+from .history_skills import load_history_skills
+from .integrations import load_integrations
+from .knowledge import load_knowledge
+from .logging_tools import load_logging_tools
+from .models import load_models
+from .network import load_network
+
+__all__ = [
+ "load_access",
+ "load_core",
+ "load_domains",
+ "load_finalize",
+ "load_history_skills",
+ "load_integrations",
+ "load_knowledge",
+ "load_logging_tools",
+ "load_models",
+ "load_network",
+]
diff --git a/src/Undefined/config/load_sections/access.py b/src/Undefined/config/load_sections/access.py
new file mode 100644
index 00000000..e3ea89c3
--- /dev/null
+++ b/src/Undefined/config/load_sections/access.py
@@ -0,0 +1,86 @@
+"""Load access config section."""
+
+from __future__ import annotations
+
+# 配置分段加载：按 table 解析 TOML → ctx 字段 dict
+
+import logging
+from pathlib import Path
+from typing import Any, Optional
+
+from ..coercers import (
+ _coerce_bool,
+ _coerce_int_list,
+ _coerce_str,
+ _get_value,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def load_access(
+ data: dict[str, Any], *, config_path: Optional[Path] = None
+) -> dict[str, Any]:
+ access_mode_raw = _get_value(data, ("access", "mode"), "ACCESS_MODE")
+ allowed_group_ids = _coerce_int_list(
+ _get_value(data, ("access", "allowed_group_ids"), "ALLOWED_GROUP_IDS")
+ )
+ blocked_group_ids = _coerce_int_list(
+ _get_value(data, ("access", "blocked_group_ids"), "BLOCKED_GROUP_IDS")
+ )
+ allowed_private_ids = _coerce_int_list(
+ _get_value(data, ("access", "allowed_private_ids"), "ALLOWED_PRIVATE_IDS")
+ )
+ blocked_private_ids = _coerce_int_list(
+ _get_value(data, ("access", "blocked_private_ids"), "BLOCKED_PRIVATE_IDS")
+ )
+ superadmin_bypass_allowlist = _coerce_bool(
+ _get_value(
+ data,
+ ("access", "superadmin_bypass_allowlist"),
+ "SUPERADMIN_BYPASS_ALLOWLIST",
+ ),
+ True,
+ )
+ superadmin_bypass_private_blacklist = _coerce_bool(
+ _get_value(
+ data,
+ ("access", "superadmin_bypass_private_blacklist"),
+ "SUPERADMIN_BYPASS_PRIVATE_BLACKLIST",
+ ),
+ False,
+ )
+ if access_mode_raw is None:
+ # 兼容旧配置：未配置 mode 时沿用历史行为（群黑名单 + 白名单联动）。
+ if (
+ allowed_group_ids
+ or blocked_group_ids
+ or allowed_private_ids
+ or blocked_private_ids
+ ):
+ access_mode = "legacy"
+ logger.warning(
+ "[配置] access.mode 未设置，已启用兼容模式（legacy）。建议显式设置为 off/blacklist/allowlist。"
+ )
+ # 否则分支
+ else:
+ access_mode = "off"
+ # 否则分支
+ else:
+ access_mode = _coerce_str(access_mode_raw, "off").lower()
+ if access_mode not in {"off", "blacklist", "allowlist"}:
+ logger.warning(
+ "[配置] access.mode 非法（仅支持 off/blacklist/allowlist），已回退为 off: %s",
+ access_mode,
+ )
+ access_mode = "off"
+
+ return {
+ "allowed_group_ids": allowed_group_ids,
+ "blocked_group_ids": blocked_group_ids,
+ "allowed_private_ids": allowed_private_ids,
+ "blocked_private_ids": blocked_private_ids,
+ "superadmin_bypass_allowlist": superadmin_bypass_allowlist,
+ "superadmin_bypass_private_blacklist": superadmin_bypass_private_blacklist,
+ "access_mode": access_mode,
+ }
diff --git a/src/Undefined/config/load_sections/core.py b/src/Undefined/config/load_sections/core.py
new file mode 100644
index 00000000..28a8f832
--- /dev/null
+++ b/src/Undefined/config/load_sections/core.py
@@ -0,0 +1,183 @@
+"""Load core config section."""
+
+from __future__ import annotations
+
+# 配置分段加载：按 table 解析 TOML → ctx 字段 dict
+
+import logging
+from pathlib import Path
+from typing import Any, Optional
+
+from ..coercers import (
+ _coerce_bool,
+ _coerce_int,
+ _coerce_int_list,
+ _coerce_str,
+ _get_value,
+)
+
+logger = logging.getLogger(__name__)
+
+
+# 加载 [core] table：机器人身份、消息开关、彩蛋、OneBot 连接
+def load_core(
+ data: dict[str, Any], *, config_path: Optional[Path] = None
+) -> dict[str, Any]:
+ # [core] 机器人 QQ 与管理员
+ bot_qq = _coerce_int(_get_value(data, ("core", "bot_qq"), "BOT_QQ"), 0)
+ superadmin_qq = _coerce_int(
+ _get_value(data, ("core", "superadmin_qq"), "SUPERADMIN_QQ"), 0
+ )
+ admin_qqs = _coerce_int_list(_get_value(data, ("core", "admin_qq"), "ADMIN_QQ"))
+ forward_proxy = _coerce_int(
+ _get_value(data, ("core", "forward_proxy_qq"), "FORWARD_PROXY_QQ"),
+ 0,
+ )
+ forward_proxy_qq = forward_proxy if forward_proxy > 0 else None
+ # [core] 群聊/私聊/拍一拍处理开关
+ process_every_message = _coerce_bool(
+ _get_value(
+ data,
+ ("core", "process_every_message"),
+ "PROCESS_EVERY_MESSAGE",
+ ),
+ True,
+ )
+ process_private_message = _coerce_bool(
+ _get_value(
+ data,
+ ("core", "process_private_message"),
+ "PROCESS_PRIVATE_MESSAGE",
+ ),
+ True,
+ )
+ process_poke_message = _coerce_bool(
+ _get_value(
+ data,
+ ("core", "process_poke_message"),
+ "PROCESS_POKE_MESSAGE",
+ ),
+ True,
+ )
+ # [easter_egg] 关键词回复与复读彩蛋
+ keyword_reply_raw = _get_value(
+ data,
+ ("easter_egg", "keyword_reply_enabled"),
+ "KEYWORD_REPLY_ENABLED",
+ )
+ if keyword_reply_raw is None:
+ # 兼容旧配置：历史上放在 [core].keyword_reply_enabled
+ keyword_reply_raw = _get_value(
+ data,
+ ("core", "keyword_reply_enabled"),
+ None,
+ )
+ keyword_reply_enabled = _coerce_bool(keyword_reply_raw, False)
+ repeat_enabled = _coerce_bool(
+ _get_value(
+ data,
+ ("easter_egg", "repeat_enabled"),
+ "EASTER_EGG_REPEAT_ENABLED",
+ ),
+ False,
+ )
+ inverted_question_enabled = _coerce_bool(
+ _get_value(
+ data,
+ ("easter_egg", "inverted_question_enabled"),
+ "EASTER_EGG_INVERTED_QUESTION_ENABLED",
+ ),
+ False,
+ )
+ repeat_threshold = _coerce_int(
+ _get_value(
+ data,
+ ("easter_egg", "repeat_threshold"),
+ "EASTER_EGG_REPEAT_THRESHOLD",
+ ),
+ 3,
+ )
+ if repeat_threshold < 2:
+ repeat_threshold = 2
+ if repeat_threshold > 20:
+ repeat_threshold = 20
+ repeat_cooldown_minutes = _coerce_int(
+ _get_value(
+ data,
+ ("easter_egg", "repeat_cooldown_minutes"),
+ "EASTER_EGG_REPEAT_COOLDOWN_MINUTES",
+ ),
+ 60,
+ )
+ if repeat_cooldown_minutes < 0:
+ repeat_cooldown_minutes = 0
+ context_recent_messages_limit = _coerce_int(
+ _get_value(
+ data,
+ ("core", "context_recent_messages_limit"),
+ "CONTEXT_RECENT_MESSAGES_LIMIT",
+ ),
+ 20,
+ )
+ if context_recent_messages_limit < 0:
+ context_recent_messages_limit = 0
+
+ # [core] AI 请求与 tool_call 重试上限
+ ai_request_max_retries = _coerce_int(
+ _get_value(
+ data,
+ ("core", "ai_request_max_retries"),
+ "AI_REQUEST_MAX_RETRIES",
+ ),
+ 2,
+ )
+ if ai_request_max_retries < 0:
+ ai_request_max_retries = 0
+
+ missing_tool_call_retries = _coerce_int(
+ _get_value(
+ data,
+ ("core", "missing_tool_call_retries"),
+ "MISSING_TOOL_CALL_RETRIES",
+ ),
+ 3,
+ )
+ if missing_tool_call_retries < 0:
+ missing_tool_call_retries = 0
+
+ nagaagent_mode_enabled = _coerce_bool(
+ _get_value(
+ data,
+ ("features", "nagaagent_mode_enabled"),
+ "NAGAAGENT_MODE_ENABLED",
+ ),
+ False,
+ )
+ # [onebot] WebSocket 连接
+ onebot_ws_url = _coerce_str(
+ _get_value(data, ("onebot", "ws_url"), "ONEBOT_WS_URL"), ""
+ )
+ onebot_token = _coerce_str(
+ _get_value(data, ("onebot", "token"), "ONEBOT_TOKEN"), ""
+ )
+
+ return {
+ "bot_qq": bot_qq,
+ "superadmin_qq": superadmin_qq,
+ "admin_qqs": admin_qqs,
+ "forward_proxy_qq": forward_proxy_qq,
+ "process_every_message": process_every_message,
+ "process_private_message": process_private_message,
+ "process_poke_message": process_poke_message,
+ "keyword_reply_enabled": keyword_reply_enabled,
+ "repeat_enabled": repeat_enabled,
+ "inverted_question_enabled": inverted_question_enabled,
+ "repeat_threshold": repeat_threshold,
+ "repeat_cooldown_minutes": repeat_cooldown_minutes,
+ "context_recent_messages_limit": context_recent_messages_limit,
+ "ai_request_max_retries": ai_request_max_retries,
+ "missing_tool_call_retries": missing_tool_call_retries,
+ "nagaagent_mode_enabled": nagaagent_mode_enabled,
+ "onebot_ws_url": onebot_ws_url,
+ "onebot_token": onebot_token,
+ }
diff --git a/src/Undefined/config/load_sections/domains.py b/src/Undefined/config/load_sections/domains.py
new file mode 100644
index 00000000..6ada6453
--- /dev/null
+++ b/src/Undefined/config/load_sections/domains.py
@@ -0,0 +1,58 @@
+"""Load domains config section."""
+
+from __future__ import annotations
+
+# 配置分段加载：按 table 解析 TOML → ctx 字段 dict
+
+import logging
+from pathlib import Path
+from typing import Any, Optional
+
+from ..domain_parsers import (
+ _parse_api_config,
+ _parse_cognitive_config,
+ _parse_memes_config,
+ _parse_message_batcher_config,
+ _parse_naga_config,
+ _parse_render_cache_config,
+)
+from ..model_parsers import (
+ _parse_image_edit_model_config,
+ _parse_image_gen_config,
+ _parse_image_gen_model_config,
+)
+from ..webui_settings import load_webui_settings
+
+logger = logging.getLogger(__name__)
+
+
+def load_domains(
+ data: dict[str, Any], *, config_path: Optional[Path] = None
+) -> dict[str, Any]:
+ # 子域配置：WebUI/API/认知/表情包/合并器/Naga/生图等，与 core/models 段解耦
+ webui_settings = load_webui_settings(config_path)
+ api_config = _parse_api_config(data)
+
+ cognitive = _parse_cognitive_config(data)
+ memes = _parse_memes_config(data)
+ message_batcher = _parse_message_batcher_config(data)
+ render_cache = _parse_render_cache_config(data)
+ naga = _parse_naga_config(data)
+ models_image_gen = _parse_image_gen_model_config(data)
+ models_image_edit = _parse_image_edit_model_config(data)
+ image_gen = _parse_image_gen_config(data)
+
+ return {
+ "webui_url": webui_settings.url,
+ "webui_port": webui_settings.port,
+ "webui_password": webui_settings.password,
+ "api": api_config,
+ "cognitive": cognitive,
+ "memes": memes,
+ "message_batcher": message_batcher,
+ "render_cache": render_cache,
+ "naga": naga,
+ "image_gen": image_gen,
+ "models_image_gen": models_image_gen,
+ "models_image_edit": models_image_edit,
+ }
diff --git a/src/Undefined/config/load_sections/finalize.py b/src/Undefined/config/load_sections/finalize.py
new file mode 100644
index 00000000..d6da9a07
--- /dev/null
+++ b/src/Undefined/config/load_sections/finalize.py
@@ -0,0 +1,36 @@
+"""Finalize: validation and debug logging."""
+
+from __future__ import annotations
+
+# 配置分段加载：按 table 解析 TOML → ctx 字段 dict
+
+from typing import Any
+
+from ..model_parsers import _log_debug_info, _verify_required_fields
+
+
+def load_finalize(ctx: dict[str, Any], *, strict: bool = True) -> None:
+ # strict=True（首次启动）校验必填；热重载传 strict=False 跳过以免半写文件误杀
+ if strict:
+ _verify_required_fields(
+ bot_qq=ctx["bot_qq"],
+ superadmin_qq=ctx["superadmin_qq"],
+ onebot_ws_url=ctx["onebot_ws_url"],
+ chat_model=ctx["chat_model"],
+ vision_model=ctx["vision_model"],
+ agent_model=ctx["agent_model"],
+ knowledge_enabled=ctx["knowledge_enabled"],
+ embedding_model=ctx["embedding_model"],
+ )
+
+ debug_keys = (
+ "chat_model",
+ "vision_model",
+ "security_model",
+ "naga_model",
+ "agent_model",
+ "summary_model",
+ "grok_model",
+ )
+ if all(key in ctx for key in debug_keys):
+ _log_debug_info(*(ctx[key] for key in debug_keys))
diff --git a/src/Undefined/config/load_sections/history_skills.py b/src/Undefined/config/load_sections/history_skills.py
new file mode 100644
index 00000000..615580e0
--- /dev/null
+++ b/src/Undefined/config/load_sections/history_skills.py
@@ -0,0 +1,277 @@
+"""Load history_skills config section."""
+
+from __future__ import annotations
+
+# 配置分段加载：按 table 解析 TOML → ctx 字段 dict
+
+import logging
+from pathlib import Path
+from typing import Any, Optional
+
+from ..coercers import (
+ _coerce_bool,
+ _coerce_float,
+ _coerce_int,
+ _coerce_str,
+ _coerce_str_list,
+ _get_value,
+ _normalize_queue_interval,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def load_history_skills(
+ data: dict[str, Any], *, config_path: Optional[Path] = None
+) -> dict[str, Any]:
+ token_usage_max_size_mb = _coerce_int(
+ _get_value(data, ("token_usage", "max_size_mb"), "TOKEN_USAGE_MAX_SIZE_MB"),
+ 5,
+ )
+ token_usage_max_archives = _coerce_int(
+ _get_value(data, ("token_usage", "max_archives"), "TOKEN_USAGE_MAX_ARCHIVES"),
+ 30,
+ )
+ token_usage_max_total_mb = _coerce_int(
+ _get_value(data, ("token_usage", "max_total_mb"), "TOKEN_USAGE_MAX_TOTAL_MB"),
+ 0,
+ )
+ token_usage_archive_prune_mode = _coerce_str(
+ _get_value(
+ data,
+ ("token_usage", "archive_prune_mode"),
+ "TOKEN_USAGE_ARCHIVE_PRUNE_MODE",
+ ),
+ "delete",
+ )
+
+ history_max_records = max(
+ 0,
+ _coerce_int(
+ _get_value(data, ("history", "max_records"), "HISTORY_MAX_RECORDS"),
+ 10000,
+ ),
+ )
+ history_filtered_result_limit = max(
+ 1,
+ _coerce_int(
+ _get_value(
+ data,
+ ("history", "filtered_result_limit"),
+ "HISTORY_FILTERED_RESULT_LIMIT",
+ ),
+ 200,
+ ),
+ )
+ history_search_scan_limit = max(
+ 1,
+ _coerce_int(
+ _get_value(
+ data,
+ ("history", "search_scan_limit"),
+ "HISTORY_SEARCH_SCAN_LIMIT",
+ ),
+ 10000,
+ ),
+ )
+ history_summary_fetch_limit = max(
+ 1,
+ _coerce_int(
+ _get_value(
+ data,
+ ("history", "summary_fetch_limit"),
+ "HISTORY_SUMMARY_FETCH_LIMIT",
+ ),
+ 1000,
+ ),
+ )
+ history_summary_time_fetch_limit = max(
+ 1,
+ _coerce_int(
+ _get_value(
+ data,
+ ("history", "summary_time_fetch_limit"),
+ "HISTORY_SUMMARY_TIME_FETCH_LIMIT",
+ ),
+ 5000,
+ ),
+ )
+ history_onebot_fetch_limit = max(
+ 1,
+ _coerce_int(
+ _get_value(
+ data,
+ ("history", "onebot_fetch_limit"),
+ "HISTORY_ONEBOT_FETCH_LIMIT",
+ ),
+ 10000,
+ ),
+ )
+ history_group_analysis_limit = max(
+ 1,
+ _coerce_int(
+ _get_value(
+ data,
+ ("history", "group_analysis_limit"),
+ "HISTORY_GROUP_ANALYSIS_LIMIT",
+ ),
+ 500,
+ ),
+ )
+ attachment_remote_download_max_size_mb = max(
+ 0,
+ _coerce_int(
+ _get_value(
+ data,
+ ("attachments", "remote_download_max_size_mb"),
+ "ATTACHMENTS_REMOTE_DOWNLOAD_MAX_SIZE_MB",
+ ),
+ 25,
+ ),
+ )
+ attachment_cache_max_total_size_mb = max(
+ 0,
+ _coerce_int(
+ _get_value(
+ data,
+ ("attachments", "cache_max_total_size_mb"),
+ "ATTACHMENTS_CACHE_MAX_TOTAL_SIZE_MB",
+ ),
+ 0,
+ ),
+ )
+ attachment_cache_max_records = max(
+ 0,
+ _coerce_int(
+ _get_value(
+ data,
+ ("attachments", "cache_max_records"),
+ "ATTACHMENTS_CACHE_MAX_RECORDS",
+ ),
+ 2000,
+ ),
+ )
+ attachment_cache_max_age_days = max(
+ 0,
+ _coerce_int(
+ _get_value(
+ data,
+ ("attachments", "cache_max_age_days"),
+ "ATTACHMENTS_CACHE_MAX_AGE_DAYS",
+ ),
+ 7,
+ ),
+ )
+ attachment_url_reference_max_records = max(
+ 0,
+ _coerce_int(
+ _get_value(
+ data,
+ ("attachments", "url_reference_max_records"),
+ "ATTACHMENTS_URL_REFERENCE_MAX_RECORDS",
+ ),
+ 2000,
+ ),
+ )
+ attachment_url_max_length = max(
+ 0,
+ _coerce_int(
+ _get_value(
+ data,
+ ("attachments", "url_max_length"),
+ "ATTACHMENTS_URL_MAX_LENGTH",
+ ),
+ 8192,
+ ),
+ )
+
+ skills_hot_reload = _coerce_bool(
+ _get_value(data, ("skills", "hot_reload"), "SKILLS_HOT_RELOAD"), True
+ )
+ # interval/debounce 同时驱动 skills 目录扫描与 config.toml 热重载 watcher
+ skills_hot_reload_interval = _coerce_float(
+ _get_value(
+ data, ("skills", "hot_reload_interval"), "SKILLS_HOT_RELOAD_INTERVAL"
+ ),
+ 2.0,
+ )
+ skills_hot_reload_interval = _normalize_queue_interval(skills_hot_reload_interval)
+ skills_hot_reload_debounce = _coerce_float(
+ _get_value(
+ data, ("skills", "hot_reload_debounce"), "SKILLS_HOT_RELOAD_DEBOUNCE"
+ ),
+ 0.5,
+ )
+ skills_hot_reload_debounce = _normalize_queue_interval(skills_hot_reload_debounce)
+
+ agent_intro_autogen_enabled = _coerce_bool(
+ _get_value(
+ data,
+ ("skills", "intro_autogen_enabled"),
+ "AGENT_INTRO_AUTOGEN_ENABLED",
+ ),
+ True,
+ )
+ agent_intro_autogen_queue_interval = _coerce_float(
+ _get_value(
+ data,
+ ("skills", "intro_autogen_queue_interval"),
+ "AGENT_INTRO_AUTOGEN_QUEUE_INTERVAL",
+ ),
+ 1.0,
+ )
+ agent_intro_autogen_queue_interval = _normalize_queue_interval(
+ agent_intro_autogen_queue_interval
+ )
+ agent_intro_autogen_max_tokens = _coerce_int(
+ _get_value(
+ data,
+ ("skills", "intro_autogen_max_tokens"),
+ "AGENT_INTRO_AUTOGEN_MAX_TOKENS",
+ ),
+ 8192,
+ )
+ agent_intro_hash_path = _coerce_str(
+ _get_value(data, ("skills", "intro_hash_path"), "AGENT_INTRO_HASH_PATH"),
+ ".cache/agent_intro_hashes.json",
+ )
+
+ prefetch_tools_raw = _get_value(
+ data, ("skills", "prefetch_tools"), "PREFETCH_TOOLS"
+ )
+ prefetch_tools = _coerce_str_list(prefetch_tools_raw)
+ if not prefetch_tools and prefetch_tools_raw is None:
+ prefetch_tools = ["get_current_time"]
+ prefetch_tools_hide = _coerce_bool(
+ _get_value(data, ("skills", "prefetch_tools_hide"), "PREFETCH_TOOLS_HIDE"),
+ True,
+ )
+
+ return {
+ "token_usage_max_size_mb": token_usage_max_size_mb,
+ "token_usage_max_archives": token_usage_max_archives,
+ "token_usage_max_total_mb": token_usage_max_total_mb,
+ "token_usage_archive_prune_mode": token_usage_archive_prune_mode,
+ "history_max_records": history_max_records,
+ "history_filtered_result_limit": history_filtered_result_limit,
+ "history_search_scan_limit": history_search_scan_limit,
+ "history_summary_fetch_limit": history_summary_fetch_limit,
+ "history_summary_time_fetch_limit": history_summary_time_fetch_limit,
+ "history_onebot_fetch_limit": history_onebot_fetch_limit,
+ "history_group_analysis_limit": history_group_analysis_limit,
+ "attachment_remote_download_max_size_mb": attachment_remote_download_max_size_mb,
+ "attachment_cache_max_total_size_mb": attachment_cache_max_total_size_mb,
+ "attachment_cache_max_records": attachment_cache_max_records,
+ "attachment_cache_max_age_days": attachment_cache_max_age_days,
+ "attachment_url_reference_max_records": attachment_url_reference_max_records,
+ "attachment_url_max_length": attachment_url_max_length,
+ "skills_hot_reload": skills_hot_reload,
+ "skills_hot_reload_interval": skills_hot_reload_interval,
+ "skills_hot_reload_debounce": skills_hot_reload_debounce,
+ "agent_intro_autogen_enabled": agent_intro_autogen_enabled,
+ "agent_intro_autogen_queue_interval": agent_intro_autogen_queue_interval,
+ "agent_intro_autogen_max_tokens": agent_intro_autogen_max_tokens,
+ "agent_intro_hash_path": agent_intro_hash_path,
+ "prefetch_tools": prefetch_tools,
+ "prefetch_tools_hide": prefetch_tools_hide,
+ }
diff --git a/src/Undefined/config/load_sections/integrations.py b/src/Undefined/config/load_sections/integrations.py
new file mode 100644
index 00000000..ebbafdf7
--- /dev/null
+++ b/src/Undefined/config/load_sections/integrations.py
@@ -0,0 +1,275 @@
+"""Load integrations config section."""
+
+from __future__ import annotations
+
+# 配置分段加载：按 table 解析 TOML → ctx 字段 dict
+
+import logging
+from pathlib import Path
+from typing import Any, Optional
+
+from ..coercers import (
+ _coerce_bool,
+ _coerce_float,
+ _coerce_int,
+ _coerce_int_list,
+ _coerce_str,
+ _get_value,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def load_integrations(
+ data: dict[str, Any], *, config_path: Optional[Path] = None
+) -> dict[str, Any]:
+ bilibili_auto_extract_enabled = _coerce_bool(
+ _get_value(data, ("bilibili", "auto_extract_enabled"), None), False
+ )
+ # 功能级白名单为空时，运行时回退到全局 access 控制（见 Config.is_*_allowed）
+ bilibili_cookie = _coerce_str(_get_value(data, ("bilibili", "cookie"), None), "")
+ if not bilibili_cookie:
+ # 兼容旧配置项：bilibili.sessdata
+ bilibili_cookie = _coerce_str(
+ _get_value(data, ("bilibili", "sessdata"), None), ""
+ )
+ bilibili_prefer_quality = _coerce_int(
+ _get_value(data, ("bilibili", "prefer_quality"), None), 80
+ )
+ bilibili_max_duration = _coerce_int(
+ _get_value(data, ("bilibili", "max_duration"), None), 600
+ )
+ bilibili_max_file_size = _coerce_int(
+ _get_value(data, ("bilibili", "max_file_size"), None), 100
+ )
+ bilibili_oversize_strategy = _coerce_str(
+ _get_value(data, ("bilibili", "oversize_strategy"), None), "downgrade"
+ )
+ if bilibili_oversize_strategy not in ("downgrade", "info"):
+ bilibili_oversize_strategy = "downgrade"
+ bilibili_danmaku_enabled = _coerce_bool(
+ _get_value(data, ("bilibili", "danmaku_enabled"), None), True
+ )
+ bilibili_danmaku_batch_size = _coerce_int(
+ _get_value(data, ("bilibili", "danmaku_batch_size"), None), 100
+ )
+ if bilibili_danmaku_batch_size <= 0:
+ bilibili_danmaku_batch_size = 100
+ bilibili_danmaku_max_count = _coerce_int(
+ _get_value(data, ("bilibili", "danmaku_max_count"), None), 0
+ )
+ if bilibili_danmaku_max_count < 0:
+ bilibili_danmaku_max_count = 0
+ bilibili_auto_extract_group_ids = _coerce_int_list(
+ _get_value(data, ("bilibili", "auto_extract_group_ids"), None)
+ )
+ bilibili_auto_extract_private_ids = _coerce_int_list(
+ _get_value(data, ("bilibili", "auto_extract_private_ids"), None)
+ )
+
+ # arXiv 配置
+ arxiv_auto_extract_enabled = _coerce_bool(
+ _get_value(data, ("arxiv", "auto_extract_enabled"), None), False
+ )
+ arxiv_max_file_size = _coerce_int(
+ _get_value(data, ("arxiv", "max_file_size"), None), 100
+ )
+ if arxiv_max_file_size < 0:
+ arxiv_max_file_size = 100
+ arxiv_auto_extract_group_ids = _coerce_int_list(
+ _get_value(data, ("arxiv", "auto_extract_group_ids"), None)
+ )
+ arxiv_auto_extract_private_ids = _coerce_int_list(
+ _get_value(data, ("arxiv", "auto_extract_private_ids"), None)
+ )
+ arxiv_auto_extract_max_items = _coerce_int(
+ _get_value(data, ("arxiv", "auto_extract_max_items"), None), 5
+ )
+ if arxiv_auto_extract_max_items <= 0:
+ arxiv_auto_extract_max_items = 5
+ if arxiv_auto_extract_max_items > 20:
+ arxiv_auto_extract_max_items = 20
+ arxiv_author_preview_limit = _coerce_int(
+ _get_value(data, ("arxiv", "author_preview_limit"), None), 20
+ )
+ if arxiv_author_preview_limit <= 0:
+ arxiv_author_preview_limit = 20
+ if arxiv_author_preview_limit > 100:
+ arxiv_author_preview_limit = 100
+ arxiv_summary_preview_chars = _coerce_int(
+ _get_value(data, ("arxiv", "summary_preview_chars"), None), 1000
+ )
+ if arxiv_summary_preview_chars <= 0:
+ arxiv_summary_preview_chars = 1000
+ if arxiv_summary_preview_chars > 8000:
+ arxiv_summary_preview_chars = 8000
+
+ # GitHub 配置
+ github_auto_extract_enabled = _coerce_bool(
+ _get_value(data, ("github", "auto_extract_enabled"), None), False
+ )
+ github_request_timeout_seconds = _coerce_float(
+ _get_value(data, ("github", "request_timeout_seconds"), None), 10.0
+ )
+ if github_request_timeout_seconds <= 0:
+ github_request_timeout_seconds = 10.0
+ if github_request_timeout_seconds > 60.0:
+ github_request_timeout_seconds = 60.0
+ github_auto_extract_group_ids = _coerce_int_list(
+ _get_value(data, ("github", "auto_extract_group_ids"), None)
+ )
+ github_auto_extract_private_ids = _coerce_int_list(
+ _get_value(data, ("github", "auto_extract_private_ids"), None)
+ )
+ github_auto_extract_max_items = _coerce_int(
+ _get_value(data, ("github", "auto_extract_max_items"), None), 3
+ )
+ if github_auto_extract_max_items <= 0:
+ github_auto_extract_max_items = 3
+ if github_auto_extract_max_items > 10:
+ github_auto_extract_max_items = 10
+
+ # Code Delivery Agent 配置
+ code_delivery_enabled = _coerce_bool(
+ _get_value(data, ("code_delivery", "enabled"), None), True
+ )
+ code_delivery_task_root = _coerce_str(
+ _get_value(data, ("code_delivery", "task_root"), None),
+ "data/code_delivery",
+ )
+ code_delivery_docker_image = _coerce_str(
+ _get_value(data, ("code_delivery", "docker_image"), None),
+ "ubuntu:24.04",
+ )
+ code_delivery_container_name_prefix = _coerce_str(
+ _get_value(data, ("code_delivery", "container_name_prefix"), None),
+ "code_delivery_",
+ )
+ code_delivery_container_name_suffix = _coerce_str(
+ _get_value(data, ("code_delivery", "container_name_suffix"), None),
+ "_runner",
+ )
+ code_delivery_command_timeout = _coerce_int(
+ _get_value(data, ("code_delivery", "default_command_timeout_seconds"), None),
+ 600,
+ )
+ if code_delivery_command_timeout < 1:
+ code_delivery_command_timeout = 600
+ code_delivery_max_command_output = _coerce_int(
+ _get_value(data, ("code_delivery", "max_command_output_chars"), None),
+ 20000,
+ )
+ if code_delivery_max_command_output < 1:
+ code_delivery_max_command_output = 20000
+ code_delivery_default_archive_format = _coerce_str(
+ _get_value(data, ("code_delivery", "default_archive_format"), None),
+ "zip",
+ )
+ if code_delivery_default_archive_format not in ("zip", "tar.gz"):
+ code_delivery_default_archive_format = "zip"
+ code_delivery_max_archive_size_mb = _coerce_int(
+ _get_value(data, ("code_delivery", "max_archive_size_mb"), None), 200
+ )
+ if code_delivery_max_archive_size_mb < 1:
+ code_delivery_max_archive_size_mb = 200
+ code_delivery_cleanup_on_finish = _coerce_bool(
+ _get_value(data, ("code_delivery", "cleanup_on_finish"), None), True
+ )
+ code_delivery_cleanup_on_start = _coerce_bool(
+ _get_value(data, ("code_delivery", "cleanup_on_start"), None), True
+ )
+ code_delivery_llm_max_retries = _coerce_int(
+ _get_value(data, ("code_delivery", "llm_max_retries_per_request"), None),
+ 5,
+ )
+ if code_delivery_llm_max_retries < 0:
+ code_delivery_llm_max_retries = 5
+ code_delivery_notify_on_llm_failure = _coerce_bool(
+ _get_value(data, ("code_delivery", "notify_on_llm_failure"), None),
+ True,
+ )
+ code_delivery_container_memory_limit = _coerce_str(
+ _get_value(data, ("code_delivery", "container_memory_limit"), None),
+ "",
+ )
+ code_delivery_container_cpu_limit = _coerce_str(
+ _get_value(data, ("code_delivery", "container_cpu_limit"), None),
+ "",
+ )
+ code_delivery_command_blacklist_raw = _get_value(
+ data, ("code_delivery", "command_blacklist"), None
+ )
+ if isinstance(code_delivery_command_blacklist_raw, list):
+ code_delivery_command_blacklist = [
+ str(x) for x in code_delivery_command_blacklist_raw
+ ]
+ # 否则分支
+ else:
+ code_delivery_command_blacklist = []
+
+ # messages 工具集配置
+ messages_send_text_file_max_size_kb = _coerce_int(
+ _get_value(
+ data,
+ ("messages", "send_text_file_max_size_kb"),
+ "MESSAGES_SEND_TEXT_FILE_MAX_SIZE_KB",
+ ),
+ 512,
+ )
+ if messages_send_text_file_max_size_kb <= 0:
+ messages_send_text_file_max_size_kb = 512
+
+ messages_send_url_file_max_size_mb = _coerce_int(
+ _get_value(
+ data,
+ ("messages", "send_url_file_max_size_mb"),
+ "MESSAGES_SEND_URL_FILE_MAX_SIZE_MB",
+ ),
+ 100,
+ )
+ if messages_send_url_file_max_size_mb <= 0:
+ messages_send_url_file_max_size_mb = 100
+
+ return {
+ "bilibili_auto_extract_enabled": bilibili_auto_extract_enabled,
+ "bilibili_cookie": bilibili_cookie,
+ "bilibili_prefer_quality": bilibili_prefer_quality,
+ "bilibili_max_duration": bilibili_max_duration,
+ "bilibili_max_file_size": bilibili_max_file_size,
+ "bilibili_oversize_strategy": bilibili_oversize_strategy,
+ "bilibili_danmaku_enabled": bilibili_danmaku_enabled,
+ "bilibili_danmaku_batch_size": bilibili_danmaku_batch_size,
+ "bilibili_danmaku_max_count": bilibili_danmaku_max_count,
+ "bilibili_auto_extract_group_ids": bilibili_auto_extract_group_ids,
+ "bilibili_auto_extract_private_ids": bilibili_auto_extract_private_ids,
+ "arxiv_auto_extract_enabled": arxiv_auto_extract_enabled,
+ "arxiv_max_file_size": arxiv_max_file_size,
+ "arxiv_auto_extract_group_ids": arxiv_auto_extract_group_ids,
+ "arxiv_auto_extract_private_ids": arxiv_auto_extract_private_ids,
+ "arxiv_auto_extract_max_items": arxiv_auto_extract_max_items,
+ "arxiv_author_preview_limit": arxiv_author_preview_limit,
+ "arxiv_summary_preview_chars": arxiv_summary_preview_chars,
+ "github_auto_extract_enabled": github_auto_extract_enabled,
+ "github_request_timeout_seconds": github_request_timeout_seconds,
+ "github_auto_extract_group_ids": github_auto_extract_group_ids,
+ "github_auto_extract_private_ids": github_auto_extract_private_ids,
+ "github_auto_extract_max_items": github_auto_extract_max_items,
+ "code_delivery_enabled": code_delivery_enabled,
+ "code_delivery_task_root": code_delivery_task_root,
+ "code_delivery_docker_image": code_delivery_docker_image,
+ "code_delivery_container_name_prefix": code_delivery_container_name_prefix,
+ "code_delivery_container_name_suffix": code_delivery_container_name_suffix,
+ "code_delivery_command_timeout": code_delivery_command_timeout,
+ "code_delivery_max_command_output": code_delivery_max_command_output,
+ "code_delivery_default_archive_format": code_delivery_default_archive_format,
+ "code_delivery_max_archive_size_mb": code_delivery_max_archive_size_mb,
+ "code_delivery_cleanup_on_finish": code_delivery_cleanup_on_finish,
+ "code_delivery_cleanup_on_start": code_delivery_cleanup_on_start,
+ "code_delivery_llm_max_retries": code_delivery_llm_max_retries,
+ "code_delivery_notify_on_llm_failure": code_delivery_notify_on_llm_failure,
+ "code_delivery_container_memory_limit": code_delivery_container_memory_limit,
+ "code_delivery_container_cpu_limit": code_delivery_container_cpu_limit,
+ "code_delivery_command_blacklist": code_delivery_command_blacklist,
+ "messages_send_text_file_max_size_kb": messages_send_text_file_max_size_kb,
+ "messages_send_url_file_max_size_mb": messages_send_url_file_max_size_mb,
+ }
diff --git a/src/Undefined/config/load_sections/knowledge.py b/src/Undefined/config/load_sections/knowledge.py
new file mode 100644
index 00000000..266976f2
--- /dev/null
+++ b/src/Undefined/config/load_sections/knowledge.py
@@ -0,0 +1,122 @@
+"""Load knowledge config section."""
+
+from __future__ import annotations
+
+# 配置分段加载：按 table 解析 TOML → ctx 字段 dict
+
+import logging
+from pathlib import Path
+from typing import Any, Optional
+
+from ..coercers import (
+ _coerce_bool,
+ _coerce_float,
+ _coerce_int,
+ _coerce_str,
+ _get_value,
+)
+from ..model_parsers import (
+ _parse_embedding_model_config,
+ _parse_rerank_model_config,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def load_knowledge(
+ data: dict[str, Any], *, config_path: Optional[Path] = None
+) -> dict[str, Any]:
+ # 知识库段多数项仅读 TOML（env_key=None），避免与 embedding 模型 env 混淆
+ embedding_model = _parse_embedding_model_config(data)
+ rerank_model = _parse_rerank_model_config(data)
+
+ knowledge_enabled = _coerce_bool(
+ _get_value(data, ("knowledge", "enabled"), None), False
+ )
+ knowledge_base_dir = _coerce_str(
+ _get_value(data, ("knowledge", "base_dir"), None), "knowledge"
+ )
+ knowledge_auto_scan = _coerce_bool(
+ _get_value(data, ("knowledge", "auto_scan"), None), False
+ )
+ knowledge_auto_embed = _coerce_bool(
+ _get_value(data, ("knowledge", "auto_embed"), None), False
+ )
+ knowledge_scan_interval = _coerce_float(
+ _get_value(data, ("knowledge", "scan_interval"), None), 60.0
+ )
+ if knowledge_scan_interval <= 0:
+ knowledge_scan_interval = 60.0
+ knowledge_embed_batch_size = _coerce_int(
+ _get_value(data, ("knowledge", "embed_batch_size"), None), 64
+ )
+ if knowledge_embed_batch_size <= 0:
+ knowledge_embed_batch_size = 64
+ knowledge_chunk_size = _coerce_int(
+ _get_value(data, ("knowledge", "chunk_size"), None), 10
+ )
+ if knowledge_chunk_size <= 0:
+ knowledge_chunk_size = 10
+ knowledge_chunk_overlap = _coerce_int(
+ _get_value(data, ("knowledge", "chunk_overlap"), None), 2
+ )
+ if knowledge_chunk_overlap < 0:
+ knowledge_chunk_overlap = 0
+ if knowledge_chunk_overlap >= knowledge_chunk_size:
+ knowledge_chunk_overlap = max(0, knowledge_chunk_size - 1)
+ knowledge_default_top_k = _coerce_int(
+ _get_value(data, ("knowledge", "default_top_k"), None), 5
+ )
+ if knowledge_default_top_k <= 0:
+ knowledge_default_top_k = 5
+ knowledge_enable_rerank = _coerce_bool(
+ _get_value(data, ("knowledge", "enable_rerank"), None), False
+ )
+ knowledge_rerank_top_k = _coerce_int(
+ _get_value(data, ("knowledge", "rerank_top_k"), None), 3
+ )
+ if knowledge_rerank_top_k <= 0:
+ knowledge_rerank_top_k = 3
+ if knowledge_default_top_k <= 1 and knowledge_enable_rerank:
+ logger.warning(
+ "[配置] knowledge.default_top_k=%s，无法满足 rerank_top_k < default_top_k，"
+ "已自动禁用重排",
+ knowledge_default_top_k,
+ )
+ knowledge_enable_rerank = False
+ if knowledge_rerank_top_k >= knowledge_default_top_k:
+ fallback = knowledge_default_top_k - 1
+ if fallback <= 0:
+ fallback = 1
+ knowledge_enable_rerank = False
+ logger.warning(
+ "[配置] knowledge.rerank_top_k 需小于 knowledge.default_top_k，"
+ "且当前 default_top_k=%s 无法满足约束，已自动禁用重排",
+ knowledge_default_top_k,
+ )
+ # 否则分支
+ else:
+ logger.warning(
+ "[配置] knowledge.rerank_top_k 需小于 knowledge.default_top_k，"
+ "已回退: rerank_top_k=%s -> %s (default_top_k=%s)",
+ knowledge_rerank_top_k,
+ fallback,
+ knowledge_default_top_k,
+ )
+ knowledge_rerank_top_k = fallback
+
+ return {
+ "embedding_model": embedding_model,
+ "rerank_model": rerank_model,
+ "knowledge_enabled": knowledge_enabled,
+ "knowledge_base_dir": knowledge_base_dir,
+ "knowledge_auto_scan": knowledge_auto_scan,
+ "knowledge_auto_embed": knowledge_auto_embed,
+ "knowledge_scan_interval": knowledge_scan_interval,
+ "knowledge_embed_batch_size": knowledge_embed_batch_size,
+ "knowledge_chunk_size": knowledge_chunk_size,
+ "knowledge_chunk_overlap": knowledge_chunk_overlap,
+ "knowledge_default_top_k": knowledge_default_top_k,
+ "knowledge_enable_rerank": knowledge_enable_rerank,
+ "knowledge_rerank_top_k": knowledge_rerank_top_k,
+ }
diff --git a/src/Undefined/config/load_sections/logging_tools.py b/src/Undefined/config/load_sections/logging_tools.py
new file mode 100644
index 00000000..f5d6c06e
--- /dev/null
+++ b/src/Undefined/config/load_sections/logging_tools.py
@@ -0,0 +1,126 @@
+"""Load logging_tools config section."""
+
+from __future__ import annotations
+
+# 配置分段加载：按 table 解析 TOML → ctx 字段 dict
+
+import logging
+import os
+import re
+from pathlib import Path
+from typing import Any, Optional
+
+from ..coercers import (
+ _coerce_bool,
+ _coerce_int,
+ _coerce_str,
+ _get_value,
+ _warn_env_fallback,
+)
+from ..domain_parsers import (
+ _parse_easter_egg_call_mode,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def load_logging_tools(
+ data: dict[str, Any], *, config_path: Optional[Path] = None
+) -> dict[str, Any]:
+ log_level = _coerce_str(
+ _get_value(data, ("logging", "level"), "LOG_LEVEL"), "INFO"
+ ).upper()
+ log_file_path = _coerce_str(
+ _get_value(data, ("logging", "file_path"), "LOG_FILE_PATH"),
+ "logs/bot.log",
+ )
+ log_max_size_mb = _coerce_int(
+ _get_value(data, ("logging", "max_size_mb"), "LOG_MAX_SIZE_MB"), 10
+ )
+ if log_max_size_mb <= 0:
+ log_max_size_mb = 10
+ log_backup_count = _coerce_int(
+ _get_value(data, ("logging", "backup_count"), "LOG_BACKUP_COUNT"), 5
+ )
+ if log_backup_count < 0:
+ log_backup_count = 0
+ log_tty_enabled = _coerce_bool(
+ _get_value(data, ("logging", "tty_enabled"), "LOG_TTY_ENABLED"),
+ False,
+ )
+ log_thinking = _coerce_bool(
+ _get_value(data, ("logging", "log_thinking"), "LOG_THINKING"), True
+ )
+
+ tools_dot_delimiter = _coerce_str(
+ _get_value(data, ("tools", "dot_delimiter"), "TOOLS_DOT_DELIMITER"), "-_-"
+ ).strip()
+ if not tools_dot_delimiter:
+ tools_dot_delimiter = "-_-"
+ # dot_delimiter 必须满足 OpenAI 兼容的 function.name 约束。
+ if "." in tools_dot_delimiter or not re.fullmatch(
+ r"[a-zA-Z0-9_-]+", tools_dot_delimiter
+ ):
+ logger.warning(
+ "[配置] tools.dot_delimiter 非法（仅允许 [a-zA-Z0-9_-] 且不能包含 '.'），已回退默认值: '-_-'（当前=%s）",
+ tools_dot_delimiter,
+ )
+ tools_dot_delimiter = "-_-"
+ tools_description_max_len = _coerce_int(
+ _get_value(data, ("tools", "description_max_len"), "TOOLS_DESCRIPTION_MAX_LEN"),
+ 1024,
+ )
+ tools_description_truncate_enabled = _coerce_bool(
+ _get_value(
+ data,
+ ("tools", "description_truncate_enabled"),
+ "TOOLS_DESCRIPTION_TRUNCATE_ENABLED",
+ ),
+ False,
+ )
+ tools_sanitize_verbose = _coerce_bool(
+ _get_value(data, ("tools", "sanitize_verbose"), "TOOLS_SANITIZE_VERBOSE"),
+ False,
+ )
+ tools_description_preview_len = _coerce_int(
+ _get_value(
+ data,
+ ("tools", "description_preview_len"),
+ "TOOLS_DESCRIPTION_PREVIEW_LEN",
+ ),
+ 160,
+ )
+
+ easter_egg_mode_raw = _get_value(
+ data,
+ ("easter_egg", "agent_call_message_enabled"),
+ "EASTER_EGG_AGENT_CALL_MESSAGE_ENABLED",
+ )
+ if easter_egg_mode_raw is None:
+ easter_egg_mode_raw = os.getenv("EASTER_EGG_AGENT_CALL_MESSAGE_MODE")
+ if easter_egg_mode_raw is not None:
+ _warn_env_fallback("EASTER_EGG_AGENT_CALL_MESSAGE_MODE")
+ # 否则分支
+ else:
+ easter_egg_mode_raw = os.getenv("EASTER_EGG_CALL_MESSAGE_MODE")
+ if easter_egg_mode_raw is not None:
+ _warn_env_fallback("EASTER_EGG_CALL_MESSAGE_MODE")
+
+ easter_egg_agent_call_message_mode = _parse_easter_egg_call_mode(
+ easter_egg_mode_raw
+ )
+
+ return {
+ "log_level": log_level,
+ "log_file_path": log_file_path,
+ "log_max_size": log_max_size_mb * 1024 * 1024,
+ "log_backup_count": log_backup_count,
+ "log_tty_enabled": log_tty_enabled,
+ "log_thinking": log_thinking,
+ "tools_dot_delimiter": tools_dot_delimiter,
+ "tools_description_max_len": tools_description_max_len,
+ "tools_description_truncate_enabled": tools_description_truncate_enabled,
+ "tools_sanitize_verbose": tools_sanitize_verbose,
+ "tools_description_preview_len": tools_description_preview_len,
+ "easter_egg_agent_call_message_mode": easter_egg_agent_call_message_mode,
+ }
diff --git a/src/Undefined/config/load_sections/models.py b/src/Undefined/config/load_sections/models.py
new file mode 100644
index 00000000..1a3fff14
--- /dev/null
+++ b/src/Undefined/config/load_sections/models.py
@@ -0,0 +1,68 @@
+"""Load models config section."""
+
+from __future__ import annotations
+
+# 配置分段加载：按 table 解析 TOML → ctx 字段 dict
+
+import logging
+from pathlib import Path
+from typing import Any, Optional
+
+from ..coercers import (
+ _coerce_bool,
+ _get_value,
+)
+from ..model_parsers import (
+ _parse_agent_model_config,
+ _parse_chat_model_config,
+ _parse_grok_model_config,
+ _parse_historian_model_config,
+ _parse_naga_model_config,
+ _parse_security_model_config,
+ _parse_summary_model_config,
+ _parse_vision_model_config,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def load_models(
+ data: dict[str, Any], *, config_path: Optional[Path] = None
+) -> dict[str, Any]:
+ chat_model = _parse_chat_model_config(data)
+ vision_model = _parse_vision_model_config(data)
+ security_model_enabled = _coerce_bool(
+ _get_value(
+ data,
+ ("models", "security", "enabled"),
+ "SECURITY_MODEL_ENABLED",
+ ),
+ True,
+ )
+ # 未单独配置的模型段会回退到 chat/security/agent 等主模型
+ security_model = _parse_security_model_config(data, chat_model)
+ naga_model = _parse_naga_model_config(data, security_model)
+ agent_model = _parse_agent_model_config(data)
+ historian_model = _parse_historian_model_config(data, agent_model)
+ summary_model, summary_model_configured = _parse_summary_model_config(
+ data, agent_model
+ )
+ grok_model = _parse_grok_model_config(data)
+
+ model_pool_enabled = _coerce_bool(
+ _get_value(data, ("features", "pool_enabled"), "MODEL_POOL_ENABLED"), False
+ )
+
+ return {
+ "chat_model": chat_model,
+ "vision_model": vision_model,
+ "security_model_enabled": security_model_enabled,
+ "security_model": security_model,
+ "naga_model": naga_model,
+ "agent_model": agent_model,
+ "historian_model": historian_model,
+ "summary_model": summary_model,
+ "summary_model_configured": summary_model_configured,
+ "grok_model": grok_model,
+ "model_pool_enabled": model_pool_enabled,
+ }
diff --git a/src/Undefined/config/load_sections/network.py b/src/Undefined/config/load_sections/network.py
new file mode 100644
index 00000000..818b5555
--- /dev/null
+++ b/src/Undefined/config/load_sections/network.py
@@ -0,0 +1,161 @@
+"""Load network config section."""
+
+from __future__ import annotations
+
+# 配置分段加载：按 table 解析 TOML → ctx 字段 dict
+
+import logging
+import os
+from pathlib import Path
+from typing import Any, Optional
+
+from ..coercers import (
+ _coerce_bool,
+ _coerce_float,
+ _coerce_int,
+ _coerce_str,
+ _get_value,
+ _normalize_base_url,
+ _warn_env_fallback,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def load_network(
+ data: dict[str, Any], *, config_path: Optional[Path] = None
+) -> dict[str, Any]:
+ searxng_url = _coerce_str(
+ _get_value(data, ("search", "searxng_url"), "SEARXNG_URL"), ""
+ )
+ grok_search_enabled = _coerce_bool(
+ _get_value(
+ data,
+ ("search", "grok_search_enabled"),
+ "GROK_SEARCH_ENABLED",
+ ),
+ False,
+ )
+
+ use_proxy = _coerce_bool(
+ _get_value(data, ("proxy", "use_proxy"), "USE_PROXY"), True
+ )
+ http_proxy = _coerce_str(
+ _get_value(data, ("proxy", "http_proxy"), "http_proxy"), ""
+ )
+ # TOML 未配置时回退标准 HTTP_PROXY 环境变量（小写键名不走 ENV_REGISTRY）
+ if not http_proxy:
+ http_proxy = _coerce_str(os.getenv("HTTP_PROXY"), "")
+ if http_proxy:
+ _warn_env_fallback("HTTP_PROXY")
+ https_proxy = _coerce_str(
+ _get_value(data, ("proxy", "https_proxy"), "https_proxy"), ""
+ )
+ if not https_proxy:
+ https_proxy = _coerce_str(os.getenv("HTTPS_PROXY"), "")
+ if https_proxy:
+ _warn_env_fallback("HTTPS_PROXY")
+
+ network_request_timeout = _coerce_float(
+ _get_value(
+ data,
+ ("network", "request_timeout_seconds"),
+ "NETWORK_REQUEST_TIMEOUT_SECONDS",
+ ),
+ 30.0,
+ )
+ if network_request_timeout <= 0:
+ network_request_timeout = 480.0
+
+ network_request_retries = _coerce_int(
+ _get_value(
+ data,
+ ("network", "request_retries"),
+ "NETWORK_REQUEST_RETRIES",
+ ),
+ 0,
+ )
+ if network_request_retries < 0:
+ network_request_retries = 0
+ if network_request_retries > 5:
+ network_request_retries = 5
+
+ render_browser_max_concurrency = max(
+ 0,
+ _coerce_int(
+ _get_value(
+ data,
+ ("render", "browser_max_concurrency"),
+ "RENDER_BROWSER_MAX_CONCURRENCY",
+ ),
+ 0,
+ ),
+ )
+
+ api_xxapi_base_url = _normalize_base_url(
+ _coerce_str(
+ _get_value(data, ("api_endpoints", "xxapi_base_url"), "XXAPI_BASE_URL"),
+ "https://v2.xxapi.cn",
+ ),
+ "https://v2.xxapi.cn",
+ )
+ api_xingzhige_base_url = _normalize_base_url(
+ _coerce_str(
+ _get_value(
+ data,
+ ("api_endpoints", "xingzhige_base_url"),
+ "XINGZHIGE_BASE_URL",
+ ),
+ "https://api.xingzhige.com",
+ ),
+ "https://api.xingzhige.com",
+ )
+ api_jkyai_base_url = _normalize_base_url(
+ _coerce_str(
+ _get_value(data, ("api_endpoints", "jkyai_base_url"), "JKYAI_BASE_URL"),
+ "https://api.jkyai.top",
+ ),
+ "https://api.jkyai.top",
+ )
+ api_seniverse_base_url = _normalize_base_url(
+ _coerce_str(
+ _get_value(
+ data,
+ ("api_endpoints", "seniverse_base_url"),
+ "SENIVERSE_BASE_URL",
+ ),
+ "https://api.seniverse.com/v3",
+ ),
+ "https://api.seniverse.com/v3",
+ )
+
+ weather_api_key = _coerce_str(
+ _get_value(data, ("weather", "api_key"), "WEATHER_API_KEY"), ""
+ )
+ xxapi_api_token = _coerce_str(
+ _get_value(data, ("xxapi", "api_token"), "XXAPI_API_TOKEN"), ""
+ )
+
+ mcp_config_path = _coerce_str(
+ _get_value(data, ("mcp", "config_path"), "MCP_CONFIG_PATH"),
+ "config/mcp.json",
+ )
+
+ # Bilibili 配置
+ return {
+ "searxng_url": searxng_url,
+ "grok_search_enabled": grok_search_enabled,
+ "use_proxy": use_proxy,
+ "http_proxy": http_proxy,
+ "https_proxy": https_proxy,
+ "network_request_timeout": network_request_timeout,
+ "network_request_retries": network_request_retries,
+ "render_browser_max_concurrency": render_browser_max_concurrency,
+ "api_xxapi_base_url": api_xxapi_base_url,
+ "api_xingzhige_base_url": api_xingzhige_base_url,
+ "api_jkyai_base_url": api_jkyai_base_url,
+ "api_seniverse_base_url": api_seniverse_base_url,
+ "weather_api_key": weather_api_key,
+ "xxapi_api_token": xxapi_api_token,
+ "mcp_config_path": mcp_config_path,
+ }
diff --git a/src/Undefined/config/loader.py b/src/Undefined/config/loader.py
index dc49fa38..708da82a 100644
--- a/src/Undefined/config/loader.py
+++ b/src/Undefined/config/loader.py
@@ -1,119 +1,22 @@
-"""配置加载逻辑"""
+"""配置加载逻辑（向后兼容 shim；Config 实现见 config_class）。"""
from __future__ import annotations
-import logging
-import os
-import re
-import tomllib
-from dataclasses import dataclass, field as dataclass_field, fields
-from pathlib import Path
-from typing import Any, Optional, IO
-
-try:
- from dotenv import load_dotenv
-except Exception: # pragma: no cover
- StrPath = str | os.PathLike[str]
-
- def load_dotenv(
- dotenv_path: StrPath | None = None,
- stream: IO[str] | None = None,
- verbose: bool = False,
- override: bool = False,
- interpolate: bool = True,
- encoding: str | None = "utf-8",
- ) -> bool:
- return False
-
-
-from .models import (
- APIConfig,
- AgentModelConfig,
- ChatModelConfig,
- CognitiveConfig,
- EmbeddingModelConfig,
- GrokModelConfig,
- ImageGenConfig,
- ImageGenModelConfig,
- MemeConfig,
- MessageBatcherConfig,
- NagaConfig,
- RenderCacheConfig,
- RerankModelConfig,
- SecurityModelConfig,
- VisionModelConfig,
-)
-from .coercers import ( # noqa: F401 — re-exported for backward compat
- _coerce_bool,
- _coerce_float,
- _coerce_int,
- _coerce_int_list,
- _coerce_str,
- _coerce_str_list,
- _get_model_request_params,
- _get_value,
- _normalize_base_url,
- _normalize_queue_interval,
- _normalize_str,
- _warn_env_fallback,
-)
-from .resolvers import ( # noqa: F401 — re-exported for backward compat
- _resolve_api_mode,
- _resolve_reasoning_effort,
- _resolve_reasoning_effort_style,
- _resolve_responses_force_stateless_replay,
- _resolve_responses_tool_choice_compat,
- _resolve_thinking_compat_flags,
-)
-from .admin import ( # noqa: F401 — re-exported for backward compat
- LOCAL_CONFIG_PATH,
- load_local_admins,
- save_local_admins,
-)
-from .webui_settings import ( # noqa: F401 — re-exported for backward compat
+from .admin import LOCAL_CONFIG_PATH, load_local_admins, save_local_admins
+from .config_class import Config, ConfigBuilder
+from .toml_io import CONFIG_PATH, load_toml_data
+from .webui_settings import (
DEFAULT_WEBUI_PASSWORD,
DEFAULT_WEBUI_PORT,
DEFAULT_WEBUI_URL,
WebUISettings,
load_webui_settings,
)
-from .model_parsers import (
- _log_debug_info,
- _merge_admins,
- _parse_agent_model_config,
- _parse_chat_model_config,
- _parse_embedding_model_config,
- _parse_grok_model_config,
- _parse_historian_model_config,
- _parse_image_edit_model_config,
- _parse_image_gen_config,
- _parse_image_gen_model_config,
- _parse_naga_model_config,
- _parse_rerank_model_config,
- _parse_security_model_config,
- _parse_summary_model_config,
- _parse_vision_model_config,
- _verify_required_fields,
-)
-from .domain_parsers import (
- _parse_api_config,
- _parse_cognitive_config,
- _parse_easter_egg_call_mode,
- _parse_memes_config,
- _parse_message_batcher_config,
- _parse_naga_config,
- _parse_render_cache_config,
- _update_dataclass,
-)
-
-logger = logging.getLogger(__name__)
-CONFIG_PATH = Path("config.toml")
-
-# Re-export symbols that external modules import from this module.
__all__ = [
"CONFIG_PATH",
"Config",
+ "ConfigBuilder",
"DEFAULT_WEBUI_PASSWORD",
"DEFAULT_WEBUI_PORT",
"DEFAULT_WEBUI_URL",
@@ -124,1681 +27,3 @@ def load_dotenv(
"load_webui_settings",
"save_local_admins",
]
-
-
-def _load_env() -> None:
- try:
- load_dotenv()
- except Exception:
- logger.debug("加载 .env 失败，继续使用 config.toml", exc_info=True)
-
-
-def _build_toml_decode_hint(line: str) -> str:
- hints: list[str] = []
- if "\\" in line:
- hints.append(
- 'Windows 路径建议用单引号(不转义)或双反斜杠，或直接用正斜杠，例如：path = \'D:\\AI\\bot\' / path = "D:\\\\AI\\\\bot" / path = "D:/AI/bot"'
- )
- hints.append('多行文本请用三引号，例如：prompt = """..."""')
- return "；".join(hints)
-
-
-def _format_toml_decode_error(
- path: Path, text: str, exc: tomllib.TOMLDecodeError
-) -> str:
- lineno: int | None = getattr(exc, "lineno", None)
- colno: int | None = getattr(exc, "colno", None)
- if not isinstance(lineno, int) or not isinstance(colno, int):
- match = re.search(r"\(at line (\d+), column (\d+)\)", str(exc))
- if match:
- lineno = int(match.group(1))
- colno = int(match.group(2))
-
- if isinstance(lineno, int) and lineno > 0:
- lines = text.splitlines()
- line = lines[lineno - 1] if 0 <= (lineno - 1) < len(lines) else ""
- caret_pos = max((colno or 1) - 1, 0)
- caret = " " * min(caret_pos, len(line)) + "^"
- hint = _build_toml_decode_hint(line)
- location = f"line={lineno} col={colno or 1}"
- return f"{exc} ({location})\n> {line}\n {caret}\n提示：{hint}"
- return str(exc)
-
-
-def load_toml_data(
- config_path: Optional[Path] = None, *, strict: bool = False
-) -> dict[str, Any]:
- """读取 config.toml 并返回字典"""
- path = config_path or CONFIG_PATH
- if not path.exists():
- return {}
- text = ""
- try:
- text = path.read_bytes().decode("utf-8-sig")
- data = tomllib.loads(text)
- if isinstance(data, dict):
- return data
- logger.warning("config.toml 内容不是对象结构")
- return {}
- except tomllib.TOMLDecodeError as exc:
- message = _format_toml_decode_error(path, text, exc)
- logger.error("config.toml 解析失败 (%s): %s", path.resolve(), message)
- if strict:
- raise ValueError(message) from exc
- return {}
- except UnicodeDecodeError as exc:
- logger.error("config.toml 编码错误 (%s): %s", path.resolve(), exc)
- if strict:
- raise ValueError(str(exc)) from exc
- return {}
- except OSError as exc:
- logger.error("读取 config.toml 失败: %s", exc)
- if strict:
- raise ValueError(str(exc)) from exc
- return {}
-
-
-@dataclass
-class Config:
- """应用配置"""
-
- bot_qq: int
- superadmin_qq: int
- admin_qqs: list[int]
- # 访问控制模式：off / blacklist / allowlist
- access_mode: str
- # 访问控制（会话白名单 + 黑名单）
- allowed_group_ids: list[int]
- blocked_group_ids: list[int]
- allowed_private_ids: list[int]
- blocked_private_ids: list[int]
- # 是否允许超级管理员在私聊中绕过 allowed_private_ids（仅私聊收发）
- superadmin_bypass_allowlist: bool
- # 是否允许超级管理员在私聊中绕过 blocked_private_ids（仅私聊收发）
- superadmin_bypass_private_blacklist: bool
- forward_proxy_qq: int | None
- process_every_message: bool
- process_private_message: bool
- process_poke_message: bool
- keyword_reply_enabled: bool
- repeat_enabled: bool
- repeat_threshold: int
- repeat_cooldown_minutes: int
- inverted_question_enabled: bool
- context_recent_messages_limit: int
- ai_request_max_retries: int
- missing_tool_call_retries: int
- nagaagent_mode_enabled: bool
- onebot_ws_url: str
- onebot_token: str
- chat_model: ChatModelConfig
- vision_model: VisionModelConfig
- security_model_enabled: bool
- security_model: SecurityModelConfig
- naga_model: SecurityModelConfig
- agent_model: AgentModelConfig
- historian_model: AgentModelConfig
- summary_model: AgentModelConfig
- summary_model_configured: bool
- grok_model: GrokModelConfig
- model_pool_enabled: bool
- log_level: str
- log_file_path: str
- log_max_size: int
- log_backup_count: int
- log_tty_enabled: bool
- log_thinking: bool
- tools_dot_delimiter: str
- tools_description_truncate_enabled: bool
- tools_description_max_len: int
- tools_sanitize_verbose: bool
- tools_description_preview_len: int
- easter_egg_agent_call_message_mode: str
- token_usage_max_size_mb: int
- token_usage_max_archives: int
- token_usage_max_total_mb: int
- token_usage_archive_prune_mode: str
- history_max_records: int
- history_filtered_result_limit: int
- history_search_scan_limit: int
- history_summary_fetch_limit: int
- history_summary_time_fetch_limit: int
- history_onebot_fetch_limit: int
- history_group_analysis_limit: int
- attachment_remote_download_max_size_mb: int
- attachment_cache_max_total_size_mb: int
- attachment_cache_max_records: int
- attachment_cache_max_age_days: int
- attachment_url_reference_max_records: int
- attachment_url_max_length: int
- skills_hot_reload: bool
- skills_hot_reload_interval: float
- skills_hot_reload_debounce: float
- agent_intro_autogen_enabled: bool
- agent_intro_autogen_queue_interval: float
- agent_intro_autogen_max_tokens: int
- agent_intro_hash_path: str
- searxng_url: str
- grok_search_enabled: bool
- use_proxy: bool
- http_proxy: str
- https_proxy: str
- network_request_timeout: float
- network_request_retries: int
- render_browser_max_concurrency: int
- api_xxapi_base_url: str
- api_xingzhige_base_url: str
- api_jkyai_base_url: str
- api_seniverse_base_url: str
- weather_api_key: str
- xxapi_api_token: str
- mcp_config_path: str
- prefetch_tools: list[str]
- prefetch_tools_hide: bool
- webui_url: str
- webui_port: int
- webui_password: str
- api: APIConfig
- # Code Delivery Agent
- code_delivery_enabled: bool
- code_delivery_task_root: str
- code_delivery_docker_image: str
- code_delivery_container_name_prefix: str
- code_delivery_container_name_suffix: str
- code_delivery_command_timeout: int
- code_delivery_max_command_output: int
- code_delivery_default_archive_format: str
- code_delivery_max_archive_size_mb: int
- code_delivery_cleanup_on_finish: bool
- code_delivery_cleanup_on_start: bool
- code_delivery_llm_max_retries: int
- code_delivery_notify_on_llm_failure: bool
- code_delivery_container_memory_limit: str
- code_delivery_container_cpu_limit: str
- code_delivery_command_blacklist: list[str]
- # messages 工具集
- messages_send_text_file_max_size_kb: int
- messages_send_url_file_max_size_mb: int
- # 嵌入模型
- embedding_model: EmbeddingModelConfig
- rerank_model: RerankModelConfig
- # 知识库
- knowledge_enabled: bool
- knowledge_base_dir: str
- knowledge_auto_scan: bool
- knowledge_auto_embed: bool
- knowledge_scan_interval: float
- knowledge_embed_batch_size: int
- knowledge_chunk_size: int
- knowledge_chunk_overlap: int
- knowledge_default_top_k: int
- knowledge_enable_rerank: bool
- knowledge_rerank_top_k: int
- # Bilibili 视频提取
- bilibili_auto_extract_enabled: bool
- bilibili_cookie: str
- bilibili_prefer_quality: int
- bilibili_max_duration: int
- bilibili_max_file_size: int
- bilibili_oversize_strategy: str
- bilibili_danmaku_enabled: bool
- bilibili_danmaku_batch_size: int
- bilibili_danmaku_max_count: int
- bilibili_auto_extract_group_ids: list[int]
- bilibili_auto_extract_private_ids: list[int]
- # arXiv 论文提取
- arxiv_auto_extract_enabled: bool
- arxiv_max_file_size: int
- arxiv_auto_extract_group_ids: list[int]
- arxiv_auto_extract_private_ids: list[int]
- arxiv_auto_extract_max_items: int
- arxiv_author_preview_limit: int
- arxiv_summary_preview_chars: int
- # GitHub 仓库自动提取
- github_auto_extract_enabled: bool
- github_request_timeout_seconds: float
- github_auto_extract_group_ids: list[int]
- github_auto_extract_private_ids: list[int]
- github_auto_extract_max_items: int
- # 认知记忆
- cognitive: CognitiveConfig
- # 表情包库
- memes: MemeConfig
- # 同 sender 短时多消息合并器
- message_batcher: MessageBatcherConfig
- # HTML 渲染结果缓存
- render_cache: RenderCacheConfig
- # Naga 集成
- naga: NagaConfig
- # 生图工具配置
- image_gen: ImageGenConfig
- models_image_gen: ImageGenModelConfig
- models_image_edit: ImageGenModelConfig
- _allowed_group_ids_set: set[int] = dataclass_field(
- default_factory=set,
- init=False,
- repr=False,
- )
- _blocked_group_ids_set: set[int] = dataclass_field(
- default_factory=set,
- init=False,
- repr=False,
- )
- _allowed_private_ids_set: set[int] = dataclass_field(
- default_factory=set,
- init=False,
- repr=False,
- )
- _blocked_private_ids_set: set[int] = dataclass_field(
- default_factory=set,
- init=False,
- repr=False,
- )
- _bilibili_group_ids_set: set[int] = dataclass_field(
- default_factory=set,
- init=False,
- repr=False,
- )
- _bilibili_private_ids_set: set[int] = dataclass_field(
- default_factory=set,
- init=False,
- repr=False,
- )
- _arxiv_group_ids_set: set[int] = dataclass_field(
- default_factory=set,
- init=False,
- repr=False,
- )
- _arxiv_private_ids_set: set[int] = dataclass_field(
- default_factory=set,
- init=False,
- repr=False,
- )
- _github_group_ids_set: set[int] = dataclass_field(
- default_factory=set,
- init=False,
- repr=False,
- )
- _github_private_ids_set: set[int] = dataclass_field(
- default_factory=set,
- init=False,
- repr=False,
- )
-
- def __post_init__(self) -> None:
- # 访问控制属于高频热路径，启动后缓存为 set 降低重复构建开销。
- normalized_mode = str(self.access_mode).strip().lower()
- if normalized_mode not in {"off", "blacklist", "allowlist", "legacy"}:
- normalized_mode = "off"
- self.access_mode = normalized_mode
- self._allowed_group_ids_set = {int(item) for item in self.allowed_group_ids}
- self._blocked_group_ids_set = {int(item) for item in self.blocked_group_ids}
- self._allowed_private_ids_set = {int(item) for item in self.allowed_private_ids}
- self._blocked_private_ids_set = {int(item) for item in self.blocked_private_ids}
- self._bilibili_group_ids_set = {
- int(item) for item in self.bilibili_auto_extract_group_ids
- }
- self._bilibili_private_ids_set = {
- int(item) for item in self.bilibili_auto_extract_private_ids
- }
- self._arxiv_group_ids_set = {
- int(item) for item in self.arxiv_auto_extract_group_ids
- }
- self._arxiv_private_ids_set = {
- int(item) for item in self.arxiv_auto_extract_private_ids
- }
- self._github_group_ids_set = {
- int(item) for item in self.github_auto_extract_group_ids
- }
- self._github_private_ids_set = {
- int(item) for item in self.github_auto_extract_private_ids
- }
-
- @classmethod
- def load(cls, config_path: Optional[Path] = None, strict: bool = True) -> "Config":
- """从 config.toml 和本地配置加载配置"""
- _load_env()
- data = load_toml_data(config_path, strict=strict)
-
- bot_qq = _coerce_int(_get_value(data, ("core", "bot_qq"), "BOT_QQ"), 0)
- superadmin_qq = _coerce_int(
- _get_value(data, ("core", "superadmin_qq"), "SUPERADMIN_QQ"), 0
- )
- admin_qqs = _coerce_int_list(_get_value(data, ("core", "admin_qq"), "ADMIN_QQ"))
- forward_proxy = _coerce_int(
- _get_value(data, ("core", "forward_proxy_qq"), "FORWARD_PROXY_QQ"),
- 0,
- )
- forward_proxy_qq = forward_proxy if forward_proxy > 0 else None
- process_every_message = _coerce_bool(
- _get_value(
- data,
- ("core", "process_every_message"),
- "PROCESS_EVERY_MESSAGE",
- ),
- True,
- )
- process_private_message = _coerce_bool(
- _get_value(
- data,
- ("core", "process_private_message"),
- "PROCESS_PRIVATE_MESSAGE",
- ),
- True,
- )
- process_poke_message = _coerce_bool(
- _get_value(
- data,
- ("core", "process_poke_message"),
- "PROCESS_POKE_MESSAGE",
- ),
- True,
- )
- keyword_reply_raw = _get_value(
- data,
- ("easter_egg", "keyword_reply_enabled"),
- "KEYWORD_REPLY_ENABLED",
- )
- if keyword_reply_raw is None:
- # 兼容旧配置：历史上放在 [core].keyword_reply_enabled
- keyword_reply_raw = _get_value(
- data,
- ("core", "keyword_reply_enabled"),
- None,
- )
- keyword_reply_enabled = _coerce_bool(keyword_reply_raw, False)
- repeat_enabled = _coerce_bool(
- _get_value(
- data,
- ("easter_egg", "repeat_enabled"),
- "EASTER_EGG_REPEAT_ENABLED",
- ),
- False,
- )
- inverted_question_enabled = _coerce_bool(
- _get_value(
- data,
- ("easter_egg", "inverted_question_enabled"),
- "EASTER_EGG_INVERTED_QUESTION_ENABLED",
- ),
- False,
- )
- repeat_threshold = _coerce_int(
- _get_value(
- data,
- ("easter_egg", "repeat_threshold"),
- "EASTER_EGG_REPEAT_THRESHOLD",
- ),
- 3,
- )
- if repeat_threshold < 2:
- repeat_threshold = 2
- if repeat_threshold > 20:
- repeat_threshold = 20
- repeat_cooldown_minutes = _coerce_int(
- _get_value(
- data,
- ("easter_egg", "repeat_cooldown_minutes"),
- "EASTER_EGG_REPEAT_COOLDOWN_MINUTES",
- ),
- 60,
- )
- if repeat_cooldown_minutes < 0:
- repeat_cooldown_minutes = 0
- context_recent_messages_limit = _coerce_int(
- _get_value(
- data,
- ("core", "context_recent_messages_limit"),
- "CONTEXT_RECENT_MESSAGES_LIMIT",
- ),
- 20,
- )
- if context_recent_messages_limit < 0:
- context_recent_messages_limit = 0
-
- ai_request_max_retries = _coerce_int(
- _get_value(
- data,
- ("core", "ai_request_max_retries"),
- "AI_REQUEST_MAX_RETRIES",
- ),
- 2,
- )
- if ai_request_max_retries < 0:
- ai_request_max_retries = 0
-
- missing_tool_call_retries = _coerce_int(
- _get_value(
- data,
- ("core", "missing_tool_call_retries"),
- "MISSING_TOOL_CALL_RETRIES",
- ),
- 3,
- )
- if missing_tool_call_retries < 0:
- missing_tool_call_retries = 0
-
- nagaagent_mode_enabled = _coerce_bool(
- _get_value(
- data,
- ("features", "nagaagent_mode_enabled"),
- "NAGAAGENT_MODE_ENABLED",
- ),
- False,
- )
- onebot_ws_url = _coerce_str(
- _get_value(data, ("onebot", "ws_url"), "ONEBOT_WS_URL"), ""
- )
- onebot_token = _coerce_str(
- _get_value(data, ("onebot", "token"), "ONEBOT_TOKEN"), ""
- )
-
- embedding_model = _parse_embedding_model_config(data)
- rerank_model = _parse_rerank_model_config(data)
-
- knowledge_enabled = _coerce_bool(
- _get_value(data, ("knowledge", "enabled"), None), False
- )
- knowledge_base_dir = _coerce_str(
- _get_value(data, ("knowledge", "base_dir"), None), "knowledge"
- )
- knowledge_auto_scan = _coerce_bool(
- _get_value(data, ("knowledge", "auto_scan"), None), False
- )
- knowledge_auto_embed = _coerce_bool(
- _get_value(data, ("knowledge", "auto_embed"), None), False
- )
- knowledge_scan_interval = _coerce_float(
- _get_value(data, ("knowledge", "scan_interval"), None), 60.0
- )
- if knowledge_scan_interval <= 0:
- knowledge_scan_interval = 60.0
- knowledge_embed_batch_size = _coerce_int(
- _get_value(data, ("knowledge", "embed_batch_size"), None), 64
- )
- if knowledge_embed_batch_size <= 0:
- knowledge_embed_batch_size = 64
- knowledge_chunk_size = _coerce_int(
- _get_value(data, ("knowledge", "chunk_size"), None), 10
- )
- if knowledge_chunk_size <= 0:
- knowledge_chunk_size = 10
- knowledge_chunk_overlap = _coerce_int(
- _get_value(data, ("knowledge", "chunk_overlap"), None), 2
- )
- if knowledge_chunk_overlap < 0:
- knowledge_chunk_overlap = 0
- knowledge_default_top_k = _coerce_int(
- _get_value(data, ("knowledge", "default_top_k"), None), 5
- )
- if knowledge_default_top_k <= 0:
- knowledge_default_top_k = 5
- knowledge_enable_rerank = _coerce_bool(
- _get_value(data, ("knowledge", "enable_rerank"), None), False
- )
- knowledge_rerank_top_k = _coerce_int(
- _get_value(data, ("knowledge", "rerank_top_k"), None), 3
- )
- if knowledge_rerank_top_k <= 0:
- knowledge_rerank_top_k = 3
- if knowledge_default_top_k <= 1 and knowledge_enable_rerank:
- logger.warning(
- "[配置] knowledge.default_top_k=%s，无法满足 rerank_top_k < default_top_k，"
- "已自动禁用重排",
- knowledge_default_top_k,
- )
- knowledge_enable_rerank = False
- if knowledge_rerank_top_k >= knowledge_default_top_k:
- fallback = knowledge_default_top_k - 1
- if fallback <= 0:
- fallback = 1
- knowledge_enable_rerank = False
- logger.warning(
- "[配置] knowledge.rerank_top_k 需小于 knowledge.default_top_k，"
- "且当前 default_top_k=%s 无法满足约束，已自动禁用重排",
- knowledge_default_top_k,
- )
- else:
- logger.warning(
- "[配置] knowledge.rerank_top_k 需小于 knowledge.default_top_k，"
- "已回退: rerank_top_k=%s -> %s (default_top_k=%s)",
- knowledge_rerank_top_k,
- fallback,
- knowledge_default_top_k,
- )
- knowledge_rerank_top_k = fallback
-
- chat_model = _parse_chat_model_config(data)
- vision_model = _parse_vision_model_config(data)
- security_model_enabled = _coerce_bool(
- _get_value(
- data,
- ("models", "security", "enabled"),
- "SECURITY_MODEL_ENABLED",
- ),
- True,
- )
- security_model = _parse_security_model_config(data, chat_model)
- naga_model = _parse_naga_model_config(data, security_model)
- agent_model = _parse_agent_model_config(data)
- historian_model = _parse_historian_model_config(data, agent_model)
- summary_model, summary_model_configured = _parse_summary_model_config(
- data, agent_model
- )
- grok_model = _parse_grok_model_config(data)
-
- model_pool_enabled = _coerce_bool(
- _get_value(data, ("features", "pool_enabled"), "MODEL_POOL_ENABLED"), False
- )
-
- superadmin_qq, admin_qqs = _merge_admins(
- superadmin_qq=superadmin_qq, admin_qqs=admin_qqs
- )
-
- access_mode_raw = _get_value(data, ("access", "mode"), "ACCESS_MODE")
- allowed_group_ids = _coerce_int_list(
- _get_value(data, ("access", "allowed_group_ids"), "ALLOWED_GROUP_IDS")
- )
- blocked_group_ids = _coerce_int_list(
- _get_value(data, ("access", "blocked_group_ids"), "BLOCKED_GROUP_IDS")
- )
- allowed_private_ids = _coerce_int_list(
- _get_value(data, ("access", "allowed_private_ids"), "ALLOWED_PRIVATE_IDS")
- )
- blocked_private_ids = _coerce_int_list(
- _get_value(data, ("access", "blocked_private_ids"), "BLOCKED_PRIVATE_IDS")
- )
- superadmin_bypass_allowlist = _coerce_bool(
- _get_value(
- data,
- ("access", "superadmin_bypass_allowlist"),
- "SUPERADMIN_BYPASS_ALLOWLIST",
- ),
- True,
- )
- superadmin_bypass_private_blacklist = _coerce_bool(
- _get_value(
- data,
- ("access", "superadmin_bypass_private_blacklist"),
- "SUPERADMIN_BYPASS_PRIVATE_BLACKLIST",
- ),
- False,
- )
- if access_mode_raw is None:
- # 兼容旧配置：未配置 mode 时沿用历史行为（群黑名单 + 白名单联动）。
- if (
- allowed_group_ids
- or blocked_group_ids
- or allowed_private_ids
- or blocked_private_ids
- ):
- access_mode = "legacy"
- logger.warning(
- "[配置] access.mode 未设置，已启用兼容模式（legacy）。建议显式设置为 off/blacklist/allowlist。"
- )
- else:
- access_mode = "off"
- else:
- access_mode = _coerce_str(access_mode_raw, "off").lower()
- if access_mode not in {"off", "blacklist", "allowlist"}:
- logger.warning(
- "[配置] access.mode 非法（仅支持 off/blacklist/allowlist），已回退为 off: %s",
- access_mode,
- )
- access_mode = "off"
-
- log_level = _coerce_str(
- _get_value(data, ("logging", "level"), "LOG_LEVEL"), "INFO"
- ).upper()
- log_file_path = _coerce_str(
- _get_value(data, ("logging", "file_path"), "LOG_FILE_PATH"),
- "logs/bot.log",
- )
- log_max_size_mb = _coerce_int(
- _get_value(data, ("logging", "max_size_mb"), "LOG_MAX_SIZE_MB"), 10
- )
- log_backup_count = _coerce_int(
- _get_value(data, ("logging", "backup_count"), "LOG_BACKUP_COUNT"), 5
- )
- log_tty_enabled = _coerce_bool(
- _get_value(data, ("logging", "tty_enabled"), "LOG_TTY_ENABLED"),
- False,
- )
- log_thinking = _coerce_bool(
- _get_value(data, ("logging", "log_thinking"), "LOG_THINKING"), True
- )
-
- tools_dot_delimiter = _coerce_str(
- _get_value(data, ("tools", "dot_delimiter"), "TOOLS_DOT_DELIMITER"), "-_-"
- ).strip()
- if not tools_dot_delimiter:
- tools_dot_delimiter = "-_-"
- # dot_delimiter 必须满足 OpenAI 兼容的 function.name 约束。
- if "." in tools_dot_delimiter or not re.fullmatch(
- r"[a-zA-Z0-9_-]+", tools_dot_delimiter
- ):
- logger.warning(
- "[配置] tools.dot_delimiter 非法（仅允许 [a-zA-Z0-9_-] 且不能包含 '.'），已回退默认值: '-_-'（当前=%s）",
- tools_dot_delimiter,
- )
- tools_dot_delimiter = "-_-"
- tools_description_max_len = _coerce_int(
- _get_value(
- data, ("tools", "description_max_len"), "TOOLS_DESCRIPTION_MAX_LEN"
- ),
- 1024,
- )
- tools_description_truncate_enabled = _coerce_bool(
- _get_value(
- data,
- ("tools", "description_truncate_enabled"),
- "TOOLS_DESCRIPTION_TRUNCATE_ENABLED",
- ),
- False,
- )
- tools_sanitize_verbose = _coerce_bool(
- _get_value(data, ("tools", "sanitize_verbose"), "TOOLS_SANITIZE_VERBOSE"),
- False,
- )
- tools_description_preview_len = _coerce_int(
- _get_value(
- data,
- ("tools", "description_preview_len"),
- "TOOLS_DESCRIPTION_PREVIEW_LEN",
- ),
- 160,
- )
-
- easter_egg_mode_raw = _get_value(
- data,
- ("easter_egg", "agent_call_message_enabled"),
- "EASTER_EGG_AGENT_CALL_MESSAGE_ENABLED",
- )
- if easter_egg_mode_raw is None:
- easter_egg_mode_raw = os.getenv("EASTER_EGG_AGENT_CALL_MESSAGE_MODE")
- if easter_egg_mode_raw is not None:
- _warn_env_fallback("EASTER_EGG_AGENT_CALL_MESSAGE_MODE")
- else:
- easter_egg_mode_raw = os.getenv("EASTER_EGG_CALL_MESSAGE_MODE")
- if easter_egg_mode_raw is not None:
- _warn_env_fallback("EASTER_EGG_CALL_MESSAGE_MODE")
-
- easter_egg_agent_call_message_mode = _parse_easter_egg_call_mode(
- easter_egg_mode_raw
- )
-
- token_usage_max_size_mb = _coerce_int(
- _get_value(data, ("token_usage", "max_size_mb"), "TOKEN_USAGE_MAX_SIZE_MB"),
- 5,
- )
- token_usage_max_archives = _coerce_int(
- _get_value(
- data, ("token_usage", "max_archives"), "TOKEN_USAGE_MAX_ARCHIVES"
- ),
- 30,
- )
- token_usage_max_total_mb = _coerce_int(
- _get_value(
- data, ("token_usage", "max_total_mb"), "TOKEN_USAGE_MAX_TOTAL_MB"
- ),
- 0,
- )
- token_usage_archive_prune_mode = _coerce_str(
- _get_value(
- data,
- ("token_usage", "archive_prune_mode"),
- "TOKEN_USAGE_ARCHIVE_PRUNE_MODE",
- ),
- "delete",
- )
-
- history_max_records = max(
- 0,
- _coerce_int(
- _get_value(data, ("history", "max_records"), "HISTORY_MAX_RECORDS"),
- 10000,
- ),
- )
- history_filtered_result_limit = max(
- 1,
- _coerce_int(
- _get_value(
- data,
- ("history", "filtered_result_limit"),
- "HISTORY_FILTERED_RESULT_LIMIT",
- ),
- 200,
- ),
- )
- history_search_scan_limit = max(
- 1,
- _coerce_int(
- _get_value(
- data,
- ("history", "search_scan_limit"),
- "HISTORY_SEARCH_SCAN_LIMIT",
- ),
- 10000,
- ),
- )
- history_summary_fetch_limit = max(
- 1,
- _coerce_int(
- _get_value(
- data,
- ("history", "summary_fetch_limit"),
- "HISTORY_SUMMARY_FETCH_LIMIT",
- ),
- 1000,
- ),
- )
- history_summary_time_fetch_limit = max(
- 1,
- _coerce_int(
- _get_value(
- data,
- ("history", "summary_time_fetch_limit"),
- "HISTORY_SUMMARY_TIME_FETCH_LIMIT",
- ),
- 5000,
- ),
- )
- history_onebot_fetch_limit = max(
- 1,
- _coerce_int(
- _get_value(
- data,
- ("history", "onebot_fetch_limit"),
- "HISTORY_ONEBOT_FETCH_LIMIT",
- ),
- 10000,
- ),
- )
- history_group_analysis_limit = max(
- 1,
- _coerce_int(
- _get_value(
- data,
- ("history", "group_analysis_limit"),
- "HISTORY_GROUP_ANALYSIS_LIMIT",
- ),
- 500,
- ),
- )
- attachment_remote_download_max_size_mb = max(
- 0,
- _coerce_int(
- _get_value(
- data,
- ("attachments", "remote_download_max_size_mb"),
- "ATTACHMENTS_REMOTE_DOWNLOAD_MAX_SIZE_MB",
- ),
- 25,
- ),
- )
- attachment_cache_max_total_size_mb = max(
- 0,
- _coerce_int(
- _get_value(
- data,
- ("attachments", "cache_max_total_size_mb"),
- "ATTACHMENTS_CACHE_MAX_TOTAL_SIZE_MB",
- ),
- 0,
- ),
- )
- attachment_cache_max_records = max(
- 0,
- _coerce_int(
- _get_value(
- data,
- ("attachments", "cache_max_records"),
- "ATTACHMENTS_CACHE_MAX_RECORDS",
- ),
- 2000,
- ),
- )
- attachment_cache_max_age_days = max(
- 0,
- _coerce_int(
- _get_value(
- data,
- ("attachments", "cache_max_age_days"),
- "ATTACHMENTS_CACHE_MAX_AGE_DAYS",
- ),
- 7,
- ),
- )
- attachment_url_reference_max_records = max(
- 0,
- _coerce_int(
- _get_value(
- data,
- ("attachments", "url_reference_max_records"),
- "ATTACHMENTS_URL_REFERENCE_MAX_RECORDS",
- ),
- 2000,
- ),
- )
- attachment_url_max_length = max(
- 0,
- _coerce_int(
- _get_value(
- data,
- ("attachments", "url_max_length"),
- "ATTACHMENTS_URL_MAX_LENGTH",
- ),
- 8192,
- ),
- )
-
- skills_hot_reload = _coerce_bool(
- _get_value(data, ("skills", "hot_reload"), "SKILLS_HOT_RELOAD"), True
- )
- skills_hot_reload_interval = _coerce_float(
- _get_value(
- data, ("skills", "hot_reload_interval"), "SKILLS_HOT_RELOAD_INTERVAL"
- ),
- 2.0,
- )
- skills_hot_reload_debounce = _coerce_float(
- _get_value(
- data, ("skills", "hot_reload_debounce"), "SKILLS_HOT_RELOAD_DEBOUNCE"
- ),
- 0.5,
- )
-
- agent_intro_autogen_enabled = _coerce_bool(
- _get_value(
- data,
- ("skills", "intro_autogen_enabled"),
- "AGENT_INTRO_AUTOGEN_ENABLED",
- ),
- True,
- )
- agent_intro_autogen_queue_interval = _coerce_float(
- _get_value(
- data,
- ("skills", "intro_autogen_queue_interval"),
- "AGENT_INTRO_AUTOGEN_QUEUE_INTERVAL",
- ),
- 1.0,
- )
- agent_intro_autogen_queue_interval = _normalize_queue_interval(
- agent_intro_autogen_queue_interval
- )
- agent_intro_autogen_max_tokens = _coerce_int(
- _get_value(
- data,
- ("skills", "intro_autogen_max_tokens"),
- "AGENT_INTRO_AUTOGEN_MAX_TOKENS",
- ),
- 8192,
- )
- agent_intro_hash_path = _coerce_str(
- _get_value(data, ("skills", "intro_hash_path"), "AGENT_INTRO_HASH_PATH"),
- ".cache/agent_intro_hashes.json",
- )
-
- prefetch_tools_raw = _get_value(
- data, ("skills", "prefetch_tools"), "PREFETCH_TOOLS"
- )
- prefetch_tools = _coerce_str_list(prefetch_tools_raw)
- if not prefetch_tools and prefetch_tools_raw is None:
- prefetch_tools = ["get_current_time"]
- prefetch_tools_hide = _coerce_bool(
- _get_value(data, ("skills", "prefetch_tools_hide"), "PREFETCH_TOOLS_HIDE"),
- True,
- )
-
- searxng_url = _coerce_str(
- _get_value(data, ("search", "searxng_url"), "SEARXNG_URL"), ""
- )
- grok_search_enabled = _coerce_bool(
- _get_value(
- data,
- ("search", "grok_search_enabled"),
- "GROK_SEARCH_ENABLED",
- ),
- False,
- )
-
- use_proxy = _coerce_bool(
- _get_value(data, ("proxy", "use_proxy"), "USE_PROXY"), True
- )
- http_proxy = _coerce_str(
- _get_value(data, ("proxy", "http_proxy"), "http_proxy"), ""
- )
- if not http_proxy:
- http_proxy = _coerce_str(os.getenv("HTTP_PROXY"), "")
- if http_proxy:
- _warn_env_fallback("HTTP_PROXY")
- https_proxy = _coerce_str(
- _get_value(data, ("proxy", "https_proxy"), "https_proxy"), ""
- )
- if not https_proxy:
- https_proxy = _coerce_str(os.getenv("HTTPS_PROXY"), "")
- if https_proxy:
- _warn_env_fallback("HTTPS_PROXY")
-
- network_request_timeout = _coerce_float(
- _get_value(
- data,
- ("network", "request_timeout_seconds"),
- "NETWORK_REQUEST_TIMEOUT_SECONDS",
- ),
- 30.0,
- )
- if network_request_timeout <= 0:
- network_request_timeout = 480.0
-
- network_request_retries = _coerce_int(
- _get_value(
- data,
- ("network", "request_retries"),
- "NETWORK_REQUEST_RETRIES",
- ),
- 0,
- )
- if network_request_retries < 0:
- network_request_retries = 0
- if network_request_retries > 5:
- network_request_retries = 5
-
- render_browser_max_concurrency = max(
- 0,
- _coerce_int(
- _get_value(
- data,
- ("render", "browser_max_concurrency"),
- "RENDER_BROWSER_MAX_CONCURRENCY",
- ),
- 0,
- ),
- )
-
- api_xxapi_base_url = _normalize_base_url(
- _coerce_str(
- _get_value(data, ("api_endpoints", "xxapi_base_url"), "XXAPI_BASE_URL"),
- "https://v2.xxapi.cn",
- ),
- "https://v2.xxapi.cn",
- )
- api_xingzhige_base_url = _normalize_base_url(
- _coerce_str(
- _get_value(
- data,
- ("api_endpoints", "xingzhige_base_url"),
- "XINGZHIGE_BASE_URL",
- ),
- "https://api.xingzhige.com",
- ),
- "https://api.xingzhige.com",
- )
- api_jkyai_base_url = _normalize_base_url(
- _coerce_str(
- _get_value(data, ("api_endpoints", "jkyai_base_url"), "JKYAI_BASE_URL"),
- "https://api.jkyai.top",
- ),
- "https://api.jkyai.top",
- )
- api_seniverse_base_url = _normalize_base_url(
- _coerce_str(
- _get_value(
- data,
- ("api_endpoints", "seniverse_base_url"),
- "SENIVERSE_BASE_URL",
- ),
- "https://api.seniverse.com/v3",
- ),
- "https://api.seniverse.com/v3",
- )
-
- weather_api_key = _coerce_str(
- _get_value(data, ("weather", "api_key"), "WEATHER_API_KEY"), ""
- )
- xxapi_api_token = _coerce_str(
- _get_value(data, ("xxapi", "api_token"), "XXAPI_API_TOKEN"), ""
- )
-
- mcp_config_path = _coerce_str(
- _get_value(data, ("mcp", "config_path"), "MCP_CONFIG_PATH"),
- "config/mcp.json",
- )
-
- # Bilibili 配置
- bilibili_auto_extract_enabled = _coerce_bool(
- _get_value(data, ("bilibili", "auto_extract_enabled"), None), False
- )
- bilibili_cookie = _coerce_str(
- _get_value(data, ("bilibili", "cookie"), None), ""
- )
- if not bilibili_cookie:
- # 兼容旧配置项：bilibili.sessdata
- bilibili_cookie = _coerce_str(
- _get_value(data, ("bilibili", "sessdata"), None), ""
- )
- bilibili_prefer_quality = _coerce_int(
- _get_value(data, ("bilibili", "prefer_quality"), None), 80
- )
- bilibili_max_duration = _coerce_int(
- _get_value(data, ("bilibili", "max_duration"), None), 600
- )
- bilibili_max_file_size = _coerce_int(
- _get_value(data, ("bilibili", "max_file_size"), None), 100
- )
- bilibili_oversize_strategy = _coerce_str(
- _get_value(data, ("bilibili", "oversize_strategy"), None), "downgrade"
- )
- if bilibili_oversize_strategy not in ("downgrade", "info"):
- bilibili_oversize_strategy = "downgrade"
- bilibili_danmaku_enabled = _coerce_bool(
- _get_value(data, ("bilibili", "danmaku_enabled"), None), True
- )
- bilibili_danmaku_batch_size = _coerce_int(
- _get_value(data, ("bilibili", "danmaku_batch_size"), None), 100
- )
- if bilibili_danmaku_batch_size <= 0:
- bilibili_danmaku_batch_size = 100
- bilibili_danmaku_max_count = _coerce_int(
- _get_value(data, ("bilibili", "danmaku_max_count"), None), 0
- )
- if bilibili_danmaku_max_count < 0:
- bilibili_danmaku_max_count = 0
- bilibili_auto_extract_group_ids = _coerce_int_list(
- _get_value(data, ("bilibili", "auto_extract_group_ids"), None)
- )
- bilibili_auto_extract_private_ids = _coerce_int_list(
- _get_value(data, ("bilibili", "auto_extract_private_ids"), None)
- )
-
- # arXiv 配置
- arxiv_auto_extract_enabled = _coerce_bool(
- _get_value(data, ("arxiv", "auto_extract_enabled"), None), False
- )
- arxiv_max_file_size = _coerce_int(
- _get_value(data, ("arxiv", "max_file_size"), None), 100
- )
- if arxiv_max_file_size < 0:
- arxiv_max_file_size = 100
- arxiv_auto_extract_group_ids = _coerce_int_list(
- _get_value(data, ("arxiv", "auto_extract_group_ids"), None)
- )
- arxiv_auto_extract_private_ids = _coerce_int_list(
- _get_value(data, ("arxiv", "auto_extract_private_ids"), None)
- )
- arxiv_auto_extract_max_items = _coerce_int(
- _get_value(data, ("arxiv", "auto_extract_max_items"), None), 5
- )
- if arxiv_auto_extract_max_items <= 0:
- arxiv_auto_extract_max_items = 5
- if arxiv_auto_extract_max_items > 20:
- arxiv_auto_extract_max_items = 20
- arxiv_author_preview_limit = _coerce_int(
- _get_value(data, ("arxiv", "author_preview_limit"), None), 20
- )
- if arxiv_author_preview_limit <= 0:
- arxiv_author_preview_limit = 20
- if arxiv_author_preview_limit > 100:
- arxiv_author_preview_limit = 100
- arxiv_summary_preview_chars = _coerce_int(
- _get_value(data, ("arxiv", "summary_preview_chars"), None), 1000
- )
- if arxiv_summary_preview_chars <= 0:
- arxiv_summary_preview_chars = 1000
- if arxiv_summary_preview_chars > 8000:
- arxiv_summary_preview_chars = 8000
-
- # GitHub 配置
- github_auto_extract_enabled = _coerce_bool(
- _get_value(data, ("github", "auto_extract_enabled"), None), False
- )
- github_request_timeout_seconds = _coerce_float(
- _get_value(data, ("github", "request_timeout_seconds"), None), 10.0
- )
- if github_request_timeout_seconds <= 0:
- github_request_timeout_seconds = 10.0
- if github_request_timeout_seconds > 60.0:
- github_request_timeout_seconds = 60.0
- github_auto_extract_group_ids = _coerce_int_list(
- _get_value(data, ("github", "auto_extract_group_ids"), None)
- )
- github_auto_extract_private_ids = _coerce_int_list(
- _get_value(data, ("github", "auto_extract_private_ids"), None)
- )
- github_auto_extract_max_items = _coerce_int(
- _get_value(data, ("github", "auto_extract_max_items"), None), 3
- )
- if github_auto_extract_max_items <= 0:
- github_auto_extract_max_items = 3
- if github_auto_extract_max_items > 10:
- github_auto_extract_max_items = 10
-
- # Code Delivery Agent 配置
- code_delivery_enabled = _coerce_bool(
- _get_value(data, ("code_delivery", "enabled"), None), True
- )
- code_delivery_task_root = _coerce_str(
- _get_value(data, ("code_delivery", "task_root"), None),
- "data/code_delivery",
- )
- code_delivery_docker_image = _coerce_str(
- _get_value(data, ("code_delivery", "docker_image"), None),
- "ubuntu:24.04",
- )
- code_delivery_container_name_prefix = _coerce_str(
- _get_value(data, ("code_delivery", "container_name_prefix"), None),
- "code_delivery_",
- )
- code_delivery_container_name_suffix = _coerce_str(
- _get_value(data, ("code_delivery", "container_name_suffix"), None),
- "_runner",
- )
- code_delivery_command_timeout = _coerce_int(
- _get_value(
- data, ("code_delivery", "default_command_timeout_seconds"), None
- ),
- 600,
- )
- code_delivery_max_command_output = _coerce_int(
- _get_value(data, ("code_delivery", "max_command_output_chars"), None),
- 20000,
- )
- code_delivery_default_archive_format = _coerce_str(
- _get_value(data, ("code_delivery", "default_archive_format"), None),
- "zip",
- )
- if code_delivery_default_archive_format not in ("zip", "tar.gz"):
- code_delivery_default_archive_format = "zip"
- code_delivery_max_archive_size_mb = _coerce_int(
- _get_value(data, ("code_delivery", "max_archive_size_mb"), None), 200
- )
- code_delivery_cleanup_on_finish = _coerce_bool(
- _get_value(data, ("code_delivery", "cleanup_on_finish"), None), True
- )
- code_delivery_cleanup_on_start = _coerce_bool(
- _get_value(data, ("code_delivery", "cleanup_on_start"), None), True
- )
- code_delivery_llm_max_retries = _coerce_int(
- _get_value(data, ("code_delivery", "llm_max_retries_per_request"), None),
- 5,
- )
- code_delivery_notify_on_llm_failure = _coerce_bool(
- _get_value(data, ("code_delivery", "notify_on_llm_failure"), None),
- True,
- )
- code_delivery_container_memory_limit = _coerce_str(
- _get_value(data, ("code_delivery", "container_memory_limit"), None),
- "",
- )
- code_delivery_container_cpu_limit = _coerce_str(
- _get_value(data, ("code_delivery", "container_cpu_limit"), None),
- "",
- )
- code_delivery_command_blacklist_raw = _get_value(
- data, ("code_delivery", "command_blacklist"), None
- )
- if isinstance(code_delivery_command_blacklist_raw, list):
- code_delivery_command_blacklist = [
- str(x) for x in code_delivery_command_blacklist_raw
- ]
- else:
- code_delivery_command_blacklist = []
-
- # messages 工具集配置
- messages_send_text_file_max_size_kb = _coerce_int(
- _get_value(
- data,
- ("messages", "send_text_file_max_size_kb"),
- "MESSAGES_SEND_TEXT_FILE_MAX_SIZE_KB",
- ),
- 512,
- )
- if messages_send_text_file_max_size_kb <= 0:
- messages_send_text_file_max_size_kb = 512
-
- messages_send_url_file_max_size_mb = _coerce_int(
- _get_value(
- data,
- ("messages", "send_url_file_max_size_mb"),
- "MESSAGES_SEND_URL_FILE_MAX_SIZE_MB",
- ),
- 100,
- )
- if messages_send_url_file_max_size_mb <= 0:
- messages_send_url_file_max_size_mb = 100
-
- webui_settings = load_webui_settings(config_path)
- api_config = _parse_api_config(data)
-
- cognitive = _parse_cognitive_config(data)
- memes = _parse_memes_config(data)
- message_batcher = _parse_message_batcher_config(data)
- render_cache = _parse_render_cache_config(data)
- naga = _parse_naga_config(data)
- models_image_gen = _parse_image_gen_model_config(data)
- models_image_edit = _parse_image_edit_model_config(data)
- image_gen = _parse_image_gen_config(data)
-
- if strict:
- _verify_required_fields(
- bot_qq=bot_qq,
- superadmin_qq=superadmin_qq,
- onebot_ws_url=onebot_ws_url,
- chat_model=chat_model,
- vision_model=vision_model,
- agent_model=agent_model,
- knowledge_enabled=knowledge_enabled,
- embedding_model=embedding_model,
- )
-
- _log_debug_info(
- chat_model,
- vision_model,
- security_model,
- naga_model,
- agent_model,
- summary_model,
- grok_model,
- )
-
- return cls(
- bot_qq=bot_qq,
- superadmin_qq=superadmin_qq,
- admin_qqs=admin_qqs,
- access_mode=access_mode,
- allowed_group_ids=allowed_group_ids,
- blocked_group_ids=blocked_group_ids,
- allowed_private_ids=allowed_private_ids,
- blocked_private_ids=blocked_private_ids,
- superadmin_bypass_allowlist=superadmin_bypass_allowlist,
- superadmin_bypass_private_blacklist=superadmin_bypass_private_blacklist,
- forward_proxy_qq=forward_proxy_qq,
- process_every_message=process_every_message,
- process_private_message=process_private_message,
- process_poke_message=process_poke_message,
- keyword_reply_enabled=keyword_reply_enabled,
- repeat_enabled=repeat_enabled,
- repeat_threshold=repeat_threshold,
- repeat_cooldown_minutes=repeat_cooldown_minutes,
- inverted_question_enabled=inverted_question_enabled,
- context_recent_messages_limit=context_recent_messages_limit,
- ai_request_max_retries=ai_request_max_retries,
- missing_tool_call_retries=missing_tool_call_retries,
- nagaagent_mode_enabled=nagaagent_mode_enabled,
- onebot_ws_url=onebot_ws_url,
- onebot_token=onebot_token,
- chat_model=chat_model,
- vision_model=vision_model,
- security_model_enabled=security_model_enabled,
- security_model=security_model,
- naga_model=naga_model,
- agent_model=agent_model,
- historian_model=historian_model,
- summary_model=summary_model,
- summary_model_configured=summary_model_configured,
- grok_model=grok_model,
- model_pool_enabled=model_pool_enabled,
- log_level=log_level,
- log_file_path=log_file_path,
- log_max_size=log_max_size_mb * 1024 * 1024,
- log_backup_count=log_backup_count,
- log_tty_enabled=log_tty_enabled,
- log_thinking=log_thinking,
- tools_dot_delimiter=tools_dot_delimiter,
- tools_description_truncate_enabled=tools_description_truncate_enabled,
- tools_description_max_len=tools_description_max_len,
- tools_sanitize_verbose=tools_sanitize_verbose,
- tools_description_preview_len=tools_description_preview_len,
- easter_egg_agent_call_message_mode=easter_egg_agent_call_message_mode,
- token_usage_max_size_mb=token_usage_max_size_mb,
- token_usage_max_archives=token_usage_max_archives,
- token_usage_max_total_mb=token_usage_max_total_mb,
- token_usage_archive_prune_mode=token_usage_archive_prune_mode,
- skills_hot_reload=skills_hot_reload,
- history_max_records=history_max_records,
- history_filtered_result_limit=history_filtered_result_limit,
- history_search_scan_limit=history_search_scan_limit,
- history_summary_fetch_limit=history_summary_fetch_limit,
- history_summary_time_fetch_limit=history_summary_time_fetch_limit,
- history_onebot_fetch_limit=history_onebot_fetch_limit,
- history_group_analysis_limit=history_group_analysis_limit,
- attachment_remote_download_max_size_mb=attachment_remote_download_max_size_mb,
- attachment_cache_max_total_size_mb=attachment_cache_max_total_size_mb,
- attachment_cache_max_records=attachment_cache_max_records,
- attachment_cache_max_age_days=attachment_cache_max_age_days,
- attachment_url_reference_max_records=attachment_url_reference_max_records,
- attachment_url_max_length=attachment_url_max_length,
- skills_hot_reload_interval=skills_hot_reload_interval,
- skills_hot_reload_debounce=skills_hot_reload_debounce,
- agent_intro_autogen_enabled=agent_intro_autogen_enabled,
- agent_intro_autogen_queue_interval=agent_intro_autogen_queue_interval,
- agent_intro_autogen_max_tokens=agent_intro_autogen_max_tokens,
- agent_intro_hash_path=agent_intro_hash_path,
- searxng_url=searxng_url,
- grok_search_enabled=grok_search_enabled,
- use_proxy=use_proxy,
- http_proxy=http_proxy,
- https_proxy=https_proxy,
- network_request_timeout=network_request_timeout,
- network_request_retries=network_request_retries,
- render_browser_max_concurrency=render_browser_max_concurrency,
- api_xxapi_base_url=api_xxapi_base_url,
- api_xingzhige_base_url=api_xingzhige_base_url,
- api_jkyai_base_url=api_jkyai_base_url,
- api_seniverse_base_url=api_seniverse_base_url,
- weather_api_key=weather_api_key,
- xxapi_api_token=xxapi_api_token,
- mcp_config_path=mcp_config_path,
- prefetch_tools=prefetch_tools,
- prefetch_tools_hide=prefetch_tools_hide,
- webui_url=webui_settings.url,
- webui_port=webui_settings.port,
- webui_password=webui_settings.password,
- api=api_config,
- code_delivery_enabled=code_delivery_enabled,
- code_delivery_task_root=code_delivery_task_root,
- code_delivery_docker_image=code_delivery_docker_image,
- code_delivery_container_name_prefix=code_delivery_container_name_prefix,
- code_delivery_container_name_suffix=code_delivery_container_name_suffix,
- code_delivery_command_timeout=code_delivery_command_timeout,
- code_delivery_max_command_output=code_delivery_max_command_output,
- code_delivery_default_archive_format=code_delivery_default_archive_format,
- code_delivery_max_archive_size_mb=code_delivery_max_archive_size_mb,
- code_delivery_cleanup_on_finish=code_delivery_cleanup_on_finish,
- code_delivery_cleanup_on_start=code_delivery_cleanup_on_start,
- code_delivery_llm_max_retries=code_delivery_llm_max_retries,
- code_delivery_notify_on_llm_failure=code_delivery_notify_on_llm_failure,
- code_delivery_container_memory_limit=code_delivery_container_memory_limit,
- code_delivery_container_cpu_limit=code_delivery_container_cpu_limit,
- code_delivery_command_blacklist=code_delivery_command_blacklist,
- messages_send_text_file_max_size_kb=messages_send_text_file_max_size_kb,
- messages_send_url_file_max_size_mb=messages_send_url_file_max_size_mb,
- bilibili_auto_extract_enabled=bilibili_auto_extract_enabled,
- bilibili_cookie=bilibili_cookie,
- bilibili_prefer_quality=bilibili_prefer_quality,
- bilibili_max_duration=bilibili_max_duration,
- bilibili_max_file_size=bilibili_max_file_size,
- bilibili_oversize_strategy=bilibili_oversize_strategy,
- bilibili_danmaku_enabled=bilibili_danmaku_enabled,
- bilibili_danmaku_batch_size=bilibili_danmaku_batch_size,
- bilibili_danmaku_max_count=bilibili_danmaku_max_count,
- bilibili_auto_extract_group_ids=bilibili_auto_extract_group_ids,
- bilibili_auto_extract_private_ids=bilibili_auto_extract_private_ids,
- arxiv_auto_extract_enabled=arxiv_auto_extract_enabled,
- arxiv_max_file_size=arxiv_max_file_size,
- arxiv_auto_extract_group_ids=arxiv_auto_extract_group_ids,
- arxiv_auto_extract_private_ids=arxiv_auto_extract_private_ids,
- arxiv_auto_extract_max_items=arxiv_auto_extract_max_items,
- arxiv_author_preview_limit=arxiv_author_preview_limit,
- arxiv_summary_preview_chars=arxiv_summary_preview_chars,
- github_auto_extract_enabled=github_auto_extract_enabled,
- github_request_timeout_seconds=github_request_timeout_seconds,
- github_auto_extract_group_ids=github_auto_extract_group_ids,
- github_auto_extract_private_ids=github_auto_extract_private_ids,
- github_auto_extract_max_items=github_auto_extract_max_items,
- embedding_model=embedding_model,
- rerank_model=rerank_model,
- knowledge_enabled=knowledge_enabled,
- knowledge_base_dir=knowledge_base_dir,
- knowledge_auto_scan=knowledge_auto_scan,
- knowledge_auto_embed=knowledge_auto_embed,
- knowledge_scan_interval=knowledge_scan_interval,
- knowledge_embed_batch_size=knowledge_embed_batch_size,
- knowledge_chunk_size=knowledge_chunk_size,
- knowledge_chunk_overlap=knowledge_chunk_overlap,
- knowledge_default_top_k=knowledge_default_top_k,
- knowledge_enable_rerank=knowledge_enable_rerank,
- knowledge_rerank_top_k=knowledge_rerank_top_k,
- cognitive=cognitive,
- memes=memes,
- message_batcher=message_batcher,
- render_cache=render_cache,
- naga=naga,
- image_gen=image_gen,
- models_image_gen=models_image_gen,
- models_image_edit=models_image_edit,
- )
-
- @property
- def bilibili_sessdata(self) -> str:
- """兼容旧字段名，等价于 bilibili_cookie。"""
- return self.bilibili_cookie
-
- def allowlist_mode_enabled(self) -> bool:
- """是否启用白名单限制模式。"""
-
- return self.access_mode in {"allowlist", "legacy"} and (
- bool(self.allowed_group_ids) or bool(self.allowed_private_ids)
- )
-
- def group_allowlist_enabled(self) -> bool:
- """群聊白名单是否生效（显式 allowlist 模式按维度独立控制）。"""
-
- return bool(self.allowed_group_ids)
-
- def private_allowlist_enabled(self) -> bool:
- """私聊白名单是否生效（显式 allowlist 模式按维度独立控制）。"""
-
- return bool(self.allowed_private_ids)
-
- def blacklist_mode_enabled(self) -> bool:
- """是否启用黑名单限制模式。"""
-
- return self.access_mode in {"blacklist", "legacy"} and (
- bool(self.blocked_group_ids) or bool(self.blocked_private_ids)
- )
-
- def access_control_enabled(self) -> bool:
- """是否启用访问控制。"""
-
- return self.allowlist_mode_enabled() or self.blacklist_mode_enabled()
-
- def group_access_denied_reason(self, group_id: int) -> str | None:
- """群聊访问被拒绝原因。
-
- 返回:
- - "blacklist": 命中 access.blocked_group_ids
- - "allowlist": allowlist 模式下不在 access.allowed_group_ids
- - None: 允许访问
- """
-
- normalized_group_id = int(group_id)
- if self.access_mode == "off":
- return None
- if self.access_mode == "blacklist":
- if normalized_group_id in self._blocked_group_ids_set:
- return "blacklist"
- return None
- if self.access_mode == "legacy":
- if normalized_group_id in self._blocked_group_ids_set:
- return "blacklist"
- if not self.allowlist_mode_enabled():
- return None
- if normalized_group_id not in self._allowed_group_ids_set:
- return "allowlist"
- return None
- if not self.group_allowlist_enabled():
- return None
- if normalized_group_id not in self._allowed_group_ids_set:
- return "allowlist"
- return None
-
- def is_group_allowed(self, group_id: int) -> bool:
- """群聊是否允许收发消息。"""
-
- return self.group_access_denied_reason(group_id) is None
-
- def private_access_denied_reason(self, user_id: int) -> str | None:
- """私聊访问被拒绝原因。"""
-
- normalized_user_id = int(user_id)
- if self.access_mode == "off":
- return None
- if self.access_mode == "blacklist":
- if normalized_user_id not in self._blocked_private_ids_set:
- return None
- if (
- self.superadmin_bypass_private_blacklist
- and normalized_user_id == int(self.superadmin_qq)
- and self.superadmin_qq > 0
- ):
- return None
- return "blacklist"
- if self.access_mode == "legacy":
- if normalized_user_id in self._blocked_private_ids_set:
- if (
- self.superadmin_bypass_private_blacklist
- and normalized_user_id == int(self.superadmin_qq)
- and self.superadmin_qq > 0
- ):
- return None
- return "blacklist"
- if not self.allowlist_mode_enabled():
- return None
- if (
- self.superadmin_bypass_allowlist
- and normalized_user_id == int(self.superadmin_qq)
- and self.superadmin_qq > 0
- ):
- return None
- if normalized_user_id not in self._allowed_private_ids_set:
- return "allowlist"
- return None
- if not self.private_allowlist_enabled():
- return None
- if (
- self.superadmin_bypass_allowlist
- and normalized_user_id == int(self.superadmin_qq)
- and self.superadmin_qq > 0
- ):
- return None
- if normalized_user_id not in self._allowed_private_ids_set:
- return "allowlist"
- return None
-
- def is_private_allowed(self, user_id: int) -> bool:
- """私聊是否允许收发消息。"""
-
- return self.private_access_denied_reason(user_id) is None
-
- def is_bilibili_auto_extract_allowed_group(self, group_id: int) -> bool:
- """群聊是否允许 bilibili 自动提取。"""
- if self._bilibili_group_ids_set:
- return int(group_id) in self._bilibili_group_ids_set
- # 功能白名单为空时跟随全局 access 控制
- return self.is_group_allowed(group_id)
-
- def is_bilibili_auto_extract_allowed_private(self, user_id: int) -> bool:
- """私聊是否允许 bilibili 自动提取。"""
- if self._bilibili_private_ids_set:
- return int(user_id) in self._bilibili_private_ids_set
- # 功能白名单为空时跟随全局 access 控制
- return self.is_private_allowed(user_id)
-
- def is_arxiv_auto_extract_allowed_group(self, group_id: int) -> bool:
- """群聊是否允许 arXiv 自动提取。"""
- if self._arxiv_group_ids_set:
- return int(group_id) in self._arxiv_group_ids_set
- return self.is_group_allowed(group_id)
-
- def is_arxiv_auto_extract_allowed_private(self, user_id: int) -> bool:
- """私聊是否允许 arXiv 自动提取。"""
- if self._arxiv_private_ids_set:
- return int(user_id) in self._arxiv_private_ids_set
- return self.is_private_allowed(user_id)
-
- def is_github_auto_extract_allowed_group(self, group_id: int) -> bool:
- """群聊是否允许 GitHub 仓库自动提取。"""
- if self._github_group_ids_set:
- return int(group_id) in self._github_group_ids_set
- return self.is_group_allowed(group_id)
-
- def is_github_auto_extract_allowed_private(self, user_id: int) -> bool:
- """私聊是否允许 GitHub 仓库自动提取。"""
- if self._github_private_ids_set:
- return int(user_id) in self._github_private_ids_set
- return self.is_private_allowed(user_id)
-
- def should_process_group_message(self, is_at_bot: bool) -> bool:
- """是否处理该条群消息。"""
-
- if self.process_every_message:
- return True
- return bool(is_at_bot)
-
- def should_process_private_message(self) -> bool:
- """是否处理私聊消息回复。"""
-
- return bool(self.process_private_message)
-
- def should_process_poke_message(self) -> bool:
- """是否处理拍一拍触发。"""
-
- return bool(self.process_poke_message)
-
- def get_context_recent_messages_limit(self) -> int:
- """获取上下文最近历史消息条数上限。"""
-
- limit = int(self.context_recent_messages_limit)
- if limit < 0:
- return 0
- return limit
-
- def security_check_enabled(self) -> bool:
- """是否启用安全模型检查。"""
-
- return bool(self.security_model_enabled)
-
- def update_from(self, new_config: "Config") -> dict[str, tuple[Any, Any]]:
- changes: dict[str, tuple[Any, Any]] = {}
- for field in fields(self):
- name = field.name
- old_value = getattr(self, name)
- new_value = getattr(new_config, name)
- if isinstance(
- old_value,
- (
- ChatModelConfig,
- VisionModelConfig,
- SecurityModelConfig,
- AgentModelConfig,
- GrokModelConfig,
- ),
- ):
- changes.update(_update_dataclass(old_value, new_value, prefix=name))
- continue
- if old_value != new_value:
- setattr(self, name, new_value)
- changes[name] = (old_value, new_value)
- return changes
-
- def reload(self, strict: bool = False) -> dict[str, tuple[Any, Any]]:
- new_config = Config.load(strict=strict)
- return self.update_from(new_config)
-
- def add_admin(self, qq: int) -> bool:
- if qq in self.admin_qqs:
- return False
- self.admin_qqs.append(qq)
- local_admins = load_local_admins()
- if qq not in local_admins:
- local_admins.append(qq)
- save_local_admins(local_admins)
- return True
-
- def remove_admin(self, qq: int) -> bool:
- if qq == self.superadmin_qq or qq not in self.admin_qqs:
- return False
- self.admin_qqs.remove(qq)
- local_admins = load_local_admins()
- if qq in local_admins:
- local_admins.remove(qq)
- save_local_admins(local_admins)
- return True
-
- def is_superadmin(self, qq: int) -> bool:
- return qq == self.superadmin_qq
-
- def is_admin(self, qq: int) -> bool:
- return qq in self.admin_qqs
diff --git a/src/Undefined/config/manager.py b/src/Undefined/config/manager.py
index f2c4400b..f497f253 100644
--- a/src/Undefined/config/manager.py
+++ b/src/Undefined/config/manager.py
@@ -30,6 +30,10 @@ def load(self, strict: bool = True) -> Config:
self._config = Config.load(config_path=self.config_path, strict=strict)
return self._config
+ def replace(self, config: Config) -> None:
+ """替换缓存的配置实例（库嵌入 ``set_config`` 注入时使用）。"""
+ self._config = config
+
def reload(self, strict: bool = False) -> dict[str, tuple[Any, Any]]:
if self._config is None:
self._config = Config.load(config_path=self.config_path, strict=strict)
diff --git a/src/Undefined/config/parsers/__init__.py b/src/Undefined/config/parsers/__init__.py
new file mode 100644
index 00000000..faf5adeb
--- /dev/null
+++ b/src/Undefined/config/parsers/__init__.py
@@ -0,0 +1,39 @@
+"""Model configuration parsers."""
+
+# 模型配置解析：原始 dict → ChatModelConfig 等 dataclass
+from .agent import _parse_agent_model_config
+from .chat import _parse_chat_model_config
+from .embedding import _parse_embedding_model_config, _parse_rerank_model_config
+from .grok import _parse_grok_model_config
+from .helpers import _log_debug_info, _merge_admins, _verify_required_fields
+from .historian import _parse_historian_model_config
+from .image import (
+ _parse_image_edit_model_config,
+ _parse_image_gen_config,
+ _parse_image_gen_model_config,
+)
+from .naga import _parse_naga_model_config
+from .pool import _parse_model_pool
+from .security import _parse_security_model_config
+from .summary import _parse_summary_model_config
+from .vision import _parse_vision_model_config
+
+__all__ = [
+ "_log_debug_info",
+ "_merge_admins",
+ "_parse_agent_model_config",
+ "_parse_chat_model_config",
+ "_parse_embedding_model_config",
+ "_parse_grok_model_config",
+ "_parse_historian_model_config",
+ "_parse_image_edit_model_config",
+ "_parse_image_gen_config",
+ "_parse_image_gen_model_config",
+ "_parse_model_pool",
+ "_parse_naga_model_config",
+ "_parse_rerank_model_config",
+ "_parse_security_model_config",
+ "_parse_summary_model_config",
+ "_parse_vision_model_config",
+ "_verify_required_fields",
+]
diff --git a/src/Undefined/config/parsers/agent.py b/src/Undefined/config/parsers/agent.py
new file mode 100644
index 00000000..5170d181
--- /dev/null
+++ b/src/Undefined/config/parsers/agent.py
@@ -0,0 +1,163 @@
+"""Agent model parser."""
+
+from __future__ import annotations
+
+# 模型配置解析：原始 dict → ChatModelConfig 等 dataclass
+
+import logging
+from typing import Any
+
+
+from ..coercers import (
+ _coerce_bool,
+ _coerce_float,
+ _coerce_int,
+ _coerce_str,
+ _get_model_request_params,
+ _get_value,
+ _normalize_queue_interval,
+)
+from ..models import (
+ AgentModelConfig,
+)
+from ..resolvers import (
+ _resolve_api_mode,
+ _resolve_context_window_tokens,
+ _resolve_reasoning_effort,
+ _resolve_reasoning_effort_style,
+ _resolve_reasoning_content_replay,
+ _resolve_responses_force_stateless_replay,
+ _resolve_responses_tool_choice_compat,
+ _resolve_system_prompt_as_user,
+ _resolve_thinking_compat_flags,
+)
+from .pool import _parse_model_pool
+
+logger = logging.getLogger(__name__)
+
+
+def _parse_agent_model_config(data: dict[str, Any]) -> AgentModelConfig:
+ queue_interval_seconds = _normalize_queue_interval(
+ _coerce_float(
+ _get_value(
+ data,
+ ("models", "agent", "queue_interval_seconds"),
+ "AGENT_MODEL_QUEUE_INTERVAL",
+ ),
+ 1.0,
+ )
+ )
+ thinking_include_budget, thinking_tool_call_compat = _resolve_thinking_compat_flags(
+ data=data,
+ model_name="agent",
+ include_budget_env_key="AGENT_MODEL_THINKING_INCLUDE_BUDGET",
+ tool_call_compat_env_key="AGENT_MODEL_THINKING_TOOL_CALL_COMPAT",
+ legacy_env_key="AGENT_MODEL_DEEPSEEK_NEW_COT_SUPPORT",
+ )
+ api_mode = _resolve_api_mode(data, "agent", "AGENT_MODEL_API_MODE")
+ responses_tool_choice_compat = _resolve_responses_tool_choice_compat(
+ data, "agent", "AGENT_MODEL_RESPONSES_TOOL_CHOICE_COMPAT"
+ )
+ responses_force_stateless_replay = _resolve_responses_force_stateless_replay(
+ data, "agent", "AGENT_MODEL_RESPONSES_FORCE_STATELESS_REPLAY"
+ )
+ reasoning_content_replay = _resolve_reasoning_content_replay(
+ data, "agent", "AGENT_MODEL_REASONING_CONTENT_REPLAY"
+ )
+ system_prompt_as_user = _resolve_system_prompt_as_user(
+ data, "agent", "AGENT_MODEL_SYSTEM_PROMPT_AS_USER"
+ )
+ prompt_cache_enabled = _coerce_bool(
+ _get_value(
+ data,
+ ("models", "agent", "prompt_cache_enabled"),
+ "AGENT_MODEL_PROMPT_CACHE_ENABLED",
+ ),
+ True,
+ )
+ reasoning_enabled = _coerce_bool(
+ _get_value(
+ data,
+ ("models", "agent", "reasoning_enabled"),
+ "AGENT_MODEL_REASONING_ENABLED",
+ ),
+ False,
+ )
+ reasoning_effort = _resolve_reasoning_effort(
+ _get_value(
+ data,
+ ("models", "agent", "reasoning_effort"),
+ "AGENT_MODEL_REASONING_EFFORT",
+ ),
+ "medium",
+ )
+ stream_enabled = _coerce_bool(
+ _get_value(
+ data,
+ ("models", "agent", "stream_enabled"),
+ "AGENT_MODEL_STREAM_ENABLED",
+ ),
+ False,
+ )
+ context_window_tokens = _resolve_context_window_tokens(
+ data, "agent", "AGENT_MODEL_CONTEXT_WINDOW_TOKENS"
+ )
+ config = AgentModelConfig(
+ api_url=_coerce_str(
+ _get_value(data, ("models", "agent", "api_url"), "AGENT_MODEL_API_URL"),
+ "",
+ ),
+ api_key=_coerce_str(
+ _get_value(data, ("models", "agent", "api_key"), "AGENT_MODEL_API_KEY"),
+ "",
+ ),
+ model_name=_coerce_str(
+ _get_value(data, ("models", "agent", "model_name"), "AGENT_MODEL_NAME"),
+ "",
+ ),
+ max_tokens=_coerce_int(
+ _get_value(
+ data, ("models", "agent", "max_tokens"), "AGENT_MODEL_MAX_TOKENS"
+ ),
+ 4096,
+ ),
+ context_window_tokens=context_window_tokens,
+ queue_interval_seconds=queue_interval_seconds,
+ api_mode=api_mode,
+ thinking_enabled=_coerce_bool(
+ _get_value(
+ data,
+ ("models", "agent", "thinking_enabled"),
+ "AGENT_MODEL_THINKING_ENABLED",
+ ),
+ False,
+ ),
+ thinking_budget_tokens=_coerce_int(
+ _get_value(
+ data,
+ ("models", "agent", "thinking_budget_tokens"),
+ "AGENT_MODEL_THINKING_BUDGET_TOKENS",
+ ),
+ 0,
+ ),
+ thinking_include_budget=thinking_include_budget,
+ reasoning_effort_style=_resolve_reasoning_effort_style(
+ _get_value(
+ data,
+ ("models", "agent", "reasoning_effort_style"),
+ "AGENT_MODEL_REASONING_EFFORT_STYLE",
+ ),
+ ),
+ thinking_tool_call_compat=thinking_tool_call_compat,
+ reasoning_content_replay=reasoning_content_replay,
+ system_prompt_as_user=system_prompt_as_user,
+ responses_tool_choice_compat=responses_tool_choice_compat,
+ responses_force_stateless_replay=responses_force_stateless_replay,
+ prompt_cache_enabled=prompt_cache_enabled,
+ reasoning_enabled=reasoning_enabled,
+ reasoning_effort=reasoning_effort,
+ stream_enabled=stream_enabled,
+ request_params=_get_model_request_params(data, "agent"),
+ )
+ config.pool = _parse_model_pool(data, "agent", config)
+ return config
diff --git a/src/Undefined/config/parsers/chat.py b/src/Undefined/config/parsers/chat.py
new file mode 100644
index 00000000..6f1028fa
--- /dev/null
+++ b/src/Undefined/config/parsers/chat.py
@@ -0,0 +1,165 @@
+"""Chat model parser."""
+
+from __future__ import annotations
+
+# 模型配置解析：原始 dict → ChatModelConfig 等 dataclass
+
+import logging
+from typing import Any
+
+
+from ..coercers import (
+ _coerce_bool,
+ _coerce_float,
+ _coerce_int,
+ _coerce_str,
+ _get_model_request_params,
+ _get_value,
+ _normalize_queue_interval,
+)
+from ..models import (
+ ChatModelConfig,
+)
+from ..resolvers import (
+ _resolve_api_mode,
+ _resolve_context_window_tokens,
+ _resolve_reasoning_effort,
+ _resolve_reasoning_effort_style,
+ _resolve_reasoning_content_replay,
+ _resolve_responses_force_stateless_replay,
+ _resolve_responses_tool_choice_compat,
+ _resolve_system_prompt_as_user,
+ _resolve_thinking_compat_flags,
+)
+from .pool import _parse_model_pool
+
+logger = logging.getLogger(__name__)
+
+
+# 解析 [models.chat]：主对话模型 API、thinking/reasoning、队列间隔与模型池
+def _parse_chat_model_config(data: dict[str, Any]) -> ChatModelConfig:
+ # 该模型独立的发车间隔（秒），0=立即发车
+ queue_interval_seconds = _normalize_queue_interval(
+ _coerce_float(
+ _get_value(
+ data,
+ ("models", "chat", "queue_interval_seconds"),
+ "CHAT_MODEL_QUEUE_INTERVAL",
+ ),
+ 1.0,
+ )
+ )
+ # DeepSeek/兼容模型的 thinking 预算与 tool_call 互斥开关
+ thinking_include_budget, thinking_tool_call_compat = _resolve_thinking_compat_flags(
+ data=data,
+ model_name="chat",
+ include_budget_env_key="CHAT_MODEL_THINKING_INCLUDE_BUDGET",
+ tool_call_compat_env_key="CHAT_MODEL_THINKING_TOOL_CALL_COMPAT",
+ legacy_env_key="CHAT_MODEL_DEEPSEEK_NEW_COT_SUPPORT",
+ )
+ # OpenAI 兼容层：chat_completions / responses 及 reasoning 回放策略
+ api_mode = _resolve_api_mode(data, "chat", "CHAT_MODEL_API_MODE")
+ responses_tool_choice_compat = _resolve_responses_tool_choice_compat(
+ data, "chat", "CHAT_MODEL_RESPONSES_TOOL_CHOICE_COMPAT"
+ )
+ responses_force_stateless_replay = _resolve_responses_force_stateless_replay(
+ data, "chat", "CHAT_MODEL_RESPONSES_FORCE_STATELESS_REPLAY"
+ )
+ reasoning_content_replay = _resolve_reasoning_content_replay(
+ data, "chat", "CHAT_MODEL_REASONING_CONTENT_REPLAY"
+ )
+ system_prompt_as_user = _resolve_system_prompt_as_user(
+ data, "chat", "CHAT_MODEL_SYSTEM_PROMPT_AS_USER"
+ )
+ prompt_cache_enabled = _coerce_bool(
+ _get_value(
+ data,
+ ("models", "chat", "prompt_cache_enabled"),
+ "CHAT_MODEL_PROMPT_CACHE_ENABLED",
+ ),
+ True,
+ )
+ reasoning_enabled = _coerce_bool(
+ _get_value(
+ data,
+ ("models", "chat", "reasoning_enabled"),
+ "CHAT_MODEL_REASONING_ENABLED",
+ ),
+ False,
+ )
+ reasoning_effort = _resolve_reasoning_effort(
+ _get_value(
+ data,
+ ("models", "chat", "reasoning_effort"),
+ "CHAT_MODEL_REASONING_EFFORT",
+ ),
+ "medium",
+ )
+ stream_enabled = _coerce_bool(
+ _get_value(
+ data,
+ ("models", "chat", "stream_enabled"),
+ "CHAT_MODEL_STREAM_ENABLED",
+ ),
+ False,
+ )
+ context_window_tokens = _resolve_context_window_tokens(
+ data, "chat", "CHAT_MODEL_CONTEXT_WINDOW_TOKENS"
+ )
+ config = ChatModelConfig(
+ api_url=_coerce_str(
+ _get_value(data, ("models", "chat", "api_url"), "CHAT_MODEL_API_URL"),
+ "",
+ ),
+ api_key=_coerce_str(
+ _get_value(data, ("models", "chat", "api_key"), "CHAT_MODEL_API_KEY"),
+ "",
+ ),
+ model_name=_coerce_str(
+ _get_value(data, ("models", "chat", "model_name"), "CHAT_MODEL_NAME"),
+ "",
+ ),
+ context_window_tokens=context_window_tokens,
+ max_tokens=_coerce_int(
+ _get_value(data, ("models", "chat", "max_tokens"), "CHAT_MODEL_MAX_TOKENS"),
+ 8192,
+ ),
+ queue_interval_seconds=queue_interval_seconds,
+ api_mode=api_mode,
+ thinking_enabled=_coerce_bool(
+ _get_value(
+ data,
+ ("models", "chat", "thinking_enabled"),
+ "CHAT_MODEL_THINKING_ENABLED",
+ ),
+ False,
+ ),
+ thinking_budget_tokens=_coerce_int(
+ _get_value(
+ data,
+ ("models", "chat", "thinking_budget_tokens"),
+ "CHAT_MODEL_THINKING_BUDGET_TOKENS",
+ ),
+ 20000,
+ ),
+ thinking_include_budget=thinking_include_budget,
+ reasoning_effort_style=_resolve_reasoning_effort_style(
+ _get_value(
+ data,
+ ("models", "chat", "reasoning_effort_style"),
+ "CHAT_MODEL_REASONING_EFFORT_STYLE",
+ ),
+ ),
+ thinking_tool_call_compat=thinking_tool_call_compat,
+ reasoning_content_replay=reasoning_content_replay,
+ system_prompt_as_user=system_prompt_as_user,
+ responses_tool_choice_compat=responses_tool_choice_compat,
+ responses_force_stateless_replay=responses_force_stateless_replay,
+ prompt_cache_enabled=prompt_cache_enabled,
+ reasoning_enabled=reasoning_enabled,
+ reasoning_effort=reasoning_effort,
+ stream_enabled=stream_enabled,
+ request_params=_get_model_request_params(data, "chat"),
+ )
+ config.pool = _parse_model_pool(data, "chat", config)
+ return config
diff --git a/src/Undefined/config/parsers/embedding.py b/src/Undefined/config/parsers/embedding.py
new file mode 100644
index 00000000..8c5f3aa7
--- /dev/null
+++ b/src/Undefined/config/parsers/embedding.py
@@ -0,0 +1,106 @@
+"""Embedding model parser."""
+
+from __future__ import annotations
+
+# 模型配置解析：原始 dict → ChatModelConfig 等 dataclass
+
+import logging
+from typing import Any
+
+
+from ..coercers import (
+ _coerce_float,
+ _coerce_int,
+ _coerce_str,
+ _get_model_request_params,
+ _get_value,
+ _normalize_queue_interval,
+)
+from ..models import (
+ EmbeddingModelConfig,
+ RerankModelConfig,
+)
+from ..resolvers import (
+ _resolve_context_window_tokens,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def _parse_embedding_model_config(data: dict[str, Any]) -> EmbeddingModelConfig:
+ return EmbeddingModelConfig(
+ api_url=_coerce_str(
+ _get_value(
+ data, ("models", "embedding", "api_url"), "EMBEDDING_MODEL_API_URL"
+ ),
+ "",
+ ),
+ api_key=_coerce_str(
+ _get_value(
+ data, ("models", "embedding", "api_key"), "EMBEDDING_MODEL_API_KEY"
+ ),
+ "",
+ ),
+ model_name=_coerce_str(
+ _get_value(
+ data, ("models", "embedding", "model_name"), "EMBEDDING_MODEL_NAME"
+ ),
+ "",
+ ),
+ queue_interval_seconds=_normalize_queue_interval(
+ _coerce_float(
+ _get_value(
+ data, ("models", "embedding", "queue_interval_seconds"), None
+ ),
+ 0.0,
+ ),
+ 0.0,
+ ),
+ dimensions=_coerce_int(
+ _get_value(data, ("models", "embedding", "dimensions"), None), 0
+ )
+ or None,
+ query_instruction=_coerce_str(
+ _get_value(data, ("models", "embedding", "query_instruction"), None), ""
+ ),
+ context_window_tokens=_resolve_context_window_tokens(
+ data, "embedding", "EMBEDDING_MODEL_CONTEXT_WINDOW_TOKENS"
+ ),
+ document_instruction=_coerce_str(
+ _get_value(data, ("models", "embedding", "document_instruction"), None),
+ "",
+ ),
+ request_params=_get_model_request_params(data, "embedding"),
+ )
+
+
+def _parse_rerank_model_config(data: dict[str, Any]) -> RerankModelConfig:
+ queue_interval_seconds = _normalize_queue_interval(
+ _coerce_float(
+ _get_value(data, ("models", "rerank", "queue_interval_seconds"), None),
+ 0.0,
+ ),
+ 0.0,
+ )
+ return RerankModelConfig(
+ api_url=_coerce_str(
+ _get_value(data, ("models", "rerank", "api_url"), "RERANK_MODEL_API_URL"),
+ "",
+ ),
+ api_key=_coerce_str(
+ _get_value(data, ("models", "rerank", "api_key"), "RERANK_MODEL_API_KEY"),
+ "",
+ ),
+ model_name=_coerce_str(
+ _get_value(data, ("models", "rerank", "model_name"), "RERANK_MODEL_NAME"),
+ "",
+ ),
+ queue_interval_seconds=queue_interval_seconds,
+ context_window_tokens=_resolve_context_window_tokens(
+ data, "rerank", "RERANK_MODEL_CONTEXT_WINDOW_TOKENS"
+ ),
+ query_instruction=_coerce_str(
+ _get_value(data, ("models", "rerank", "query_instruction"), None), ""
+ ),
+ request_params=_get_model_request_params(data, "rerank"),
+ )
diff --git a/src/Undefined/config/parsers/grok.py b/src/Undefined/config/parsers/grok.py
new file mode 100644
index 00000000..efcbbadb
--- /dev/null
+++ b/src/Undefined/config/parsers/grok.py
@@ -0,0 +1,129 @@
+"""Grok model parser."""
+
+from __future__ import annotations
+
+# 模型配置解析：原始 dict → ChatModelConfig 等 dataclass
+
+import logging
+from typing import Any
+
+
+from ..coercers import (
+ _coerce_bool,
+ _coerce_float,
+ _coerce_int,
+ _coerce_str,
+ _get_model_request_params,
+ _get_value,
+ _normalize_queue_interval,
+)
+from ..models import (
+ GrokModelConfig,
+)
+from ..resolvers import (
+ _resolve_context_window_tokens,
+ _resolve_reasoning_effort,
+ _resolve_reasoning_effort_style,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def _parse_grok_model_config(data: dict[str, Any]) -> GrokModelConfig:
+ queue_interval_seconds = _normalize_queue_interval(
+ _coerce_float(
+ _get_value(
+ data,
+ ("models", "grok", "queue_interval_seconds"),
+ "GROK_MODEL_QUEUE_INTERVAL",
+ ),
+ 1.0,
+ )
+ )
+ context_window_tokens = _resolve_context_window_tokens(
+ data, "grok", "GROK_MODEL_CONTEXT_WINDOW_TOKENS"
+ )
+ return GrokModelConfig(
+ api_url=_coerce_str(
+ _get_value(data, ("models", "grok", "api_url"), "GROK_MODEL_API_URL"),
+ "",
+ ),
+ api_key=_coerce_str(
+ _get_value(data, ("models", "grok", "api_key"), "GROK_MODEL_API_KEY"),
+ "",
+ ),
+ model_name=_coerce_str(
+ _get_value(data, ("models", "grok", "model_name"), "GROK_MODEL_NAME"),
+ "",
+ ),
+ max_tokens=_coerce_int(
+ _get_value(data, ("models", "grok", "max_tokens"), "GROK_MODEL_MAX_TOKENS"),
+ 8192,
+ ),
+ context_window_tokens=context_window_tokens,
+ queue_interval_seconds=queue_interval_seconds,
+ thinking_enabled=_coerce_bool(
+ _get_value(
+ data,
+ ("models", "grok", "thinking_enabled"),
+ "GROK_MODEL_THINKING_ENABLED",
+ ),
+ False,
+ ),
+ thinking_budget_tokens=_coerce_int(
+ _get_value(
+ data,
+ ("models", "grok", "thinking_budget_tokens"),
+ "GROK_MODEL_THINKING_BUDGET_TOKENS",
+ ),
+ 20000,
+ ),
+ thinking_include_budget=_coerce_bool(
+ _get_value(
+ data,
+ ("models", "grok", "thinking_include_budget"),
+ "GROK_MODEL_THINKING_INCLUDE_BUDGET",
+ ),
+ True,
+ ),
+ reasoning_effort_style=_resolve_reasoning_effort_style(
+ _get_value(
+ data,
+ ("models", "grok", "reasoning_effort_style"),
+ "GROK_MODEL_REASONING_EFFORT_STYLE",
+ ),
+ ),
+ prompt_cache_enabled=_coerce_bool(
+ _get_value(
+ data,
+ ("models", "grok", "prompt_cache_enabled"),
+ "GROK_MODEL_PROMPT_CACHE_ENABLED",
+ ),
+ True,
+ ),
+ reasoning_enabled=_coerce_bool(
+ _get_value(
+ data,
+ ("models", "grok", "reasoning_enabled"),
+ "GROK_MODEL_REASONING_ENABLED",
+ ),
+ False,
+ ),
+ reasoning_effort=_resolve_reasoning_effort(
+ _get_value(
+ data,
+ ("models", "grok", "reasoning_effort"),
+ "GROK_MODEL_REASONING_EFFORT",
+ ),
+ "medium",
+ ),
+ stream_enabled=_coerce_bool(
+ _get_value(
+ data,
+ ("models", "grok", "stream_enabled"),
+ "GROK_MODEL_STREAM_ENABLED",
+ ),
+ False,
+ ),
+ request_params=_get_model_request_params(data, "grok"),
+ )
diff --git a/src/Undefined/config/parsers/helpers.py b/src/Undefined/config/parsers/helpers.py
new file mode 100644
index 00000000..19c82989
--- /dev/null
+++ b/src/Undefined/config/parsers/helpers.py
@@ -0,0 +1,122 @@
+"""Admin merge, validation, and debug helpers for model parsers."""
+
+from __future__ import annotations
+
+# 模型配置解析：原始 dict → ChatModelConfig 等 dataclass
+
+import logging
+
+from ..admin import load_local_admins
+from ..models import (
+ AgentModelConfig,
+ ChatModelConfig,
+ EmbeddingModelConfig,
+ GrokModelConfig,
+ SecurityModelConfig,
+ VisionModelConfig,
+)
+
+# 合并多来源配置
+logger = logging.getLogger(__name__)
+
+
+# 合并多来源配置
+def _merge_admins(superadmin_qq: int, admin_qqs: list[int]) -> tuple[int, list[int]]:
+ # admins.json 与 config.toml 的 admin_qq 合并，去重后超管必在列表中
+ local_admins = load_local_admins()
+ all_admins = list(set(admin_qqs + local_admins))
+ if superadmin_qq and superadmin_qq not in all_admins:
+ all_admins.append(superadmin_qq)
+ # 校验必填字段
+ return superadmin_qq, all_admins
+
+
+# 校验必填字段
+def _verify_required_fields(
+ bot_qq: int,
+ superadmin_qq: int,
+ onebot_ws_url: str,
+ chat_model: ChatModelConfig,
+ vision_model: VisionModelConfig,
+ agent_model: AgentModelConfig,
+ knowledge_enabled: bool,
+ embedding_model: EmbeddingModelConfig,
+) -> None:
+ missing: list[str] = []
+ if bot_qq <= 0:
+ missing.append("core.bot_qq")
+ if superadmin_qq <= 0:
+ missing.append("core.superadmin_qq")
+ if not onebot_ws_url:
+ missing.append("onebot.ws_url")
+ if not chat_model.api_url:
+ missing.append("models.chat.api_url")
+ if not chat_model.api_key:
+ missing.append("models.chat.api_key")
+ if not chat_model.model_name:
+ missing.append("models.chat.model_name")
+ if not vision_model.api_url:
+ missing.append("models.vision.api_url")
+ if not vision_model.api_key:
+ missing.append("models.vision.api_key")
+ if not vision_model.model_name:
+ missing.append("models.vision.model_name")
+ if not agent_model.api_url:
+ missing.append("models.agent.api_url")
+ if not agent_model.api_key:
+ missing.append("models.agent.api_key")
+ if not agent_model.model_name:
+ missing.append("models.agent.model_name")
+ if knowledge_enabled:
+ if not embedding_model.api_url:
+ missing.append("models.embedding.api_url")
+ if not embedding_model.model_name:
+ missing.append("models.embedding.model_name")
+ if missing:
+ # 输出调试/诊断日志
+ raise ValueError(f"缺少必需配置: {', '.join(missing)}")
+
+
+# 输出调试/诊断日志
+def _log_debug_info(
+ chat_model: ChatModelConfig,
+ vision_model: VisionModelConfig,
+ security_model: SecurityModelConfig,
+ naga_model: SecurityModelConfig,
+ agent_model: AgentModelConfig,
+ summary_model: AgentModelConfig,
+ grok_model: GrokModelConfig,
+) -> None:
+ configs: list[
+ tuple[
+ str,
+ ChatModelConfig
+ | VisionModelConfig
+ | SecurityModelConfig
+ | AgentModelConfig
+ | GrokModelConfig,
+ ]
+ ] = [
+ ("chat", chat_model),
+ ("vision", vision_model),
+ ("security", security_model),
+ ("naga", naga_model),
+ ("agent", agent_model),
+ ("summary", summary_model),
+ ("grok", grok_model),
+ ]
+ for name, cfg in configs:
+ logger.debug(
+ "[配置] %s_model=%s api_url=%s api_key_set=%s api_mode=%s thinking=%s reasoning=%s/%s cot_compat=%s responses_tool_choice_compat=%s responses_force_stateless_replay=%s",
+ name,
+ cfg.model_name,
+ cfg.api_url,
+ bool(cfg.api_key),
+ getattr(cfg, "api_mode", "chat_completions"),
+ cfg.thinking_enabled,
+ getattr(cfg, "reasoning_enabled", False),
+ getattr(cfg, "reasoning_effort", "medium"),
+ getattr(cfg, "thinking_tool_call_compat", False),
+ getattr(cfg, "responses_tool_choice_compat", False),
+ getattr(cfg, "responses_force_stateless_replay", False),
+ )
diff --git a/src/Undefined/config/parsers/historian.py b/src/Undefined/config/parsers/historian.py
new file mode 100644
index 00000000..74e06bf4
--- /dev/null
+++ b/src/Undefined/config/parsers/historian.py
@@ -0,0 +1,144 @@
+"""Historian model parser."""
+
+from __future__ import annotations
+
+# 模型配置解析：原始 dict → ChatModelConfig 等 dataclass
+
+import logging
+from typing import Any
+
+from Undefined.utils.request_params import merge_request_params
+
+from ..coercers import (
+ _coerce_bool,
+ _coerce_float,
+ _coerce_int,
+ _coerce_str,
+ _get_value,
+ _normalize_queue_interval,
+)
+from ..models import (
+ AgentModelConfig,
+)
+from ..resolvers import (
+ _resolve_api_mode,
+ _resolve_reasoning_effort,
+ _resolve_reasoning_effort_style,
+ _resolve_responses_force_stateless_replay,
+ _resolve_responses_tool_choice_compat,
+ _resolve_thinking_compat_flags,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def _parse_historian_model_config(
+ data: dict[str, Any], fallback: AgentModelConfig
+) -> AgentModelConfig:
+ h = data.get("models", {}).get("historian", {})
+ if not isinstance(h, dict) or not h:
+ return fallback
+ queue_interval_seconds = _coerce_float(
+ h.get("queue_interval_seconds"), fallback.queue_interval_seconds
+ )
+ queue_interval_seconds = _normalize_queue_interval(
+ queue_interval_seconds, fallback.queue_interval_seconds
+ )
+ thinking_include_budget, thinking_tool_call_compat = _resolve_thinking_compat_flags(
+ data={"models": {"historian": h}},
+ model_name="historian",
+ include_budget_env_key="HISTORIAN_MODEL_THINKING_INCLUDE_BUDGET",
+ tool_call_compat_env_key="HISTORIAN_MODEL_THINKING_TOOL_CALL_COMPAT",
+ legacy_env_key="HISTORIAN_MODEL_DEEPSEEK_NEW_COT_SUPPORT",
+ )
+ api_mode = _resolve_api_mode(
+ {"models": {"historian": h}},
+ "historian",
+ "HISTORIAN_MODEL_API_MODE",
+ fallback.api_mode,
+ )
+ responses_tool_choice_compat = _resolve_responses_tool_choice_compat(
+ {"models": {"historian": h}},
+ "historian",
+ "HISTORIAN_MODEL_RESPONSES_TOOL_CHOICE_COMPAT",
+ fallback.responses_tool_choice_compat,
+ )
+ responses_force_stateless_replay = _resolve_responses_force_stateless_replay(
+ {"models": {"historian": h}},
+ "historian",
+ "HISTORIAN_MODEL_RESPONSES_FORCE_STATELESS_REPLAY",
+ fallback.responses_force_stateless_replay,
+ )
+ prompt_cache_enabled = _coerce_bool(
+ _get_value(
+ {"models": {"historian": h}},
+ ("models", "historian", "prompt_cache_enabled"),
+ "HISTORIAN_MODEL_PROMPT_CACHE_ENABLED",
+ ),
+ fallback.prompt_cache_enabled,
+ )
+ context_window_tokens = _coerce_int(
+ h.get("context_window_tokens"), fallback.context_window_tokens
+ )
+ return AgentModelConfig(
+ api_url=_coerce_str(h.get("api_url"), fallback.api_url),
+ api_key=_coerce_str(h.get("api_key"), fallback.api_key),
+ model_name=_coerce_str(h.get("model_name"), fallback.model_name),
+ max_tokens=_coerce_int(h.get("max_tokens"), fallback.max_tokens),
+ context_window_tokens=context_window_tokens,
+ queue_interval_seconds=queue_interval_seconds,
+ api_mode=api_mode,
+ thinking_enabled=_coerce_bool(
+ h.get("thinking_enabled"), fallback.thinking_enabled
+ ),
+ thinking_budget_tokens=_coerce_int(
+ h.get("thinking_budget_tokens"), fallback.thinking_budget_tokens
+ ),
+ thinking_include_budget=thinking_include_budget,
+ reasoning_effort_style=_resolve_reasoning_effort_style(
+ _get_value(
+ {"models": {"historian": h}},
+ ("models", "historian", "reasoning_effort_style"),
+ "HISTORIAN_MODEL_REASONING_EFFORT_STYLE",
+ ),
+ fallback.reasoning_effort_style,
+ ),
+ thinking_tool_call_compat=thinking_tool_call_compat,
+ reasoning_content_replay=_coerce_bool(
+ h.get("reasoning_content_replay"), fallback.reasoning_content_replay
+ ),
+ system_prompt_as_user=_coerce_bool(
+ h.get("system_prompt_as_user"), fallback.system_prompt_as_user
+ ),
+ responses_tool_choice_compat=responses_tool_choice_compat,
+ responses_force_stateless_replay=responses_force_stateless_replay,
+ prompt_cache_enabled=prompt_cache_enabled,
+ reasoning_enabled=_coerce_bool(
+ _get_value(
+ {"models": {"historian": h}},
+ ("models", "historian", "reasoning_enabled"),
+ "HISTORIAN_MODEL_REASONING_ENABLED",
+ ),
+ fallback.reasoning_enabled,
+ ),
+ reasoning_effort=_resolve_reasoning_effort(
+ _get_value(
+ {"models": {"historian": h}},
+ ("models", "historian", "reasoning_effort"),
+ "HISTORIAN_MODEL_REASONING_EFFORT",
+ ),
+ fallback.reasoning_effort,
+ ),
+ stream_enabled=_coerce_bool(
+ _get_value(
+ {"models": {"historian": h}},
+ ("models", "historian", "stream_enabled"),
+ "HISTORIAN_MODEL_STREAM_ENABLED",
+ ),
+ fallback.stream_enabled,
+ ),
+ request_params=merge_request_params(
+ fallback.request_params,
+ h.get("request_params"),
+ ),
+ )
diff --git a/src/Undefined/config/parsers/image.py b/src/Undefined/config/parsers/image.py
new file mode 100644
index 00000000..cccf8460
--- /dev/null
+++ b/src/Undefined/config/parsers/image.py
@@ -0,0 +1,120 @@
+"""Image model parser."""
+
+from __future__ import annotations
+
+# 模型配置解析：原始 dict → ChatModelConfig 等 dataclass
+
+import logging
+from typing import Any
+
+
+from ..coercers import (
+ _coerce_float,
+ _coerce_int,
+ _coerce_str,
+ _get_model_request_params,
+ _get_value,
+)
+from ..models import (
+ ImageGenConfig,
+ ImageGenModelConfig,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def _parse_image_gen_model_config(data: dict[str, Any]) -> ImageGenModelConfig:
+ """解析 [models.image_gen] 生图模型配置"""
+ return ImageGenModelConfig(
+ api_url=_coerce_str(
+ _get_value(
+ data, ("models", "image_gen", "api_url"), "IMAGE_GEN_MODEL_API_URL"
+ ),
+ "",
+ ),
+ api_key=_coerce_str(
+ _get_value(
+ data, ("models", "image_gen", "api_key"), "IMAGE_GEN_MODEL_API_KEY"
+ ),
+ "",
+ ),
+ model_name=_coerce_str(
+ _get_value(
+ data, ("models", "image_gen", "model_name"), "IMAGE_GEN_MODEL_NAME"
+ ),
+ "",
+ ),
+ context_window_tokens=_coerce_int(
+ _get_value(
+ data,
+ ("models", "image_gen", "context_window_tokens"),
+ None,
+ ),
+ 0,
+ ),
+ request_params=_get_model_request_params(data, "image_gen"),
+ )
+
+
+def _parse_image_edit_model_config(data: dict[str, Any]) -> ImageGenModelConfig:
+ """解析 [models.image_edit] 参考图生图模型配置"""
+ return ImageGenModelConfig(
+ api_url=_coerce_str(
+ _get_value(
+ data,
+ ("models", "image_edit", "api_url"),
+ "IMAGE_EDIT_MODEL_API_URL",
+ ),
+ "",
+ ),
+ api_key=_coerce_str(
+ _get_value(
+ data,
+ ("models", "image_edit", "api_key"),
+ "IMAGE_EDIT_MODEL_API_KEY",
+ ),
+ "",
+ ),
+ model_name=_coerce_str(
+ _get_value(
+ data,
+ ("models", "image_edit", "model_name"),
+ "IMAGE_EDIT_MODEL_NAME",
+ ),
+ "",
+ ),
+ context_window_tokens=_coerce_int(
+ _get_value(
+ data,
+ ("models", "image_edit", "context_window_tokens"),
+ None,
+ ),
+ 0,
+ ),
+ request_params=_get_model_request_params(data, "image_edit"),
+ )
+
+
+def _parse_image_gen_config(data: dict[str, Any]) -> ImageGenConfig:
+ """解析 [image_gen] 生图工具配置"""
+ return ImageGenConfig(
+ provider=_coerce_str(
+ _get_value(data, ("image_gen", "provider"), "IMAGE_GEN_PROVIDER"),
+ "xingzhige",
+ ),
+ xingzhige_size=_coerce_str(
+ _get_value(data, ("image_gen", "xingzhige_size"), None), "1:1"
+ ),
+ openai_size=_coerce_str(
+ _get_value(data, ("image_gen", "openai_size"), None), ""
+ ),
+ openai_quality=_coerce_str(
+ _get_value(data, ("image_gen", "openai_quality"), None), ""
+ ),
+ openai_style=_coerce_str(
+ _get_value(data, ("image_gen", "openai_style"), None), ""
+ ),
+ openai_timeout=_coerce_float(
+ _get_value(data, ("image_gen", "openai_timeout"), None), 120.0
+ ),
+ )
diff --git a/src/Undefined/config/parsers/naga.py b/src/Undefined/config/parsers/naga.py
new file mode 100644
index 00000000..920862e0
--- /dev/null
+++ b/src/Undefined/config/parsers/naga.py
@@ -0,0 +1,206 @@
+"""Naga model parser."""
+
+from __future__ import annotations
+
+# 模型配置解析：原始 dict → ChatModelConfig 等 dataclass
+
+import logging
+from typing import Any
+
+from Undefined.utils.request_params import merge_request_params
+
+from ..coercers import (
+ _coerce_bool,
+ _coerce_float,
+ _coerce_int,
+ _coerce_str,
+ _get_model_request_params,
+ _get_value,
+ _normalize_queue_interval,
+)
+from ..models import (
+ SecurityModelConfig,
+)
+from ..resolvers import (
+ _resolve_api_mode,
+ _resolve_context_window_tokens,
+ _resolve_reasoning_effort,
+ _resolve_reasoning_effort_style,
+ _resolve_reasoning_content_replay,
+ _resolve_responses_force_stateless_replay,
+ _resolve_responses_tool_choice_compat,
+ _resolve_system_prompt_as_user,
+ _resolve_thinking_compat_flags,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def _parse_naga_model_config(
+ data: dict[str, Any], security_model: SecurityModelConfig
+) -> SecurityModelConfig:
+ api_url = _coerce_str(
+ _get_value(data, ("models", "naga", "api_url"), "NAGA_MODEL_API_URL"),
+ "",
+ )
+ api_key = _coerce_str(
+ _get_value(data, ("models", "naga", "api_key"), "NAGA_MODEL_API_KEY"),
+ "",
+ )
+ model_name = _coerce_str(
+ _get_value(data, ("models", "naga", "model_name"), "NAGA_MODEL_NAME"),
+ "",
+ )
+ queue_interval_seconds = _coerce_float(
+ _get_value(
+ data,
+ ("models", "naga", "queue_interval_seconds"),
+ "NAGA_MODEL_QUEUE_INTERVAL",
+ ),
+ security_model.queue_interval_seconds,
+ )
+ queue_interval_seconds = _normalize_queue_interval(queue_interval_seconds)
+
+ thinking_include_budget, thinking_tool_call_compat = _resolve_thinking_compat_flags(
+ data=data,
+ model_name="naga",
+ include_budget_env_key="NAGA_MODEL_THINKING_INCLUDE_BUDGET",
+ tool_call_compat_env_key="NAGA_MODEL_THINKING_TOOL_CALL_COMPAT",
+ legacy_env_key="NAGA_MODEL_DEEPSEEK_NEW_COT_SUPPORT",
+ )
+ api_mode = _resolve_api_mode(data, "naga", "NAGA_MODEL_API_MODE")
+ responses_tool_choice_compat = _resolve_responses_tool_choice_compat(
+ data, "naga", "NAGA_MODEL_RESPONSES_TOOL_CHOICE_COMPAT"
+ )
+ responses_force_stateless_replay = _resolve_responses_force_stateless_replay(
+ data, "naga", "NAGA_MODEL_RESPONSES_FORCE_STATELESS_REPLAY"
+ )
+ reasoning_content_replay = _resolve_reasoning_content_replay(
+ data,
+ "naga",
+ "NAGA_MODEL_REASONING_CONTENT_REPLAY",
+ default=security_model.reasoning_content_replay,
+ )
+ system_prompt_as_user = _resolve_system_prompt_as_user(
+ data,
+ "naga",
+ "NAGA_MODEL_SYSTEM_PROMPT_AS_USER",
+ default=security_model.system_prompt_as_user,
+ )
+ prompt_cache_enabled = _coerce_bool(
+ _get_value(
+ data,
+ ("models", "naga", "prompt_cache_enabled"),
+ "NAGA_MODEL_PROMPT_CACHE_ENABLED",
+ ),
+ getattr(security_model, "prompt_cache_enabled", True),
+ )
+ reasoning_enabled = _coerce_bool(
+ _get_value(
+ data,
+ ("models", "naga", "reasoning_enabled"),
+ "NAGA_MODEL_REASONING_ENABLED",
+ ),
+ getattr(security_model, "reasoning_enabled", False),
+ )
+ reasoning_effort = _resolve_reasoning_effort(
+ _get_value(
+ data,
+ ("models", "naga", "reasoning_effort"),
+ "NAGA_MODEL_REASONING_EFFORT",
+ ),
+ getattr(security_model, "reasoning_effort", "medium"),
+ )
+ stream_enabled = _coerce_bool(
+ _get_value(
+ data,
+ ("models", "naga", "stream_enabled"),
+ "NAGA_MODEL_STREAM_ENABLED",
+ ),
+ getattr(security_model, "stream_enabled", False),
+ )
+
+ if api_url and api_key and model_name:
+ context_window_tokens = _resolve_context_window_tokens(
+ data,
+ "naga",
+ "NAGA_MODEL_CONTEXT_WINDOW_TOKENS",
+ default=security_model.context_window_tokens,
+ )
+ return SecurityModelConfig(
+ api_url=api_url,
+ api_key=api_key,
+ model_name=model_name,
+ max_tokens=_coerce_int(
+ _get_value(
+ data,
+ ("models", "naga", "max_tokens"),
+ "NAGA_MODEL_MAX_TOKENS",
+ ),
+ 160,
+ ),
+ context_window_tokens=context_window_tokens,
+ queue_interval_seconds=queue_interval_seconds,
+ api_mode=api_mode,
+ thinking_enabled=_coerce_bool(
+ _get_value(
+ data,
+ ("models", "naga", "thinking_enabled"),
+ "NAGA_MODEL_THINKING_ENABLED",
+ ),
+ False,
+ ),
+ thinking_budget_tokens=_coerce_int(
+ _get_value(
+ data,
+ ("models", "naga", "thinking_budget_tokens"),
+ "NAGA_MODEL_THINKING_BUDGET_TOKENS",
+ ),
+ 0,
+ ),
+ thinking_include_budget=thinking_include_budget,
+ reasoning_effort_style=_resolve_reasoning_effort_style(
+ _get_value(
+ data,
+ ("models", "naga", "reasoning_effort_style"),
+ "NAGA_MODEL_REASONING_EFFORT_STYLE",
+ ),
+ ),
+ thinking_tool_call_compat=thinking_tool_call_compat,
+ reasoning_content_replay=reasoning_content_replay,
+ system_prompt_as_user=system_prompt_as_user,
+ responses_tool_choice_compat=responses_tool_choice_compat,
+ responses_force_stateless_replay=responses_force_stateless_replay,
+ prompt_cache_enabled=prompt_cache_enabled,
+ reasoning_enabled=reasoning_enabled,
+ reasoning_effort=reasoning_effort,
+ stream_enabled=stream_enabled,
+ request_params=_get_model_request_params(data, "naga"),
+ )
+
+ logger.info(
+ "未配置 Naga 审核模型，将使用已解析的安全模型配置作为后备（安全模型本身可能已回退）"
+ )
+ return SecurityModelConfig(
+ api_url=security_model.api_url,
+ api_key=security_model.api_key,
+ model_name=security_model.model_name,
+ max_tokens=security_model.max_tokens,
+ context_window_tokens=security_model.context_window_tokens,
+ queue_interval_seconds=security_model.queue_interval_seconds,
+ api_mode=security_model.api_mode,
+ thinking_enabled=security_model.thinking_enabled,
+ thinking_budget_tokens=security_model.thinking_budget_tokens,
+ thinking_include_budget=security_model.thinking_include_budget,
+ reasoning_effort_style=security_model.reasoning_effort_style,
+ thinking_tool_call_compat=security_model.thinking_tool_call_compat,
+ reasoning_content_replay=security_model.reasoning_content_replay,
+ system_prompt_as_user=security_model.system_prompt_as_user,
+ responses_tool_choice_compat=security_model.responses_tool_choice_compat,
+ responses_force_stateless_replay=security_model.responses_force_stateless_replay,
+ prompt_cache_enabled=security_model.prompt_cache_enabled,
+ reasoning_enabled=security_model.reasoning_enabled,
+ reasoning_effort=security_model.reasoning_effort,
+ stream_enabled=security_model.stream_enabled,
+ request_params=merge_request_params(security_model.request_params),
+ )
diff --git a/src/Undefined/config/parsers/pool.py b/src/Undefined/config/parsers/pool.py
new file mode 100644
index 00000000..72abcce4
--- /dev/null
+++ b/src/Undefined/config/parsers/pool.py
@@ -0,0 +1,142 @@
+"""Model pool parser."""
+
+from __future__ import annotations
+
+# 模型配置解析：原始 dict → ChatModelConfig 等 dataclass
+
+import logging
+from typing import Any
+
+from Undefined.utils.request_params import merge_request_params
+
+from ..coercers import (
+ _coerce_bool,
+ _coerce_float,
+ _coerce_int,
+ _coerce_str,
+ _normalize_queue_interval,
+ _VALID_API_MODES,
+)
+from ..models import AgentModelConfig, ChatModelConfig, ModelPool, ModelPoolEntry
+from ..resolvers import (
+ _resolve_reasoning_effort,
+ _resolve_reasoning_effort_style,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def _parse_model_pool(
+ data: dict[str, Any],
+ model_section: str,
+ primary_config: ChatModelConfig | AgentModelConfig,
+) -> ModelPool | None:
+ """解析模型池配置，缺省字段继承 primary_config"""
+ pool_data = data.get("models", {}).get(model_section, {}).get("pool")
+ if not isinstance(pool_data, dict):
+ return None
+
+ enabled = _coerce_bool(pool_data.get("enabled"), False)
+ strategy = _coerce_str(pool_data.get("strategy"), "default").strip().lower()
+ if strategy not in ("default", "round_robin", "random"):
+ strategy = "default"
+
+ raw_models = pool_data.get("models")
+ if not isinstance(raw_models, list):
+ return ModelPool(enabled=enabled, strategy=strategy)
+
+ entries: list[ModelPoolEntry] = []
+ for item in raw_models:
+ if not isinstance(item, dict):
+ continue
+ name = _coerce_str(item.get("model_name"), "").strip()
+ if not name:
+ continue
+ entries.append(
+ ModelPoolEntry(
+ api_url=_coerce_str(item.get("api_url"), primary_config.api_url),
+ api_key=_coerce_str(item.get("api_key"), primary_config.api_key),
+ model_name=name,
+ context_window_tokens=_coerce_int(
+ item.get("context_window_tokens"),
+ primary_config.context_window_tokens,
+ ),
+ max_tokens=_coerce_int(
+ item.get("max_tokens"), primary_config.max_tokens
+ ),
+ queue_interval_seconds=_normalize_queue_interval(
+ _coerce_float(
+ item.get("queue_interval_seconds"),
+ primary_config.queue_interval_seconds,
+ ),
+ primary_config.queue_interval_seconds,
+ ),
+ api_mode=(
+ _coerce_str(item.get("api_mode"), primary_config.api_mode)
+ .strip()
+ .lower()
+ )
+ if _coerce_str(item.get("api_mode"), primary_config.api_mode)
+ .strip()
+ .lower()
+ in _VALID_API_MODES
+ else primary_config.api_mode,
+ thinking_enabled=_coerce_bool(
+ item.get("thinking_enabled"), primary_config.thinking_enabled
+ ),
+ thinking_budget_tokens=_coerce_int(
+ item.get("thinking_budget_tokens"),
+ primary_config.thinking_budget_tokens,
+ ),
+ thinking_include_budget=_coerce_bool(
+ item.get("thinking_include_budget"),
+ primary_config.thinking_include_budget,
+ ),
+ reasoning_effort_style=_resolve_reasoning_effort_style(
+ item.get("reasoning_effort_style"),
+ primary_config.reasoning_effort_style,
+ ),
+ thinking_tool_call_compat=_coerce_bool(
+ item.get("thinking_tool_call_compat"),
+ primary_config.thinking_tool_call_compat,
+ ),
+ reasoning_content_replay=_coerce_bool(
+ item.get("reasoning_content_replay"),
+ primary_config.reasoning_content_replay,
+ ),
+ system_prompt_as_user=_coerce_bool(
+ item.get("system_prompt_as_user"),
+ primary_config.system_prompt_as_user,
+ ),
+ responses_tool_choice_compat=_coerce_bool(
+ item.get("responses_tool_choice_compat"),
+ primary_config.responses_tool_choice_compat,
+ ),
+ responses_force_stateless_replay=_coerce_bool(
+ item.get("responses_force_stateless_replay"),
+ primary_config.responses_force_stateless_replay,
+ ),
+ prompt_cache_enabled=_coerce_bool(
+ item.get("prompt_cache_enabled"),
+ primary_config.prompt_cache_enabled,
+ ),
+ reasoning_enabled=_coerce_bool(
+ item.get("reasoning_enabled"),
+ primary_config.reasoning_enabled,
+ ),
+ reasoning_effort=_resolve_reasoning_effort(
+ item.get("reasoning_effort"),
+ primary_config.reasoning_effort,
+ ),
+ stream_enabled=_coerce_bool(
+ item.get("stream_enabled"),
+ getattr(primary_config, "stream_enabled", False),
+ ),
+ request_params=merge_request_params(
+ primary_config.request_params,
+ item.get("request_params"),
+ ),
+ )
+ )
+
+ return ModelPool(enabled=enabled, strategy=strategy, models=entries)
diff --git a/src/Undefined/config/parsers/security.py b/src/Undefined/config/parsers/security.py
new file mode 100644
index 00000000..f5044a91
--- /dev/null
+++ b/src/Undefined/config/parsers/security.py
@@ -0,0 +1,196 @@
+"""Security model parser."""
+
+from __future__ import annotations
+
+# 模型配置解析：原始 dict → ChatModelConfig 等 dataclass
+
+import logging
+from typing import Any
+
+from Undefined.utils.request_params import merge_request_params
+
+from ..coercers import (
+ _coerce_bool,
+ _coerce_float,
+ _coerce_int,
+ _coerce_str,
+ _get_model_request_params,
+ _get_value,
+ _normalize_queue_interval,
+)
+from ..models import (
+ ChatModelConfig,
+ SecurityModelConfig,
+)
+from ..resolvers import (
+ _resolve_api_mode,
+ _resolve_context_window_tokens,
+ _resolve_reasoning_effort,
+ _resolve_reasoning_effort_style,
+ _resolve_reasoning_content_replay,
+ _resolve_responses_force_stateless_replay,
+ _resolve_responses_tool_choice_compat,
+ _resolve_system_prompt_as_user,
+ _resolve_thinking_compat_flags,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def _parse_security_model_config(
+ data: dict[str, Any], chat_model: ChatModelConfig
+) -> SecurityModelConfig:
+ api_url = _coerce_str(
+ _get_value(data, ("models", "security", "api_url"), "SECURITY_MODEL_API_URL"),
+ "",
+ )
+ api_key = _coerce_str(
+ _get_value(data, ("models", "security", "api_key"), "SECURITY_MODEL_API_KEY"),
+ "",
+ )
+ model_name = _coerce_str(
+ _get_value(data, ("models", "security", "model_name"), "SECURITY_MODEL_NAME"),
+ "",
+ )
+ queue_interval_seconds = _coerce_float(
+ _get_value(
+ data,
+ ("models", "security", "queue_interval_seconds"),
+ "SECURITY_MODEL_QUEUE_INTERVAL",
+ ),
+ 1.0,
+ )
+ queue_interval_seconds = _normalize_queue_interval(queue_interval_seconds)
+
+ thinking_include_budget, thinking_tool_call_compat = _resolve_thinking_compat_flags(
+ data=data,
+ model_name="security",
+ include_budget_env_key="SECURITY_MODEL_THINKING_INCLUDE_BUDGET",
+ tool_call_compat_env_key="SECURITY_MODEL_THINKING_TOOL_CALL_COMPAT",
+ legacy_env_key="SECURITY_MODEL_DEEPSEEK_NEW_COT_SUPPORT",
+ )
+ api_mode = _resolve_api_mode(data, "security", "SECURITY_MODEL_API_MODE")
+ responses_tool_choice_compat = _resolve_responses_tool_choice_compat(
+ data, "security", "SECURITY_MODEL_RESPONSES_TOOL_CHOICE_COMPAT"
+ )
+ responses_force_stateless_replay = _resolve_responses_force_stateless_replay(
+ data, "security", "SECURITY_MODEL_RESPONSES_FORCE_STATELESS_REPLAY"
+ )
+ reasoning_content_replay = _resolve_reasoning_content_replay(
+ data, "security", "SECURITY_MODEL_REASONING_CONTENT_REPLAY"
+ )
+ system_prompt_as_user = _resolve_system_prompt_as_user(
+ data, "security", "SECURITY_MODEL_SYSTEM_PROMPT_AS_USER"
+ )
+ prompt_cache_enabled = _coerce_bool(
+ _get_value(
+ data,
+ ("models", "security", "prompt_cache_enabled"),
+ "SECURITY_MODEL_PROMPT_CACHE_ENABLED",
+ ),
+ True,
+ )
+ reasoning_enabled = _coerce_bool(
+ _get_value(
+ data,
+ ("models", "security", "reasoning_enabled"),
+ "SECURITY_MODEL_REASONING_ENABLED",
+ ),
+ False,
+ )
+ reasoning_effort = _resolve_reasoning_effort(
+ _get_value(
+ data,
+ ("models", "security", "reasoning_effort"),
+ "SECURITY_MODEL_REASONING_EFFORT",
+ ),
+ "medium",
+ )
+ stream_enabled = _coerce_bool(
+ _get_value(
+ data,
+ ("models", "security", "stream_enabled"),
+ "SECURITY_MODEL_STREAM_ENABLED",
+ ),
+ False,
+ )
+
+ context_window_tokens = _resolve_context_window_tokens(
+ data, "security", "SECURITY_MODEL_CONTEXT_WINDOW_TOKENS"
+ )
+ if api_url and api_key and model_name:
+ return SecurityModelConfig(
+ api_url=api_url,
+ api_key=api_key,
+ model_name=model_name,
+ max_tokens=_coerce_int(
+ _get_value(
+ data,
+ ("models", "security", "max_tokens"),
+ "SECURITY_MODEL_MAX_TOKENS",
+ ),
+ 100,
+ ),
+ context_window_tokens=context_window_tokens,
+ queue_interval_seconds=queue_interval_seconds,
+ api_mode=api_mode,
+ thinking_enabled=_coerce_bool(
+ _get_value(
+ data,
+ ("models", "security", "thinking_enabled"),
+ "SECURITY_MODEL_THINKING_ENABLED",
+ ),
+ False,
+ ),
+ thinking_budget_tokens=_coerce_int(
+ _get_value(
+ data,
+ ("models", "security", "thinking_budget_tokens"),
+ "SECURITY_MODEL_THINKING_BUDGET_TOKENS",
+ ),
+ 0,
+ ),
+ thinking_include_budget=thinking_include_budget,
+ reasoning_effort_style=_resolve_reasoning_effort_style(
+ _get_value(
+ data,
+ ("models", "security", "reasoning_effort_style"),
+ "SECURITY_MODEL_REASONING_EFFORT_STYLE",
+ ),
+ ),
+ thinking_tool_call_compat=thinking_tool_call_compat,
+ reasoning_content_replay=reasoning_content_replay,
+ system_prompt_as_user=system_prompt_as_user,
+ responses_tool_choice_compat=responses_tool_choice_compat,
+ responses_force_stateless_replay=responses_force_stateless_replay,
+ prompt_cache_enabled=prompt_cache_enabled,
+ reasoning_enabled=reasoning_enabled,
+ reasoning_effort=reasoning_effort,
+ stream_enabled=stream_enabled,
+ request_params=_get_model_request_params(data, "security"),
+ )
+
+ logger.warning("未配置安全模型，将使用对话模型作为后备")
+ return SecurityModelConfig(
+ api_url=chat_model.api_url,
+ api_key=chat_model.api_key,
+ model_name=chat_model.model_name,
+ context_window_tokens=chat_model.context_window_tokens,
+ max_tokens=chat_model.max_tokens,
+ queue_interval_seconds=chat_model.queue_interval_seconds,
+ api_mode=chat_model.api_mode,
+ thinking_enabled=False,
+ thinking_budget_tokens=0,
+ thinking_include_budget=True,
+ reasoning_effort_style="openai",
+ thinking_tool_call_compat=chat_model.thinking_tool_call_compat,
+ reasoning_content_replay=chat_model.reasoning_content_replay,
+ system_prompt_as_user=chat_model.system_prompt_as_user,
+ responses_tool_choice_compat=chat_model.responses_tool_choice_compat,
+ responses_force_stateless_replay=chat_model.responses_force_stateless_replay,
+ prompt_cache_enabled=chat_model.prompt_cache_enabled,
+ reasoning_enabled=chat_model.reasoning_enabled,
+ reasoning_effort=chat_model.reasoning_effort,
+ stream_enabled=chat_model.stream_enabled,
+ request_params=merge_request_params(chat_model.request_params),
+ )
diff --git a/src/Undefined/config/parsers/summary.py b/src/Undefined/config/parsers/summary.py
new file mode 100644
index 00000000..dd7757dc
--- /dev/null
+++ b/src/Undefined/config/parsers/summary.py
@@ -0,0 +1,147 @@
+"""Summary model parser."""
+
+from __future__ import annotations
+
+# 模型配置解析：原始 dict → ChatModelConfig 等 dataclass
+
+import logging
+from typing import Any
+
+from Undefined.utils.request_params import merge_request_params
+
+from ..coercers import (
+ _coerce_bool,
+ _coerce_float,
+ _coerce_int,
+ _coerce_str,
+ _get_value,
+ _normalize_queue_interval,
+)
+from ..models import (
+ AgentModelConfig,
+)
+from ..resolvers import (
+ _resolve_api_mode,
+ _resolve_reasoning_effort,
+ _resolve_reasoning_effort_style,
+ _resolve_responses_force_stateless_replay,
+ _resolve_responses_tool_choice_compat,
+ _resolve_thinking_compat_flags,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def _parse_summary_model_config(
+ data: dict[str, Any], fallback: AgentModelConfig
+) -> tuple[AgentModelConfig, bool]:
+ s = data.get("models", {}).get("summary", {})
+ if not isinstance(s, dict) or not s:
+ return fallback, False
+ queue_interval_seconds = _coerce_float(
+ s.get("queue_interval_seconds"), fallback.queue_interval_seconds
+ )
+ queue_interval_seconds = _normalize_queue_interval(
+ queue_interval_seconds, fallback.queue_interval_seconds
+ )
+ thinking_include_budget, thinking_tool_call_compat = _resolve_thinking_compat_flags(
+ data={"models": {"summary": s}},
+ model_name="summary",
+ include_budget_env_key="SUMMARY_MODEL_THINKING_INCLUDE_BUDGET",
+ tool_call_compat_env_key="SUMMARY_MODEL_THINKING_TOOL_CALL_COMPAT",
+ legacy_env_key="SUMMARY_MODEL_DEEPSEEK_NEW_COT_SUPPORT",
+ )
+ api_mode = _resolve_api_mode(
+ {"models": {"summary": s}},
+ "summary",
+ "SUMMARY_MODEL_API_MODE",
+ fallback.api_mode,
+ )
+ responses_tool_choice_compat = _resolve_responses_tool_choice_compat(
+ {"models": {"summary": s}},
+ "summary",
+ "SUMMARY_MODEL_RESPONSES_TOOL_CHOICE_COMPAT",
+ fallback.responses_tool_choice_compat,
+ )
+ responses_force_stateless_replay = _resolve_responses_force_stateless_replay(
+ {"models": {"summary": s}},
+ "summary",
+ "SUMMARY_MODEL_RESPONSES_FORCE_STATELESS_REPLAY",
+ fallback.responses_force_stateless_replay,
+ )
+ prompt_cache_enabled = _coerce_bool(
+ _get_value(
+ {"models": {"summary": s}},
+ ("models", "summary", "prompt_cache_enabled"),
+ "SUMMARY_MODEL_PROMPT_CACHE_ENABLED",
+ ),
+ fallback.prompt_cache_enabled,
+ )
+ context_window_tokens = _coerce_int(
+ s.get("context_window_tokens"), fallback.context_window_tokens
+ )
+ return (
+ AgentModelConfig(
+ api_url=_coerce_str(s.get("api_url"), fallback.api_url),
+ api_key=_coerce_str(s.get("api_key"), fallback.api_key),
+ model_name=_coerce_str(s.get("model_name"), fallback.model_name),
+ max_tokens=_coerce_int(s.get("max_tokens"), fallback.max_tokens),
+ context_window_tokens=context_window_tokens,
+ queue_interval_seconds=queue_interval_seconds,
+ api_mode=api_mode,
+ thinking_enabled=_coerce_bool(
+ s.get("thinking_enabled"), fallback.thinking_enabled
+ ),
+ thinking_budget_tokens=_coerce_int(
+ s.get("thinking_budget_tokens"), fallback.thinking_budget_tokens
+ ),
+ thinking_include_budget=thinking_include_budget,
+ reasoning_effort_style=_resolve_reasoning_effort_style(
+ _get_value(
+ {"models": {"summary": s}},
+ ("models", "summary", "reasoning_effort_style"),
+ "SUMMARY_MODEL_REASONING_EFFORT_STYLE",
+ ),
+ fallback.reasoning_effort_style,
+ ),
+ thinking_tool_call_compat=thinking_tool_call_compat,
+ reasoning_content_replay=_coerce_bool(
+ s.get("reasoning_content_replay"), fallback.reasoning_content_replay
+ ),
+ system_prompt_as_user=_coerce_bool(
+ s.get("system_prompt_as_user"), fallback.system_prompt_as_user
+ ),
+ responses_tool_choice_compat=responses_tool_choice_compat,
+ responses_force_stateless_replay=responses_force_stateless_replay,
+ prompt_cache_enabled=prompt_cache_enabled,
+ reasoning_enabled=_coerce_bool(
+ _get_value(
+ {"models": {"summary": s}},
+ ("models", "summary", "reasoning_enabled"),
+ "SUMMARY_MODEL_REASONING_ENABLED",
+ ),
+ fallback.reasoning_enabled,
+ ),
+ reasoning_effort=_resolve_reasoning_effort(
+ _get_value(
+ {"models": {"summary": s}},
+ ("models", "summary", "reasoning_effort"),
+ "SUMMARY_MODEL_REASONING_EFFORT",
+ ),
+ fallback.reasoning_effort,
+ ),
+ stream_enabled=_coerce_bool(
+ _get_value(
+ {"models": {"summary": s}},
+ ("models", "summary", "stream_enabled"),
+ "SUMMARY_MODEL_STREAM_ENABLED",
+ ),
+ fallback.stream_enabled,
+ ),
+ request_params=merge_request_params(
+ fallback.request_params,
+ s.get("request_params"),
+ ),
+ ),
+ True,
+ )
diff --git a/src/Undefined/config/parsers/vision.py b/src/Undefined/config/parsers/vision.py
new file mode 100644
index 00000000..9299c980
--- /dev/null
+++ b/src/Undefined/config/parsers/vision.py
@@ -0,0 +1,162 @@
+"""Vision model parser."""
+
+from __future__ import annotations
+
+# 模型配置解析：原始 dict → ChatModelConfig 等 dataclass
+
+import logging
+from typing import Any
+
+
+from ..coercers import (
+ _coerce_bool,
+ _coerce_float,
+ _coerce_int,
+ _coerce_str,
+ _get_model_request_params,
+ _get_value,
+ _normalize_queue_interval,
+)
+from ..models import (
+ VisionModelConfig,
+)
+from ..resolvers import (
+ _resolve_api_mode,
+ _resolve_context_window_tokens,
+ _resolve_reasoning_effort,
+ _resolve_reasoning_effort_style,
+ _resolve_reasoning_content_replay,
+ _resolve_responses_force_stateless_replay,
+ _resolve_responses_tool_choice_compat,
+ _resolve_system_prompt_as_user,
+ _resolve_thinking_compat_flags,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def _parse_vision_model_config(data: dict[str, Any]) -> VisionModelConfig:
+ queue_interval_seconds = _normalize_queue_interval(
+ _coerce_float(
+ _get_value(
+ data,
+ ("models", "vision", "queue_interval_seconds"),
+ "VISION_MODEL_QUEUE_INTERVAL",
+ ),
+ 1.0,
+ )
+ )
+ thinking_include_budget, thinking_tool_call_compat = _resolve_thinking_compat_flags(
+ data=data,
+ model_name="vision",
+ include_budget_env_key="VISION_MODEL_THINKING_INCLUDE_BUDGET",
+ tool_call_compat_env_key="VISION_MODEL_THINKING_TOOL_CALL_COMPAT",
+ legacy_env_key="VISION_MODEL_DEEPSEEK_NEW_COT_SUPPORT",
+ )
+ api_mode = _resolve_api_mode(data, "vision", "VISION_MODEL_API_MODE")
+ responses_tool_choice_compat = _resolve_responses_tool_choice_compat(
+ data, "vision", "VISION_MODEL_RESPONSES_TOOL_CHOICE_COMPAT"
+ )
+ responses_force_stateless_replay = _resolve_responses_force_stateless_replay(
+ data, "vision", "VISION_MODEL_RESPONSES_FORCE_STATELESS_REPLAY"
+ )
+ reasoning_content_replay = _resolve_reasoning_content_replay(
+ data, "vision", "VISION_MODEL_REASONING_CONTENT_REPLAY"
+ )
+ system_prompt_as_user = _resolve_system_prompt_as_user(
+ data, "vision", "VISION_MODEL_SYSTEM_PROMPT_AS_USER"
+ )
+ prompt_cache_enabled = _coerce_bool(
+ _get_value(
+ data,
+ ("models", "vision", "prompt_cache_enabled"),
+ "VISION_MODEL_PROMPT_CACHE_ENABLED",
+ ),
+ True,
+ )
+ reasoning_enabled = _coerce_bool(
+ _get_value(
+ data,
+ ("models", "vision", "reasoning_enabled"),
+ "VISION_MODEL_REASONING_ENABLED",
+ ),
+ False,
+ )
+ reasoning_effort = _resolve_reasoning_effort(
+ _get_value(
+ data,
+ ("models", "vision", "reasoning_effort"),
+ "VISION_MODEL_REASONING_EFFORT",
+ ),
+ "medium",
+ )
+ stream_enabled = _coerce_bool(
+ _get_value(
+ data,
+ ("models", "vision", "stream_enabled"),
+ "VISION_MODEL_STREAM_ENABLED",
+ ),
+ False,
+ )
+ context_window_tokens = _resolve_context_window_tokens(
+ data, "vision", "VISION_MODEL_CONTEXT_WINDOW_TOKENS"
+ )
+ return VisionModelConfig(
+ api_url=_coerce_str(
+ _get_value(data, ("models", "vision", "api_url"), "VISION_MODEL_API_URL"),
+ "",
+ ),
+ api_key=_coerce_str(
+ _get_value(data, ("models", "vision", "api_key"), "VISION_MODEL_API_KEY"),
+ "",
+ ),
+ model_name=_coerce_str(
+ _get_value(data, ("models", "vision", "model_name"), "VISION_MODEL_NAME"),
+ "",
+ ),
+ max_tokens=_coerce_int(
+ _get_value(
+ data,
+ ("models", "vision", "max_tokens"),
+ "VISION_MODEL_MAX_TOKENS",
+ ),
+ 8192,
+ ),
+ context_window_tokens=context_window_tokens,
+ queue_interval_seconds=queue_interval_seconds,
+ api_mode=api_mode,
+ thinking_enabled=_coerce_bool(
+ _get_value(
+ data,
+ ("models", "vision", "thinking_enabled"),
+ "VISION_MODEL_THINKING_ENABLED",
+ ),
+ False,
+ ),
+ thinking_budget_tokens=_coerce_int(
+ _get_value(
+ data,
+ ("models", "vision", "thinking_budget_tokens"),
+ "VISION_MODEL_THINKING_BUDGET_TOKENS",
+ ),
+ 20000,
+ ),
+ thinking_include_budget=thinking_include_budget,
+ reasoning_effort_style=_resolve_reasoning_effort_style(
+ _get_value(
+ data,
+ ("models", "vision", "reasoning_effort_style"),
+ "VISION_MODEL_REASONING_EFFORT_STYLE",
+ ),
+ ),
+ thinking_tool_call_compat=thinking_tool_call_compat,
+ reasoning_content_replay=reasoning_content_replay,
+ system_prompt_as_user=system_prompt_as_user,
+ responses_tool_choice_compat=responses_tool_choice_compat,
+ responses_force_stateless_replay=responses_force_stateless_replay,
+ prompt_cache_enabled=prompt_cache_enabled,
+ reasoning_enabled=reasoning_enabled,
+ reasoning_effort=reasoning_effort,
+ stream_enabled=stream_enabled,
+ request_params=_get_model_request_params(data, "vision"),
+ )
diff --git a/src/Undefined/config/toml_io.py b/src/Undefined/config/toml_io.py
new file mode 100644
index 00000000..b9c96d46
--- /dev/null
+++ b/src/Undefined/config/toml_io.py
@@ -0,0 +1,110 @@
+"""TOML file I/O and environment bootstrap for configuration."""
+
+from __future__ import annotations
+
+# 配置 I/O：读取 config.toml、加载 .env bootstrap、格式化解析错误
+
+import logging
+import os
+import re
+import tomllib
+from pathlib import Path
+from typing import Any, IO, Optional
+
+try:
+ from dotenv import load_dotenv
+except Exception: # pragma: no cover
+ StrPath = str | os.PathLike[str]
+
+ def load_dotenv(
+ dotenv_path: StrPath | None = None,
+ stream: IO[str] | None = None,
+ verbose: bool = False,
+ override: bool = False,
+ interpolate: bool = True,
+ encoding: str | None = "utf-8",
+ ) -> bool:
+ return False
+
+
+logger = logging.getLogger(__name__)
+
+CONFIG_PATH = Path("config.toml")
+
+__all__ = ["CONFIG_PATH", "_load_env", "load_toml_data"]
+
+
+def _load_env() -> None:
+ # dotenv 仅 bootstrap 环境变量，不覆盖已有 os.environ（TOML 仍优先于 env）
+ try:
+ load_dotenv()
+ except Exception:
+ logger.debug("加载 .env 失败，继续使用 config.toml", exc_info=True)
+
+
+def _build_toml_decode_hint(line: str) -> str:
+ """根据出错行内容生成 TOML 修复提示。"""
+ hints: list[str] = []
+ if "\\" in line:
+ hints.append(
+ 'Windows 路径建议用单引号(不转义)或双反斜杠，或直接用正斜杠，例如：path = \'D:\\AI\\bot\' / path = "D:\\\\AI\\\\bot" / path = "D:/AI/bot"'
+ )
+ hints.append('多行文本请用三引号，例如：prompt = """..."""')
+ return "；".join(hints)
+
+
+def _format_toml_decode_error(
+ path: Path, text: str, exc: tomllib.TOMLDecodeError
+) -> str:
+ """将 tomllib 解析异常格式化为带行号、caret 与中文提示的可读消息。"""
+ lineno: int | None = getattr(exc, "lineno", None)
+ colno: int | None = getattr(exc, "colno", None)
+ if not isinstance(lineno, int) or not isinstance(colno, int):
+ match = re.search(r"\(at line (\d+), column (\d+)\)", str(exc))
+ if match:
+ lineno = int(match.group(1))
+ colno = int(match.group(2))
+
+ if isinstance(lineno, int) and lineno > 0:
+ lines = text.splitlines()
+ line = lines[lineno - 1] if 0 <= (lineno - 1) < len(lines) else ""
+ caret_pos = max((colno or 1) - 1, 0)
+ caret = " " * min(caret_pos, len(line)) + "^"
+ hint = _build_toml_decode_hint(line)
+ location = f"line={lineno} col={colno or 1}"
+ return f"{exc} ({location})\n> {line}\n {caret}\n提示：{hint}"
+ return str(exc)
+
+
+def load_toml_data(
+ config_path: Optional[Path] = None, *, strict: bool = False
+) -> dict[str, Any]:
+ """读取 config.toml 并返回字典；文件不存在时返回空 dict。"""
+ path = config_path or CONFIG_PATH
+ if not path.exists():
+ return {}
+ text = ""
+ try:
+ # utf-8-sig 兼容带 BOM 的编辑器输出
+ text = path.read_bytes().decode("utf-8-sig")
+ data = tomllib.loads(text)
+ if isinstance(data, dict):
+ return data
+ logger.warning("config.toml 内容不是对象结构")
+ return {}
+ except tomllib.TOMLDecodeError as exc:
+ message = _format_toml_decode_error(path, text, exc)
+ logger.error("config.toml 解析失败 (%s): %s", path.resolve(), message)
+ if strict:
+ raise ValueError(message) from exc
+ return {}
+ except UnicodeDecodeError as exc:
+ logger.error("config.toml 编码错误 (%s): %s", path.resolve(), exc)
+ if strict:
+ raise ValueError(str(exc)) from exc
+ return {}
+ except OSError as exc:
+ logger.error("读取 config.toml 失败: %s", exc)
+ if strict:
+ raise ValueError(str(exc)) from exc
+ return {}
diff --git a/src/Undefined/handlers.py b/src/Undefined/handlers.py
deleted file mode 100644
index a793ae44..00000000
--- a/src/Undefined/handlers.py
+++ /dev/null
@@ -1,1399 +0,0 @@
-"""消息处理和命令分发"""
-
-from __future__ import annotations
-
-import asyncio
-from dataclasses import dataclass
-import logging
-import os
-from pathlib import Path
-import random
-import time
-from typing import Any, Coroutine, Literal
-
-from Undefined.attachments import (
- append_attachment_text,
- build_attachment_scope,
- register_message_attachments,
-)
-from Undefined.ai import AIClient
-from Undefined.config import Config
-from Undefined.faq import FAQStorage
-from Undefined.rate_limit import RateLimiter
-from Undefined.services.queue_manager import QueueManager
-from Undefined.onebot import (
- OneBotClient,
- get_message_content,
- get_message_sender_id,
-)
-from Undefined.utils.common import (
- extract_text,
- parse_message_content_for_history,
- matches_xinliweiyuan,
-)
-from Undefined.utils.fake_at import BotNicknameCache, strip_fake_at
-from Undefined.utils.history import MessageHistoryManager
-from Undefined.utils.scheduler import TaskScheduler
-from Undefined.utils.sender import MessageSender
-from Undefined.services.security import SecurityService
-from Undefined.services.command import CommandDispatcher
-from Undefined.services.ai_coordinator import AICoordinator
-from Undefined.services.message_batcher import MessageBatcher, make_scope
-from Undefined.services.model_pool import ModelPoolService
-from Undefined.skills.pipelines import PipelineRegistry
-from Undefined.skills.pipelines.context import build_pipeline_context
-from Undefined.utils.resources import resolve_resource_path
-from Undefined.utils.queue_intervals import build_model_queue_intervals
-
-from Undefined.scheduled_task_storage import ScheduledTaskStorage
-from Undefined.utils.logging import log_debug_json, redact_string
-from Undefined.utils.coerce import safe_int
-
-logger = logging.getLogger(__name__)
-
-KEYWORD_REPLY_HISTORY_PREFIX = "[系统关键词自动回复] "
-REPEAT_REPLY_HISTORY_PREFIX = "[系统复读] "
-
-
-def _is_private_model_pool_control_text(text: str) -> bool:
- return ModelPoolService.is_private_control_text(text)
-
-
-def _format_poke_history_text(display_name: str, user_id: int) -> str:
- """格式化拍一拍历史文本。"""
- return f"{display_name}(暱称)[{user_id}(QQ号)] 拍了拍你。"
-
-
-@dataclass(frozen=True)
-class PrivatePokeRecord:
- poke_text: str
- sender_name: str
-
-
-@dataclass(frozen=True)
-class GroupPokeRecord:
- poke_text: str
- sender_name: str
- group_name: str
- sender_role: str
- sender_title: str
- sender_level: str
-
-
-class MessageHandler:
- """消息处理器"""
-
- def __init__(
- self,
- config: Config,
- onebot: OneBotClient,
- ai: AIClient,
- faq_storage: FAQStorage,
- task_storage: ScheduledTaskStorage,
- ) -> None:
- self.config = config
- self.onebot = onebot
- self.ai = ai
- self.faq_storage = faq_storage
- # 初始化工具组件
- self.history_manager = MessageHistoryManager(config.history_max_records)
- self.sender = MessageSender(
- onebot,
- self.history_manager,
- config.bot_qq,
- config,
- attachment_registry=getattr(ai, "attachment_registry", None),
- )
-
- # 初始化服务
- self.security = SecurityService(config, ai._http_client)
- self.rate_limiter = RateLimiter(config)
- self.queue_manager = QueueManager(
- max_retries=config.ai_request_max_retries,
- )
- self.queue_manager.update_model_intervals(build_model_queue_intervals(config))
-
- # 设置队列管理器到 AIClient（触发 Agent 介绍生成器启动）
- ai.set_queue_manager(self.queue_manager)
-
- self.command_dispatcher = CommandDispatcher(
- config,
- self.sender,
- ai,
- faq_storage,
- onebot,
- self.security,
- queue_manager=self.queue_manager,
- rate_limiter=self.rate_limiter,
- history_manager=self.history_manager,
- )
- self.ai_coordinator = AICoordinator(
- config,
- ai,
- self.queue_manager,
- self.history_manager,
- self.sender,
- onebot,
- TaskScheduler(ai, self.sender, onebot, self.history_manager, task_storage),
- self.security,
- command_dispatcher=self.command_dispatcher,
- )
-
- # 同 sender 短时多消息合并器；coordinator 决定是否旁路
- self.message_batcher = MessageBatcher(
- config.message_batcher,
- flush_callback=self.ai_coordinator.handle_batched_dispatch,
- )
- self.ai_coordinator.set_batcher(self.message_batcher)
-
- self._background_tasks: set[asyncio.Task[None]] = set()
- self._profile_name_refresh_cache: dict[tuple[str, int], str] = {}
- self._bot_nickname_cache = BotNicknameCache(onebot, config.bot_qq)
- self.pipeline_registry = PipelineRegistry()
- self._pipelines_initialized = False
- self._pipelines_init_lock = asyncio.Lock()
-
- # 复读功能状态（按群跟踪最近消息文本与发送者）
- self._repeat_counter: dict[int, list[tuple[str, int]]] = {}
- self._repeat_locks: dict[int, asyncio.Lock] = {}
- # 复读冷却：group_id → {normalized_text → monotonic_timestamp}
- self._repeat_cooldown: dict[int, dict[str, float]] = {}
-
- # 启动队列
- self.ai_coordinator.queue_manager.start(self.ai_coordinator.execute_reply)
-
- async def initialize(self) -> None:
- """完成需要事件循环承载的异步初始化。"""
- await self.init_pipelines()
-
- async def init_pipelines(self) -> None:
- """异步加载自动处理管线并按配置启动热重载。"""
- if getattr(self, "_pipelines_initialized", False):
- return
- init_lock = getattr(self, "_pipelines_init_lock", None)
- if init_lock is None:
- init_lock = asyncio.Lock()
- self._pipelines_init_lock = init_lock
- async with init_lock:
- if getattr(self, "_pipelines_initialized", False):
- return
- await self.pipeline_registry.load_items_async()
- self._pipelines_initialized = True
- if getattr(self.config, "skills_hot_reload", False):
- self.pipeline_registry.start_hot_reload(
- interval=self.config.skills_hot_reload_interval,
- debounce=self.config.skills_hot_reload_debounce,
- )
-
- def _get_repeat_lock(self, group_id: int) -> asyncio.Lock:
- """获取或创建指定群的复读竞态保护锁。"""
- lock = self._repeat_locks.get(group_id)
- if lock is None:
- lock = asyncio.Lock()
- self._repeat_locks[group_id] = lock
- return lock
-
- @staticmethod
- def _normalize_repeat_text(text: str) -> str:
- """规范化复读文本用于冷却比较（？→?）。"""
- return text.replace("？", "?")
-
- def _is_repeat_on_cooldown(self, group_id: int, text: str) -> bool:
- """检查指定群的文本是否在复读冷却期内。"""
- cooldown_minutes = self.config.repeat_cooldown_minutes
- if cooldown_minutes <= 0:
- return False
- group_cd = self._repeat_cooldown.get(group_id)
- if not group_cd:
- return False
- key = self._normalize_repeat_text(text)
- last_time = group_cd.get(key)
- if last_time is None:
- return False
- return (time.monotonic() - last_time) < cooldown_minutes * 60
-
- def _record_repeat_cooldown(self, group_id: int, text: str) -> None:
- """记录复读冷却时间戳，同时清理已过期条目防止内存泄漏。"""
- cooldown_seconds = self.config.repeat_cooldown_minutes * 60
- if cooldown_seconds <= 0:
- return
- key = self._normalize_repeat_text(text)
- group_cd = self._repeat_cooldown.setdefault(group_id, {})
- now = time.monotonic()
- # 清理已过期条目
- expired = [k for k, ts in group_cd.items() if (now - ts) >= cooldown_seconds]
- for k in expired:
- del group_cd[k]
- group_cd[key] = now
-
- async def _annotate_meme_descriptions(
- self,
- attachments: list[dict[str, str]],
- scope_key: str,
- ) -> list[dict[str, str]]:
- """为图片附件添加表情包描述（如果在表情库中找到）。
-
- 采用批量查询：收集所有 SHA256 哈希值，一次性查询，然后映射结果。
- 最佳努力：任何失败时返回原始列表。
- """
- if not attachments:
- return attachments
-
- ai_client = getattr(self, "ai", None)
- if ai_client is None:
- return attachments
-
- attachment_registry = getattr(ai_client, "attachment_registry", None)
- if attachment_registry is None:
- return attachments
-
- meme_service = getattr(ai_client, "_meme_service", None)
- if meme_service is None or not getattr(meme_service, "enabled", False):
- return attachments
-
- meme_store = getattr(meme_service, "_store", None)
- if meme_store is None:
- return attachments
-
- try:
- # 1. 从图片附件收集唯一的 SHA256 哈希值
- uid_to_hash: dict[str, str] = {}
- for att in attachments:
- uid = att.get("uid", "")
- if not uid.startswith("pic_"):
- continue
- record = attachment_registry.resolve(uid, scope_key)
- if record and record.sha256:
- uid_to_hash[uid] = record.sha256
-
- if not uid_to_hash:
- return attachments
-
- # 2. 批量查询：去重哈希值
- unique_hashes = set(uid_to_hash.values())
- hash_to_desc: dict[str, str] = {}
- for h in unique_hashes:
- meme = await meme_store.find_by_sha256(h)
- if meme and meme.description:
- hash_to_desc[h] = meme.description
-
- if not hash_to_desc:
- return attachments
-
- # 3. 构建带注释的新列表
- result: list[dict[str, str]] = []
- for att in attachments:
- uid = att.get("uid", "")
- sha = uid_to_hash.get(uid, "")
- desc = hash_to_desc.get(sha, "")
- if desc:
- new_att = dict(att)
- new_att["description"] = f"[表情包] {desc}"
- result.append(new_att)
- else:
- result.append(att)
- return result
- except Exception:
- logger.warning("表情包自动匹配失败，跳过", exc_info=True)
- return attachments
-
- async def _collect_message_attachments(
- self,
- message_content: list[dict[str, Any]],
- *,
- group_id: int | None = None,
- user_id: int | None = None,
- request_type: str,
- ) -> list[dict[str, str]]:
- scope_key = build_attachment_scope(
- group_id=group_id,
- user_id=user_id,
- request_type=request_type,
- )
- if not scope_key:
- return []
- ai_client = getattr(self, "ai", None)
- attachment_registry = (
- getattr(ai_client, "attachment_registry", None) if ai_client else None
- )
- if attachment_registry is None:
- return []
- onebot = getattr(self, "onebot", None)
- resolve_image_url = getattr(onebot, "get_image", None) if onebot else None
- result = await register_message_attachments(
- registry=attachment_registry,
- segments=message_content,
- scope_key=scope_key,
- resolve_image_url=resolve_image_url,
- get_forward_messages=getattr(onebot, "get_forward_msg", None)
- if onebot
- else None,
- )
- attachments = result.attachments
- # 为图片附件添加表情包描述
- attachments = await self._annotate_meme_descriptions(attachments, scope_key)
- return attachments
-
- def _schedule_meme_ingest(
- self,
- *,
- attachments: list[dict[str, str]],
- chat_type: str,
- chat_id: int,
- sender_id: int,
- message_id: int | None,
- scope_key: str | None,
- ) -> None:
- if not attachments or not scope_key:
- return
- meme_service = getattr(self.ai, "_meme_service", None)
- if meme_service is None or not getattr(meme_service, "enabled", False):
- return
- self._spawn_background_task(
- f"meme_ingest:{chat_type}:{chat_id}:{sender_id}:{message_id or 0}",
- meme_service.enqueue_incoming_attachments(
- attachments=attachments,
- chat_type=chat_type,
- chat_id=chat_id,
- sender_id=sender_id,
- message_id=message_id,
- scope_key=scope_key,
- ),
- )
-
- async def _refresh_profile_display_names(
- self,
- *,
- sender_id: int | None = None,
- sender_name: str = "",
- group_id: int | None = None,
- group_name: str = "",
- ) -> None:
- ai_client = getattr(self, "ai", None)
- cognitive_service = getattr(ai_client, "_cognitive_service", None)
- if not cognitive_service or not getattr(cognitive_service, "enabled", False):
- return
-
- if sender_id and sender_name.strip():
- await cognitive_service.sync_profile_display_name(
- entity_type="user",
- entity_id=str(sender_id),
- preferred_name=sender_name.strip(),
- )
- if group_id and group_name.strip():
- await cognitive_service.sync_profile_display_name(
- entity_type="group",
- entity_id=str(group_id),
- preferred_name=group_name.strip(),
- )
-
- def _can_refresh_profile_display_names(self) -> bool:
- ai_client = getattr(self, "ai", None)
- cognitive_service = getattr(ai_client, "_cognitive_service", None)
- return bool(cognitive_service and getattr(cognitive_service, "enabled", False))
-
- def _schedule_profile_display_name_refresh(
- self,
- *,
- task_name: str,
- sender_id: int | None = None,
- sender_name: str = "",
- group_id: int | None = None,
- group_name: str = "",
- ) -> None:
- if not self._can_refresh_profile_display_names():
- return
-
- cache = getattr(self, "_profile_name_refresh_cache", None)
- if cache is None:
- cache = {}
- self._profile_name_refresh_cache = cache
-
- updates: dict[str, Any] = {}
- rollback: list[tuple[tuple[str, int], str | None]] = []
-
- normalized_sender_name = sender_name.strip()
- if sender_id and normalized_sender_name:
- sender_key = ("user", int(sender_id))
- previous = cache.get(sender_key)
- if previous != normalized_sender_name:
- cache[sender_key] = normalized_sender_name
- rollback.append((sender_key, previous))
- updates["sender_id"] = sender_id
- updates["sender_name"] = normalized_sender_name
-
- normalized_group_name = group_name.strip()
- if group_id and normalized_group_name:
- group_key = ("group", int(group_id))
- previous = cache.get(group_key)
- if previous != normalized_group_name:
- cache[group_key] = normalized_group_name
- rollback.append((group_key, previous))
- updates["group_id"] = group_id
- updates["group_name"] = normalized_group_name
-
- if not updates:
- return
-
- async def _run_refresh() -> None:
- try:
- await self._refresh_profile_display_names(**updates)
- except Exception:
- for key, previous in rollback:
- if previous is None:
- cache.pop(key, None)
- else:
- cache[key] = previous
- raise
-
- self._spawn_background_task(task_name, _run_refresh())
-
- async def handle_message(self, event: dict[str, Any]) -> None:
- """处理收到的消息事件"""
- if logger.isEnabledFor(logging.DEBUG):
- log_debug_json(logger, "[事件数据]", event)
- post_type = event.get("post_type", "message")
-
- # 处理拍一拍事件（效果同被 @）
- if post_type == "notice" and event.get("notice_type") == "poke":
- target_id = event.get("target_id", 0)
- # 只有拍机器人才响应
- if target_id != self.config.bot_qq:
- logger.debug(
- "[通知] 忽略拍一拍目标非机器人: target=%s",
- target_id,
- )
- return
-
- if not self.config.should_process_poke_message():
- logger.debug("[消息策略] 已关闭拍一拍处理，忽略此次 poke 事件")
- return
-
- poke_group_id: int = event.get("group_id", 0)
- poke_sender_id: int = event.get("user_id", 0)
-
- # 访问控制：命中群黑名单或不满足白名单限制时忽略
- if poke_group_id == 0:
- if not self.config.is_private_allowed(poke_sender_id):
- private_reason = (
- self.config.private_access_denied_reason(poke_sender_id)
- or "unknown"
- )
- logger.debug(
- "[访问控制] 忽略私聊拍一拍: user=%s reason=%s (access enabled=%s)",
- poke_sender_id,
- private_reason,
- self.config.access_control_enabled(),
- )
- return
- else:
- if not self.config.is_group_allowed(poke_group_id):
- group_reason = (
- self.config.group_access_denied_reason(poke_group_id)
- or "unknown"
- )
- logger.debug(
- "[访问控制] 忽略群聊拍一拍: group=%s sender=%s reason=%s (access enabled=%s)",
- poke_group_id,
- poke_sender_id,
- group_reason,
- self.config.access_control_enabled(),
- )
- return
-
- logger.info(
- "[通知] 收到拍一拍: group=%s sender=%s",
- poke_group_id,
- poke_sender_id,
- )
- logger.debug("[通知] 拍一拍事件数据: %s", str(event)[:200])
-
- if poke_group_id == 0:
- private_poke = await self._record_private_poke_history(
- poke_sender_id, event
- )
- logger.info("[通知] 私聊拍一拍，触发私聊回复")
- await self.ai_coordinator.handle_private_reply(
- poke_sender_id,
- private_poke.poke_text,
- [],
- is_poke=True,
- sender_name=private_poke.sender_name,
- )
- else:
- group_poke = await self._record_group_poke_history(
- poke_group_id,
- poke_sender_id,
- event,
- )
- logger.info(
- "[通知] 群聊拍一拍，触发群聊回复: group=%s",
- poke_group_id,
- )
- await self.ai_coordinator.handle_auto_reply(
- poke_group_id,
- poke_sender_id,
- group_poke.poke_text,
- [],
- is_poke=True,
- sender_name=group_poke.sender_name,
- group_name=group_poke.group_name,
- sender_role=group_poke.sender_role,
- sender_title=group_poke.sender_title,
- sender_level=group_poke.sender_level,
- )
- return
-
- # 处理私聊消息
- if event.get("message_type") == "private":
- private_sender_id: int = get_message_sender_id(event)
- private_message_content: list[dict[str, Any]] = get_message_content(event)
- trigger_message_id = event.get("message_id")
-
- # 访问控制：命中黑/白名单规则时忽略（不入历史、不触发任何处理）
- if not self.config.is_private_allowed(private_sender_id):
- private_reason = (
- self.config.private_access_denied_reason(private_sender_id)
- or "unknown"
- )
- logger.debug(
- "[访问控制] 忽略私聊消息: user=%s reason=%s (access enabled=%s)",
- private_sender_id,
- private_reason,
- self.config.access_control_enabled(),
- )
- return
-
- # 获取发送者昵称
- private_sender: dict[str, Any] = event.get("sender", {})
- private_sender_nickname: str = private_sender.get("nickname", "")
-
- # 获取私聊用户昵称
- user_name = private_sender_nickname
- if not user_name:
- try:
- user_info = await self.onebot.get_stranger_info(private_sender_id)
- if user_info:
- user_name = user_info.get("nickname", "")
- except Exception as exc:
- logger.warning("获取用户昵称失败: %s", exc)
-
- text = extract_text(private_message_content, self.config.bot_qq)
- # 并行执行附件收集和历史内容解析
- private_attachments, parsed_content_raw = await asyncio.gather(
- self._collect_message_attachments(
- private_message_content,
- user_id=private_sender_id,
- request_type="private",
- ),
- parse_message_content_for_history(
- private_message_content,
- self.config.bot_qq,
- self.onebot.get_msg,
- self.onebot.get_forward_msg,
- ),
- )
- safe_text = redact_string(text)
- logger.info(
- "[私聊消息] 发送者=%s 昵称=%s 内容=%s",
- private_sender_id,
- user_name or private_sender_nickname,
- safe_text[:100],
- )
- resolved_private_name = (user_name or private_sender_nickname or "").strip()
- self._schedule_profile_display_name_refresh(
- task_name=f"profile_name_refresh_private:{private_sender_id}",
- sender_id=private_sender_id,
- sender_name=resolved_private_name,
- )
-
- # 保存私聊消息到历史记录
- parsed_content = append_attachment_text(
- parsed_content_raw, private_attachments
- )
- safe_parsed = redact_string(parsed_content)
- logger.debug(
- "[历史记录] 保存私聊: user=%s content=%s...",
- private_sender_id,
- safe_parsed[:50],
- )
- await self.history_manager.add_private_message(
- user_id=private_sender_id,
- text_content=parsed_content,
- display_name=private_sender_nickname,
- user_name=user_name,
- message_id=trigger_message_id,
- attachments=private_attachments,
- )
-
- # 如果是 bot 自己的消息，只保存不触发回复，避免无限循环
- if private_sender_id == self.config.bot_qq:
- return
-
- self._schedule_meme_ingest(
- attachments=private_attachments,
- chat_type="private",
- chat_id=private_sender_id,
- sender_id=private_sender_id,
- message_id=safe_int(trigger_message_id),
- scope_key=build_attachment_scope(
- user_id=private_sender_id,
- request_type="private",
- ),
- )
-
- if not self.config.should_process_private_message():
- logger.debug(
- "[消息策略] 已关闭私聊处理: user=%s",
- private_sender_id,
- )
- return
-
- if (
- getattr(self.config, "model_pool_enabled", False)
- and _is_private_model_pool_control_text(text)
- ) and await self.ai_coordinator.model_pool.handle_private_message(
- private_sender_id,
- text,
- ):
- return
-
- private_command = self.command_dispatcher.parse_command(text)
- if private_command:
- await self._flush_command_buffer(
- scope=make_scope(user_id=private_sender_id),
- sender_id=private_sender_id,
- )
- await self.command_dispatcher.dispatch_private(
- user_id=private_sender_id,
- sender_id=private_sender_id,
- command=private_command,
- )
- return
-
- await self._run_pipelines(
- target_id=private_sender_id,
- target_type="private",
- text=text,
- message_content=private_message_content,
- )
-
- await self.ai_coordinator.handle_private_reply(
- private_sender_id,
- text,
- private_message_content,
- attachments=private_attachments,
- sender_name=user_name,
- trigger_message_id=trigger_message_id,
- )
- return
-
- # 只处理群消息
- if event.get("message_type") != "group":
- return
-
- group_id: int = event.get("group_id", 0)
- sender_id: int = get_message_sender_id(event)
- message_content: list[dict[str, Any]] = get_message_content(event)
- trigger_message_id = event.get("message_id")
-
- # 访问控制：命中黑/白名单规则时忽略（不入历史、不触发任何处理）
- if not self.config.is_group_allowed(group_id):
- group_reason = self.config.group_access_denied_reason(group_id) or "unknown"
- logger.debug(
- "[访问控制] 忽略群消息: group=%s sender=%s reason=%s (access enabled=%s)",
- group_id,
- sender_id,
- group_reason,
- self.config.access_control_enabled(),
- )
- return
-
- # 获取发送者信息
- group_sender: dict[str, Any] = event.get("sender", {})
- sender_card: str = group_sender.get("card", "")
- sender_nickname: str = group_sender.get("nickname", "")
- sender_role: str = group_sender.get("role", "member")
- sender_title: str = group_sender.get("title", "")
- sender_level: str = str(group_sender.get("level", "")).strip()
-
- # 提取文本内容
- text = extract_text(message_content, self.config.bot_qq)
- safe_text = redact_string(text)
- logger.info(
- f"[群消息] group={group_id} sender={sender_id} name={sender_card or sender_nickname} "
- f"role={sender_role} | {safe_text[:100]}"
- )
-
- # 并行执行 3 个独立的异步操作：附件收集、群信息获取、历史内容解析
- async def _fetch_group_name() -> str:
- try:
- info = await self.onebot.get_group_info(group_id)
- if info:
- return str(info.get("group_name", "") or "")
- except Exception as e:
- logger.warning(f"获取群聊名失败: {e}")
- return ""
-
- group_attachments, group_name, parsed_content_raw = await asyncio.gather(
- self._collect_message_attachments(
- message_content,
- group_id=group_id,
- request_type="group",
- ),
- _fetch_group_name(),
- parse_message_content_for_history(
- message_content,
- self.config.bot_qq,
- self.onebot.get_msg,
- self.onebot.get_forward_msg,
- ),
- )
-
- resolved_group_sender_name = (sender_card or sender_nickname or "").strip()
- self._schedule_profile_display_name_refresh(
- task_name=f"profile_name_refresh_group:{group_id}:{sender_id}",
- sender_id=sender_id,
- sender_name=resolved_group_sender_name,
- group_id=group_id,
- group_name=str(group_name or "").strip(),
- )
-
- # 保存消息到历史记录
- parsed_content = append_attachment_text(parsed_content_raw, group_attachments)
- safe_parsed = redact_string(parsed_content)
- logger.debug(
- f"[历史记录] 保存群聊: group={group_id}, sender={sender_id}, content={safe_parsed[:50]}..."
- )
- await self.history_manager.add_group_message(
- group_id=group_id,
- sender_id=sender_id,
- text_content=parsed_content,
- sender_card=sender_card,
- sender_nickname=sender_nickname,
- group_name=group_name,
- role=sender_role,
- title=sender_title,
- level=sender_level,
- message_id=trigger_message_id,
- attachments=group_attachments,
- )
-
- # 如果是 bot 自己的消息，只保存不触发回复，避免无限循环
- # 同时把 bot 自身的发言写入复读计数器，使窗口中留有 bot 标记，
- # 后续触发检查时会排除含 bot 的窗口，防止"bot 先发 → 用户跟发"或
- # "用户发到一半 bot 插入"等情况误触复读。
- if sender_id == self.config.bot_qq:
- if self.config.repeat_enabled and text:
- async with self._get_repeat_lock(group_id):
- counter = self._repeat_counter.setdefault(group_id, [])
- counter.append((text, sender_id))
- n = self.config.repeat_threshold
- if len(counter) > n:
- self._repeat_counter[group_id] = counter[-n:]
- return
-
- self._schedule_meme_ingest(
- attachments=group_attachments,
- chat_type="group",
- chat_id=group_id,
- sender_id=sender_id,
- message_id=safe_int(trigger_message_id),
- scope_key=build_attachment_scope(group_id=group_id, request_type="group"),
- )
-
- # 检查是否 @ 了机器人（后续分流共用）
- is_at_bot = self.ai_coordinator._is_at_bot(message_content)
-
- # 假@检测：识别 "@昵称" 纯文本形式
- # normalized_text 用于命令解析和 AI 路由，原始 text 已用于历史/日志
- is_fake_at = False
- normalized_text = text
- if not is_at_bot and ("@" in text or "＠" in text):
- nicknames = await self._bot_nickname_cache.get_nicknames(group_id)
- if nicknames:
- is_fake_at, normalized_text = strip_fake_at(text, nicknames)
- if is_fake_at:
- is_at_bot = True
- logger.info(
- "[假@] 识别到假@: group=%s sender=%s",
- group_id,
- sender_id,
- )
-
- # 关闭“每条消息处理”后，仅处理 @ 消息（私聊/拍一拍在其他分支中处理）
- if not self.config.should_process_group_message(is_at_bot=is_at_bot):
- logger.debug(
- "[消息策略] 跳过群消息处理: group=%s sender=%s process_every_message=%s at_bot=%s",
- group_id,
- sender_id,
- self.config.process_every_message,
- is_at_bot,
- )
- return
-
- # 只有被@时才处理斜杠命令（使用 normalized_text 以支持假@后的命令）。
- # 命令优先于自动处理管线，命中后不触发后续自动提取或 AI 回复。
- if is_at_bot:
- command = self.command_dispatcher.parse_command(normalized_text)
- if command:
- await self._flush_command_buffer(
- scope=make_scope(group_id=group_id),
- sender_id=sender_id,
- )
- await self.command_dispatcher.dispatch(group_id, sender_id, command)
- return
-
- # 关键词自动回复：心理委员 (使用原始消息内容提取文本，保证关键词触发不受影响)
- if self.config.keyword_reply_enabled and matches_xinliweiyuan(text):
- rand_val = random.random()
- if rand_val < 0.01: # 1% 飞起来
- message = f"[@{sender_id}] 再发让你飞起来"
- logger.info("关键词回复: 再发让你飞起来")
- await self.sender.send_group_message(
- group_id,
- message,
- history_prefix=KEYWORD_REPLY_HISTORY_PREFIX,
- )
- return
- elif rand_val < 0.11: # 10% 发送图片
- try:
- image_path = (
- resolve_resource_path("img/xlwy.jpg").resolve().as_uri()
- )
- except Exception:
- image_path = Path(os.path.abspath("img/xlwy.jpg")).as_uri()
- message = f"[CQ:image,file={image_path}]"
- # 50% 概率 @ 发送者
- if random.random() < 0.5:
- message = f"[@{sender_id}] {message}"
- logger.info("关键词回复: 发送图片 xlwy.jpg")
- else: # 90% 原有逻辑
- if random.random() < 0.7:
- reply = "受着"
- else:
- reply = "那咋了"
- # 50% 概率 @ 发送者
- if random.random() < 0.5:
- message = f"[@{sender_id}] {reply}"
- else:
- message = reply
- logger.info(f"关键词回复: {reply}")
- # 使用 sender 发送
- await self.sender.send_group_message(
- group_id,
- message,
- history_prefix=KEYWORD_REPLY_HISTORY_PREFIX,
- )
- return
-
- # 复读功能：连续 N 条相同消息（来自不同发送者）时复读，N = repeat_threshold
- if self.config.repeat_enabled and text:
- n = self.config.repeat_threshold
- async with self._get_repeat_lock(group_id):
- counter = self._repeat_counter.setdefault(group_id, [])
- counter.append((text, sender_id))
- # 只保留最近 n 条
- if len(counter) > n:
- self._repeat_counter[group_id] = counter[-n:]
- counter = self._repeat_counter[group_id]
-
- if len(counter) >= n:
- last_n = counter[-n:]
- texts = [t for t, _ in last_n]
- senders = [s for _, s in last_n]
- if (
- len(set(texts)) == 1
- and len(set(senders)) == n
- and self.config.bot_qq not in senders
- ):
- reply_text = texts[0]
- # 冷却检查：同一内容在冷却期内不再复读
- if self._is_repeat_on_cooldown(group_id, reply_text):
- self._repeat_counter[group_id] = []
- logger.debug(
- "[复读] 冷却中跳过: group=%s text=%s",
- group_id,
- redact_string(reply_text)[:50],
- )
- else:
- if self.config.inverted_question_enabled:
- stripped = reply_text.strip()
- if set(stripped) <= {"?", "？"}:
- reply_text = "¿" * len(stripped)
- # 清空计数器防止重复触发
- self._repeat_counter[group_id] = []
- self._record_repeat_cooldown(group_id, texts[0])
- logger.info(
- "[复读] 触发复读: group=%s text=%s",
- group_id,
- redact_string(reply_text)[:50],
- )
- await self.sender.send_group_message(
- group_id,
- reply_text,
- history_prefix=REPEAT_REPLY_HISTORY_PREFIX,
- )
- return
-
- await self._run_pipelines(
- target_id=group_id,
- target_type="group",
- text=text,
- message_content=message_content,
- )
-
- # 提取文本内容
- # (已在上方提取用于日志记录)
-
- # 自动回复处理（使用 normalized_text 以去除假@前缀）
- display_name = sender_card or sender_nickname or str(sender_id)
- await self.ai_coordinator.handle_auto_reply(
- group_id,
- sender_id,
- normalized_text,
- message_content,
- attachments=group_attachments,
- sender_name=display_name,
- group_name=group_name,
- sender_role=sender_role,
- sender_title=sender_title,
- sender_level=sender_level,
- trigger_message_id=trigger_message_id,
- is_fake_at=is_fake_at,
- )
-
- async def _record_private_poke_history(
- self, user_id: int, event: dict[str, Any]
- ) -> PrivatePokeRecord:
- """记录私聊拍一拍到历史。"""
- sender = event.get("sender", {})
- sender_nickname = ""
- if isinstance(sender, dict):
- sender_nickname = str(sender.get("nickname", "")).strip()
-
- user_name = sender_nickname
- if not user_name:
- try:
- user_info = await self.onebot.get_stranger_info(user_id)
- if isinstance(user_info, dict):
- user_name = str(user_info.get("nickname", "")).strip()
- except Exception as exc:
- logger.warning(
- "[通知] 获取私聊拍一拍用户昵称失败: user=%s err=%s",
- user_id,
- exc,
- )
-
- resolved_sender_name = (sender_nickname or user_name).strip()
- display_name = resolved_sender_name or f"QQ{user_id}"
- normalized_user_name = user_name or display_name
- poke_text = _format_poke_history_text(display_name, user_id)
- self._schedule_profile_display_name_refresh(
- task_name=f"profile_name_refresh_private_poke:{user_id}",
- sender_id=user_id,
- sender_name=resolved_sender_name,
- )
-
- try:
- await self.history_manager.add_private_message(
- user_id=user_id,
- text_content=poke_text,
- display_name=display_name,
- user_name=normalized_user_name,
- )
- except Exception as exc:
- logger.warning(
- "[历史记录] 写入私聊拍一拍失败: user=%s err=%s",
- user_id,
- exc,
- )
- return PrivatePokeRecord(poke_text=poke_text, sender_name=display_name)
-
- async def _record_group_poke_history(
- self,
- group_id: int,
- sender_id: int,
- event: dict[str, Any],
- ) -> GroupPokeRecord:
- """记录群聊拍一拍到历史。"""
- sender = event.get("sender", {})
- sender_card = ""
- sender_nickname = ""
- sender_role = "member"
- sender_title = ""
- sender_level = ""
- if isinstance(sender, dict):
- sender_card = str(sender.get("card", "")).strip()
- sender_nickname = str(sender.get("nickname", "")).strip()
- sender_role = str(sender.get("role", "member")).strip() or "member"
- sender_title = str(sender.get("title", "")).strip()
- sender_level = str(sender.get("level", "")).strip()
-
- if not sender_card and not sender_nickname:
- try:
- member_info = await self.onebot.get_group_member_info(
- group_id, sender_id
- )
- if isinstance(member_info, dict):
- sender_card = str(member_info.get("card", "")).strip()
- sender_nickname = str(member_info.get("nickname", "")).strip()
- sender_role = (
- str(member_info.get("role", "member")).strip() or "member"
- )
- sender_title = str(member_info.get("title", "")).strip()
- sender_level = str(member_info.get("level", "")).strip()
- except Exception as exc:
- logger.warning(
- "[通知] 获取拍一拍群成员信息失败: group=%s user=%s err=%s",
- group_id,
- sender_id,
- exc,
- )
-
- group_name = ""
- try:
- group_info = await self.onebot.get_group_info(group_id)
- if isinstance(group_info, dict):
- group_name = str(group_info.get("group_name", "")).strip()
- except Exception as exc:
- logger.warning(
- "[通知] 获取拍一拍群名失败: group=%s err=%s",
- group_id,
- exc,
- )
-
- resolved_sender_name = (sender_card or sender_nickname).strip()
- resolved_group_name = group_name.strip()
- display_name = resolved_sender_name or f"QQ{sender_id}"
- poke_text = _format_poke_history_text(display_name, sender_id)
- normalized_group_name = resolved_group_name or f"群{group_id}"
- self._schedule_profile_display_name_refresh(
- task_name=f"profile_name_refresh_group_poke:{group_id}:{sender_id}",
- sender_id=sender_id,
- sender_name=resolved_sender_name,
- group_id=group_id,
- group_name=resolved_group_name,
- )
-
- try:
- await self.history_manager.add_group_message(
- group_id=group_id,
- sender_id=sender_id,
- text_content=poke_text,
- sender_card=sender_card,
- sender_nickname=sender_nickname,
- group_name=normalized_group_name,
- role=sender_role,
- title=sender_title,
- level=sender_level,
- )
- except Exception as exc:
- logger.warning(
- "[历史记录] 写入群聊拍一拍失败: group=%s sender=%s err=%s",
- group_id,
- sender_id,
- exc,
- )
- return GroupPokeRecord(
- poke_text=poke_text,
- sender_name=display_name,
- group_name=normalized_group_name,
- sender_role=sender_role,
- sender_title=sender_title,
- sender_level=sender_level,
- )
-
- async def _extract_bilibili_ids(
- self, text: str, message_content: list[dict[str, Any]]
- ) -> list[str]:
- """从文本和消息段中提取 B 站视频 BV 号。"""
- from Undefined.bilibili.parser import (
- extract_bilibili_ids,
- extract_from_json_message,
- )
-
- bvids = await extract_bilibili_ids(text)
- if not bvids:
- bvids = await extract_from_json_message(message_content)
- return bvids
-
- async def _flush_command_buffer(self, *, scope: str, sender_id: int) -> None:
- batcher_config = getattr(self.config, "message_batcher", None)
- if not getattr(batcher_config, "flush_on_command", False):
- return
- batcher = getattr(self, "message_batcher", None)
- if batcher is None:
- return
- flushed = await batcher.flush_sender(scope, sender_id)
- if not flushed:
- logger.warning(
- "[MessageBatcher] 命令触发 flush 当前 buffer 失败: scope=%s sender=%s",
- scope,
- sender_id,
- )
-
- async def _run_pipelines(
- self,
- *,
- target_id: int,
- target_type: Literal["group", "private"],
- text: str,
- message_content: list[dict[str, Any]],
- ) -> bool:
- """并行检测并处理所有命中的自动处理管线。"""
- if not getattr(self, "_pipelines_initialized", False):
- await self.init_pipelines()
- context = build_pipeline_context(
- self,
- target_id=target_id,
- target_type=target_type,
- text=text,
- message_content=message_content,
- )
- detections = await self.pipeline_registry.run(context)
- return bool(detections)
-
- async def apply_skills_hot_reload_config(
- self,
- *,
- enabled: bool,
- interval: float,
- debounce: float,
- ) -> None:
- """跟随全局 skills 热重载配置更新管线。"""
- if not enabled:
- await self.pipeline_registry.stop_hot_reload()
- logger.info("[pipelines] 热重载已随配置禁用")
- return
-
- await self.pipeline_registry.stop_hot_reload()
- self.pipeline_registry.start_hot_reload(
- interval=interval,
- debounce=debounce,
- )
-
- def _extract_arxiv_ids(
- self, text: str, message_content: list[dict[str, Any]]
- ) -> list[str]:
- """从文本和消息段中提取 arXiv 论文 ID。"""
- from Undefined.arxiv.parser import extract_arxiv_ids, extract_from_json_message
-
- paper_ids: list[str] = []
- seen: set[str] = set()
-
- for paper_id in extract_arxiv_ids(text):
- if paper_id in seen:
- continue
- seen.add(paper_id)
- paper_ids.append(paper_id)
-
- for paper_id in extract_from_json_message(message_content):
- if paper_id in seen:
- continue
- seen.add(paper_id)
- paper_ids.append(paper_id)
-
- return paper_ids
-
- def _extract_github_repo_ids(
- self, text: str, message_content: list[dict[str, Any]]
- ) -> list[str]:
- """从文本和消息段中提取 GitHub 仓库 ID。"""
- from Undefined.github.parser import (
- extract_from_json_message,
- extract_github_repo_ids,
- )
-
- repo_ids: list[str] = []
- seen: set[str] = set()
-
- for repo_id in extract_github_repo_ids(text):
- key = repo_id.lower()
- if key in seen:
- continue
- seen.add(key)
- repo_ids.append(repo_id)
-
- for repo_id in extract_from_json_message(message_content):
- key = repo_id.lower()
- if key in seen:
- continue
- seen.add(key)
- repo_ids.append(repo_id)
-
- return repo_ids
-
- async def _handle_bilibili_extract(
- self,
- target_id: int,
- bvids: list[str],
- target_type: str,
- ) -> None:
- """处理 bilibili 视频自动提取和发送。"""
- from Undefined.bilibili.sender import send_bilibili_video
-
- for bvid in bvids[:3]: # 最多同时处理 3 个
- try:
- await send_bilibili_video(
- video_id=bvid,
- sender=self.sender,
- onebot=self.onebot,
- target_type=target_type, # type: ignore[arg-type]
- target_id=target_id,
- cookie=self.config.bilibili_cookie,
- prefer_quality=self.config.bilibili_prefer_quality,
- max_duration=self.config.bilibili_max_duration,
- max_file_size=self.config.bilibili_max_file_size,
- oversize_strategy=self.config.bilibili_oversize_strategy,
- danmaku_enabled=self.config.bilibili_danmaku_enabled,
- danmaku_batch_size=self.config.bilibili_danmaku_batch_size,
- danmaku_max_count=self.config.bilibili_danmaku_max_count,
- )
- except Exception as exc:
- logger.error(
- "[Bilibili] 自动提取失败 %s → %s:%s: %s",
- bvid,
- target_type,
- target_id,
- exc,
- )
- try:
- error_msg = f"视频提取失败: {exc}"
- if target_type == "group":
- await self.sender.send_group_message(target_id, error_msg)
- else:
- await self.sender.send_private_message(target_id, error_msg)
- except Exception:
- pass
-
- async def _handle_arxiv_extract(
- self,
- target_id: int,
- paper_ids: list[str],
- target_type: str,
- ) -> None:
- """处理 arXiv 论文自动提取和发送。"""
- from Undefined.arxiv.sender import send_arxiv_paper
-
- max_items = max(1, int(self.config.arxiv_auto_extract_max_items))
-
- for paper_id in paper_ids[:max_items]:
- try:
- result = await send_arxiv_paper(
- paper_id=paper_id,
- sender=self.sender,
- target_type=target_type, # type: ignore[arg-type]
- target_id=target_id,
- max_file_size=self.config.arxiv_max_file_size,
- author_preview_limit=self.config.arxiv_author_preview_limit,
- summary_preview_chars=self.config.arxiv_summary_preview_chars,
- context={
- "request_id": (
- f"arxiv_auto_extract:{target_type}:{target_id}:{paper_id}"
- )
- },
- )
- logger.info(
- "[arXiv] 自动提取完成 %s → %s:%s: %s",
- paper_id,
- target_type,
- target_id,
- result,
- )
- except Exception:
- logger.exception(
- "[arXiv] 自动提取失败 %s → %s:%s",
- paper_id,
- target_type,
- target_id,
- )
-
- async def _handle_github_extract(
- self,
- target_id: int,
- repo_ids: list[str],
- target_type: str,
- ) -> None:
- """处理 GitHub 仓库自动提取和发送。"""
- from Undefined.github.sender import send_github_repo_card
-
- max_items = max(
- 1, int(getattr(self.config, "github_auto_extract_max_items", 3))
- )
- request_timeout = float(
- getattr(self.config, "github_request_timeout_seconds", 10.0)
- )
-
- for repo_id in repo_ids[:max_items]:
- try:
- result = await send_github_repo_card(
- repo_id=repo_id,
- sender=self.sender,
- target_type=target_type, # type: ignore[arg-type]
- target_id=target_id,
- request_timeout=request_timeout,
- context={
- "request_id": (
- f"github_auto_extract:{target_type}:{target_id}:{repo_id}"
- )
- },
- )
- logger.info(
- "[GitHub] 自动提取完成 %s → %s:%s: %s",
- repo_id,
- target_type,
- target_id,
- result,
- )
- except Exception as exc:
- logger.info(
- "[GitHub] 自动提取跳过 %s → %s:%s: %s",
- repo_id,
- target_type,
- target_id,
- exc,
- )
-
- def _spawn_background_task(
- self,
- name: str,
- coroutine: Coroutine[Any, Any, None],
- ) -> None:
- task = asyncio.create_task(coroutine, name=name)
- self._background_tasks.add(task)
-
- def _finalize(done_task: asyncio.Task[None]) -> None:
- self._background_tasks.discard(done_task)
- try:
- exc = done_task.exception()
- except asyncio.CancelledError:
- logger.debug("[后台任务] 已取消: %s", name)
- return
- if exc is not None:
- logger.exception(
- "[后台任务] 执行失败: name=%s",
- name,
- exc_info=(type(exc), exc, exc.__traceback__),
- )
-
- task.add_done_callback(_finalize)
-
- async def close(self) -> None:
- """关闭消息处理器"""
- logger.info("正在关闭消息处理器...")
- if self._background_tasks:
- logger.info(
- "[后台任务] 等待自动提取任务收敛: count=%s",
- len(self._background_tasks),
- )
- await asyncio.gather(
- *list(self._background_tasks),
- return_exceptions=True,
- )
- await self.pipeline_registry.stop_hot_reload()
- await self.message_batcher.flush_all()
- await self.ai_coordinator.queue_manager.drain()
- await self.ai_coordinator.queue_manager.stop()
- await self.history_manager.flush_pending_saves()
- logger.info("消息处理器已关闭")
diff --git a/src/Undefined/handlers/__init__.py b/src/Undefined/handlers/__init__.py
new file mode 100644
index 00000000..631cda8e
--- /dev/null
+++ b/src/Undefined/handlers/__init__.py
@@ -0,0 +1,28 @@
+"""消息处理和命令分发包。
+
+聚合 ``MessageHandler`` 与各 mixin 子模块；保留 ``parse_message_content_for_history`` 等
+模块级符号供测试 monkeypatch（``import Undefined.handlers``）。
+"""
+
+from Undefined.handlers.message_flow import (
+ KEYWORD_REPLY_HISTORY_PREFIX,
+ MessageHandler,
+)
+from Undefined.handlers.poke import GroupPokeRecord, PrivatePokeRecord
+from Undefined.handlers.repeat import REPEAT_REPLY_HISTORY_PREFIX
+from Undefined.utils.common import (
+ extract_text,
+ matches_xinliweiyuan,
+ parse_message_content_for_history,
+)
+
+__all__ = [
+ "GroupPokeRecord",
+ "KEYWORD_REPLY_HISTORY_PREFIX",
+ "MessageHandler",
+ "PrivatePokeRecord",
+ "REPEAT_REPLY_HISTORY_PREFIX",
+ "extract_text",
+ "matches_xinliweiyuan",
+ "parse_message_content_for_history",
+]
diff --git a/src/Undefined/handlers/auto_extract.py b/src/Undefined/handlers/auto_extract.py
new file mode 100644
index 00000000..8375f8cb
--- /dev/null
+++ b/src/Undefined/handlers/auto_extract.py
@@ -0,0 +1,223 @@
+"""B 站 / arXiv / GitHub 链接自动提取 mixin。
+
+从消息中解析外部资源 ID 并调用对应 sender 发送；由 ``MessageHandler`` 混入使用。
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+ from Undefined.config import Config
+ from Undefined.onebot import OneBotClient
+ from Undefined.utils.sender import MessageSender
+
+logger = logging.getLogger(__name__)
+
+
+class AutoExtractMixin:
+ """外部资源自动提取 mixin。"""
+
+ if TYPE_CHECKING:
+ config: Config
+ sender: MessageSender
+ onebot: OneBotClient
+
+ async def _extract_bilibili_ids(
+ self, text: str, message_content: list[dict[str, Any]]
+ ) -> list[str]:
+ """从文本和消息段中提取 B 站视频 BV 号。"""
+ from Undefined.bilibili.parser import (
+ extract_bilibili_ids,
+ extract_from_json_message,
+ )
+
+ bvids = await extract_bilibili_ids(text)
+ if not bvids:
+ bvids = await extract_from_json_message(message_content)
+ return list(bvids)
+
+ def _extract_arxiv_ids(
+ self, text: str, message_content: list[dict[str, Any]]
+ ) -> list[str]:
+ """从文本和消息段中提取 arXiv 论文 ID。"""
+ from Undefined.arxiv.parser import extract_arxiv_ids, extract_from_json_message
+
+ paper_ids: list[str] = []
+ seen: set[str] = set()
+
+ for paper_id in extract_arxiv_ids(text):
+ if paper_id in seen:
+ continue
+ seen.add(paper_id)
+ paper_ids.append(paper_id)
+
+ for paper_id in extract_from_json_message(message_content):
+ if paper_id in seen:
+ continue
+ seen.add(paper_id)
+ paper_ids.append(paper_id)
+
+ return paper_ids
+
+ def _extract_github_repo_ids(
+ self, text: str, message_content: list[dict[str, Any]]
+ ) -> list[str]:
+ """从文本和消息段中提取 GitHub 仓库 ID。"""
+ from Undefined.github.parser import (
+ extract_from_json_message,
+ extract_github_repo_ids,
+ )
+
+ repo_ids: list[str] = []
+ seen: set[str] = set()
+
+ for repo_id in extract_github_repo_ids(text):
+ # 仓库 ID 大小写不敏感去重
+ key = repo_id.lower()
+ if key in seen:
+ continue
+ seen.add(key)
+ repo_ids.append(repo_id)
+
+ for repo_id in extract_from_json_message(message_content):
+ key = repo_id.lower()
+ if key in seen:
+ continue
+ seen.add(key)
+ repo_ids.append(repo_id)
+
+ return repo_ids
+
+ async def _handle_bilibili_extract(
+ self,
+ target_id: int,
+ bvids: list[str],
+ target_type: str,
+ ) -> None:
+ """处理 bilibili 视频自动提取和发送。"""
+ from Undefined.bilibili.sender import send_bilibili_video
+
+ for bvid in bvids[:3]:
+ try:
+ # 单条消息最多自动提取 3 个 BV
+ await send_bilibili_video(
+ video_id=bvid,
+ sender=self.sender,
+ onebot=self.onebot,
+ target_type=target_type, # type: ignore[arg-type]
+ target_id=target_id,
+ cookie=self.config.bilibili_cookie,
+ prefer_quality=self.config.bilibili_prefer_quality,
+ max_duration=self.config.bilibili_max_duration,
+ max_file_size=self.config.bilibili_max_file_size,
+ oversize_strategy=self.config.bilibili_oversize_strategy,
+ danmaku_enabled=self.config.bilibili_danmaku_enabled,
+ danmaku_batch_size=self.config.bilibili_danmaku_batch_size,
+ danmaku_max_count=self.config.bilibili_danmaku_max_count,
+ )
+ except Exception as exc:
+ logger.error(
+ "[Bilibili] 自动提取失败 %s → %s:%s: %s",
+ bvid,
+ target_type,
+ target_id,
+ exc,
+ )
+ try:
+ error_msg = f"视频提取失败: {exc}"
+ if target_type == "group":
+ await self.sender.send_group_message(target_id, error_msg)
+ else:
+ await self.sender.send_private_message(target_id, error_msg)
+ except Exception:
+ pass
+
+ async def _handle_arxiv_extract(
+ self,
+ target_id: int,
+ paper_ids: list[str],
+ target_type: str,
+ ) -> None:
+ """处理 arXiv 论文自动提取和发送。"""
+ from Undefined.arxiv.sender import send_arxiv_paper
+
+ max_items = max(1, int(self.config.arxiv_auto_extract_max_items))
+
+ for paper_id in paper_ids[:max_items]:
+ try:
+ result = await send_arxiv_paper(
+ paper_id=paper_id,
+ sender=self.sender,
+ target_type=target_type, # type: ignore[arg-type]
+ target_id=target_id,
+ max_file_size=self.config.arxiv_max_file_size,
+ author_preview_limit=self.config.arxiv_author_preview_limit,
+ summary_preview_chars=self.config.arxiv_summary_preview_chars,
+ context={
+ "request_id": (
+ f"arxiv_auto_extract:{target_type}:{target_id}:{paper_id}"
+ )
+ },
+ )
+ logger.info(
+ "[arXiv] 自动提取完成 %s → %s:%s: %s",
+ paper_id,
+ target_type,
+ target_id,
+ result,
+ )
+ except Exception:
+ logger.exception(
+ "[arXiv] 自动提取失败 %s → %s:%s",
+ paper_id,
+ target_type,
+ target_id,
+ )
+
+ async def _handle_github_extract(
+ self,
+ target_id: int,
+ repo_ids: list[str],
+ target_type: str,
+ ) -> None:
+ """处理 GitHub 仓库自动提取和发送。"""
+ from Undefined.github.sender import send_github_repo_card
+
+ max_items = max(
+ 1, int(getattr(self.config, "github_auto_extract_max_items", 3))
+ )
+ request_timeout = float(
+ getattr(self.config, "github_request_timeout_seconds", 10.0)
+ )
+
+ for repo_id in repo_ids[:max_items]:
+ try:
+ result = await send_github_repo_card(
+ repo_id=repo_id,
+ sender=self.sender,
+ target_type=target_type, # type: ignore[arg-type]
+ target_id=target_id,
+ request_timeout=request_timeout,
+ context={
+ "request_id": (
+ f"github_auto_extract:{target_type}:{target_id}:{repo_id}"
+ )
+ },
+ )
+ logger.info(
+ "[GitHub] 自动提取完成 %s → %s:%s: %s",
+ repo_id,
+ target_type,
+ target_id,
+ result,
+ )
+ except Exception as exc:
+ logger.info(
+ "[GitHub] 自动提取跳过 %s → %s:%s: %s",
+ repo_id,
+ target_type,
+ target_id,
+ exc,
+ )
diff --git a/src/Undefined/handlers/message_flow.py b/src/Undefined/handlers/message_flow.py
new file mode 100644
index 00000000..b9b2a5a5
--- /dev/null
+++ b/src/Undefined/handlers/message_flow.py
@@ -0,0 +1,845 @@
+"""消息主流程与 ``MessageHandler`` 核心实现。
+
+协调私聊/群聊事件分发、附件收集、管线与 AI 回复；拍一拍、复读、自动提取由 mixin 提供。
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import os
+from pathlib import Path
+import random
+from typing import Any, Coroutine, Literal
+
+import Undefined.handlers as handlers_module
+from Undefined.attachments import (
+ append_attachment_text,
+ build_attachment_scope,
+ register_message_attachments,
+)
+from Undefined.ai import AIClient
+from Undefined.config import Config
+from Undefined.faq import FAQStorage
+from Undefined.handlers.auto_extract import AutoExtractMixin
+from Undefined.handlers.poke import PokeMixin
+from Undefined.handlers.repeat import RepeatMixin
+from Undefined.onebot import (
+ OneBotClient,
+ get_message_content,
+ get_message_sender_id,
+)
+from Undefined.rate_limit import RateLimiter
+from Undefined.scheduled_task_storage import ScheduledTaskStorage
+from Undefined.services.ai_coordinator import AICoordinator
+from Undefined.services.command import CommandDispatcher
+from Undefined.services.message_batcher import MessageBatcher, make_scope
+from Undefined.services.model_pool import ModelPoolService
+from Undefined.services.queue_manager import QueueManager
+from Undefined.services.security import SecurityService
+from Undefined.skills.pipelines import PipelineRegistry
+from Undefined.skills.pipelines.context import build_pipeline_context
+from Undefined.utils.coerce import safe_int
+from Undefined.utils.fake_at import BotNicknameCache, strip_fake_at
+from Undefined.utils.history import MessageHistoryManager
+from Undefined.utils.logging import log_debug_json, redact_string
+from Undefined.utils.queue_intervals import build_model_queue_intervals
+from Undefined.utils.resources import resolve_resource_path
+from Undefined.utils.scheduler import TaskScheduler
+from Undefined.utils.sender import MessageSender
+
+logger = logging.getLogger(__name__)
+
+KEYWORD_REPLY_HISTORY_PREFIX = "[系统关键词自动回复] "
+
+
+def _is_private_model_pool_control_text(text: str) -> bool:
+ return bool(ModelPoolService.is_private_control_text(text))
+
+
+class MessageHandler(PokeMixin, RepeatMixin, AutoExtractMixin):
+ """消息处理器。
+
+ 接收 OneBot 事件、写入历史并协调命令分发、自动管线与 AI 回复；
+ 依赖 ``AICoordinator``、``CommandDispatcher`` 与 ``MessageBatcher``。
+ """
+
+ def __init__(
+ self,
+ config: Config,
+ onebot: OneBotClient,
+ ai: AIClient,
+ faq_storage: FAQStorage,
+ task_storage: ScheduledTaskStorage,
+ ) -> None:
+ self.config = config
+ self.onebot = onebot
+ self.ai = ai
+ self.faq_storage = faq_storage
+ self.history_manager = MessageHistoryManager(config.history_max_records)
+ self.sender = MessageSender(
+ onebot,
+ self.history_manager,
+ config.bot_qq,
+ config,
+ attachment_registry=getattr(ai, "attachment_registry", None),
+ )
+
+ self.security = SecurityService(config, ai._http_client)
+ self.rate_limiter = RateLimiter(config)
+ self.queue_manager = QueueManager(
+ max_retries=config.ai_request_max_retries,
+ )
+ self.queue_manager.update_model_intervals(build_model_queue_intervals(config))
+
+ ai.set_queue_manager(self.queue_manager)
+
+ self.command_dispatcher = CommandDispatcher(
+ config,
+ self.sender,
+ ai,
+ faq_storage,
+ onebot,
+ self.security,
+ queue_manager=self.queue_manager,
+ rate_limiter=self.rate_limiter,
+ history_manager=self.history_manager,
+ )
+ self.ai_coordinator = AICoordinator(
+ config,
+ ai,
+ self.queue_manager,
+ self.history_manager,
+ self.sender,
+ onebot,
+ TaskScheduler(ai, self.sender, onebot, self.history_manager, task_storage),
+ self.security,
+ command_dispatcher=self.command_dispatcher,
+ )
+
+ self.message_batcher = MessageBatcher(
+ config.message_batcher,
+ flush_callback=self.ai_coordinator.handle_batched_dispatch,
+ )
+ self.ai_coordinator.set_batcher(self.message_batcher)
+
+ self._background_tasks: set[asyncio.Task[None]] = set()
+ self._profile_name_refresh_cache: dict[tuple[str, int], str] = {}
+ self._bot_nickname_cache = BotNicknameCache(onebot, config.bot_qq)
+ self.pipeline_registry = PipelineRegistry()
+ self._pipelines_initialized = False
+ self._pipelines_init_lock = asyncio.Lock()
+
+ self._repeat_counter: dict[int, list[tuple[str, int]]] = {}
+ self._repeat_locks: dict[int, asyncio.Lock] = {}
+ self._repeat_cooldown: dict[int, dict[str, float]] = {}
+
+ self.ai_coordinator.queue_manager.start(self.ai_coordinator.execute_reply)
+
+ async def initialize(self) -> None:
+ """完成需要事件循环承载的异步初始化。"""
+ await self.init_pipelines()
+
+ async def init_pipelines(self) -> None:
+ """异步加载自动处理管线并按配置启动热重载。"""
+ if getattr(self, "_pipelines_initialized", False):
+ return
+ init_lock = getattr(self, "_pipelines_init_lock", None)
+ if init_lock is None:
+ init_lock = asyncio.Lock()
+ self._pipelines_init_lock = init_lock
+ async with init_lock:
+ if getattr(self, "_pipelines_initialized", False):
+ return
+ await self.pipeline_registry.load_items_async()
+ self._pipelines_initialized = True
+ if getattr(self.config, "skills_hot_reload", False):
+ self.pipeline_registry.start_hot_reload(
+ interval=self.config.skills_hot_reload_interval,
+ debounce=self.config.skills_hot_reload_debounce,
+ )
+
+ async def _annotate_meme_descriptions(
+ self,
+ attachments: list[dict[str, str]],
+ scope_key: str,
+ ) -> list[dict[str, str]]:
+ """为图片附件添加表情包描述（如果在表情库中找到）。
+
+ 采用批量查询：收集所有 SHA256 哈希值，一次性查询，然后映射结果。
+ 最佳努力：任何失败时返回原始列表。
+ """
+ if not attachments:
+ return attachments
+
+ ai_client = getattr(self, "ai", None)
+ if ai_client is None:
+ return attachments
+
+ attachment_registry = getattr(ai_client, "attachment_registry", None)
+ if attachment_registry is None:
+ return attachments
+
+ meme_service = getattr(ai_client, "_meme_service", None)
+ if meme_service is None or not getattr(meme_service, "enabled", False):
+ return attachments
+
+ meme_store = getattr(meme_service, "_store", None)
+ if meme_store is None:
+ return attachments
+
+ try:
+ # 仅 pic_ 前缀图片参与表情库匹配
+ uid_to_hash: dict[str, str] = {}
+ for att in attachments:
+ uid = att.get("uid", "")
+ if not uid.startswith("pic_"):
+ continue
+ record = attachment_registry.resolve(uid, scope_key)
+ if record and record.sha256:
+ uid_to_hash[uid] = record.sha256
+
+ if not uid_to_hash:
+ return attachments
+
+ unique_hashes = set(uid_to_hash.values())
+ hash_to_desc: dict[str, str] = {}
+ for h in unique_hashes:
+ meme = await meme_store.find_by_sha256(h)
+ if meme and meme.description:
+ hash_to_desc[h] = meme.description
+
+ if not hash_to_desc:
+ return attachments
+
+ result: list[dict[str, str]] = []
+ for att in attachments:
+ uid = att.get("uid", "")
+ sha = uid_to_hash.get(uid, "")
+ desc = hash_to_desc.get(sha, "")
+ if desc:
+ new_att = dict(att)
+ new_att["description"] = f"[表情包] {desc}"
+ result.append(new_att)
+ else:
+ result.append(att)
+ return result
+ except Exception:
+ logger.warning("表情包自动匹配失败，跳过", exc_info=True)
+ return attachments
+
+ async def _collect_message_attachments(
+ self,
+ message_content: list[dict[str, Any]],
+ *,
+ group_id: int | None = None,
+ user_id: int | None = None,
+ request_type: str,
+ ) -> list[dict[str, str]]:
+ scope_key = build_attachment_scope(
+ group_id=group_id,
+ user_id=user_id,
+ request_type=request_type,
+ )
+ if not scope_key:
+ return []
+ ai_client = getattr(self, "ai", None)
+ attachment_registry = (
+ getattr(ai_client, "attachment_registry", None) if ai_client else None
+ )
+ if attachment_registry is None:
+ return []
+ onebot = getattr(self, "onebot", None)
+ resolve_image_url = getattr(onebot, "get_image", None) if onebot else None
+ result = await register_message_attachments(
+ registry=attachment_registry,
+ segments=message_content,
+ scope_key=scope_key,
+ resolve_image_url=resolve_image_url,
+ get_forward_messages=getattr(onebot, "get_forward_msg", None)
+ if onebot
+ else None,
+ )
+ attachments = result.attachments
+ # 命中表情库时为 AI 上下文补充 [表情包] 描述
+ attachments = await self._annotate_meme_descriptions(attachments, scope_key)
+ return attachments
+
+ def _schedule_meme_ingest(
+ self,
+ *,
+ attachments: list[dict[str, str]],
+ chat_type: str,
+ chat_id: int,
+ sender_id: int,
+ message_id: int | None,
+ scope_key: str | None,
+ ) -> None:
+ # 后台异步入库，不阻塞主消息处理
+ if not attachments or not scope_key:
+ return
+ meme_service = getattr(self.ai, "_meme_service", None)
+ if meme_service is None or not getattr(meme_service, "enabled", False):
+ return
+ self._spawn_background_task(
+ f"meme_ingest:{chat_type}:{chat_id}:{sender_id}:{message_id or 0}",
+ meme_service.enqueue_incoming_attachments(
+ attachments=attachments,
+ chat_type=chat_type,
+ chat_id=chat_id,
+ sender_id=sender_id,
+ message_id=message_id,
+ scope_key=scope_key,
+ ),
+ )
+
+ async def _refresh_profile_display_names(
+ self,
+ *,
+ sender_id: int | None = None,
+ sender_name: str = "",
+ group_id: int | None = None,
+ group_name: str = "",
+ ) -> None:
+ ai_client = getattr(self, "ai", None)
+ cognitive_service = getattr(ai_client, "_cognitive_service", None)
+ if not cognitive_service or not getattr(cognitive_service, "enabled", False):
+ return
+
+ if sender_id and sender_name.strip():
+ await cognitive_service.sync_profile_display_name(
+ entity_type="user",
+ entity_id=str(sender_id),
+ preferred_name=sender_name.strip(),
+ )
+ if group_id and group_name.strip():
+ await cognitive_service.sync_profile_display_name(
+ entity_type="group",
+ entity_id=str(group_id),
+ preferred_name=group_name.strip(),
+ )
+
+ def _can_refresh_profile_display_names(self) -> bool:
+ ai_client = getattr(self, "ai", None)
+ cognitive_service = getattr(ai_client, "_cognitive_service", None)
+ return bool(cognitive_service and getattr(cognitive_service, "enabled", False))
+
+ def _schedule_profile_display_name_refresh(
+ self,
+ *,
+ task_name: str,
+ sender_id: int | None = None,
+ sender_name: str = "",
+ group_id: int | None = None,
+ group_name: str = "",
+ ) -> None:
+ if not self._can_refresh_profile_display_names():
+ return
+
+ cache = getattr(self, "_profile_name_refresh_cache", None)
+ if cache is None:
+ cache = {}
+ self._profile_name_refresh_cache = cache
+
+ updates: dict[str, Any] = {}
+ rollback: list[tuple[tuple[str, int], str | None]] = []
+
+ normalized_sender_name = sender_name.strip()
+ if sender_id and normalized_sender_name:
+ sender_key = ("user", int(sender_id))
+ previous = cache.get(sender_key)
+ if previous != normalized_sender_name:
+ cache[sender_key] = normalized_sender_name
+ rollback.append((sender_key, previous))
+ updates["sender_id"] = sender_id
+ updates["sender_name"] = normalized_sender_name
+
+ normalized_group_name = group_name.strip()
+ if group_id and normalized_group_name:
+ group_key = ("group", int(group_id))
+ previous = cache.get(group_key)
+ if previous != normalized_group_name:
+ cache[group_key] = normalized_group_name
+ rollback.append((group_key, previous))
+ updates["group_id"] = group_id
+ updates["group_name"] = normalized_group_name
+
+ if not updates:
+ return
+
+ async def _run_refresh() -> None:
+ try:
+ await self._refresh_profile_display_names(**updates)
+ except Exception:
+ # 刷新失败时回滚内存缓存，避免脏昵称长期生效
+ for key, previous in rollback:
+ if previous is None:
+ cache.pop(key, None)
+ else:
+ cache[key] = previous
+ raise
+
+ self._spawn_background_task(task_name, _run_refresh())
+
+ async def handle_message(self, event: dict[str, Any]) -> None:
+ """处理收到的消息事件。"""
+ if logger.isEnabledFor(logging.DEBUG):
+ log_debug_json(logger, "[事件数据]", event)
+ post_type = event.get("post_type", "message")
+
+ # 拍一拍走 notice 旁路，不进入普通消息流水线
+ if post_type == "notice" and event.get("notice_type") == "poke":
+ await self._handle_poke_notice(event)
+ return
+
+ if event.get("message_type") == "private":
+ await self._handle_private_message(event)
+ return
+
+ if event.get("message_type") != "group":
+ return
+
+ await self._handle_group_message(event)
+
+ async def _handle_private_message(self, event: dict[str, Any]) -> None:
+ """处理私聊消息事件。"""
+ private_sender_id: int = get_message_sender_id(event)
+ private_message_content: list[dict[str, Any]] = get_message_content(event)
+ trigger_message_id = event.get("message_id")
+
+ if not self.config.is_private_allowed(private_sender_id):
+ private_reason = (
+ self.config.private_access_denied_reason(private_sender_id) or "unknown"
+ )
+ logger.debug(
+ "[访问控制] 忽略私聊消息: user=%s reason=%s (access enabled=%s)",
+ private_sender_id,
+ private_reason,
+ self.config.access_control_enabled(),
+ )
+ return
+
+ private_sender: dict[str, Any] = event.get("sender", {})
+ private_sender_nickname: str = private_sender.get("nickname", "")
+
+ user_name = private_sender_nickname
+ if not user_name:
+ try:
+ user_info = await self.onebot.get_stranger_info(private_sender_id)
+ if user_info:
+ user_name = user_info.get("nickname", "")
+ except Exception as exc:
+ logger.warning("获取用户昵称失败: %s", exc)
+
+ text = handlers_module.extract_text(private_message_content, self.config.bot_qq)
+ private_attachments, parsed_content_raw = await asyncio.gather(
+ self._collect_message_attachments(
+ private_message_content,
+ user_id=private_sender_id,
+ request_type="private",
+ ),
+ handlers_module.parse_message_content_for_history(
+ private_message_content,
+ self.config.bot_qq,
+ self.onebot.get_msg,
+ self.onebot.get_forward_msg,
+ ),
+ )
+ safe_text = redact_string(text)
+ logger.info(
+ "[私聊消息] 发送者=%s 昵称=%s 内容=%s",
+ private_sender_id,
+ user_name or private_sender_nickname,
+ safe_text[:100],
+ )
+ resolved_private_name = (user_name or private_sender_nickname or "").strip()
+ self._schedule_profile_display_name_refresh(
+ task_name=f"profile_name_refresh_private:{private_sender_id}",
+ sender_id=private_sender_id,
+ sender_name=resolved_private_name,
+ )
+
+ parsed_content = append_attachment_text(parsed_content_raw, private_attachments)
+ safe_parsed = redact_string(parsed_content)
+ logger.debug(
+ "[历史记录] 保存私聊: user=%s content=%s...",
+ private_sender_id,
+ safe_parsed[:50],
+ )
+ await self.history_manager.add_private_message(
+ user_id=private_sender_id,
+ text_content=parsed_content,
+ display_name=private_sender_nickname,
+ user_name=user_name,
+ message_id=trigger_message_id,
+ attachments=private_attachments,
+ )
+
+ # 机器人自身消息只写历史，不触发后续自动回复/入库
+ if private_sender_id == self.config.bot_qq:
+ return
+
+ self._schedule_meme_ingest(
+ attachments=private_attachments,
+ chat_type="private",
+ chat_id=private_sender_id,
+ sender_id=private_sender_id,
+ message_id=safe_int(trigger_message_id),
+ scope_key=build_attachment_scope(
+ user_id=private_sender_id,
+ request_type="private",
+ ),
+ )
+
+ if not self.config.should_process_private_message():
+ logger.debug(
+ "[消息策略] 已关闭私聊处理: user=%s",
+ private_sender_id,
+ )
+ return
+
+ # 多模型池控制指令优先于斜杠命令与 AI 回复
+ if (
+ getattr(self.config, "model_pool_enabled", False)
+ and _is_private_model_pool_control_text(text)
+ ) and await self.ai_coordinator.model_pool.handle_private_message(
+ private_sender_id,
+ text,
+ ):
+ return
+
+ private_command = self.command_dispatcher.parse_command(text)
+ if private_command:
+ await self._flush_command_buffer(
+ scope=make_scope(user_id=private_sender_id),
+ sender_id=private_sender_id,
+ )
+ await self.command_dispatcher.dispatch_private(
+ user_id=private_sender_id,
+ sender_id=private_sender_id,
+ command=private_command,
+ )
+ return
+
+ await self._run_pipelines(
+ target_id=private_sender_id,
+ target_type="private",
+ text=text,
+ message_content=private_message_content,
+ )
+
+ await self.ai_coordinator.handle_private_reply(
+ private_sender_id,
+ text,
+ private_message_content,
+ attachments=private_attachments,
+ sender_name=user_name,
+ trigger_message_id=trigger_message_id,
+ )
+
+ async def _handle_group_message(self, event: dict[str, Any]) -> None:
+ """处理群聊消息事件。"""
+ group_id: int = event.get("group_id", 0)
+ sender_id: int = get_message_sender_id(event)
+ message_content: list[dict[str, Any]] = get_message_content(event)
+ trigger_message_id = event.get("message_id")
+
+ if not self.config.is_group_allowed(group_id):
+ group_reason = self.config.group_access_denied_reason(group_id) or "unknown"
+ logger.debug(
+ "[访问控制] 忽略群消息: group=%s sender=%s reason=%s (access enabled=%s)",
+ group_id,
+ sender_id,
+ group_reason,
+ self.config.access_control_enabled(),
+ )
+ return
+
+ group_sender: dict[str, Any] = event.get("sender", {})
+ sender_card: str = group_sender.get("card", "")
+ sender_nickname: str = group_sender.get("nickname", "")
+ sender_role: str = group_sender.get("role", "member")
+ sender_title: str = group_sender.get("title", "")
+ sender_level: str = str(group_sender.get("level", "")).strip()
+
+ text = handlers_module.extract_text(message_content, self.config.bot_qq)
+ safe_text = redact_string(text)
+ logger.info(
+ f"[群消息] group={group_id} sender={sender_id} name={sender_card or sender_nickname} "
+ f"role={sender_role} | {safe_text[:100]}"
+ )
+
+ async def _fetch_group_name() -> str:
+ try:
+ info = await self.onebot.get_group_info(group_id)
+ if info:
+ return str(info.get("group_name", "") or "")
+ except Exception as e:
+ logger.warning(f"获取群聊名失败: {e}")
+ return ""
+
+ group_attachments, group_name, parsed_content_raw = await asyncio.gather(
+ self._collect_message_attachments(
+ message_content,
+ group_id=group_id,
+ request_type="group",
+ ),
+ _fetch_group_name(),
+ handlers_module.parse_message_content_for_history(
+ message_content,
+ self.config.bot_qq,
+ self.onebot.get_msg,
+ self.onebot.get_forward_msg,
+ ),
+ )
+
+ resolved_group_sender_name = (sender_card or sender_nickname or "").strip()
+ self._schedule_profile_display_name_refresh(
+ task_name=f"profile_name_refresh_group:{group_id}:{sender_id}",
+ sender_id=sender_id,
+ sender_name=resolved_group_sender_name,
+ group_id=group_id,
+ group_name=str(group_name or "").strip(),
+ )
+
+ parsed_content = append_attachment_text(parsed_content_raw, group_attachments)
+ safe_parsed = redact_string(parsed_content)
+ logger.debug(
+ f"[历史记录] 保存群聊: group={group_id}, sender={sender_id}, content={safe_parsed[:50]}..."
+ )
+ await self.history_manager.add_group_message(
+ group_id=group_id,
+ sender_id=sender_id,
+ text_content=parsed_content,
+ sender_card=sender_card,
+ sender_nickname=sender_nickname,
+ group_name=group_name,
+ role=sender_role,
+ title=sender_title,
+ level=sender_level,
+ message_id=trigger_message_id,
+ attachments=group_attachments,
+ )
+
+ # 机器人发言计入复读计数，防止 bot 复读自身
+ if sender_id == self.config.bot_qq:
+ await self._append_bot_repeat_counter(group_id, text)
+ return
+
+ self._schedule_meme_ingest(
+ attachments=group_attachments,
+ chat_type="group",
+ chat_id=group_id,
+ sender_id=sender_id,
+ message_id=safe_int(trigger_message_id),
+ scope_key=build_attachment_scope(group_id=group_id, request_type="group"),
+ )
+
+ is_at_bot = self.ai_coordinator._is_at_bot(message_content)
+
+ # 文本 @ 未命中 CQ at 段时，尝试识别「假 @」昵称
+ is_fake_at = False
+ normalized_text = text
+ if not is_at_bot and ("@" in text or "＠" in text):
+ nicknames = await self._bot_nickname_cache.get_nicknames(group_id)
+ if nicknames:
+ is_fake_at, normalized_text = strip_fake_at(text, nicknames)
+ if is_fake_at:
+ is_at_bot = True
+ logger.info(
+ "[假@] 识别到假@: group=%s sender=%s",
+ group_id,
+ sender_id,
+ )
+
+ if not self.config.should_process_group_message(is_at_bot=is_at_bot):
+ logger.debug(
+ "[消息策略] 跳过群消息处理: group=%s sender=%s process_every_message=%s at_bot=%s",
+ group_id,
+ sender_id,
+ self.config.process_every_message,
+ is_at_bot,
+ )
+ return
+
+ # 斜杠命令仅在 @bot 时生效；未 @ 时不拦截普通群聊
+ if is_at_bot:
+ command = self.command_dispatcher.parse_command(normalized_text)
+ if command:
+ await self._flush_command_buffer(
+ scope=make_scope(group_id=group_id),
+ sender_id=sender_id,
+ )
+ await self.command_dispatcher.dispatch(group_id, sender_id, command)
+ return
+
+ if self.config.keyword_reply_enabled and handlers_module.matches_xinliweiyuan(
+ text
+ ):
+ if await self._handle_keyword_reply(group_id, sender_id):
+ return
+
+ # 复读命中则跳过管线与 AI 自动回复
+ if await self._maybe_trigger_repeat(group_id, sender_id, text):
+ return
+
+ await self._run_pipelines(
+ target_id=group_id,
+ target_type="group",
+ text=text,
+ message_content=message_content,
+ )
+
+ display_name = sender_card or sender_nickname or str(sender_id)
+ await self.ai_coordinator.handle_auto_reply(
+ group_id,
+ sender_id,
+ normalized_text,
+ message_content,
+ attachments=group_attachments,
+ sender_name=display_name,
+ group_name=group_name,
+ sender_role=sender_role,
+ sender_title=sender_title,
+ sender_level=sender_level,
+ trigger_message_id=trigger_message_id,
+ is_fake_at=is_fake_at,
+ )
+
+ async def _handle_keyword_reply(self, group_id: int, sender_id: int) -> bool:
+ """处理心理委员关键词自动回复；若已发送回复则返回 True。"""
+ rand_val = random.random()
+ if rand_val < 0.01:
+ message = f"[@{sender_id}] 再发让你飞起来"
+ logger.info("关键词回复: 再发让你飞起来")
+ await self.sender.send_group_message(
+ group_id,
+ message,
+ history_prefix=KEYWORD_REPLY_HISTORY_PREFIX,
+ )
+ return True
+ if rand_val < 0.11:
+ try:
+ image_path = resolve_resource_path("img/xlwy.jpg").resolve().as_uri()
+ except Exception:
+ image_path = Path(os.path.abspath("img/xlwy.jpg")).as_uri()
+ message = f"[CQ:image,file={image_path}]"
+ if random.random() < 0.5:
+ message = f"[@{sender_id}] {message}"
+ logger.info("关键词回复: 发送图片 xlwy.jpg")
+ else:
+ if random.random() < 0.7:
+ reply = "受着"
+ else:
+ reply = "那咋了"
+ if random.random() < 0.5:
+ message = f"[@{sender_id}] {reply}"
+ else:
+ message = reply
+ logger.info(f"关键词回复: {reply}")
+ await self.sender.send_group_message(
+ group_id,
+ message,
+ history_prefix=KEYWORD_REPLY_HISTORY_PREFIX,
+ )
+ return True
+
+ async def _flush_command_buffer(self, *, scope: str, sender_id: int) -> None:
+ batcher_config = getattr(self.config, "message_batcher", None)
+ if not getattr(batcher_config, "flush_on_command", False):
+ return
+ batcher = getattr(self, "message_batcher", None)
+ if batcher is None:
+ return
+ # 斜杠命令命中时强制 flush 未合并 buffer，避免命令与待发 batch 交错
+ flushed = await batcher.flush_sender(scope, sender_id)
+ if not flushed:
+ logger.warning(
+ "[MessageBatcher] 命令触发 flush 当前 buffer 失败: scope=%s sender=%s",
+ scope,
+ sender_id,
+ )
+
+ async def _run_pipelines(
+ self,
+ *,
+ target_id: int,
+ target_type: Literal["group", "private"],
+ text: str,
+ message_content: list[dict[str, Any]],
+ ) -> bool:
+ """并行检测并处理所有命中的自动处理管线。"""
+ if not getattr(self, "_pipelines_initialized", False):
+ await self.init_pipelines()
+ context = build_pipeline_context(
+ self,
+ target_id=target_id,
+ target_type=target_type,
+ text=text,
+ message_content=message_content,
+ )
+ detections = await self.pipeline_registry.run(context)
+ return bool(detections)
+
+ async def apply_skills_hot_reload_config(
+ self,
+ *,
+ enabled: bool,
+ interval: float,
+ debounce: float,
+ ) -> None:
+ """跟随全局 skills 热重载配置更新管线。"""
+ if not enabled:
+ await self.pipeline_registry.stop_hot_reload()
+ logger.info("[pipelines] 热重载已随配置禁用")
+ return
+
+ await self.pipeline_registry.stop_hot_reload()
+ self.pipeline_registry.start_hot_reload(
+ interval=interval,
+ debounce=debounce,
+ )
+
+ def _spawn_background_task(
+ self,
+ name: str,
+ coroutine: Coroutine[Any, Any, None],
+ ) -> None:
+ task = asyncio.create_task(coroutine, name=name)
+ self._background_tasks.add(task)
+
+ def _finalize(done_task: asyncio.Task[None]) -> None:
+ self._background_tasks.discard(done_task)
+ try:
+ exc = done_task.exception()
+ except asyncio.CancelledError:
+ logger.debug("[后台任务] 已取消: %s", name)
+ return
+ if exc is not None:
+ logger.exception(
+ "[后台任务] 执行失败: name=%s",
+ name,
+ exc_info=(type(exc), exc, exc.__traceback__),
+ )
+
+ task.add_done_callback(_finalize)
+
+ async def close(self) -> None:
+ """关闭消息处理器。"""
+ logger.info("正在关闭消息处理器...")
+ if self._background_tasks:
+ logger.info(
+ "[后台任务] 等待自动提取任务收敛: count=%s",
+ len(self._background_tasks),
+ )
+ await asyncio.gather(
+ *list(self._background_tasks),
+ return_exceptions=True,
+ )
+ await self.pipeline_registry.stop_hot_reload()
+ await self.message_batcher.flush_all()
+ # 关闭前排空 AI 队列并落盘历史，避免丢回复/丢记录
+ await self.ai_coordinator.queue_manager.drain()
+ await self.ai_coordinator.queue_manager.stop()
+ await self.history_manager.flush_pending_saves()
+ logger.info("消息处理器已关闭")
diff --git a/src/Undefined/handlers/poke.py b/src/Undefined/handlers/poke.py
new file mode 100644
index 00000000..06ba2738
--- /dev/null
+++ b/src/Undefined/handlers/poke.py
@@ -0,0 +1,293 @@
+"""拍一拍（poke）通知处理 mixin。
+
+负责私聊/群聊拍一拍历史写入与 AI 回复触发；由 ``MessageHandler`` 混入使用。
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+ from Undefined.config import Config
+ from Undefined.onebot import OneBotClient
+ from Undefined.services.ai_coordinator import AICoordinator
+ from Undefined.utils.history import MessageHistoryManager
+
+logger = logging.getLogger(__name__)
+
+
+def _format_poke_history_text(display_name: str, user_id: int) -> str:
+ """格式化拍一拍历史文本。"""
+ return f"{display_name}(暱称)[{user_id}(QQ号)] 拍了拍你。"
+
+
+@dataclass(frozen=True)
+class PrivatePokeRecord:
+ """私聊拍一拍历史记录摘要。"""
+
+ poke_text: str
+ sender_name: str
+
+
+@dataclass(frozen=True)
+class GroupPokeRecord:
+ """群聊拍一拍历史记录摘要。"""
+
+ poke_text: str
+ sender_name: str
+ group_name: str
+ sender_role: str
+ sender_title: str
+ sender_level: str
+
+
+class PokeMixin:
+ """拍一拍事件处理 mixin。"""
+
+ if TYPE_CHECKING:
+ config: Config
+ onebot: OneBotClient
+ ai_coordinator: AICoordinator
+ history_manager: MessageHistoryManager
+
+ def _schedule_profile_display_name_refresh(
+ self,
+ *,
+ task_name: str,
+ sender_id: int | None = None,
+ sender_name: str = "",
+ group_id: int | None = None,
+ group_name: str = "",
+ ) -> None: ...
+
+ async def _handle_poke_notice(self, event: dict[str, Any]) -> None:
+ """处理拍一拍通知并触发对应私聊/群聊 AI 回复。"""
+ # 仅处理拍机器人自身的 poke
+ target_id = event.get("target_id", 0)
+ if target_id != self.config.bot_qq:
+ logger.debug(
+ "[通知] 忽略拍一拍目标非机器人: target=%s",
+ target_id,
+ )
+ return
+
+ if not self.config.should_process_poke_message():
+ logger.debug("[消息策略] 已关闭拍一拍处理，忽略此次 poke 事件")
+ return
+
+ poke_group_id: int = event.get("group_id", 0)
+ poke_sender_id: int = event.get("user_id", 0)
+
+ if poke_group_id == 0:
+ # group_id=0 表示私聊拍一拍
+ if not self.config.is_private_allowed(poke_sender_id):
+ private_reason = (
+ self.config.private_access_denied_reason(poke_sender_id)
+ or "unknown"
+ )
+ logger.debug(
+ "[访问控制] 忽略私聊拍一拍: user=%s reason=%s (access enabled=%s)",
+ poke_sender_id,
+ private_reason,
+ self.config.access_control_enabled(),
+ )
+ return
+ else:
+ if not self.config.is_group_allowed(poke_group_id):
+ group_reason = (
+ self.config.group_access_denied_reason(poke_group_id) or "unknown"
+ )
+ logger.debug(
+ "[访问控制] 忽略群聊拍一拍: group=%s sender=%s reason=%s (access enabled=%s)",
+ poke_group_id,
+ poke_sender_id,
+ group_reason,
+ self.config.access_control_enabled(),
+ )
+ return
+
+ logger.info(
+ "[通知] 收到拍一拍: group=%s sender=%s",
+ poke_group_id,
+ poke_sender_id,
+ )
+ logger.debug("[通知] 拍一拍事件数据: %s", str(event)[:200])
+
+ if poke_group_id == 0:
+ private_poke = await self._record_private_poke_history(
+ poke_sender_id, event
+ )
+ logger.info("[通知] 私聊拍一拍，触发私聊回复")
+ # 拍一拍旁路 MessageBatcher，直接走 mention 级队列
+ await self.ai_coordinator.handle_private_reply(
+ poke_sender_id,
+ private_poke.poke_text,
+ [],
+ is_poke=True,
+ sender_name=private_poke.sender_name,
+ )
+ else:
+ group_poke = await self._record_group_poke_history(
+ poke_group_id,
+ poke_sender_id,
+ event,
+ )
+ logger.info(
+ "[通知] 群聊拍一拍，触发群聊回复: group=%s",
+ poke_group_id,
+ )
+ await self.ai_coordinator.handle_auto_reply(
+ poke_group_id,
+ poke_sender_id,
+ group_poke.poke_text,
+ [],
+ is_poke=True,
+ sender_name=group_poke.sender_name,
+ group_name=group_poke.group_name,
+ sender_role=group_poke.sender_role,
+ sender_title=group_poke.sender_title,
+ sender_level=group_poke.sender_level,
+ )
+
+ async def _record_private_poke_history(
+ self, user_id: int, event: dict[str, Any]
+ ) -> PrivatePokeRecord:
+ """记录私聊拍一拍到历史。"""
+ sender = event.get("sender", {})
+ sender_nickname = ""
+ if isinstance(sender, dict):
+ sender_nickname = str(sender.get("nickname", "")).strip()
+
+ user_name = sender_nickname
+ if not user_name:
+ try:
+ user_info = await self.onebot.get_stranger_info(user_id)
+ if isinstance(user_info, dict):
+ user_name = str(user_info.get("nickname", "")).strip()
+ except Exception as exc:
+ logger.warning(
+ "[通知] 获取私聊拍一拍用户昵称失败: user=%s err=%s",
+ user_id,
+ exc,
+ )
+
+ resolved_sender_name = (sender_nickname or user_name).strip()
+ display_name = resolved_sender_name or f"QQ{user_id}"
+ normalized_user_name = user_name or display_name
+ poke_text = _format_poke_history_text(display_name, user_id)
+ self._schedule_profile_display_name_refresh(
+ task_name=f"profile_name_refresh_private_poke:{user_id}",
+ sender_id=user_id,
+ sender_name=resolved_sender_name,
+ )
+
+ try:
+ await self.history_manager.add_private_message(
+ user_id=user_id,
+ text_content=poke_text,
+ display_name=display_name,
+ user_name=normalized_user_name,
+ )
+ except Exception as exc:
+ logger.warning(
+ "[历史记录] 写入私聊拍一拍失败: user=%s err=%s",
+ user_id,
+ exc,
+ )
+ return PrivatePokeRecord(poke_text=poke_text, sender_name=display_name)
+
+ async def _record_group_poke_history(
+ self,
+ group_id: int,
+ sender_id: int,
+ event: dict[str, Any],
+ ) -> GroupPokeRecord:
+ """记录群聊拍一拍到历史。"""
+ sender = event.get("sender", {})
+ sender_card = ""
+ sender_nickname = ""
+ sender_role = "member"
+ sender_title = ""
+ sender_level = ""
+ if isinstance(sender, dict):
+ sender_card = str(sender.get("card", "")).strip()
+ sender_nickname = str(sender.get("nickname", "")).strip()
+ sender_role = str(sender.get("role", "member")).strip() or "member"
+ sender_title = str(sender.get("title", "")).strip()
+ sender_level = str(sender.get("level", "")).strip()
+
+ if not sender_card and not sender_nickname:
+ try:
+ member_info = await self.onebot.get_group_member_info(
+ group_id, sender_id
+ )
+ if isinstance(member_info, dict):
+ sender_card = str(member_info.get("card", "")).strip()
+ sender_nickname = str(member_info.get("nickname", "")).strip()
+ sender_role = (
+ str(member_info.get("role", "member")).strip() or "member"
+ )
+ sender_title = str(member_info.get("title", "")).strip()
+ sender_level = str(member_info.get("level", "")).strip()
+ except Exception as exc:
+ logger.warning(
+ "[通知] 获取拍一拍群成员信息失败: group=%s user=%s err=%s",
+ group_id,
+ sender_id,
+ exc,
+ )
+
+ group_name = ""
+ try:
+ group_info = await self.onebot.get_group_info(group_id)
+ if isinstance(group_info, dict):
+ group_name = str(group_info.get("group_name", "")).strip()
+ except Exception as exc:
+ logger.warning(
+ "[通知] 获取拍一拍群名失败: group=%s err=%s",
+ group_id,
+ exc,
+ )
+
+ resolved_sender_name = (sender_card or sender_nickname).strip()
+ resolved_group_name = group_name.strip()
+ display_name = resolved_sender_name or f"QQ{sender_id}"
+ poke_text = _format_poke_history_text(display_name, sender_id)
+ normalized_group_name = resolved_group_name or f"群{group_id}"
+ self._schedule_profile_display_name_refresh(
+ task_name=f"profile_name_refresh_group_poke:{group_id}:{sender_id}",
+ sender_id=sender_id,
+ sender_name=resolved_sender_name,
+ group_id=group_id,
+ group_name=resolved_group_name,
+ )
+
+ try:
+ await self.history_manager.add_group_message(
+ group_id=group_id,
+ sender_id=sender_id,
+ text_content=poke_text,
+ sender_card=sender_card,
+ sender_nickname=sender_nickname,
+ group_name=normalized_group_name,
+ role=sender_role,
+ title=sender_title,
+ level=sender_level,
+ )
+ except Exception as exc:
+ logger.warning(
+ "[历史记录] 写入群聊拍一拍失败: group=%s sender=%s err=%s",
+ group_id,
+ sender_id,
+ exc,
+ )
+ return GroupPokeRecord(
+ poke_text=poke_text,
+ sender_name=display_name,
+ group_name=normalized_group_name,
+ sender_role=sender_role,
+ sender_title=sender_title,
+ sender_level=sender_level,
+ )
diff --git a/src/Undefined/handlers/repeat.py b/src/Undefined/handlers/repeat.py
new file mode 100644
index 00000000..5307e792
--- /dev/null
+++ b/src/Undefined/handlers/repeat.py
@@ -0,0 +1,146 @@
+"""群聊复读功能 mixin。
+
+按群跟踪连续相同消息并在阈值满足时复读；由 ``MessageHandler`` 混入使用。
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import time
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+ from Undefined.config import Config
+ from Undefined.utils.sender import MessageSender
+
+from Undefined.utils.logging import redact_string
+
+logger = logging.getLogger(__name__)
+
+REPEAT_REPLY_HISTORY_PREFIX = "[系统复读] "
+
+
+class RepeatMixin:
+ """群聊复读计数与触发 mixin。"""
+
+ if TYPE_CHECKING:
+ config: Config
+ sender: MessageSender
+ _repeat_counter: dict[int, list[tuple[str, int]]]
+ _repeat_locks: dict[int, asyncio.Lock]
+ _repeat_cooldown: dict[int, dict[str, float]]
+
+ def _get_repeat_lock(self, group_id: int) -> asyncio.Lock:
+ """获取或创建指定群的复读竞态保护锁。"""
+ lock = self._repeat_locks.get(group_id)
+ if lock is None:
+ lock = asyncio.Lock()
+ self._repeat_locks[group_id] = lock
+ return lock
+
+ @staticmethod
+ def _normalize_repeat_text(text: str) -> str:
+ """规范化复读文本用于冷却比较（？→?）。"""
+ return text.replace("？", "?")
+
+ def _is_repeat_on_cooldown(self, group_id: int, text: str) -> bool:
+ """检查指定群的文本是否在复读冷却期内。"""
+ cooldown_minutes = self.config.repeat_cooldown_minutes
+ if cooldown_minutes <= 0:
+ return False
+ group_cd = self._repeat_cooldown.get(group_id)
+ if not group_cd:
+ return False
+ key = self._normalize_repeat_text(text)
+ last_time = group_cd.get(key)
+ if last_time is None:
+ return False
+ return bool((time.monotonic() - last_time) < cooldown_minutes * 60)
+
+ def _record_repeat_cooldown(self, group_id: int, text: str) -> None:
+ """记录复读冷却时间戳，同时清理已过期条目防止内存泄漏。"""
+ cooldown_seconds = self.config.repeat_cooldown_minutes * 60
+ if cooldown_seconds <= 0:
+ return
+ key = self._normalize_repeat_text(text)
+ group_cd = self._repeat_cooldown.setdefault(group_id, {})
+ now = time.monotonic()
+ expired = [k for k, ts in group_cd.items() if (now - ts) >= cooldown_seconds]
+ for k in expired:
+ del group_cd[k]
+ group_cd[key] = now
+
+ async def _append_bot_repeat_counter(self, group_id: int, text: str) -> None:
+ """将 bot 自身发言写入复读计数器，防止误触复读。"""
+ if not self.config.repeat_enabled or not text:
+ return
+ async with self._get_repeat_lock(group_id):
+ counter = self._repeat_counter.setdefault(group_id, [])
+ counter.append((text, self.config.bot_qq))
+ n = self.config.repeat_threshold
+ if len(counter) > n:
+ self._repeat_counter[group_id] = counter[-n:]
+
+ async def _maybe_trigger_repeat(
+ self,
+ group_id: int,
+ sender_id: int,
+ text: str,
+ ) -> bool:
+ """尝试触发群聊复读；若已发送复读消息则返回 True。"""
+ if not self.config.repeat_enabled or not text:
+ return False
+
+ n = self.config.repeat_threshold
+ async with self._get_repeat_lock(group_id):
+ counter = self._repeat_counter.setdefault(group_id, [])
+ counter.append((text, sender_id))
+ if len(counter) > n:
+ self._repeat_counter[group_id] = counter[-n:]
+ counter = self._repeat_counter[group_id]
+
+ if len(counter) < n:
+ return False
+
+ last_n = counter[-n:]
+ texts = [t for t, _ in last_n]
+ senders = [s for _, s in last_n]
+ # 连续 n 条文本相同且来自 n 个不同发送者，且 bot 未参与
+ if not (
+ len(set(texts)) == 1
+ and len(set(senders)) == n
+ and self.config.bot_qq not in senders
+ ):
+ return False
+
+ reply_text = texts[0]
+ if self._is_repeat_on_cooldown(group_id, reply_text):
+ # 冷却期内清空计数，避免同一文本反复试探
+ self._repeat_counter[group_id] = []
+ logger.debug(
+ "[复读] 冷却中跳过: group=%s text=%s",
+ group_id,
+ redact_string(reply_text)[:50],
+ )
+ return False
+
+ if self.config.inverted_question_enabled:
+ stripped = reply_text.strip()
+ # 纯问号复读时翻转成 ¿
+ if set(stripped) <= {"?", "？"}:
+ reply_text = "¿" * len(stripped)
+
+ self._repeat_counter[group_id] = []
+ self._record_repeat_cooldown(group_id, texts[0])
+ logger.info(
+ "[复读] 触发复读: group=%s text=%s",
+ group_id,
+ redact_string(reply_text)[:50],
+ )
+ await self.sender.send_group_message(
+ group_id,
+ reply_text,
+ history_prefix=REPEAT_REPLY_HISTORY_PREFIX,
+ )
+ return True
diff --git a/src/Undefined/memes/_image_utils.py b/src/Undefined/memes/_image_utils.py
new file mode 100644
index 00000000..aff46c57
--- /dev/null
+++ b/src/Undefined/memes/_image_utils.py
@@ -0,0 +1,147 @@
+"""Meme 图片处理与标签归一化共享工具。"""
+
+from __future__ import annotations
+
+import math
+import mimetypes
+import re
+from datetime import datetime
+from pathlib import Path
+
+from openai import APIConnectionError, APIStatusError, APITimeoutError
+from PIL import Image
+
+from Undefined.memes.models import normalize_string_list
+
+_IMAGE_EXTENSIONS_BY_MIME = {
+ "image/png": ".png",
+ "image/jpeg": ".jpg",
+ "image/gif": ".gif",
+ "image/webp": ".webp",
+ "image/bmp": ".bmp",
+ "image/svg+xml": ".svg",
+}
+_TAG_SPLIT_RE = re.compile(r"[,，\n]+")
+
+
+def now_iso() -> str:
+ return datetime.now().isoformat(timespec="seconds")
+
+
+def guess_suffix(path: Path, mime_type: str) -> str:
+ suffix = path.suffix.lower()
+ if suffix:
+ return suffix
+ guessed = _IMAGE_EXTENSIONS_BY_MIME.get(mime_type)
+ if guessed:
+ return guessed
+ mime_guess = mimetypes.guess_extension(mime_type or "")
+ if mime_guess:
+ return mime_guess.lower()
+ return ".bin"
+
+
+def normalize_tags(raw_tags: list[str] | str | None) -> list[str]:
+ if raw_tags is None:
+ return []
+ if isinstance(raw_tags, str):
+ parts = [part.strip() for part in _TAG_SPLIT_RE.split(raw_tags)]
+ return normalize_string_list(parts)
+ return normalize_string_list(raw_tags)
+
+
+def is_retryable_llm_error(exc: Exception) -> bool:
+ """判断 LLM 调用异常是否应触发 worker 级重试。"""
+ if isinstance(exc, (APIConnectionError, APITimeoutError)):
+ return True
+ if isinstance(exc, APIStatusError):
+ return exc.status_code == 429 or exc.status_code >= 500
+ return False
+
+
+def extract_gif_frames(source_path: Path, n_frames: int) -> list[Image.Image]:
+ """从 GIF 中均匀采样 *n_frames* 帧（含首末帧），返回 RGBA Image 列表。"""
+ with Image.open(source_path) as image:
+ total = getattr(image, "n_frames", 1)
+ if total <= 1:
+ image.seek(0)
+ return [image.convert("RGBA").copy()]
+ n = min(n_frames, total)
+ if n <= 1:
+ image.seek(0)
+ return [image.convert("RGBA").copy()]
+ indices = sample_frame_indices(total, n)
+ frames: list[Image.Image] = []
+ for idx in indices:
+ image.seek(idx)
+ frames.append(image.convert("RGBA").copy())
+ return frames
+
+
+def sample_frame_indices(total: int, n: int) -> list[int]:
+ """生成均匀采样的帧索引列表（始终包含首帧和末帧）。"""
+ if n >= total:
+ return list(range(total))
+ if n == 1:
+ return [0]
+ if n == 2:
+ return [0, total - 1]
+ indices = [round(i * (total - 1) / (n - 1)) for i in range(n)]
+ seen: set[int] = set()
+ result: list[int] = []
+ for idx in indices:
+ if idx not in seen:
+ seen.add(idx)
+ result.append(idx)
+ return result
+
+
+def compose_grid(frames: list[Image.Image], output_path: Path) -> None:
+ """将多帧拼接为网格图并保存为 PNG。"""
+ n = len(frames)
+ if n == 0:
+ return
+ if n == 1:
+ frames[0].save(output_path, format="PNG")
+ return
+ cols = math.ceil(math.sqrt(n))
+ rows = math.ceil(n / cols)
+ fw, fh = frames[0].size
+ grid = Image.new("RGBA", (cols * fw, rows * fh), (0, 0, 0, 0))
+ for i, frame in enumerate(frames):
+ resized = (
+ frame.resize((fw, fh), Image.Resampling.LANCZOS)
+ if frame.size != (fw, fh)
+ else frame
+ )
+ x = (i % cols) * fw
+ y = (i // cols) * fh
+ grid.paste(resized, (x, y))
+ grid.save(output_path, format="PNG")
+
+
+# 向后兼容：旧模块级私有名仍可从 service 等路径导入
+_now_iso = now_iso
+_guess_suffix = guess_suffix
+_normalize_tags = normalize_tags
+_is_retryable_llm_error = is_retryable_llm_error
+_extract_gif_frames = extract_gif_frames
+_sample_frame_indices = sample_frame_indices
+_compose_grid = compose_grid
+
+__all__ = [
+ "compose_grid",
+ "extract_gif_frames",
+ "guess_suffix",
+ "is_retryable_llm_error",
+ "normalize_tags",
+ "now_iso",
+ "sample_frame_indices",
+ "_compose_grid",
+ "_extract_gif_frames",
+ "_guess_suffix",
+ "_is_retryable_llm_error",
+ "_normalize_tags",
+ "_now_iso",
+ "_sample_frame_indices",
+]
diff --git a/src/Undefined/memes/ingest.py b/src/Undefined/memes/ingest.py
new file mode 100644
index 00000000..dd157bf5
--- /dev/null
+++ b/src/Undefined/memes/ingest.py
@@ -0,0 +1,625 @@
+"""MemeService 入库与后台任务处理。"""
+
+from __future__ import annotations
+
+import asyncio
+from collections.abc import Mapping
+from dataclasses import replace
+import hashlib
+import logging
+import mimetypes
+from pathlib import Path
+import shutil
+from typing import TYPE_CHECKING, Any
+from uuid import uuid4
+
+from PIL import Image
+
+from Undefined.memes._image_utils import (
+ _compose_grid,
+ _extract_gif_frames,
+ _guess_suffix,
+ _is_retryable_llm_error,
+ _normalize_tags,
+ _now_iso,
+)
+from Undefined.memes.models import (
+ IngestDigestLockEntry,
+ MemeRecord,
+ MemeSourceRecord,
+ build_search_text,
+)
+
+if TYPE_CHECKING:
+ from Undefined.memes.store import MemeStore
+ from Undefined.memes.vector_store import MemeVectorStore
+
+logger = logging.getLogger(__name__)
+
+
+class MemeIngestMixin:
+ if TYPE_CHECKING:
+ _ai_client: Any | None
+ _attachment_registry: Any | None
+ _ingest_digest_locks: dict[str, Any]
+ _ingest_digest_locks_guard: asyncio.Lock
+ _job_queue: Any | None
+ _store: MemeStore
+ _vector_store: MemeVectorStore
+
+ def _blob_dir(self) -> Path: ...
+ def _cfg(self) -> Any: ...
+ def _invalidate_global_image_cache(self, uid: str) -> None: ...
+ def _preview_dir(self) -> Path: ...
+ def _queue_enabled(self) -> bool: ...
+ async def delete_meme(self, uid: str) -> bool: ...
+ def enabled(self) -> bool: ...
+
+ async def _acquire_ingest_digest_lock(self, digest: str) -> IngestDigestLockEntry:
+ async with self._ingest_digest_locks_guard:
+ entry = self._ingest_digest_locks.get(digest)
+ if entry is None:
+ entry = IngestDigestLockEntry(lock=asyncio.Lock())
+ self._ingest_digest_locks[digest] = entry
+ entry.users += 1
+ try:
+ await entry.lock.acquire()
+ except BaseException:
+ await self._release_ingest_digest_lock_reference(digest, entry)
+ raise
+ return entry
+
+ async def _release_ingest_digest_lock_reference(
+ self,
+ digest: str,
+ entry: IngestDigestLockEntry,
+ *,
+ release_lock: bool = False,
+ ) -> None:
+ if release_lock and entry.lock.locked():
+ entry.lock.release()
+ async with self._ingest_digest_locks_guard:
+ entry.users = max(0, entry.users - 1)
+ current = self._ingest_digest_locks.get(digest)
+ if current is entry and entry.users == 0 and not entry.lock.locked():
+ self._ingest_digest_locks.pop(digest, None)
+
+ def _delete_file_if_exists(self, path: Path) -> None:
+ try:
+ path.unlink(missing_ok=True)
+ except OSError:
+ logger.debug("[memes] 删除文件失败: path=%s", path, exc_info=True)
+
+ def _cleanup_gif_frame_files(self, uid: str) -> None:
+ """清理 GIF 多帧分析产生的临时帧文件 ({uid}_f{i}.png)。"""
+ preview_dir = self._preview_dir()
+ for frame_file in preview_dir.glob(f"{uid}_f*.png"):
+ try:
+ frame_file.unlink(missing_ok=True)
+ except OSError:
+ logger.debug(
+ "[memes] 删除帧文件失败: path=%s", frame_file, exc_info=True
+ )
+
+ async def _cleanup_meme_artifacts(
+ self,
+ *,
+ uid: str | None,
+ blob_path: Path,
+ preview_path: Path | None,
+ ) -> None:
+ if uid:
+ try:
+ await self._store.delete(uid)
+ self._invalidate_global_image_cache(uid)
+ except Exception:
+ logger.exception(
+ "[memes] 清理记录失败: uid=%s",
+ uid,
+ )
+ try:
+ await self._vector_store.delete(uid)
+ except Exception:
+ logger.exception(
+ "[memes] 清理向量索引失败: uid=%s",
+ uid,
+ )
+ await asyncio.to_thread(self._delete_file_if_exists, blob_path)
+ if preview_path is not None and preview_path != blob_path:
+ await asyncio.to_thread(self._delete_file_if_exists, preview_path)
+ if uid:
+ await asyncio.to_thread(self._cleanup_gif_frame_files, uid)
+
+ async def enqueue_incoming_attachments(
+ self,
+ *,
+ attachments: list[dict[str, str]],
+ chat_type: str,
+ chat_id: int,
+ sender_id: int,
+ message_id: int | None,
+ scope_key: str,
+ ) -> None:
+ if not self.enabled() or not self._queue_enabled():
+ return
+ cfg = self._cfg()
+ if chat_type == "group" and not bool(cfg.auto_ingest_group):
+ return
+ if chat_type == "private" and not bool(cfg.auto_ingest_private):
+ return
+
+ for item in attachments:
+ media_type = str(item.get("media_type") or item.get("kind") or "").strip()
+ uid = str(item.get("uid") or "").strip()
+ if media_type != "image" or not uid:
+ continue
+ job = {
+ "request_id": f"meme_ingest_{uid}",
+ "kind": "ingest",
+ "attachment_uid": uid,
+ "scope_key": scope_key,
+ "chat_type": chat_type,
+ "chat_id": str(chat_id),
+ "sender_id": str(sender_id),
+ "message_id": str(message_id or ""),
+ "queued_at": _now_iso(),
+ }
+ queue = self._job_queue
+ if queue is None:
+ return
+ await queue.enqueue(job)
+
+ async def enqueue_reanalyze(self, uid: str) -> str | None:
+ if not self._queue_enabled():
+ return None
+ queue = self._job_queue
+ if queue is None:
+ return None
+ result = await queue.enqueue(
+ {
+ "request_id": f"meme_reanalyze_{uid}",
+ "kind": "reanalyze",
+ "uid": uid,
+ "queued_at": _now_iso(),
+ }
+ )
+ return str(result)
+
+ async def enqueue_reindex(self, uid: str) -> str | None:
+ if not self._queue_enabled():
+ return None
+ queue = self._job_queue
+ if queue is None:
+ return None
+ result = await queue.enqueue(
+ {
+ "request_id": f"meme_reindex_{uid}",
+ "kind": "reindex",
+ "uid": uid,
+ "queued_at": _now_iso(),
+ }
+ )
+ return str(result)
+
+ async def process_job(self, job: Mapping[str, Any]) -> None:
+ kind = str(job.get("kind") or "").strip().lower()
+ if kind == "ingest":
+ await self._process_ingest_job(job)
+ return
+ if kind == "reanalyze":
+ await self._process_reanalyze_job(job)
+ return
+ if kind == "reindex":
+ await self._process_reindex_job(job)
+ return
+ raise ValueError(f"unsupported meme job kind: {kind}")
+
+ async def _process_reindex_job(self, job: Mapping[str, Any]) -> None:
+ uid = str(job.get("uid") or "").strip()
+ if not uid:
+ return
+ record = await self._store.get(uid)
+ if record is None:
+ return
+ await self._vector_store.upsert(record)
+
+ async def _process_reanalyze_job(self, job: Mapping[str, Any]) -> None:
+ uid = str(job.get("uid") or "").strip()
+ if not uid:
+ return
+ record = await self._store.get(uid)
+ if record is None:
+ return
+ if self._ai_client is None:
+ raise RuntimeError("reanalyze requires ai_client")
+ analyze_path: str | list[str] = (
+ record.preview_path if record.preview_path else record.blob_path
+ )
+ # GIF 多帧模式：与 ingest 路径保持一致
+ if record.is_animated:
+ cfg = self._cfg()
+ if str(getattr(cfg, "gif_analysis_mode", "grid")).lower() == "multi":
+ analyze_path = await self._prepare_gif_multi_frames(
+ Path(record.blob_path), uid
+ )
+ try:
+ judgement = await self._ai_client.judge_meme_image(analyze_path)
+ except Exception as exc:
+ if _is_retryable_llm_error(exc):
+ if isinstance(analyze_path, list):
+ await asyncio.to_thread(self._cleanup_gif_frame_files, uid)
+ raise
+ logger.exception(
+ "[memes] judge stage failed during reanalyze: uid=%s err=%s", uid, exc
+ )
+ if isinstance(analyze_path, list):
+ await asyncio.to_thread(self._cleanup_gif_frame_files, uid)
+ return
+ if not bool(judgement.get("is_meme", False)):
+ if isinstance(analyze_path, list):
+ await asyncio.to_thread(self._cleanup_gif_frame_files, uid)
+ await self.delete_meme(uid)
+ return
+ try:
+ described = await self._ai_client.describe_meme_image(analyze_path)
+ except Exception as exc:
+ if _is_retryable_llm_error(exc):
+ if isinstance(analyze_path, list):
+ await asyncio.to_thread(self._cleanup_gif_frame_files, uid)
+ raise
+ logger.exception(
+ "[memes] describe stage failed during reanalyze: uid=%s err=%s",
+ uid,
+ exc,
+ )
+ if isinstance(analyze_path, list):
+ await asyncio.to_thread(self._cleanup_gif_frame_files, uid)
+ return
+ # GIF 多帧文件用完即清理
+ if isinstance(analyze_path, list):
+ await asyncio.to_thread(self._cleanup_gif_frame_files, uid)
+ auto_description = str(described.get("description") or "").strip()
+ next_tags = _normalize_tags(described.get("tags"))
+ if not auto_description and not next_tags:
+ logger.warning(
+ "[memes] reanalyze describe failed, skip update: uid=%s", uid
+ )
+ return
+ next_record = replace(
+ record,
+ auto_description=auto_description,
+ ocr_text="",
+ tags=next_tags,
+ search_text=build_search_text(
+ manual_description=record.manual_description,
+ auto_description=auto_description,
+ ocr_text="",
+ tags=next_tags,
+ aliases=record.aliases,
+ ),
+ updated_at=_now_iso(),
+ )
+ saved = await self._store.upsert_record(next_record)
+ self._invalidate_global_image_cache(saved.uid)
+ await self._vector_store.upsert(saved)
+
+ async def _process_ingest_job(self, job: Mapping[str, Any]) -> None:
+ if self._attachment_registry is None:
+ raise RuntimeError("ingest requires attachment_registry")
+ if self._ai_client is None:
+ raise RuntimeError("ingest requires ai_client")
+
+ attachment_uid = str(job.get("attachment_uid") or "").strip()
+ scope_key = str(job.get("scope_key") or "").strip() or None
+ if not attachment_uid:
+ return
+ attachment = self._attachment_registry.resolve(attachment_uid, scope_key)
+ if attachment is None:
+ raise FileNotFoundError(f"attachment uid unavailable: {attachment_uid}")
+ if str(attachment.media_type).lower() != "image":
+ return
+ source_path = Path(str(attachment.local_path or ""))
+ if not source_path.is_file():
+ raise FileNotFoundError(source_path)
+ file_size = source_path.stat().st_size
+ cfg = self._cfg()
+ if file_size > int(cfg.max_source_image_bytes):
+ logger.info(
+ "[memes] skip oversized image: uid=%s size=%s limit=%s",
+ attachment_uid,
+ file_size,
+ cfg.max_source_image_bytes,
+ )
+ return
+
+ digest = await asyncio.to_thread(self._hash_file, source_path)
+ # 同一 SHA256 并发入库串行化，避免重复 AI 判定
+ digest_lock_entry = await self._acquire_ingest_digest_lock(digest)
+ try:
+ existing = await self._store.find_by_sha256(digest)
+ if existing is not None and not Path(existing.blob_path).is_file():
+ logger.warning(
+ "[memes] 检测到孤儿记录，删除后重新入库: uid=%s blob_path=%s",
+ existing.uid,
+ existing.blob_path,
+ )
+ await self._cleanup_meme_artifacts(
+ uid=existing.uid,
+ blob_path=Path(existing.blob_path),
+ preview_path=(
+ Path(existing.preview_path) if existing.preview_path else None
+ ),
+ )
+ existing = await self._store.find_by_sha256(digest)
+ if existing is not None and not Path(existing.blob_path).is_file():
+ raise RuntimeError(
+ f"stale meme record cleanup failed: uid={existing.uid}"
+ )
+ source = MemeSourceRecord(
+ uid=existing.uid if existing is not None else "",
+ source_type="message_attachment",
+ chat_type=str(job.get("chat_type") or ""),
+ chat_id=str(job.get("chat_id") or ""),
+ sender_id=str(job.get("sender_id") or ""),
+ message_id=str(job.get("message_id") or ""),
+ attachment_uid=attachment_uid,
+ source_url=str(attachment.source_ref or ""),
+ seen_at=_now_iso(),
+ )
+ if existing is not None:
+ # 内容已存在：仅追加来源记录并刷新向量索引
+ await self._store.add_source(replace(source, uid=existing.uid))
+ await self._vector_store.upsert(existing)
+ return
+
+ with Image.open(source_path) as image:
+ width, height = image.size
+ is_animated = bool(getattr(image, "is_animated", False))
+ if is_animated and not bool(cfg.allow_gif):
+ return
+
+ uid = await self._generate_uid()
+ suffix = _guess_suffix(source_path, str(attachment.mime_type or ""))
+ blob_path = self._blob_dir() / f"{uid}{suffix}"
+ cleanup_preview_path = (
+ self._preview_dir() / f"{uid}.png" if is_animated else blob_path
+ )
+ persisted_uid: str | None = None
+
+ try:
+ preview_path = await self._prepare_blob_and_preview(
+ source_path=source_path,
+ target_uid=uid,
+ suffix=suffix,
+ is_animated=is_animated,
+ )
+ if preview_path is not None:
+ cleanup_preview_path = preview_path
+ mime_type = str(
+ attachment.mime_type
+ or mimetypes.guess_type(source_path.name)[0]
+ or "application/octet-stream"
+ )
+ analyze_path: str | list[str] = str(
+ preview_path if preview_path is not None else blob_path
+ )
+ if (
+ is_animated
+ and str(getattr(cfg, "gif_analysis_mode", "grid")).lower()
+ == "multi"
+ ):
+ analyze_path = await self._prepare_gif_multi_frames(
+ source_path, uid
+ )
+ try:
+ judgement = await self._ai_client.judge_meme_image(analyze_path)
+ except Exception as exc:
+ if _is_retryable_llm_error(exc):
+ if isinstance(analyze_path, list):
+ await asyncio.to_thread(self._cleanup_gif_frame_files, uid)
+ raise
+ logger.exception(
+ "[memes] judge stage failed, treat as non-meme: uid=%s err=%s",
+ uid,
+ exc,
+ )
+ judgement = {"is_meme": False}
+ if not bool(judgement.get("is_meme", False)):
+ if isinstance(analyze_path, list):
+ await asyncio.to_thread(self._cleanup_gif_frame_files, uid)
+ # 非表情包：清理已落盘文件，不入库
+ await self._cleanup_meme_artifacts(
+ uid=None,
+ blob_path=blob_path,
+ preview_path=cleanup_preview_path,
+ )
+ return
+
+ try:
+ described = await self._ai_client.describe_meme_image(analyze_path)
+ except Exception as exc:
+ if _is_retryable_llm_error(exc):
+ if isinstance(analyze_path, list):
+ await asyncio.to_thread(self._cleanup_gif_frame_files, uid)
+ raise
+ logger.exception(
+ "[memes] describe stage failed, drop uid=%s err=%s", uid, exc
+ )
+ described = {"description": "", "tags": []}
+ # GIF 多帧文件用完即清理
+ if isinstance(analyze_path, list):
+ await asyncio.to_thread(self._cleanup_gif_frame_files, uid)
+ tags = _normalize_tags(described.get("tags"))
+ auto_description = str(described.get("description") or "").strip()
+ if not auto_description and not tags:
+ logger.warning(
+ "[memes] describe stage returned empty result, drop uid=%s", uid
+ )
+ await self._cleanup_meme_artifacts(
+ uid=None,
+ blob_path=blob_path,
+ preview_path=cleanup_preview_path,
+ )
+ return
+ now = _now_iso()
+ record = MemeRecord(
+ uid=uid,
+ content_sha256=digest,
+ blob_path=str(blob_path),
+ preview_path=(
+ str(preview_path) if preview_path is not None else None
+ ),
+ mime_type=mime_type,
+ file_size=file_size,
+ width=width,
+ height=height,
+ is_animated=is_animated,
+ enabled=True,
+ pinned=False,
+ auto_description=auto_description,
+ manual_description="",
+ ocr_text="",
+ tags=tags,
+ aliases=[],
+ search_text=build_search_text(
+ manual_description="",
+ auto_description=auto_description,
+ ocr_text="",
+ tags=tags,
+ aliases=[],
+ ),
+ use_count=0,
+ last_used_at="",
+ created_at=now,
+ updated_at=now,
+ status="ready",
+ segment_data={"subType": "1"},
+ )
+ saved = await self._store.upsert_record(record)
+ self._invalidate_global_image_cache(saved.uid)
+ persisted_uid = saved.uid
+ await self._store.add_source(replace(source, uid=saved.uid))
+ await self._vector_store.upsert(saved)
+ except Exception:
+ await self._cleanup_meme_artifacts(
+ uid=persisted_uid,
+ blob_path=blob_path,
+ preview_path=cleanup_preview_path,
+ )
+ raise
+ finally:
+ await self._release_ingest_digest_lock_reference(
+ digest,
+ digest_lock_entry,
+ release_lock=True,
+ )
+ await self._prune_if_needed()
+
+ async def _prepare_blob_and_preview(
+ self,
+ *,
+ source_path: Path,
+ target_uid: str,
+ suffix: str,
+ is_animated: bool,
+ ) -> Path | None:
+ blob_path = self._blob_dir() / f"{target_uid}{suffix}"
+
+ def _copy() -> None:
+ shutil.copy2(source_path, blob_path)
+
+ await asyncio.to_thread(_copy)
+ if not is_animated:
+ return blob_path
+
+ cfg = self._cfg()
+ mode = str(getattr(cfg, "gif_analysis_mode", "grid")).lower()
+ n_frames = max(2, int(getattr(cfg, "gif_analysis_frames", 6)))
+ preview_path = self._preview_dir() / f"{target_uid}.png"
+
+ def _render_preview() -> None:
+ frames = _extract_gif_frames(source_path, n_frames)
+ if mode == "multi":
+ frames[0].save(preview_path, format="PNG")
+ else:
+ _compose_grid(frames, preview_path)
+ for f in frames:
+ f.close()
+
+ await asyncio.to_thread(_render_preview)
+ return preview_path
+
+ async def _prepare_gif_multi_frames(
+ self, source_path: Path, target_uid: str
+ ) -> list[str]:
+ """multi 模式：将 GIF 各帧单独保存为 PNG，返回路径列表。"""
+ cfg = self._cfg()
+ n_frames = max(2, int(getattr(cfg, "gif_analysis_frames", 6)))
+ preview_dir = self._preview_dir()
+
+ def _render_frames() -> list[str]:
+ frames = _extract_gif_frames(source_path, n_frames)
+ paths: list[str] = []
+ for i, frame in enumerate(frames):
+ p = preview_dir / f"{target_uid}_f{i}.png"
+ frame.save(p, format="PNG")
+ frame.close()
+ paths.append(str(p))
+ return paths
+
+ return await asyncio.to_thread(_render_frames)
+
+ def _hash_file(self, path: Path) -> str:
+ hasher = hashlib.sha256()
+ with path.open("rb") as handle:
+ while True:
+ chunk = handle.read(1024 * 1024)
+ if not chunk:
+ break
+ hasher.update(chunk)
+ return hasher.hexdigest()
+
+ async def _generate_uid(self) -> str:
+ while True:
+ candidate = f"pic_{uuid4().hex[:8]}"
+ if await self._store.get(candidate) is not None:
+ continue
+ if (
+ self._attachment_registry is not None
+ and self._attachment_registry.get(candidate) is not None
+ ):
+ continue
+ return candidate
+
+ async def _prune_if_needed(self) -> None:
+ stats = await self._store.stats()
+ cfg = self._cfg()
+ total_count = int(stats.get("total_count", 0))
+ total_bytes = int(stats.get("total_bytes", 0))
+ if total_count <= int(cfg.max_items) and total_bytes <= int(
+ cfg.max_total_bytes
+ ):
+ return
+ candidates = await self._store.list_prune_candidates()
+ for candidate in candidates:
+ if candidate.pinned:
+ continue
+ if total_count <= int(cfg.max_items) and total_bytes <= int(
+ cfg.max_total_bytes
+ ):
+ break
+ deleted = await self._store.delete(candidate.uid)
+ if deleted is None:
+ continue
+ self._invalidate_global_image_cache(candidate.uid)
+ await self._vector_store.delete(candidate.uid)
+ await asyncio.to_thread(
+ self._delete_file_if_exists, Path(deleted.blob_path)
+ )
+ if deleted.preview_path and deleted.preview_path != deleted.blob_path:
+ await asyncio.to_thread(
+ self._delete_file_if_exists,
+ Path(deleted.preview_path),
+ )
+ total_count -= 1
+ total_bytes -= int(deleted.file_size)
diff --git a/src/Undefined/memes/models.py b/src/Undefined/memes/models.py
index 99a3c989..d4637535 100644
--- a/src/Undefined/memes/models.py
+++ b/src/Undefined/memes/models.py
@@ -1,5 +1,6 @@
from __future__ import annotations
+import asyncio
from dataclasses import dataclass, field
from typing import Any
@@ -51,6 +52,12 @@ class MemeSourceRecord:
seen_at: str
+@dataclass
+class IngestDigestLockEntry:
+ lock: asyncio.Lock
+ users: int = 0
+
+
@dataclass(frozen=True)
class MemeSearchItem:
uid: str
diff --git a/src/Undefined/memes/search.py b/src/Undefined/memes/search.py
new file mode 100644
index 00000000..abd2be24
--- /dev/null
+++ b/src/Undefined/memes/search.py
@@ -0,0 +1,471 @@
+"""MemeService 检索与列表操作。"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from pathlib import Path
+from typing import TYPE_CHECKING, Any
+
+
+from Undefined.attachments import AttachmentRecord
+from Undefined.memes._image_utils import _now_iso
+from Undefined.memes.models import (
+ MemeRecord,
+ MemeSearchItem,
+)
+from Undefined.utils.message_targets import resolve_message_target
+from Undefined.utils.coerce import safe_int
+
+if TYPE_CHECKING:
+ import threading
+
+ from Undefined.memes.store import MemeStore
+ from Undefined.memes.vector_store import MemeVectorStore
+
+logger = logging.getLogger(__name__)
+
+
+class MemeSearchMixin:
+ if TYPE_CHECKING:
+ _cfg: Any
+ _global_image_cache: dict[str, AttachmentRecord]
+ _global_image_cache_lock: threading.Lock
+ _job_queue: Any | None
+ _retrieval_runtime: Any | None
+ _store: MemeStore
+ _vector_store: MemeVectorStore
+
+ def resolve_global_image_sync(self, uid: str) -> AttachmentRecord | None:
+ normalized_uid = str(uid or "").strip()
+ if not normalized_uid:
+ return None
+
+ with self._global_image_cache_lock:
+ cached = self._global_image_cache.get(normalized_uid)
+ if cached is not None:
+ return cached
+
+ record = self._store.get_sync(normalized_uid)
+ if record is None or not record.enabled or record.status != "ready":
+ return None
+ # scope_key 留空：表情库 UID 全局可解析，不受会话 scope 限制
+ attachment = AttachmentRecord(
+ uid=record.uid,
+ scope_key="",
+ kind="image",
+ media_type="image",
+ display_name=Path(record.blob_path).name,
+ source_kind="meme_library",
+ source_ref=Path(record.blob_path).resolve().as_uri(),
+ local_path=record.blob_path,
+ mime_type=record.mime_type,
+ sha256=record.content_sha256,
+ created_at=record.created_at,
+ segment_data={"subType": "1"},
+ semantic_kind="meme",
+ description=record.description,
+ )
+ with self._global_image_cache_lock:
+ self._global_image_cache[normalized_uid] = attachment
+ return attachment
+
+ async def resolve_global_image(self, uid: str) -> AttachmentRecord | None:
+ return await asyncio.to_thread(self.resolve_global_image_sync, uid)
+
+ async def get_meme(self, uid: str) -> dict[str, Any] | None:
+ record = await self._store.get(uid)
+ if record is None:
+ return None
+ sources = await self._store.get_sources(uid)
+ return {
+ "record": self.serialize_record(record),
+ "sources": [source.__dict__ for source in sources],
+ }
+
+ async def get_record(self, uid: str) -> MemeRecord | None:
+ return await self._store.get(uid)
+
+ def serialize_record(self, record: MemeRecord) -> dict[str, Any]:
+ preview_path = record.preview_path or record.blob_path
+ return {
+ "uid": record.uid,
+ "description": record.description,
+ "auto_description": record.auto_description,
+ "manual_description": record.manual_description,
+ "ocr_text": record.ocr_text,
+ "tags": list(record.tags),
+ "aliases": list(record.aliases),
+ "enabled": bool(record.enabled),
+ "pinned": bool(record.pinned),
+ "is_animated": bool(record.is_animated),
+ "mime_type": record.mime_type,
+ "file_size": record.file_size,
+ "width": record.width,
+ "height": record.height,
+ "blob_url": f"/api/v1/management/memes/{record.uid}/blob",
+ "preview_url": f"/api/v1/management/memes/{record.uid}/preview",
+ "use_count": record.use_count,
+ "last_used_at": record.last_used_at,
+ "created_at": record.created_at,
+ "updated_at": record.updated_at,
+ "status": record.status,
+ "search_text": record.search_text,
+ "preview_path": preview_path,
+ }
+
+ def serialize_list_item(self, record: MemeRecord) -> dict[str, Any]:
+ return {
+ "uid": record.uid,
+ "description": record.description,
+ "enabled": bool(record.enabled),
+ "pinned": bool(record.pinned),
+ "is_animated": bool(record.is_animated),
+ "use_count": int(record.use_count),
+ "created_at": record.created_at,
+ "updated_at": record.updated_at,
+ "status": record.status,
+ }
+
+ async def list_memes(
+ self,
+ *,
+ query: str = "",
+ enabled: bool | None = None,
+ animated: bool | None = None,
+ pinned: bool | None = None,
+ sort: str = "updated_at",
+ page: int = 1,
+ page_size: int = 50,
+ summary: bool = False,
+ ) -> dict[str, Any]:
+ items, total = await self._store.list_memes(
+ query=query,
+ enabled=enabled,
+ animated=animated,
+ pinned=pinned,
+ sort=sort,
+ page=page,
+ page_size=page_size,
+ )
+ return {
+ "ok": True,
+ "total": total,
+ "window_total": total,
+ "total_exact": True,
+ "page": max(1, int(page)),
+ "page_size": max(1, min(200, int(page_size))),
+ "has_more": max(1, int(page)) * max(1, min(200, int(page_size))) < total,
+ "sort": str(sort or "updated_at"),
+ "items": [
+ self.serialize_list_item(item)
+ if summary
+ else self.serialize_record(item)
+ for item in items
+ ],
+ }
+
+ async def stats(self) -> dict[str, Any]:
+ stats = await self._store.stats()
+ cfg = self._cfg()
+ stats["max_items"] = int(cfg.max_items)
+ stats["max_total_bytes"] = int(cfg.max_total_bytes)
+ queue = self._job_queue.snapshot() if self._job_queue is not None else None
+ stats["queue"] = queue
+ return stats
+
+ async def search_memes(
+ self,
+ query: str,
+ *,
+ query_mode: str = "hybrid",
+ keyword_query: str | None = None,
+ semantic_query: str | None = None,
+ top_k: int = 8,
+ include_disabled: bool = False,
+ sort: str = "relevance",
+ ) -> dict[str, Any]:
+ raw_query = str(query or "").strip()
+ raw_keyword_query = str(keyword_query or "").strip()
+ raw_semantic_query = str(semantic_query or "").strip()
+ normalized_mode = str(query_mode or "hybrid").strip().lower()
+ if normalized_mode not in {"keyword", "semantic", "hybrid"}:
+ normalized_mode = "hybrid"
+ resolved_keyword_query = raw_keyword_query or raw_query
+ resolved_semantic_query = raw_semantic_query or raw_query
+ if normalized_mode == "keyword" and not resolved_keyword_query:
+ return {
+ "ok": True,
+ "count": 0,
+ "query_mode": normalized_mode,
+ "keyword_query": resolved_keyword_query,
+ "semantic_query": resolved_semantic_query,
+ "items": [],
+ }
+ if normalized_mode == "semantic" and not resolved_semantic_query:
+ return {
+ "ok": True,
+ "count": 0,
+ "query_mode": normalized_mode,
+ "keyword_query": resolved_keyword_query,
+ "semantic_query": resolved_semantic_query,
+ "items": [],
+ }
+ if (
+ normalized_mode == "hybrid"
+ and not resolved_keyword_query
+ and not resolved_semantic_query
+ ):
+ return {
+ "ok": True,
+ "count": 0,
+ "query_mode": normalized_mode,
+ "keyword_query": resolved_keyword_query,
+ "semantic_query": resolved_semantic_query,
+ "items": [],
+ }
+
+ cfg = self._cfg()
+ keyword_hits: list[dict[str, Any]] = []
+ if normalized_mode in {"keyword", "hybrid"} and resolved_keyword_query:
+ keyword_hits = await self._store.search_keyword(
+ resolved_keyword_query,
+ limit=max(int(cfg.keyword_top_k), int(top_k)),
+ include_disabled=include_disabled,
+ )
+ semantic_hits: list[dict[str, Any]] = []
+ if normalized_mode in {"semantic", "hybrid"} and resolved_semantic_query:
+ semantic_hits = await self._vector_store.query(
+ resolved_semantic_query,
+ top_k=max(int(cfg.semantic_top_k), int(top_k)),
+ include_disabled=include_disabled,
+ )
+ merged: dict[str, dict[str, Any]] = {}
+
+ for item in keyword_hits:
+ record: MemeRecord = item["record"]
+ merged[record.uid] = {
+ "record": record,
+ "keyword_score": float(item.get("keyword_score", 0.0)),
+ "semantic_score": 0.0,
+ "rerank_score": None,
+ }
+
+ missing_semantic_uids = [
+ str(item.get("uid") or "").strip()
+ for item in semantic_hits
+ if str(item.get("uid") or "").strip()
+ and str(item.get("uid") or "").strip() not in merged
+ ]
+ missing_records = (
+ await self._store.get_many(missing_semantic_uids)
+ if missing_semantic_uids
+ else {}
+ )
+
+ for item in semantic_hits:
+ uid = str(item.get("uid") or "").strip()
+ if not uid:
+ continue
+ existing = merged.get(uid)
+ if existing is None:
+ stored_record = missing_records.get(uid)
+ if stored_record is None:
+ continue
+ existing = {
+ "record": stored_record,
+ "keyword_score": 0.0,
+ "semantic_score": 0.0,
+ "rerank_score": None,
+ }
+ merged[uid] = existing
+ existing["semantic_score"] = max(
+ float(existing.get("semantic_score", 0.0)),
+ float(item.get("semantic_score", 0.0)),
+ )
+
+ reranker = (
+ self._retrieval_runtime.ensure_reranker()
+ if self._retrieval_runtime is not None
+ else None
+ )
+ ranked_candidates = list(merged.values())
+ rerank_query = resolved_semantic_query or resolved_keyword_query
+ if (
+ reranker is not None
+ and normalized_mode in {"semantic", "hybrid"}
+ and rerank_query
+ and ranked_candidates
+ ):
+ documents = [
+ candidate["record"].search_text for candidate in ranked_candidates
+ ]
+ reranked = await reranker.rerank(
+ rerank_query,
+ documents,
+ top_n=min(len(documents), int(cfg.rerank_top_k)),
+ )
+ for item in reranked:
+ try:
+ index = int(item.get("index"))
+ except (TypeError, ValueError):
+ continue
+ if index < 0 or index >= len(ranked_candidates):
+ continue
+ ranked_candidates[index]["rerank_score"] = float(
+ item.get("relevance_score", 0.0) or 0.0
+ )
+
+ def _final_score(item: dict[str, Any]) -> float:
+ rerank_score = item.get("rerank_score")
+ if rerank_score is not None:
+ return float(rerank_score)
+ # hybrid 模式：keyword 与 semantic 取较高分
+ return max(
+ float(item.get("keyword_score", 0.0)),
+ float(item.get("semantic_score", 0.0)),
+ )
+
+ normalized_sort = str(sort or "relevance").strip().lower()
+ if normalized_sort == "use_count":
+ ranked_candidates.sort(
+ key=lambda item: (
+ item["record"].pinned,
+ item["record"].use_count,
+ item["record"].updated_at,
+ _final_score(item),
+ ),
+ reverse=True,
+ )
+ elif normalized_sort == "created_at":
+ ranked_candidates.sort(
+ key=lambda item: (
+ item["record"].pinned,
+ item["record"].created_at,
+ item["record"].updated_at,
+ _final_score(item),
+ ),
+ reverse=True,
+ )
+ elif normalized_sort == "updated_at":
+ ranked_candidates.sort(
+ key=lambda item: (
+ item["record"].pinned,
+ item["record"].updated_at,
+ item["record"].use_count,
+ _final_score(item),
+ ),
+ reverse=True,
+ )
+ else:
+ ranked_candidates.sort(
+ key=lambda item: (
+ _final_score(item),
+ item["record"].pinned,
+ item["record"].use_count,
+ item["record"].updated_at,
+ ),
+ reverse=True,
+ )
+
+ items: list[dict[str, Any]] = []
+ for candidate in ranked_candidates[: max(1, int(top_k))]:
+ record = candidate["record"]
+ search_item = MemeSearchItem(
+ uid=record.uid,
+ description=record.description,
+ tags=list(record.tags),
+ aliases=list(record.aliases),
+ enabled=bool(record.enabled),
+ pinned=bool(record.pinned),
+ is_animated=record.is_animated,
+ created_at=record.created_at,
+ updated_at=record.updated_at,
+ score=round(_final_score(candidate), 6),
+ keyword_score=round(float(candidate.get("keyword_score", 0.0)), 6),
+ semantic_score=round(float(candidate.get("semantic_score", 0.0)), 6),
+ rerank_score=(
+ round(float(candidate["rerank_score"]), 6)
+ if candidate.get("rerank_score") is not None
+ else None
+ ),
+ use_count=record.use_count,
+ )
+ items.append(search_item.__dict__)
+ return {
+ "ok": True,
+ "count": len(items),
+ "query_mode": normalized_mode,
+ "keyword_query": resolved_keyword_query,
+ "semantic_query": resolved_semantic_query,
+ "sort": normalized_sort,
+ "items": items,
+ }
+
+ async def send_meme_by_uid(self, uid: str, context: dict[str, Any]) -> str:
+ record = await self._store.get(uid)
+ if record is None or not record.enabled or record.status != "ready":
+ return f"发送失败：未找到可用表情包 UID：{uid}"
+
+ sender = context.get("sender")
+ if sender is None:
+ return "发送失败：当前上下文缺少 sender"
+
+ tool_args = {
+ "target_type": context.get("target_type"),
+ "target_id": context.get("target_id"),
+ }
+ target, target_error = resolve_message_target(tool_args, context)
+ if target_error or target is None:
+ return f"发送失败：{target_error or '无法确定目标会话'}"
+ target_type, target_id = target
+
+ local_path = Path(record.blob_path)
+ if not local_path.is_file():
+ return f"发送失败：表情包文件不存在：{uid}"
+ file_uri = local_path.resolve().as_uri()
+ cq_message = f"[CQ:image,file={file_uri},subType=1]"
+ history_message = f"[图片 uid={record.uid} name={local_path.name}]"
+ history_attachment = await self.resolve_global_image(uid)
+ history_attachments = (
+ [history_attachment.prompt_ref()]
+ if history_attachment is not None
+ else None
+ )
+
+ if target_type == "group":
+ sent_message_id = await sender.send_group_message(
+ int(target_id),
+ cq_message,
+ history_message=history_message,
+ attachments=history_attachments,
+ )
+ else:
+ preferred_temp_group_id = safe_int(context.get("group_id")) or None
+ sent_message_id = await sender.send_private_message(
+ int(target_id),
+ cq_message,
+ preferred_temp_group_id=preferred_temp_group_id,
+ history_message=history_message,
+ attachments=history_attachments,
+ )
+
+ now = _now_iso()
+ updated_record = await self._store.increment_use(uid, now)
+ if updated_record is not None:
+ await self._vector_store.upsert(updated_record)
+ if sent_message_id is not None:
+ return f"表情包已发送（message_id={sent_message_id}）"
+ return "表情包已发送"
+
+ async def blob_path_for_uid(
+ self, uid: str, *, preview: bool = False
+ ) -> Path | None:
+ record = await self._store.get(uid)
+ if record is None:
+ return None
+ path_text = (
+ record.preview_path if preview and record.preview_path else record.blob_path
+ )
+ path = Path(path_text)
+ return path if path.is_file() else None
diff --git a/src/Undefined/memes/service.py b/src/Undefined/memes/service.py
index 96f80abe..1f7ba719 100644
--- a/src/Undefined/memes/service.py
+++ b/src/Undefined/memes/service.py
@@ -2,29 +2,34 @@
import asyncio
from collections.abc import Mapping
-from dataclasses import dataclass, replace
-from datetime import datetime
+from dataclasses import replace
import hashlib
import logging
-import math
import mimetypes
from pathlib import Path
-import re
import shutil
import threading
from typing import Any
from uuid import uuid4
-from openai import APIConnectionError, APIStatusError, APITimeoutError
from PIL import Image
from Undefined.attachments import AttachmentRecord
+from Undefined.memes._image_utils import (
+ _compose_grid,
+ _extract_gif_frames,
+ _guess_suffix,
+ _is_retryable_llm_error,
+ _normalize_tags,
+ _now_iso,
+)
+from Undefined.memes._image_utils import _sample_frame_indices # noqa: F401
from Undefined.memes.models import (
+ IngestDigestLockEntry,
MemeRecord,
MemeSearchItem,
MemeSourceRecord,
build_search_text,
- normalize_string_list,
)
from Undefined.memes.store import MemeStore
from Undefined.memes.vector_store import MemeVectorStore
@@ -34,118 +39,13 @@
logger = logging.getLogger(__name__)
-_IMAGE_EXTENSIONS_BY_MIME = {
- "image/png": ".png",
- "image/jpeg": ".jpg",
- "image/gif": ".gif",
- "image/webp": ".webp",
- "image/bmp": ".bmp",
- "image/svg+xml": ".svg",
-}
-_TAG_SPLIT_RE = re.compile(r"[,，\n]+")
-
-
-def _now_iso() -> str:
- return datetime.now().isoformat(timespec="seconds")
-
-
-def _guess_suffix(path: Path, mime_type: str) -> str:
- suffix = path.suffix.lower()
- if suffix:
- return suffix
- guessed = _IMAGE_EXTENSIONS_BY_MIME.get(mime_type)
- if guessed:
- return guessed
- mime_guess = mimetypes.guess_extension(mime_type or "")
- if mime_guess:
- return mime_guess.lower()
- return ".bin"
-
-
-def _normalize_tags(raw_tags: list[str] | str | None) -> list[str]:
- if raw_tags is None:
- return []
- if isinstance(raw_tags, str):
- parts = [part.strip() for part in _TAG_SPLIT_RE.split(raw_tags)]
- return normalize_string_list(parts)
- return normalize_string_list(raw_tags)
-
-
-def _is_retryable_llm_error(exc: Exception) -> bool:
- """判断 LLM 调用异常是否应触发 worker 级重试。"""
- if isinstance(exc, (APIConnectionError, APITimeoutError)):
- return True
- if isinstance(exc, APIStatusError):
- return exc.status_code == 429 or exc.status_code >= 500
- return False
-
-
-def _extract_gif_frames(source_path: Path, n_frames: int) -> list[Image.Image]:
- """从 GIF 中均匀采样 *n_frames* 帧（含首末帧），返回 RGBA Image 列表。"""
- with Image.open(source_path) as image:
- total = getattr(image, "n_frames", 1)
- if total <= 1:
- image.seek(0)
- return [image.convert("RGBA").copy()]
- n = min(n_frames, total)
- if n <= 1:
- image.seek(0)
- return [image.convert("RGBA").copy()]
- indices = _sample_frame_indices(total, n)
- frames: list[Image.Image] = []
- for idx in indices:
- image.seek(idx)
- frames.append(image.convert("RGBA").copy())
- return frames
-
-
-def _sample_frame_indices(total: int, n: int) -> list[int]:
- """生成均匀采样的帧索引列表（始终包含首帧和末帧）。"""
- if n >= total:
- return list(range(total))
- if n == 1:
- return [0]
- if n == 2:
- return [0, total - 1]
- indices = [round(i * (total - 1) / (n - 1)) for i in range(n)]
- # 去重并保持顺序
- seen: set[int] = set()
- result: list[int] = []
- for idx in indices:
- if idx not in seen:
- seen.add(idx)
- result.append(idx)
- return result
-
-
-def _compose_grid(frames: list[Image.Image], output_path: Path) -> None:
- """将多帧拼接为网格图并保存为 PNG。"""
- n = len(frames)
- if n == 0:
- return
- if n == 1:
- frames[0].save(output_path, format="PNG")
- return
- cols = math.ceil(math.sqrt(n))
- rows = math.ceil(n / cols)
- fw, fh = frames[0].size
- grid = Image.new("RGBA", (cols * fw, rows * fh), (0, 0, 0, 0))
- for i, frame in enumerate(frames):
- resized = (
- frame.resize((fw, fh), Image.Resampling.LANCZOS)
- if frame.size != (fw, fh)
- else frame
- )
- x = (i % cols) * fw
- y = (i // cols) * fh
- grid.paste(resized, (x, y))
- grid.save(output_path, format="PNG")
-
-
-@dataclass
-class _IngestDigestLockEntry:
- lock: asyncio.Lock
- users: int = 0
+__all__ = [
+ "MemeService",
+ "_compose_grid",
+ "_extract_gif_frames",
+ "_is_retryable_llm_error",
+ "_sample_frame_indices",
+]
class MemeService:
@@ -168,7 +68,7 @@ def __init__(
self._attachment_registry = attachment_registry
self._retrieval_runtime = retrieval_runtime
# Serialize same-content ingest jobs within the process to avoid duplicates.
- self._ingest_digest_locks: dict[str, _IngestDigestLockEntry] = {}
+ self._ingest_digest_locks: dict[str, IngestDigestLockEntry] = {}
self._ingest_digest_locks_guard = asyncio.Lock()
self._global_image_cache: dict[str, AttachmentRecord] = {}
self._global_image_cache_lock = threading.Lock()
@@ -196,11 +96,11 @@ def _cfg(self) -> Any:
def _blob_dir(self) -> Path:
return ensure_dir(Path(self._cfg().blob_dir))
- async def _acquire_ingest_digest_lock(self, digest: str) -> _IngestDigestLockEntry:
+ async def _acquire_ingest_digest_lock(self, digest: str) -> IngestDigestLockEntry:
async with self._ingest_digest_locks_guard:
entry = self._ingest_digest_locks.get(digest)
if entry is None:
- entry = _IngestDigestLockEntry(lock=asyncio.Lock())
+ entry = IngestDigestLockEntry(lock=asyncio.Lock())
self._ingest_digest_locks[digest] = entry
entry.users += 1
try:
@@ -213,7 +113,7 @@ async def _acquire_ingest_digest_lock(self, digest: str) -> _IngestDigestLockEnt
async def _release_ingest_digest_lock_reference(
self,
digest: str,
- entry: _IngestDigestLockEntry,
+ entry: IngestDigestLockEntry,
*,
release_lock: bool = False,
) -> None:
@@ -1177,7 +1077,6 @@ def _copy() -> None:
def _render_preview() -> None:
frames = _extract_gif_frames(source_path, n_frames)
if mode == "multi":
- # multi 模式也需要生成一张预览用于存储/展示，取首帧
frames[0].save(preview_path, format="PNG")
else:
_compose_grid(frames, preview_path)
diff --git a/src/Undefined/onebot/__init__.py b/src/Undefined/onebot/__init__.py
new file mode 100644
index 00000000..14c5690a
--- /dev/null
+++ b/src/Undefined/onebot/__init__.py
@@ -0,0 +1,16 @@
+"""OneBot WebSocket 客户端包。"""
+
+# 统一 re-export，供 handlers 与 sender 直接 from Undefined.onebot import ...
+from Undefined.onebot.client import OneBotClient
+from Undefined.onebot.message import (
+ get_message_content,
+ get_message_sender_id,
+ parse_message_time,
+)
+
+__all__ = [
+ "OneBotClient",
+ "parse_message_time",
+ "get_message_sender_id",
+ "get_message_content",
+]
diff --git a/src/Undefined/onebot.py b/src/Undefined/onebot/client.py
similarity index 94%
rename from src/Undefined/onebot.py
rename to src/Undefined/onebot/client.py
index 9a53d885..ce293428 100644
--- a/src/Undefined/onebot.py
+++ b/src/Undefined/onebot/client.py
@@ -1,11 +1,10 @@
-"""OneBot WebSocket 客户端"""
+"""OneBot v11 WebSocket 客户端实现。"""
import asyncio
import json
import logging
import time
from typing import Any, Callable, Coroutine
-from datetime import datetime
import websockets
from websockets.asyncio.client import ClientConnection
@@ -20,9 +19,11 @@ def _mark_message_sent_this_turn() -> None:
ctx = RequestContext.current()
if ctx is None:
return
+ # 标记本轮已向用户发出消息，供 end 工具判断是否可静默结束。
ctx.set_resource("message_sent_this_turn", True)
+# OneBot v11 WebSocket 客户端
class OneBotClient:
"""OneBot v11 WebSocket 客户端"""
@@ -61,7 +62,6 @@ def connection_status(self) -> dict[str, Any]:
async def connect(self) -> None:
"""连接到 OneBot WebSocket"""
- # 构建带 token 的 URL
url = self.ws_url
if self.token:
separator = "&" if "?" in url else "?"
@@ -126,7 +126,6 @@ async def _call_api(
if logger.isEnabledFor(logging.DEBUG):
log_debug_json(logger, "[OneBot请求体]", request)
- # 创建 Future 等待响应
future: asyncio.Future[dict[str, Any]] = asyncio.Future()
self._pending_responses[echo] = future
@@ -138,7 +137,6 @@ async def _call_api(
response = await asyncio.wait_for(future, timeout=480.0)
duration = time.perf_counter() - start_time
- # 检查响应状态
status = response.get("status")
if status == "failed":
retcode = response.get("retcode", -1)
@@ -241,7 +239,6 @@ async def get_group_msg_history(
result = await self._call_api("get_group_msg_history", params)
- # 安全获取消息列表
if result is None:
logger.warning("get_group_msg_history 返回 None")
return []
@@ -874,51 +871,3 @@ def stop(self) -> None:
"""停止运行"""
self._should_stop = True
self._running = False
-
-
-def parse_message_time(message: dict[str, Any]) -> datetime:
- """解析消息时间。
-
- 兼容秒级/毫秒级时间戳与字符串输入，异常时回退到当前时间。
- """
-
- raw_timestamp = message.get("time")
-
- if raw_timestamp is None:
- return datetime.now()
-
- try:
- timestamp = float(raw_timestamp)
- except (TypeError, ValueError):
- logger.debug("[OneBot] 无法解析消息时间戳，使用当前时间: %s", raw_timestamp)
- return datetime.now()
-
- # 13 位毫秒时间戳自动降为秒。
- if timestamp > 1_000_000_000_000:
- timestamp /= 1000.0
-
- if timestamp <= 0:
- return datetime.now()
-
- try:
- return datetime.fromtimestamp(timestamp)
- except (OSError, OverflowError, ValueError):
- logger.debug("[OneBot] 时间戳越界，使用当前时间: %s", raw_timestamp)
- return datetime.now()
-
-
-def get_message_sender_id(message: dict[str, Any]) -> int:
- """获取消息发送者 QQ 号"""
- sender: dict[str, Any] = message.get("sender", {})
- user_id: int = sender.get("user_id", 0)
- return user_id
-
-
-def get_message_content(message: dict[str, Any]) -> list[dict[str, Any]]:
- """获取消息内容（CQ 码数组格式）"""
- msg = message.get("message", [])
- if isinstance(msg, str):
- # 如果是字符串格式，转换为数组格式
- return [{"type": "text", "data": {"text": msg}}]
- content: list[dict[str, Any]] = msg
- return content
diff --git a/src/Undefined/onebot/message.py b/src/Undefined/onebot/message.py
new file mode 100644
index 00000000..7fe13dc5
--- /dev/null
+++ b/src/Undefined/onebot/message.py
@@ -0,0 +1,58 @@
+"""OneBot 消息解析辅助函数。"""
+
+from __future__ import annotations
+
+import logging
+from datetime import datetime
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+
+def parse_message_time(message: dict[str, Any]) -> datetime:
+ """解析消息时间。
+
+ 兼容秒级/毫秒级时间戳与字符串输入，异常时回退到当前时间。
+ """
+
+ raw_timestamp = message.get("time")
+
+ if raw_timestamp is None:
+ return datetime.now()
+
+ try:
+ timestamp = float(raw_timestamp)
+ except (TypeError, ValueError):
+ logger.debug("[OneBot] 无法解析消息时间戳，使用当前时间: %s", raw_timestamp)
+ return datetime.now()
+
+ # 13 位毫秒时间戳自动降为秒。
+ if timestamp > 1_000_000_000_000:
+ timestamp /= 1000.0
+
+ if timestamp <= 0:
+ return datetime.now()
+
+ try:
+ return datetime.fromtimestamp(timestamp)
+ except (OSError, OverflowError, ValueError):
+ # 越界或非法 epoch 回退当前时间，避免整条消息解析失败。
+ logger.debug("[OneBot] 时间戳越界，使用当前时间: %s", raw_timestamp)
+ return datetime.now()
+
+
+def get_message_sender_id(message: dict[str, Any]) -> int:
+ """获取消息发送者 QQ 号"""
+ sender: dict[str, Any] = message.get("sender", {})
+ user_id: int = sender.get("user_id", 0)
+ return user_id
+
+
+def get_message_content(message: dict[str, Any]) -> list[dict[str, Any]]:
+ """获取消息内容（CQ 码数组格式）"""
+ msg = message.get("message", [])
+ if isinstance(msg, str):
+ # 如果是字符串格式，转换为数组格式
+ return [{"type": "text", "data": {"text": msg}}]
+ content: list[dict[str, Any]] = msg
+ return content
diff --git a/src/Undefined/py.typed b/src/Undefined/py.typed
new file mode 100644
index 00000000..e69de29b
diff --git a/src/Undefined/services/ai_coordinator.py b/src/Undefined/services/ai_coordinator.py
index f4efaf2f..7bc32036 100644
--- a/src/Undefined/services/ai_coordinator.py
+++ b/src/Undefined/services/ai_coordinator.py
@@ -105,7 +105,7 @@ def __init__(
self.security = security
self.command_dispatcher = command_dispatcher
self.model_pool = ModelPoolService(ai, config, sender)
- # batcher 由外部（handlers.py）创建并通过 set_batcher 注入；未注入时所有消息按单条流程直送。
+ # batcher 由外部（handlers/message_flow）创建并通过 set_batcher 注入；未注入时所有消息按单条流程直送。
self._batcher: MessageBatcher | None = None
def set_batcher(self, batcher: MessageBatcher | None) -> None:
@@ -305,7 +305,6 @@ async def _execute_auto_reply(self, request: dict[str, Any]) -> None:
# 用于向 batcher 注册 inflight 任务（仅当本请求源自合并桶时生效）
batcher_scope: str | None = make_scope(group_id=group_id) if group_id else None
- # 创建请求上下文
async with RequestContext(
request_type="group",
group_id=group_id,
@@ -347,7 +346,6 @@ async def send_img_cb(tid: int, mtype: str, path: str) -> None:
async def send_like_cb(uid: int, times: int = 1) -> None:
await self.onebot.send_like(uid, times)
- # 存储资源到上下文
ai_client = self.ai
memory_storage = self.ai.memory_storage
runtime_config = self.ai.runtime_config
@@ -442,7 +440,6 @@ async def _execute_private_reply(self, request: dict[str, Any]) -> None:
trigger_message_id = request.get("trigger_message_id")
batcher_scope: str | None = make_scope(user_id=user_id)
- # 创建请求上下文
async with RequestContext(
request_type="private",
user_id=user_id,
@@ -479,7 +476,6 @@ async def send_private_cb(
) -> None:
await self.sender.send_private_message(uid, msg, reply_to=reply_to)
- # 存储资源到上下文
ai_client = self.ai
memory_storage = self.ai.memory_storage
runtime_config = self.ai.runtime_config
@@ -591,7 +587,6 @@ async def _execute_stats_analysis(self, request: dict[str, Any]) -> None:
logger.warning("[统计分析] 缺少 request_id，群=%s", group_id)
return
try:
- # 加载提示词模板
prompt_template = _STATS_ANALYSIS_FALLBACK_PROMPT
try:
loaded_prompt = read_text_resource(_STATS_ANALYSIS_PROMPT_PATH).strip()
@@ -615,7 +610,6 @@ async def _execute_stats_analysis(self, request: dict[str, Any]) -> None:
data_summary=safe_data_summary
)
- # 调用 AI 进行分析
messages = [
{"role": "system", "content": "你是一位专业的数据分析师。"},
{"role": "user", "content": full_prompt},
@@ -629,7 +623,6 @@ async def _execute_stats_analysis(self, request: dict[str, Any]) -> None:
queue_lane=request.get("_queue_lane"),
)
- # 提取分析结果
choices = result.get("choices", [{}])
if choices:
content = choices[0].get("message", {}).get("content", "")
@@ -647,7 +640,6 @@ async def _execute_stats_analysis(self, request: dict[str, Any]) -> None:
request_id,
)
- # 设置分析结果（通知等待的 _handle_stats 方法）
if self.command_dispatcher:
self.command_dispatcher.set_stats_analysis_result(
group_id, request_id, analysis
@@ -655,7 +647,6 @@ async def _execute_stats_analysis(self, request: dict[str, Any]) -> None:
except Exception as exc:
logger.exception("[统计分析] AI 分析失败: %s", exc)
- # 出错时也通知等待，但返回空字符串
if self.command_dispatcher:
self.command_dispatcher.set_stats_analysis_result(
group_id, request_id, ""
@@ -708,7 +699,6 @@ async def _execute_agent_intro_generation(self, request: dict[str, Any]) -> None
return
try:
- # 获取提示词
from Undefined.skills.agents.intro_generator import AgentIntroGenerator
agent_intro_generator = self.ai._agent_intro_generator
@@ -721,7 +711,6 @@ async def _execute_agent_intro_generation(self, request: dict[str, Any]) -> None
user_prompt,
) = await agent_intro_generator.get_intro_prompt_and_context(agent_name)
- # 调用 AI 生成
messages = [
{"role": "system", "content": system_prompt or "你是一位智能助手。"},
{"role": "user", "content": user_prompt},
@@ -735,7 +724,6 @@ async def _execute_agent_intro_generation(self, request: dict[str, Any]) -> None
queue_lane=request.get("_queue_lane"),
)
- # 提取结果
choices = result.get("choices", [{}])
if choices:
content = choices[0].get("message", {}).get("content", "")
@@ -750,7 +738,6 @@ async def _execute_agent_intro_generation(self, request: dict[str, Any]) -> None
request_id,
)
- # 通知结果
agent_intro_generator.set_intro_generation_result(
request_id, generated_content if generated_content else None
)
@@ -761,7 +748,6 @@ async def _execute_agent_intro_generation(self, request: dict[str, Any]) -> None
agent_name,
exc,
)
- # 出错时也通知，返回 None
try:
agent_intro_generator = self.ai._agent_intro_generator
agent_intro_generator.set_intro_generation_result(request_id, None)
diff --git a/src/Undefined/services/commands/bugfix.py b/src/Undefined/services/commands/bugfix.py
new file mode 100644
index 00000000..f4689a3e
--- /dev/null
+++ b/src/Undefined/services/commands/bugfix.py
@@ -0,0 +1,189 @@
+"""Bug 修复归档命令（/bugfix）的实现逻辑。
+
+本模块提供 ``BugfixCommandMixin``，供 ``CommandDispatcher`` 通过多重继承组合。
+通过回溯群聊记录并调用 AI 摘要，自动生成 FAQ 归档条目。
+"""
+
+from __future__ import annotations
+
+# 斜杠命令：目录扫描注册、权限/限流/子命令路由
+
+import logging
+from datetime import datetime
+from typing import TYPE_CHECKING, Any
+from uuid import uuid4
+
+from Undefined.faq import extract_faq_title
+from Undefined.onebot import (
+ get_message_content,
+ get_message_sender_id,
+ parse_message_time,
+)
+
+if TYPE_CHECKING:
+ from Undefined.config import Config
+ from Undefined.faq import FAQStorage
+ from Undefined.onebot import OneBotClient
+ from Undefined.utils.sender import MessageSender
+
+logger = logging.getLogger(__name__)
+
+
+class BugfixCommandMixin:
+ """``/bugfix`` 命令相关方法集合，作为 ``CommandDispatcher`` 的 mixin 使用。"""
+
+ if TYPE_CHECKING:
+ ai: Any
+ config: Config
+ faq_storage: FAQStorage
+ onebot: OneBotClient
+ sender: MessageSender
+
+ async def _handle_bugfix(
+ self, group_id: int, admin_id: int, args: list[str]
+ ) -> None:
+ """处理 ``/bugfix`` 命令，通过分析聊天记录自动生成 FAQ 归档。"""
+ parsed = self._parse_bugfix_args(args)
+ if isinstance(parsed, str):
+ await self.sender.send_group_message(group_id, parsed)
+ return
+
+ target_qqs, start_date, end_date, start_str, end_str = parsed
+
+ await self.sender.send_group_message(
+ group_id, "🔍 正在获取对话记录进行回溯分析..."
+ )
+
+ try:
+ messages = await self._fetch_messages(
+ group_id, target_qqs, start_date, end_date
+ )
+ if not messages:
+ await self.sender.send_group_message(
+ group_id, "❌ 未找到符合条件的对话记录。"
+ )
+ return
+
+ processed_text = await self._process_messages(messages)
+ summary = await self._obtain_bugfix_summary(group_id, processed_text)
+
+ title = extract_faq_title(summary)
+ if not title or title == "未命名问题":
+ title = await self.ai.generate_title(summary)
+
+ faq = await self.faq_storage.create(
+ group_id=group_id,
+ target_qq=target_qqs[0],
+ start_time=start_str,
+ end_time=end_str,
+ title=title,
+ content=summary,
+ )
+
+ result_msg = f"✅ Bug 修复分析完成！\n\n📌 FAQ ID: {faq.id}\n📋 标题: {title}\n\n{summary}"
+ await self.sender.send_group_message(group_id, result_msg)
+
+ except Exception as e:
+ error_id = uuid4().hex[:8]
+ logger.exception("Bugfix 失败: error_id=%s err=%s", error_id, e)
+ await self.sender.send_group_message(
+ group_id,
+ f"❌ Bug 修复分析失败，请稍后重试（错误码: {error_id}）",
+ )
+
+ def _parse_bugfix_args(
+ self, args: list[str]
+ ) -> tuple[list[int], datetime, datetime, str, str] | str:
+ """解析 ``/bugfix`` 命令的参数。"""
+ if len(args) < 3:
+ return (
+ "❌ 用法: /bugfix [QQ号|@用户2] ... <开始时间> <结束时间>\n"
+ "时间格式: YYYY/MM/DD/HH:MM，结束时间可用 now\n"
+ "示例: /bugfix 123456 2024/12/01/09:00 now"
+ )
+
+ try:
+ target_qqs = [int(arg) for arg in args[:-2]]
+ start_str, end_str_raw = args[-2], args[-1]
+ start_date = datetime.strptime(start_str, "%Y/%m/%d/%H:%M")
+
+ if end_str_raw.lower() == "now":
+ end_date, end_str = datetime.now(), "now"
+ else:
+ end_date, end_str = (
+ datetime.strptime(end_str_raw, "%Y/%m/%d/%H:%M"),
+ end_str_raw,
+ )
+
+ return target_qqs, start_date, end_date, start_str, end_str
+ except ValueError:
+ return "❌ 参数格式错误：QQ号应为数字或 @ 提及，时间格式应为 YYYY/MM/DD/HH:MM。"
+
+ async def _obtain_bugfix_summary(self, group_id: int, processed_text: str) -> str:
+ """利用 AI 生成聊天记录的 Bug 分析摘要。"""
+ total_tokens = self.ai.count_tokens(processed_text)
+ max_tokens = self.config.chat_model.max_tokens
+
+ if total_tokens <= max_tokens:
+ return str(await self.ai.summarize_chat(processed_text))
+
+ await self.sender.send_group_message(
+ group_id, f"📊 消息较长（{total_tokens} tokens），正在分段处理..."
+ )
+ chunks = self.ai.split_messages_by_tokens(processed_text, max_tokens)
+ summaries = [await self.ai.summarize_chat(chunk) for chunk in chunks]
+ return str(await self.ai.merge_summaries(summaries))
+
+ async def _fetch_messages(
+ self,
+ group_id: int,
+ target_qqs: list[int],
+ start_date: datetime,
+ end_date: datetime,
+ ) -> list[dict[str, Any]]:
+ """从 OneBot 拉取指定时间范围内目标用户的消息。"""
+ batch = await self.onebot.get_group_msg_history(group_id, count=2500)
+ if not batch:
+ return []
+ target_qqs_set = set(target_qqs)
+ results = []
+ for msg in batch:
+ msg_time = parse_message_time(msg)
+ if (
+ start_date <= msg_time <= end_date
+ and get_message_sender_id(msg) in target_qqs_set
+ ):
+ # 后台循环处理队列
+ results.append(msg)
+ return sorted(results, key=lambda m: m.get("time", 0))
+
+ # 后台循环处理队列
+ async def _process_messages(self, messages: list[dict[str, Any]]) -> str:
+ """将原始 OneBot 消息序列化为 AI 可读的纯文本。"""
+ lines = []
+ for msg in messages:
+ sender_id = get_message_sender_id(msg)
+ msg_time = parse_message_time(msg).strftime("%Y-%m-%d %H:%M:%S")
+ content = get_message_content(msg)
+ text_parts = []
+ for segment in content:
+ seg_type, seg_data = segment.get("type", ""), segment.get("data", {})
+ if seg_type == "text":
+ text_parts.append(seg_data.get("text", ""))
+ elif seg_type == "image":
+ file = seg_data.get("file", "") or seg_data.get("url", "")
+ if file:
+ try:
+ url = await self.onebot.get_image(file)
+ if url:
+ res = await self.ai.analyze_multimodal(url, "image")
+ text_parts.append(
+ f"[pic]{res.get('description', '')}{res.get('ocr_text', '')}[/pic]"
+ )
+ except Exception:
+ text_parts.append("[pic]图片处理失败[/pic]")
+ elif seg_type == "at":
+ text_parts.append(f"@{seg_data.get('qq', '')}")
+ if text_parts:
+ lines.append(f"[{msg_time}] {sender_id}: {''.join(text_parts)}")
+ return "\n".join(lines)
diff --git a/src/Undefined/services/commands/stats.py b/src/Undefined/services/commands/stats.py
new file mode 100644
index 00000000..a992f760
--- /dev/null
+++ b/src/Undefined/services/commands/stats.py
@@ -0,0 +1,822 @@
+"""Token 使用统计命令（/stats）的实现逻辑。
+
+本模块提供 ``StatsCommandMixin``，供 ``CommandDispatcher`` 通过多重继承组合。
+群聊与私聊统计、图表生成、AI 分析队列交互均在此实现。
+"""
+
+from __future__ import annotations
+
+# 斜杠命令：目录扫描注册、权限/限流/子命令路由
+
+import asyncio
+import base64
+import logging
+import re
+from pathlib import Path
+from typing import TYPE_CHECKING, Any
+from uuid import uuid4
+
+from Undefined.ai.queue_budget import (
+ compute_queued_llm_timeout_seconds,
+ resolve_effective_retry_count,
+)
+from Undefined.token_usage_storage import TokenUsageStorage
+
+if TYPE_CHECKING:
+ from Undefined.config import Config
+ from Undefined.onebot import OneBotClient
+ from Undefined.utils.history import MessageHistoryManager
+ from Undefined.utils.sender import MessageSender
+
+# 尝试导入 matplotlib（可选依赖）
+plt: Any
+try:
+ import matplotlib.pyplot as plt
+
+ _MATPLOTLIB_AVAILABLE = True
+except ImportError:
+ plt = None
+ _MATPLOTLIB_AVAILABLE = False
+
+logger = logging.getLogger(__name__)
+
+_STATS_DEFAULT_DAYS = 7
+_STATS_MIN_DAYS = 1
+_STATS_MAX_DAYS = 365
+_STATS_MODEL_TOP_N = 8
+_STATS_CALL_TYPE_TOP_N = 12
+_STATS_DATA_SUMMARY_MAX_CHARS = 12000
+_STATS_AI_FLAGS = {"--ai", "-a"}
+_STATS_TIME_RANGE_RE = re.compile(r"^\d+[dwm]?$", re.IGNORECASE)
+
+
+class StatsCommandMixin:
+ """``/stats`` 命令相关方法集合，作为 ``CommandDispatcher`` 的 mixin 使用。"""
+
+ if TYPE_CHECKING:
+ ai: Any
+ config: Config
+ history_manager: MessageHistoryManager
+ onebot: OneBotClient
+ queue_manager: Any
+ sender: MessageSender
+
+ _token_usage_storage: TokenUsageStorage
+ _stats_analysis_results: dict[str, str]
+ _stats_analysis_events: dict[str, asyncio.Event]
+
+ def _parse_time_range(self, time_str: str) -> int:
+ """解析时间范围字符串，返回天数。
+
+ 参数:
+ time_str: 时间范围字符串（如 ``7d``、``1w``、``30d``）。
+
+ 返回:
+ clamp 在 ``[_STATS_MIN_DAYS, _STATS_MAX_DAYS]`` 内的天数。
+ """
+ if not time_str:
+ return _STATS_DEFAULT_DAYS
+
+ def _clamp_days(value: int) -> int:
+ if value < _STATS_MIN_DAYS:
+ return _STATS_DEFAULT_DAYS
+ if value > _STATS_MAX_DAYS:
+ return _STATS_MAX_DAYS
+ return value
+
+ time_str = time_str.lower().strip()
+
+ if time_str.endswith("d"):
+ try:
+ return _clamp_days(int(time_str[:-1]))
+ except ValueError:
+ return _STATS_DEFAULT_DAYS
+ if time_str.endswith("w"):
+ try:
+ return _clamp_days(int(time_str[:-1]) * 7)
+ except ValueError:
+ return _STATS_DEFAULT_DAYS
+ if time_str.endswith("m"):
+ try:
+ return _clamp_days(int(time_str[:-1]) * 30)
+ except ValueError:
+ return _STATS_DEFAULT_DAYS
+
+ try:
+ return _clamp_days(int(time_str))
+ except ValueError:
+ return _STATS_DEFAULT_DAYS
+
+ def _parse_stats_options(self, args: list[str]) -> tuple[int, bool]:
+ """解析 ``/stats`` 参数：时间范围 + AI 分析开关。"""
+ days = _STATS_DEFAULT_DAYS
+ enable_ai_analysis = False
+ picked_days = False
+
+ for raw in args:
+ token = str(raw or "").strip()
+ if not token:
+ continue
+ lower = token.lower()
+ if lower in _STATS_AI_FLAGS:
+ enable_ai_analysis = True
+ continue
+ if not picked_days and _STATS_TIME_RANGE_RE.match(lower):
+ days = self._parse_time_range(lower)
+ picked_days = True
+
+ return days, enable_ai_analysis
+
+ async def _handle_stats(
+ self, group_id: int, sender_id: int, args: list[str]
+ ) -> None:
+ """处理群聊 ``/stats`` 命令，生成 token 使用统计图表（可选 AI 分析）。"""
+ if not _MATPLOTLIB_AVAILABLE:
+ await self.sender.send_group_message(
+ group_id, "❌ 缺少必要的库，无法生成图表。请安装 matplotlib。"
+ )
+ return
+
+ days, enable_ai_analysis = self._parse_stats_options(args)
+
+ try:
+ summary = await self._token_usage_storage.get_summary(days=days)
+ if summary["total_calls"] == 0:
+ await self.sender.send_group_message(
+ group_id, f"📊 最近 {days} 天内无 Token 使用记录。"
+ )
+ return
+
+ from Undefined.utils.paths import RENDER_CACHE_DIR, ensure_dir
+
+ img_dir = ensure_dir(RENDER_CACHE_DIR)
+ await self._generate_line_chart(summary, img_dir, days)
+ await self._generate_bar_chart(summary, img_dir)
+ await self._generate_pie_chart(summary, img_dir)
+ await self._generate_stats_table(summary, img_dir)
+
+ ai_analysis = ""
+ if enable_ai_analysis:
+ ai_analysis = await self._run_stats_ai_analysis(
+ scope="group",
+ scope_id=group_id,
+ sender_id=sender_id,
+ summary=summary,
+ days=days,
+ )
+
+ forward_messages = self._build_stats_forward_nodes(
+ summary, img_dir, days, ai_analysis
+ )
+ await self._send_group_forward_message(
+ group_id,
+ forward_messages,
+ history_message=self._build_stats_history_message(
+ summary,
+ days,
+ ai_analysis,
+ ),
+ )
+
+ from Undefined.utils.cache import cleanup_cache_dir
+
+ cleanup_cache_dir(RENDER_CACHE_DIR)
+
+ except Exception as e:
+ error_id = uuid4().hex[:8]
+ logger.exception(
+ "[Stats] 生成统计图表失败: error_id=%s err=%s", error_id, e
+ )
+ await self.sender.send_group_message(
+ group_id,
+ f"❌ 生成统计图表失败，请稍后重试（错误码: {error_id}）",
+ )
+
+ async def _send_group_forward_message(
+ self,
+ group_id: int,
+ messages: list[dict[str, Any]],
+ *,
+ history_message: str,
+ ) -> None:
+ """发送群组合并转发消息，并在需要时写入历史记录。"""
+ send_forward = getattr(self.sender, "send_group_forward_message", None)
+ if callable(send_forward):
+ await send_forward(group_id, messages, history_message=history_message)
+ return
+
+ await self.onebot.send_forward_msg(group_id, messages)
+ if self.history_manager is None:
+ return
+ text_content = history_message.strip()
+ if not text_content:
+ return
+
+ await self.history_manager.add_group_message(
+ group_id=group_id,
+ sender_id=getattr(self.config, "bot_qq", 0),
+ text_content=text_content,
+ sender_nickname="Bot",
+ group_name="",
+ )
+
+ @staticmethod
+ def _build_stats_history_message(
+ summary: dict[str, Any],
+ days: int,
+ ai_analysis: str,
+ ) -> str:
+ """构建写入群聊历史的 ``/stats`` 输出摘要文本。"""
+ lines = [
+ f"[命令输出] /stats 最近 {days} 天 Token 使用统计",
+ f"总调用: {summary.get('total_calls', 0)}",
+ f"总 Token: {summary.get('total_tokens', 0)}",
+ f"输入 Token: {summary.get('prompt_tokens', 0)}",
+ f"输出 Token: {summary.get('completion_tokens', 0)}",
+ ]
+ if ai_analysis.strip():
+ lines.extend(["", "AI 分析:", ai_analysis.strip()])
+ return "\n".join(lines)
+
+ async def _handle_stats_private(
+ self,
+ user_id: int,
+ sender_id: int,
+ args: list[str],
+ send_message: Any = None,
+ *,
+ is_webui_session: bool = False,
+ ) -> None:
+ """处理私聊 ``/stats``（含 WebUI 虚拟私聊适配）。"""
+
+ async def _send_private(message: str) -> None:
+ if send_message is not None:
+ await send_message(message)
+ else:
+ await self.sender.send_private_message(user_id, message)
+
+ days, enable_ai_analysis = self._parse_stats_options(args)
+ try:
+ summary = await self._token_usage_storage.get_summary(days=days)
+ if summary["total_calls"] == 0:
+ await _send_private(f"📊 最近 {days} 天内无 Token 使用记录。")
+ return
+
+ ai_analysis = ""
+ if enable_ai_analysis:
+ ai_analysis = await self._run_stats_ai_analysis(
+ scope="private",
+ scope_id=0,
+ sender_id=sender_id,
+ summary=summary,
+ days=days,
+ )
+
+ if not _MATPLOTLIB_AVAILABLE:
+ message = "❌ 缺少必要的库，无法生成图表。请安装 matplotlib。"
+ if is_webui_session:
+ message += "\n\n" + self._build_stats_summary_text(summary)
+ if ai_analysis:
+ message += f"\n\n🤖 AI 智能分析\n{ai_analysis}"
+ await _send_private(message)
+ return
+
+ from Undefined.utils.cache import cleanup_cache_dir
+ from Undefined.utils.paths import RENDER_CACHE_DIR, ensure_dir
+
+ img_dir = ensure_dir(RENDER_CACHE_DIR)
+ await self._generate_line_chart(summary, img_dir, days)
+ await self._generate_bar_chart(summary, img_dir)
+ await self._generate_pie_chart(summary, img_dir)
+ await self._generate_stats_table(summary, img_dir)
+
+ await _send_private(f"📊 最近 {days} 天的 Token 使用统计：")
+ for img_name in ["line_chart", "bar_chart", "pie_chart", "table"]:
+ img_path = img_dir / f"stats_{img_name}.png"
+ if img_path.exists():
+ message = await self._build_private_stats_image_message(
+ img_path,
+ inline_base64=is_webui_session,
+ )
+ await _send_private(message)
+
+ await _send_private(self._build_stats_summary_text(summary))
+ if ai_analysis:
+ await _send_private(f"🤖 AI 智能分析\n{ai_analysis}")
+
+ cleanup_cache_dir(RENDER_CACHE_DIR)
+ except Exception as e:
+ error_id = uuid4().hex[:8]
+ logger.exception(
+ "[Stats] 私聊统计生成失败: error_id=%s user=%s err=%s",
+ error_id,
+ user_id,
+ e,
+ )
+ await _send_private(
+ f"❌ 生成统计图表失败，请稍后重试（错误码: {error_id}）"
+ )
+
+ async def _build_private_stats_image_message(
+ self,
+ image_path: Path,
+ *,
+ inline_base64: bool,
+ ) -> str:
+ """构建私聊统计图片的 OneBot CQ 码消息。"""
+ file_uri = image_path.absolute().as_uri()
+ if not inline_base64:
+ return f"[CQ:image,file={file_uri}]"
+
+ try:
+ encoded = await asyncio.to_thread(
+ lambda: base64.b64encode(image_path.read_bytes()).decode("ascii")
+ )
+ except Exception as exc:
+ logger.warning(
+ "[Stats] 图像 base64 编码失败，回退文件路径: path=%s err=%s",
+ file_uri,
+ exc,
+ )
+ return f"[CQ:image,file={file_uri}]"
+
+ return f"[CQ:image,file=base64://{encoded}]"
+
+ async def _run_stats_ai_analysis(
+ self,
+ *,
+ scope: str,
+ scope_id: int,
+ sender_id: int,
+ summary: dict[str, Any],
+ days: int,
+ ) -> str:
+ """投递并等待 AI 对统计数据的分析结果。"""
+ if not self.queue_manager:
+ return ""
+
+ data_summary = self._build_data_summary(summary, days)
+ request_id = uuid4().hex
+ analysis_event = asyncio.Event()
+ self._stats_analysis_events[request_id] = analysis_event
+ request_data = {
+ "type": "stats_analysis",
+ "group_id": scope_id,
+ "request_id": request_id,
+ "sender_id": sender_id,
+ "data_summary": data_summary,
+ "summary": summary,
+ "days": days,
+ "scope": scope,
+ }
+ receipt = await self.queue_manager.add_group_mention_request(
+ request_data, model_name=self.config.chat_model.model_name
+ )
+ logger.info("[Stats] 已投递 AI 分析请求: scope=%s target=%s", scope, scope_id)
+
+ wait_timeout = compute_queued_llm_timeout_seconds(
+ self.ai.runtime_config,
+ self.config.chat_model,
+ retry_count=resolve_effective_retry_count(
+ self.ai.runtime_config, self.queue_manager
+ ),
+ initial_wait_seconds=float(
+ getattr(receipt, "estimated_wait_seconds", 0.0) or 0.0
+ ),
+ )
+ try:
+ await asyncio.wait_for(analysis_event.wait(), timeout=wait_timeout)
+ ai_analysis = self._stats_analysis_results.pop(request_id, "")
+ logger.info(
+ "[Stats] 已获取 AI 分析结果: scope=%s len=%s", scope, len(ai_analysis)
+ )
+ return ai_analysis
+ except asyncio.TimeoutError:
+ logger.warning(
+ "[Stats] AI 分析超时: scope=%s target=%s timeout=%.1fs",
+ scope,
+ scope_id,
+ wait_timeout,
+ )
+ return "AI 分析超时，已先发送图表与汇总数据。"
+ finally:
+ self._stats_analysis_events.pop(request_id, None)
+ self._stats_analysis_results.pop(request_id, None)
+
+ def _build_data_summary(self, summary: dict[str, Any], days: int) -> str:
+ """构建用于 AI 分析的统计数据摘要。"""
+ lines = []
+ lines.append("📊 Token 使用综合分析数据：")
+ lines.append("")
+
+ lines.append("【整体概况】")
+ lines.append(f"统计周期: {days} 天")
+ lines.append(f"总调用次数: {summary['total_calls']}")
+ lines.append(f"总 Token 消耗: {summary['total_tokens']:,}")
+ lines.append(f"平均响应时间: {summary['avg_duration']:.2f}s")
+ lines.append(f"涉及模型数: {len(summary['models'])}")
+ lines.append("")
+
+ daily_stats = summary.get("daily_stats", {})
+ if daily_stats:
+ dates = sorted(daily_stats.keys())
+ total_daily_calls = sum(daily_stats[d]["calls"] for d in dates)
+ total_daily_tokens = sum(daily_stats[d]["tokens"] for d in dates)
+ avg_daily_calls = total_daily_calls / len(dates) if dates else 0
+ avg_daily_tokens = total_daily_tokens / len(dates) if dates else 0
+
+ peak_day = (
+ max(dates, key=lambda d: daily_stats[d]["tokens"]) if dates else ""
+ )
+ peak_day_tokens = daily_stats[peak_day]["tokens"] if peak_day else 0
+
+ lines.append("【时间维度】")
+ lines.append(f"统计天数: {len(dates)} 天")
+ lines.append(f"每日平均调用: {avg_daily_calls:.1f} 次")
+ lines.append(f"每日平均 Token: {avg_daily_tokens:,.0f} 个")
+ lines.append(f"高峰日期: {peak_day} ({peak_day_tokens:,} tokens)")
+ lines.append("")
+
+ models = summary.get("models", {})
+ if models:
+ lines.append("【模型维度】")
+ total_tokens_all = summary["total_tokens"]
+ sorted_models = sorted(
+ models.items(), key=lambda x: x[1]["tokens"], reverse=True
+ )
+ for model_name, model_data in sorted_models[:_STATS_MODEL_TOP_N]:
+ calls = model_data["calls"]
+ tokens = model_data["tokens"]
+ prompt_tokens = model_data["prompt_tokens"]
+ completion_tokens = model_data["completion_tokens"]
+ token_pct = (
+ (tokens / total_tokens_all * 100) if total_tokens_all > 0 else 0
+ )
+ avg_per_call = tokens / calls if calls > 0 else 0
+ io_ratio = completion_tokens / prompt_tokens if prompt_tokens > 0 else 0
+
+ lines.append(f"模型: {model_name}")
+ lines.append(
+ f" - 调用次数: {calls} ({calls / summary['total_calls'] * 100:.1f}%)"
+ )
+ lines.append(f" - Token 消耗: {tokens:,} ({token_pct:.1f}%)")
+ lines.append(f" - 平均每次调用: {avg_per_call:.0f} tokens")
+ lines.append(
+ f" - 输入: {prompt_tokens:,} / 输出: {completion_tokens:,}"
+ )
+ lines.append(f" - 输入/输出比: 1:{io_ratio:.2f}")
+ lines.append("")
+
+ if len(sorted_models) > _STATS_MODEL_TOP_N:
+ others = sorted_models[_STATS_MODEL_TOP_N:]
+ others_calls = sum(int(item[1].get("calls", 0)) for item in others)
+ others_tokens = sum(int(item[1].get("tokens", 0)) for item in others)
+ others_pct = (
+ (others_tokens / total_tokens_all * 100)
+ if total_tokens_all > 0
+ else 0.0
+ )
+ lines.append(
+ f"其余 {len(others)} 个模型合计: 调用 {others_calls} 次, Token {others_tokens:,} ({others_pct:.1f}%)"
+ )
+ lines.append("")
+
+ call_types = summary.get("call_types", {})
+ if call_types:
+ lines.append("【调用类型维度】")
+ sorted_types = sorted(
+ call_types.items(), key=lambda item: int(item[1]), reverse=True
+ )
+ total_calls = max(1, int(summary.get("total_calls", 0)))
+ for call_type, count in sorted_types[:_STATS_CALL_TYPE_TOP_N]:
+ ratio = int(count) / total_calls * 100
+ lines.append(f"- {call_type}: {count} 次 ({ratio:.1f}%)")
+ if len(sorted_types) > _STATS_CALL_TYPE_TOP_N:
+ rest_count = sum(
+ int(item[1]) for item in sorted_types[_STATS_CALL_TYPE_TOP_N:]
+ )
+ ratio = rest_count / total_calls * 100
+ lines.append(
+ f"- 其他 {len(sorted_types) - _STATS_CALL_TYPE_TOP_N} 类: {rest_count} 次 ({ratio:.1f}%)"
+ )
+ lines.append("")
+
+ prompt_tokens = summary.get("prompt_tokens", 0)
+ completion_tokens = summary.get("completion_tokens", 0)
+ total_tokens = summary.get("total_tokens", 0)
+ input_ratio = (prompt_tokens / total_tokens * 100) if total_tokens > 0 else 0
+ output_ratio = (
+ (completion_tokens / total_tokens * 100) if total_tokens > 0 else 0
+ )
+ output_per_input = completion_tokens / prompt_tokens if prompt_tokens > 0 else 0
+
+ lines.append("【效率指标】")
+ lines.append(f"输入 Token: {prompt_tokens:,} ({input_ratio:.1f}%)")
+ lines.append(f"输出 Token: {completion_tokens:,} ({output_ratio:.1f}%)")
+ lines.append(f"输入/输出比: 1:{output_per_input:.2f}")
+ lines.append("")
+
+ if daily_stats and len(daily_stats) > 1:
+ lines.append("【趋势分析】")
+ dates = sorted(daily_stats.keys())
+ first_day_tokens = daily_stats[dates[0]]["tokens"]
+ last_day_tokens = daily_stats[dates[-1]]["tokens"]
+ trend_change = (
+ ((last_day_tokens - first_day_tokens) / first_day_tokens * 100)
+ if first_day_tokens > 0
+ else 0
+ )
+ trend_desc = "增长" if trend_change > 0 else "下降"
+ lines.append(
+ f"总体趋势: {trend_desc} {abs(trend_change):.1f}% (从首日到末日)"
+ )
+ lines.append("")
+
+ summary_text = "\n".join(lines)
+ if len(summary_text) > _STATS_DATA_SUMMARY_MAX_CHARS:
+ trimmed = summary_text[: _STATS_DATA_SUMMARY_MAX_CHARS - 80].rstrip()
+ summary_text = (
+ f"{trimmed}\n\n[数据摘要已截断，总长度 {len(summary_text)} 字符，"
+ f"仅保留前 {_STATS_DATA_SUMMARY_MAX_CHARS} 字符]"
+ )
+ logger.info(
+ "[Stats] 数据摘要过长已截断: original_len=%s max_len=%s",
+ len("\n".join(lines)),
+ _STATS_DATA_SUMMARY_MAX_CHARS,
+ )
+ return summary_text
+
+ def _build_stats_summary_text(self, summary: dict[str, Any]) -> str:
+ """构建统计结果的纯文本摘要。"""
+ return f"""📈 摘要汇总:
+• 总调用次数: {summary["total_calls"]}
+• 总消耗 Tokens: {summary["total_tokens"]:,}
+ └─ 输入: {summary["prompt_tokens"]:,}
+ └─ 输出: {summary["completion_tokens"]:,}
+• 平均耗时: {summary["avg_duration"]:.2f}s
+• 涉及模型数: {len(summary["models"])}"""
+
+ def set_stats_analysis_result(
+ self, group_id: int, request_id: str, analysis: str
+ ) -> None:
+ """设置 AI 分析结果（由队列处理器调用）。"""
+ event = self._stats_analysis_events.get(request_id)
+ if not event:
+ logger.warning(
+ "[StatsAnalysis] 未找到等待事件，群: %s, 请求: %s",
+ group_id,
+ request_id,
+ )
+ return
+ self._stats_analysis_results[request_id] = analysis
+ event.set()
+
+ def _build_stats_forward_nodes(
+ self,
+ summary: dict[str, Any],
+ img_dir: Path,
+ days: int,
+ ai_analysis: str = "",
+ ) -> list[dict[str, Any]]:
+ """构建用于合并转发的统计图表节点列表。"""
+ # 对外入队 API
+ nodes = []
+ bot_qq = str(self.config.bot_qq)
+
+ # 对外入队 API
+ def add_node(content: str) -> None:
+ nodes.append(
+ {
+ "type": "node",
+ "data": {"name": "Bot", "uin": bot_qq, "content": content},
+ }
+ )
+
+ add_node(f"📊 最近 {days} 天的 Token 使用统计：")
+
+ for img_name in ["line_chart", "bar_chart", "pie_chart", "table"]:
+ img_path = img_dir / f"stats_{img_name}.png"
+ if img_path.exists():
+ add_node(f"[CQ:image,file={img_path.absolute().as_uri()}]")
+
+ add_node(self._build_stats_summary_text(summary))
+
+ if ai_analysis:
+ add_node(f"🤖 AI 智能分析\n{ai_analysis}")
+
+ return nodes
+
+ async def _generate_line_chart(
+ self, summary: dict[str, Any], img_dir: Path, days: int
+ ) -> None:
+ """生成折线图：时间趋势。"""
+ daily_stats = summary["daily_stats"]
+ if not daily_stats:
+ return
+
+ dates = sorted(daily_stats.keys())
+ tokens = [daily_stats[d]["tokens"] for d in dates]
+ prompt_tokens = [daily_stats[d]["prompt_tokens"] for d in dates]
+ completion_tokens = [daily_stats[d]["completion_tokens"] for d in dates]
+
+ fig, ax = plt.subplots(figsize=(12, 7))
+
+ ax.plot(
+ dates, tokens, marker="o", linewidth=2, label="Total Token", color="#2196F3"
+ )
+ ax.plot(
+ dates,
+ prompt_tokens,
+ marker="s",
+ linewidth=2,
+ label="Input Token",
+ color="#4CAF50",
+ )
+ ax.plot(
+ dates,
+ completion_tokens,
+ marker="^",
+ linewidth=2,
+ label="Output Token",
+ color="#FF9800",
+ )
+
+ ax.set_title(
+ f"Token Usage Trend for Last {days} Days", fontsize=16, fontweight="bold"
+ )
+ ax.set_xlabel("Date", fontsize=12)
+ ax.set_ylabel("Token Count", fontsize=12)
+ ax.legend(loc="upper left", fontsize=10)
+ ax.grid(True, alpha=0.3)
+
+ plt.xticks(rotation=45, ha="right")
+ plt.tight_layout()
+
+ filepath = img_dir / "stats_line_chart.png"
+ plt.savefig(filepath, dpi=150, bbox_inches="tight")
+ plt.close(fig)
+
+ async def _generate_bar_chart(self, summary: dict[str, Any], img_dir: Path) -> None:
+ """生成柱状图：模型对比。"""
+ models = summary["models"]
+ if not models:
+ return
+
+ model_names = list(models.keys())
+ tokens = [models[m]["tokens"] for m in model_names]
+ prompt_tokens = [models[m]["prompt_tokens"] for m in model_names]
+ completion_tokens = [models[m]["completion_tokens"] for m in model_names]
+
+ fig, ax = plt.subplots(figsize=(14, 8))
+
+ x = range(len(model_names))
+ width = 0.25
+
+ bars1 = ax.bar(
+ [i - width for i in x],
+ tokens,
+ width,
+ label="Total Token",
+ color="#2196F3",
+ alpha=0.8,
+ )
+ bars2 = ax.bar(
+ x,
+ prompt_tokens,
+ width,
+ label="Input Token",
+ color="#4CAF50",
+ alpha=0.8,
+ )
+ bars3 = ax.bar(
+ [i + width for i in x],
+ completion_tokens,
+ width,
+ label="Output Token",
+ color="#FF9800",
+ alpha=0.8,
+ )
+
+ ax.set_title("Token Usage Comparison by Model", fontsize=16, fontweight="bold")
+ ax.set_xlabel("Model", fontsize=12)
+ ax.set_ylabel("Token Count", fontsize=12)
+ ax.set_xticks(x)
+ ax.set_xticklabels(model_names, rotation=45, ha="right")
+ ax.legend(loc="upper right", fontsize=10)
+ ax.grid(True, alpha=0.3, axis="y")
+
+ for bars in [bars1, bars2, bars3]:
+ for bar in bars:
+ height = bar.get_height()
+ if height > 0:
+ ax.text(
+ bar.get_x() + bar.get_width() / 2.0,
+ height,
+ f"{int(height):,}",
+ ha="center",
+ va="bottom",
+ fontsize=8,
+ )
+
+ plt.tight_layout()
+
+ filepath = img_dir / "stats_bar_chart.png"
+ plt.savefig(filepath, dpi=150, bbox_inches="tight")
+ plt.close(fig)
+
+ async def _generate_pie_chart(self, summary: dict[str, Any], img_dir: Path) -> None:
+ """生成饼图：输入/输出比例。"""
+ prompt_tokens = summary["prompt_tokens"]
+ completion_tokens = summary["completion_tokens"]
+
+ if prompt_tokens == 0 and completion_tokens == 0:
+ return
+
+ fig, ax = plt.subplots(figsize=(12, 8))
+
+ labels = ["Input Token", "Output Token"]
+ sizes = [prompt_tokens, completion_tokens]
+ colors = ["#4CAF50", "#FF9800"]
+ explode = (0.05, 0.05)
+
+ wedges, *_ = ax.pie(
+ sizes,
+ explode=explode,
+ labels=labels,
+ colors=colors,
+ autopct="%1.1f%%",
+ startangle=90,
+ textprops={"fontsize": 12},
+ )
+
+ ax.set_title("Input/Output Token Ratio", fontsize=16, fontweight="bold", pad=20)
+
+ ax.legend(
+ wedges,
+ [f"{labels[i]}: {sizes[i]:,}" for i in range(len(labels))],
+ loc="center left",
+ bbox_to_anchor=(1, 0, 0.5, 1),
+ fontsize=10,
+ )
+
+ plt.tight_layout()
+
+ filepath = img_dir / "stats_pie_chart.png"
+ plt.savefig(filepath, dpi=150, bbox_inches="tight")
+ plt.close(fig)
+
+ async def _generate_stats_table(
+ self, summary: dict[str, Any], img_dir: Path
+ ) -> None:
+ """生成统计表格图片。"""
+ models = summary["models"]
+ if not models:
+ return
+
+ model_names = list(models.keys())
+ data = []
+ for model in model_names:
+ m = models[model]
+ data.append(
+ [
+ model,
+ m["calls"],
+ f"{m['tokens']:,}",
+ f"{m['prompt_tokens']:,}",
+ f"{m['completion_tokens']:,}",
+ ]
+ )
+
+ fig, ax = plt.subplots(figsize=(14, 9))
+ ax.axis("tight")
+ ax.axis("off")
+
+ table = ax.table(
+ cellText=data,
+ colLabels=["Model", "Calls", "Total Token", "Input Token", "Output Token"],
+ cellLoc="center",
+ loc="center",
+ )
+
+ table.auto_set_font_size(False)
+ table.set_fontsize(10)
+ table.scale(1.2, 1.5)
+
+ for i in range(5):
+ table[(0, i)].set_facecolor("#2196F3")
+ table[(0, i)].set_text_props(weight="bold", color="white")
+
+ for i in range(1, len(data) + 1):
+ for j in range(5):
+ if i % 2 == 0:
+ table[(i, j)].set_facecolor("#f0f0f0")
+
+ ax.set_title(
+ "Model Usage Statistics Details", fontsize=16, fontweight="bold", pad=20
+ )
+
+ plt.tight_layout()
+
+ filepath = img_dir / "stats_table.png"
+ plt.savefig(filepath, dpi=150, bbox_inches="tight")
+ plt.close(fig)
diff --git a/src/Undefined/services/coordinator/__init__.py b/src/Undefined/services/coordinator/__init__.py
new file mode 100644
index 00000000..ee91e712
--- /dev/null
+++ b/src/Undefined/services/coordinator/__init__.py
@@ -0,0 +1,88 @@
+"""AI 协调器：组合群聊、私聊、合并与后台任务 mixin。"""
+
+from __future__ import annotations
+
+
+import logging
+from pathlib import Path
+from typing import Any
+
+from Undefined.config import Config
+from Undefined.services.coordinator.background import BackgroundMixin
+from Undefined.services.coordinator.batching import BatchingMixin
+from Undefined.services.coordinator.group import GroupReplyMixin
+from Undefined.services.coordinator.private import PrivateReplyMixin
+from Undefined.services.message_batcher import MessageBatcher
+from Undefined.services.model_pool import ModelPoolService
+from Undefined.services.queue_manager import QueueManager
+from Undefined.services.security import SecurityService
+from Undefined.utils.history import MessageHistoryManager
+from Undefined.utils.scheduler import TaskScheduler
+from Undefined.utils.sender import MessageSender
+
+logger = logging.getLogger(__name__)
+
+
+class AICoordinator(
+ GroupReplyMixin,
+ PrivateReplyMixin,
+ BatchingMixin,
+ BackgroundMixin,
+):
+ """AI 协调器，处理 AI 回复逻辑、Prompt 构建和队列管理"""
+
+ def __init__(
+ self,
+ config: Config,
+ ai: Any, # AIClient
+ queue_manager: QueueManager,
+ history_manager: MessageHistoryManager,
+ sender: MessageSender,
+ onebot: Any, # OneBotClient
+ scheduler: TaskScheduler,
+ security: SecurityService,
+ command_dispatcher: Any = None,
+ ) -> None:
+ self.config = config
+ self.ai = ai
+ self.queue_manager = queue_manager
+ self.history_manager = history_manager
+ self.sender = sender
+ self.onebot = onebot
+ self.scheduler = scheduler
+ self.security = security
+ self.command_dispatcher = command_dispatcher
+ self.model_pool = ModelPoolService(ai, config, sender)
+ # batcher 由外部（handlers/message_flow）创建并通过 set_batcher 注入；未注入时所有消息按单条流程直送。
+ self._batcher: MessageBatcher | None = None
+
+ def set_batcher(self, batcher: MessageBatcher | None) -> None:
+ """注入消息合并器；传 None 等同于禁用合并。"""
+ self._batcher = batcher
+
+ @property
+ def batcher(self) -> MessageBatcher | None:
+ return self._batcher
+
+ async def _send_image(self, tid: int, mtype: str, path: str) -> None:
+ """发送图片或语音消息到群聊或私聊"""
+ import os
+
+ if not os.path.exists(path):
+ return
+ file_uri = Path(path).resolve().as_uri()
+ ext = os.path.splitext(path)[1].lower()
+ if ext in [".jpg", ".jpeg", ".png", ".gif", ".bmp", ".webp"]:
+ msg = f"[CQ:image,file={file_uri}]"
+ elif ext in [".mp3", ".wav", ".ogg", ".flac", ".m4a", ".aac"]:
+ msg = f"[CQ:record,file={file_uri}]"
+ else:
+ return
+
+ try:
+ if mtype == "group":
+ await self.sender.send_group_message(tid, msg, auto_history=False)
+ elif mtype == "private":
+ await self.sender.send_private_message(tid, msg, auto_history=False)
+ except Exception:
+ logger.exception("发送媒体文件失败")
diff --git a/src/Undefined/services/coordinator/background.py b/src/Undefined/services/coordinator/background.py
new file mode 100644
index 00000000..b6c8cefe
--- /dev/null
+++ b/src/Undefined/services/coordinator/background.py
@@ -0,0 +1,250 @@
+"""后台 LLM 任务：stats 分析、队列调用与 Agent 介绍生成。"""
+
+from __future__ import annotations
+
+
+import logging
+from typing import TYPE_CHECKING, Any, cast
+
+from Undefined.services.queue_manager import QUEUE_LANE_BACKGROUND
+from Undefined.utils.resources import read_text_resource
+
+if TYPE_CHECKING:
+ from Undefined.ai import AIClient
+ from Undefined.config import Config
+ from Undefined.services.command import CommandDispatcher
+
+logger = logging.getLogger(__name__)
+
+
+_STATS_ANALYSIS_PROMPT_PATH = "res/prompts/stats_analysis.txt"
+_STATS_ANALYSIS_FALLBACK_PROMPT = (
+ "你是一位专业的数据分析师。请根据以下 Token 使用统计数据提供分析：\n\n"
+ "{data_summary}\n\n"
+ "请从整体概况、趋势、模型效率、成本结构、异常点和优化建议进行总结，"
+ "语言简洁，建议可执行。"
+)
+
+
+class BackgroundMixin:
+ """队列分发入口与后台 LLM 任务执行。"""
+
+ if TYPE_CHECKING:
+ ai: AIClient
+ config: Config
+ command_dispatcher: CommandDispatcher
+
+ async def _execute_auto_reply(self, request: dict[str, Any]) -> None: ...
+ async def _execute_private_reply(self, request: dict[str, Any]) -> None: ...
+
+ async def execute_reply(self, request: dict[str, Any]) -> None:
+ """执行排队中的回复请求（由 QueueManager 分发调用）
+
+ 参数:
+ request: 包含请求类型和必要元数据的请求字典
+ """
+ req_type = request.get("type", "unknown")
+ logger.debug("[执行请求] type=%s keys=%s", req_type, list(request.keys()))
+ batch_token = request.get("_message_batcher_token")
+ # 投机 pre-fire 被新消息 cancel 后，coordinator 在真正执行前跳过旧 token
+ if bool(getattr(batch_token, "cancelled", False)):
+ logger.info(
+ "[MessageBatcher] 跳过已取消的投机请求: type=%s scope=%s sender=%s batch=%s",
+ req_type,
+ getattr(batch_token, "scope", ""),
+ getattr(batch_token, "sender_id", ""),
+ getattr(batch_token, "batch_id", ""),
+ )
+ return
+ if req_type == "auto_reply":
+ await self._execute_auto_reply(request)
+ elif req_type == "private_reply":
+ await self._execute_private_reply(request)
+ elif req_type == "stats_analysis":
+ await self._execute_stats_analysis(request)
+ elif req_type == "agent_intro_generation":
+ await self._execute_agent_intro_generation(request)
+ elif req_type in {"queued_llm_call", "background_llm_call"}:
+ await self._execute_queued_llm_call(request)
+
+ async def _execute_stats_analysis(self, request: dict[str, Any]) -> None:
+ """执行 stats 命令的 AI 分析"""
+ group_id = request["group_id"]
+ request_id = request.get("request_id")
+ data_summary = request.get("data_summary", "")
+
+ if not request_id:
+ logger.warning("[统计分析] 缺少 request_id，群=%s", group_id)
+ return
+ try:
+ prompt_template = _STATS_ANALYSIS_FALLBACK_PROMPT
+ try:
+ loaded_prompt = read_text_resource(_STATS_ANALYSIS_PROMPT_PATH).strip()
+ if loaded_prompt:
+ prompt_template = loaded_prompt
+ except Exception as exc:
+ logger.warning("[统计分析] 读取提示词失败，使用内置模板: %s", exc)
+
+ if "{data_summary}" not in prompt_template:
+ logger.warning(
+ "[统计分析] 提示词缺少 {data_summary} 占位符，自动追加",
+ )
+ prompt_template = f"{prompt_template}\n\n{{data_summary}}"
+
+ safe_data_summary = str(data_summary).strip() or "暂无统计数据摘要"
+ try:
+ full_prompt = prompt_template.format(data_summary=safe_data_summary)
+ except Exception as exc:
+ logger.warning("[统计分析] 提示词渲染失败，使用回退模板: %s", exc)
+ full_prompt = _STATS_ANALYSIS_FALLBACK_PROMPT.format(
+ data_summary=safe_data_summary
+ )
+
+ messages = [
+ {"role": "system", "content": "你是一位专业的数据分析师。"},
+ {"role": "user", "content": full_prompt},
+ ]
+
+ result = await self.ai.submit_queued_llm_call(
+ model_config=self.config.chat_model,
+ messages=messages,
+ max_tokens=2048,
+ call_type="stats_analysis",
+ queue_lane=request.get("_queue_lane"),
+ )
+
+ choices = result.get("choices", [{}])
+ if choices:
+ content = choices[0].get("message", {}).get("content", "")
+ analysis = content.strip()
+ else:
+ analysis = "AI 分析未能生成结果"
+
+ if not analysis:
+ analysis = "AI 分析结果为空，建议稍后重试。"
+
+ logger.info(
+ "[统计分析] 分析完成: group=%s length=%s request_id=%s",
+ group_id,
+ len(analysis),
+ request_id,
+ )
+
+ if self.command_dispatcher:
+ self.command_dispatcher.set_stats_analysis_result(
+ group_id, request_id, analysis
+ )
+
+ except Exception as exc:
+ logger.exception("[统计分析] AI 分析失败: %s", exc)
+ if self.command_dispatcher:
+ self.command_dispatcher.set_stats_analysis_result(
+ group_id, request_id, ""
+ )
+
+ async def _execute_queued_llm_call(self, request: dict[str, Any]) -> None:
+ """执行队列中的 LLM 子请求。"""
+ request_id = request.get("request_id", "")
+ retry_count = int(request.get("_retry_count", 0) or 0)
+ queue_lane = str(request.get("_queue_lane") or QUEUE_LANE_BACKGROUND)
+ call_type = str(request.get("call_type", "background") or "background")
+ try:
+ max_tokens_raw = request.get("max_tokens") or getattr(
+ request["model_config"], "max_tokens", 4096
+ )
+ max_tokens = int(max_tokens_raw) if max_tokens_raw is not None else 4096
+ result = await self.ai.request_model(
+ model_config=request["model_config"],
+ messages=request["messages"],
+ tools=request.get("tools"),
+ tool_choice=request.get("tool_choice", "auto"),
+ call_type=call_type,
+ max_tokens=max_tokens,
+ transport_state=request.get("transport_state"),
+ )
+ self.ai.set_llm_call_result(request_id, result)
+ if retry_count > 0:
+ logger.info(
+ "[queued_llm_retry_success] request_id=%s call_type=%s model=%s lane=%s retry=%s",
+ request_id,
+ call_type,
+ getattr(request["model_config"], "model_name", "default"),
+ queue_lane,
+ retry_count,
+ )
+ except Exception as exc:
+ retry_count = request.get("_retry_count", 0)
+ if retry_count >= self.config.ai_request_max_retries:
+ self.ai.set_llm_call_result(request_id, exc)
+ raise
+
+ async def _execute_agent_intro_generation(self, request: dict[str, Any]) -> None:
+ """执行 Agent 自我介绍生成请求"""
+ request_id = request.get("request_id")
+ agent_name = request.get("agent_name")
+
+ if not request_id or not agent_name:
+ logger.warning(
+ "[Agent介绍生成] 缺少必要参数: request_id=%s agent_name=%s",
+ request_id,
+ agent_name,
+ )
+ return
+
+ try:
+ from Undefined.skills.agents.intro_generator import AgentIntroGenerator
+
+ agent_intro_generator = self.ai._agent_intro_generator
+ if not isinstance(agent_intro_generator, AgentIntroGenerator):
+ logger.error("[Agent介绍生成] 无法获取 AgentIntroGenerator 实例")
+ return
+
+ (
+ system_prompt,
+ user_prompt,
+ ) = await agent_intro_generator.get_intro_prompt_and_context(agent_name)
+
+ messages = [
+ {"role": "system", "content": system_prompt or "你是一位智能助手。"},
+ {"role": "user", "content": user_prompt},
+ ]
+
+ result = await self.ai.submit_queued_llm_call(
+ model_config=self.ai.agent_config,
+ messages=messages,
+ max_tokens=agent_intro_generator.config.max_tokens,
+ call_type=f"agent:{agent_name}",
+ queue_lane=request.get("_queue_lane"),
+ )
+
+ choices = result.get("choices", [{}])
+ if choices:
+ content = choices[0].get("message", {}).get("content", "")
+ generated_content = content.strip()
+ else:
+ generated_content = ""
+
+ logger.info(
+ "[Agent介绍生成] 生成完成: agent=%s length=%s request_id=%s",
+ agent_name,
+ len(generated_content),
+ request_id,
+ )
+
+ agent_intro_generator.set_intro_generation_result(
+ request_id, generated_content if generated_content else None
+ )
+
+ except Exception as exc:
+ logger.exception(
+ "[Agent介绍生成] 生成失败: agent=%s error=%s",
+ agent_name,
+ exc,
+ )
+ try:
+ agent_intro_generator = cast(
+ AgentIntroGenerator, self.ai._agent_intro_generator
+ )
+ agent_intro_generator.set_intro_generation_result(request_id, None)
+ except Exception:
+ pass
diff --git a/src/Undefined/services/coordinator/batching.py b/src/Undefined/services/coordinator/batching.py
new file mode 100644
index 00000000..d3da2c63
--- /dev/null
+++ b/src/Undefined/services/coordinator/batching.py
@@ -0,0 +1,174 @@
+"""消息合并、分组 prompt 构建与队列投递。"""
+
+from __future__ import annotations
+
+
+import logging
+from typing import TYPE_CHECKING, Any
+
+from Undefined.services.coordinator.group import _GROUP_STRATEGY_FOOTER
+from Undefined.services.coordinator.private import _PRIVATE_STRATEGY_FOOTER
+from Undefined.services.message_batcher import BufferedMessage
+
+if TYPE_CHECKING:
+ from Undefined.config import Config
+ from Undefined.services.message_batcher import BufferedMessage as _BufferedMessage
+ from Undefined.services.model_pool import ModelPoolService
+ from Undefined.services.queue_manager import QueueManager
+
+logger = logging.getLogger(__name__)
+
+
+# BatchingMixin：MessageBatcher 回调、合并 prompt 与队列路由
+class BatchingMixin:
+ """MessageBatcher 回调、合并 prompt 与队列路由。"""
+
+ if TYPE_CHECKING:
+ config: Config
+ queue_manager: QueueManager
+ model_pool: ModelPoolService
+
+ def _format_group_message_segment(self, item: _BufferedMessage) -> str: ...
+ def _format_private_message_segment(self, item: _BufferedMessage) -> str: ...
+
+ async def handle_batched_dispatch(self, items: list[BufferedMessage]) -> None:
+ """:class:`MessageBatcher` 的 flush_callback：把一批消息组装为单次请求并入队。"""
+ if not items:
+ return
+ await self._dispatch_grouped_request(items)
+
+ @staticmethod
+ def _build_continuous_messages_note(items: list[BufferedMessage]) -> str:
+ """生成"连续消息说明"段。仅在 ``len(items) >= 2`` 时使用。"""
+ count = len(items)
+ first_t = items[0].arrival_time
+ last_t = items[-1].arrival_time
+ span = max(0.0, last_t - first_t)
+ return (
+ f"\n\n 【连续消息说明】以上 {count} 条 是同一用户在约 "
+ f"{span:.1f} 秒内连续发送的消息（按时间先后排列），代表本轮要回应的全部输入：\n"
+ f" - 这些 共同构成【当前输入批次】，不要把同批前几条误判为历史旧任务；"
+ f"批次之外的历史消息仍只作为背景，不能回溯拾荒\n"
+ f" - 先识别每条的意图，分清是【独立请求】还是【对前一条的修正/否定/补充/打断】\n"
+ f' · 若是【多个独立的不同意图/问题】（如"先帮我查 A，再翻译 B")'
+ f" → 每个都要回应，不要遗漏；与平时一样，可以多次 send_message 自然分发\n"
+ f' · 若后发是【对前发的修正/否定/补充/打断】（如"画猫" → "改成狗")'
+ f" → 以最后一次明确意图为准，旧的不再执行，可简短说明已采纳更新\n"
+ f' · 拿不准时偏向"独立请求"，宁多勿漏\n'
+ f" - 整批在本轮一次性处理完即可，不要为同一意图重复输出（不要"
+ f'"中间一波、结尾再来一波"重复相同回复）\n'
+ f" - history 中若出现与当前轮 相同的条目，视为同一来源，不要重复处理"
+ )
+
+ def _build_grouped_prompt(self, items: list[BufferedMessage]) -> str:
+ """根据 BufferedMessage 列表构造合并后的完整 prompt。"""
+ if not items:
+ return ""
+ is_private = items[0].is_private
+ # prefix：拍一拍优先；否则任一 @bot
+ any_poke = any(it.is_poke for it in items)
+ any_at_bot = any(it.is_at_bot for it in items)
+ if any_poke:
+ prefix = "(用户拍了拍你) "
+ elif any_at_bot:
+ prefix = "(用户 @ 了你) "
+ else:
+ prefix = ""
+
+ if is_private:
+ segments = [self._format_private_message_segment(it) for it in items]
+ else:
+ segments = [self._format_group_message_segment(it) for it in items]
+ body = prefix + "\n".join(segments)
+ if len(items) >= 2:
+ body += self._build_continuous_messages_note(items)
+ body += _GROUP_STRATEGY_FOOTER if not is_private else _PRIVATE_STRATEGY_FOOTER
+ return body
+
+ async def _dispatch_grouped_request(self, items: list[BufferedMessage]) -> None:
+ """根据一组 BufferedMessage 决定优先级、构造 prompt 并入队。
+
+ 既是单条直送路径的统一出口，也是 :class:`MessageBatcher` 的 flush_callback。
+ """
+ if not items:
+ return
+ first = items[0]
+ last = items[-1]
+ full_question = self._build_grouped_prompt(items)
+ any_poke = any(it.is_poke for it in items)
+ any_at_bot = any(it.is_at_bot for it in items)
+
+ if first.is_private:
+ user_id = first.sender_id
+ request_data: dict[str, Any] = {
+ "type": "private_reply",
+ "user_id": user_id,
+ "sender_name": first.sender_name,
+ "text": last.text,
+ "full_question": full_question,
+ "trigger_message_id": last.trigger_message_id,
+ "batched_count": len(items),
+ }
+ if first.batch_token is not None:
+ request_data["_message_batcher_token"] = first.batch_token
+ effective_config = self.model_pool.select_chat_config(
+ self.config.chat_model, user_id=user_id
+ )
+ request_data["selected_model_name"] = effective_config.model_name
+ logger.debug(
+ "[私聊回复] full_question_len=%s user=%s batched=%s",
+ len(full_question),
+ user_id,
+ len(items),
+ )
+ if user_id == self.config.superadmin_qq:
+ # 私聊超管 → 最高优先级 superadmin lane
+ await self.queue_manager.add_superadmin_request(
+ request_data, model_name=effective_config.model_name
+ )
+ else:
+ await self.queue_manager.add_private_request(
+ request_data, model_name=effective_config.model_name
+ )
+ return
+
+ # 群聊：按 sender 身份与 @bot 状态选择 4 级 lane 之一
+ group_id = first.group_id or 0
+ sender_id = first.sender_id
+ request_data = {
+ "type": "auto_reply",
+ "group_id": group_id,
+ "sender_id": sender_id,
+ "sender_name": first.sender_name,
+ "group_name": first.group_name,
+ "text": last.text,
+ "full_question": full_question,
+ "is_at_bot": any_at_bot,
+ "trigger_message_id": last.trigger_message_id,
+ "batched_count": len(items),
+ }
+ if first.batch_token is not None:
+ request_data["_message_batcher_token"] = first.batch_token
+ logger.debug(
+ "[自动回复] full_question_len=%s group=%s sender=%s batched=%s",
+ len(full_question),
+ group_id,
+ sender_id,
+ len(items),
+ )
+ if sender_id == self.config.superadmin_qq:
+ logger.info("[AI] 投递至群聊超级管理员队列 (batched=%s)", len(items))
+ await self.queue_manager.add_group_superadmin_request(
+ request_data, model_name=self.config.chat_model.model_name
+ )
+ elif any_at_bot:
+ trigger = "拍一拍" if any_poke else "@机器人"
+ logger.info("[AI] 触发原因: %s (batched=%s)", trigger, len(items))
+ await self.queue_manager.add_group_mention_request(
+ request_data, model_name=self.config.chat_model.model_name
+ )
+ else:
+ logger.info("[AI] 投递至普通请求队列 (batched=%s)", len(items))
+ await self.queue_manager.add_group_normal_request(
+ request_data, model_name=self.config.chat_model.model_name
+ )
diff --git a/src/Undefined/services/coordinator/group.py b/src/Undefined/services/coordinator/group.py
new file mode 100644
index 00000000..0dce633b
--- /dev/null
+++ b/src/Undefined/services/coordinator/group.py
@@ -0,0 +1,405 @@
+"""群聊自动回复与 prompt 构建。"""
+
+from __future__ import annotations
+
+
+import asyncio
+import logging
+import time
+from datetime import datetime
+from typing import TYPE_CHECKING, Any, Optional
+
+from Undefined.attachments import attachment_refs_to_xml
+from Undefined.context import RequestContext
+from Undefined.context_resource_registry import collect_context_resources
+from Undefined.render import render_html_to_image, render_markdown_to_html
+from Undefined.services.message_batcher import BufferedMessage, make_scope
+from Undefined.utils.recent_messages import get_recent_messages_prefer_local
+from Undefined.utils.xml import escape_xml_attr, escape_xml_text
+
+if TYPE_CHECKING:
+ from Undefined.config import Config
+ from Undefined.services.message_batcher import BufferedMessage
+ from Undefined.services.security import SecurityService
+ from Undefined.utils.history import MessageHistoryManager
+ from Undefined.utils.scheduler import TaskScheduler
+ from Undefined.utils.sender import MessageSender
+
+logger = logging.getLogger(__name__)
+
+
+_GROUP_STRATEGY_FOOTER = """
+
+ 【回复策略 - 更克制，纯表情包才前置检索】
+ 1. 如果用户 @ 了你或拍了拍你 → 【必须回复】
+ 2. 如果消息中明确提到了你（根据上下文判断用户是否在叫你或维持对话流） → 【必须回复】
+ 3. 如果问题明确涉及某个项目/代码/部署细节（用户明确点名或上下文明确指向） → 【酌情回复，必要时先查证再回答】
+ 4. 其他技术问题 → 【酌情回复，直接按用户提到的对象回答，不要引入无关的项目名/工具名作背景】
+ 5. 先判断当前输入批次（无连续消息说明时就是最后一条消息）是不是在对你说：
+ - 如果明显是在和别人说话 → 【不要回复】
+ - 如果你不能确定是不是在和你说话 → 【默认不回复】
+ - 只有明确在和你说，或多人公开讨论且对话明显开放时，才进入下一步
+ 6. 群聊里的主动参与只保留给公开、开放的技术或项目讨论：
+ - 只在多人公开讨论代码、AI、开发工具、项目进展、技术 bug 等，且不是别人之间定向交流时，才可以【极低频参与】
+ - 默认更倾向不参与；不要长篇大论，一两句点到为止；如果别人已经在深入讨论且不需要你，保持沉默
+ - 轻松互动、玩梗、吐槽本身不构成参与许可；只有在你已经决定要回复，且本轮明确是纯表情包/纯反应图时，才优先考虑表情包表达
+ 7. 对于已经决定要回复的场景（包括被@、被拍一拍、轻量答疑，以及少量符合条件的主动参与）：
+ - 只有明确纯表情包回复才先检索表情包，再用 memes.send_meme_by_uid 单独发一条图片消息
+ - 其他需要文字承接、解释、答疑、推进任务、确认操作或表达具体态度的场景，第一轮必须优先把必要文字回复做好并调用 send_message
+ - 如果确实还想补表情包，把 memes.search_memes 和 memes.send_meme_by_uid 放到文字发送后的后续响应轮次，不要阻塞首条文字回复
+ - 不要发送任何敷衍消息（如'懒得掺和'、'哦'等）；不想回复就直接调用 end
+ - 严肃、任务型、高信息密度场景少发表情包，避免打断信息传递
+ - 绝不要刷屏、绝不要每条都回
+ 8. 对于本来就会回复的场景（私聊、被拍一拍、被@、轻量答疑）：
+ - 如果表情包能自然增强语气、缓和语气或让表达更像真人，也只能作为后续可选补充
+ - 但不要为了发表情包而牺牲信息传递；信息密度优先时仍以文字为主
+
+ 简单说：像个极度安静的群友。主动插话只留给公开、开放的技术或项目讨论；明显对别人说或拿不准时就闭嘴。已经决定要回复时，除非明确是纯表情包回复，否则先把文字回复做好，表情包最后再搜。"""
+
+
+class GroupReplyMixin:
+ """群聊自动回复、注入防御与群聊 prompt 格式化。"""
+
+ if TYPE_CHECKING:
+ ai: Any
+ config: Config
+ history_manager: MessageHistoryManager
+ onebot: Any
+ scheduler: TaskScheduler
+ security: SecurityService
+ sender: MessageSender
+
+ async def _dispatch_grouped_request(
+ self, items: list[BufferedMessage]
+ ) -> None: ...
+ async def _send_image(self, tid: int, mtype: str, path: str) -> None: ...
+
+ async def handle_auto_reply(
+ self,
+ group_id: int,
+ sender_id: int,
+ text: str,
+ message_content: list[dict[str, Any]],
+ attachments: list[dict[str, str]] | None = None,
+ is_poke: bool = False,
+ sender_name: str = "未知用户",
+ group_name: str = "未知群聊",
+ sender_role: str = "member",
+ sender_title: str = "",
+ sender_level: str = "",
+ trigger_message_id: int | None = None,
+ is_fake_at: bool = False,
+ ) -> None:
+ """群聊自动回复入口：根据消息内容、命中情况和安全检测决定是否回复"""
+ is_at_bot = is_poke or is_fake_at or self._is_at_bot(message_content)
+ logger.debug(
+ "[自动回复] group=%s sender=%s at_bot=%s fake_at=%s text_len=%s",
+ group_id,
+ sender_id,
+ is_at_bot,
+ is_fake_at,
+ len(text),
+ )
+
+ if sender_id != self.config.superadmin_qq:
+ logger.debug(f"[Security] 注入检测: group={group_id}, user={sender_id}")
+ if await self.security.detect_injection(text, message_content):
+ logger.warning(
+ f"[Security] 检测到注入攻击: group={group_id}, user={sender_id}"
+ )
+ await self.history_manager.modify_last_group_message(
+ group_id, sender_id, "<这句话检测到用户进行注入，已删除>"
+ )
+ if is_at_bot:
+ await self._handle_injection_response(
+ group_id, text, sender_id=sender_id
+ )
+ return
+
+ scope = make_scope(group_id=group_id)
+ item = BufferedMessage(
+ scope=scope,
+ sender_id=sender_id,
+ text=text,
+ message_content=list(message_content),
+ attachments=list(attachments or []),
+ sender_name=sender_name,
+ arrival_time=time.time(),
+ is_private=False,
+ trigger_message_id=trigger_message_id,
+ is_poke=is_poke,
+ is_at_bot=is_at_bot,
+ is_fake_at=is_fake_at,
+ group_id=group_id,
+ group_name=group_name,
+ sender_role=sender_role,
+ sender_title=sender_title,
+ sender_level=sender_level,
+ )
+
+ # 路由：拍一拍 → 永远旁路；否则按 batcher 启用情况与 @bot 处理规则决定
+ if is_poke:
+ await self._dispatch_grouped_request([item])
+ return
+
+ batcher = getattr(self, "_batcher", None)
+ if batcher is not None and batcher.is_enabled_for(is_group=True):
+ if is_at_bot and batcher.has_buffer(scope, sender_id):
+ # 已有 buffer 时再来一条 @bot：单独立即处理，不打断现有 buffer
+ logger.info(
+ "[自动回复] batch 内 @bot 旁路立即处理: group=%s sender=%s",
+ group_id,
+ sender_id,
+ )
+ await self._dispatch_grouped_request([item])
+ return
+ await batcher.submit(item)
+ return
+
+ await self._dispatch_grouped_request([item])
+
+ async def _execute_auto_reply(self, request: dict[str, Any]) -> None:
+ group_id = request["group_id"]
+ sender_id = request["sender_id"]
+ sender_name = str(request.get("sender_name") or "未知用户")
+ group_name = str(request.get("group_name") or "未知群聊")
+ full_question = request["full_question"]
+ trigger_message_id = request.get("trigger_message_id")
+ # 用于向 batcher 注册 inflight 任务（仅当本请求源自合并桶时生效）
+ batcher_scope: str | None = make_scope(group_id=group_id) if group_id else None
+
+ async with RequestContext(
+ request_type="group",
+ group_id=group_id,
+ sender_id=sender_id,
+ user_id=sender_id,
+ ) as ctx:
+
+ async def send_msg_cb(message: str, reply_to: int | None = None) -> None:
+ await self.sender.send_group_message(
+ group_id,
+ message,
+ reply_to=reply_to,
+ history_message=message,
+ )
+
+ async def get_recent_cb(
+ chat_id: str, msg_type: str, start: int, end: int
+ ) -> list[dict[str, Any]]:
+ return await get_recent_messages_prefer_local(
+ chat_id=chat_id,
+ msg_type=msg_type,
+ start=start,
+ end=end,
+ onebot_client=self.onebot,
+ history_manager=self.history_manager,
+ bot_qq=self.config.bot_qq,
+ attachment_registry=getattr(self.ai, "attachment_registry", None),
+ group_name_hint=group_name,
+ )
+
+ async def send_private_cb(
+ uid: int, msg: str, reply_to: int | None = None
+ ) -> None:
+ await self.sender.send_private_message(uid, msg, reply_to=reply_to)
+
+ async def send_img_cb(tid: int, mtype: str, path: str) -> None:
+ await self._send_image(tid, mtype, path)
+
+ async def send_like_cb(uid: int, times: int = 1) -> None:
+ await self.onebot.send_like(uid, times)
+
+ ai_client = self.ai
+ memory_storage = self.ai.memory_storage
+ runtime_config = self.ai.runtime_config
+ sender = self.sender
+ history_manager = self.history_manager
+ onebot_client = self.onebot
+ scheduler = self.scheduler
+ send_message_callback = send_msg_cb
+ get_recent_messages_callback = get_recent_cb
+ get_image_url_callback = self.onebot.get_image
+ get_forward_msg_callback = self.onebot.get_forward_msg
+ send_like_callback = send_like_cb
+ send_private_message_callback = send_private_cb
+ send_image_callback = send_img_cb
+ resource_vars = dict(globals())
+ resource_vars.update(locals())
+ resources = collect_context_resources(resource_vars)
+ for key, value in resources.items():
+ if value is not None:
+ ctx.set_resource(key, value)
+ if trigger_message_id is not None:
+ ctx.set_resource("trigger_message_id", trigger_message_id)
+ if request.get("_queue_lane"):
+ ctx.set_resource("queue_lane", request.get("_queue_lane"))
+ logger.debug(
+ "[上下文资源] group=%s keys=%s",
+ group_id,
+ ", ".join(sorted(resources.keys())),
+ )
+
+ try:
+ # 把当前 task 注册到 batcher，使其有能力在新消息到达时取消本次 LLM 调用
+ batcher = getattr(self, "_batcher", None)
+ current_task = asyncio.current_task()
+ registered_task: asyncio.Task[Any] | None = None
+ if (
+ batcher is not None
+ and batcher_scope is not None
+ and current_task is not None
+ ):
+ batcher.register_inflight(
+ batcher_scope, sender_id, current_task, ctx
+ )
+ registered_task = current_task
+ try:
+ await self.ai.ask(
+ full_question,
+ send_message_callback=send_msg_cb,
+ get_recent_messages_callback=get_recent_cb,
+ get_image_url_callback=self.onebot.get_image,
+ get_forward_msg_callback=self.onebot.get_forward_msg,
+ send_like_callback=send_like_cb,
+ sender=self.sender,
+ history_manager=self.history_manager,
+ onebot_client=self.onebot,
+ scheduler=self.scheduler,
+ extra_context={
+ "render_html_to_image": render_html_to_image,
+ "render_markdown_to_html": render_markdown_to_html,
+ "group_id": group_id,
+ "user_id": sender_id,
+ "is_at_bot": bool(request.get("is_at_bot", False)),
+ "sender_name": sender_name,
+ "group_name": group_name,
+ },
+ )
+ finally:
+ if (
+ batcher is not None
+ and batcher_scope is not None
+ and registered_task is not None
+ ):
+ batcher.unregister_inflight(
+ batcher_scope, sender_id, registered_task
+ )
+ except asyncio.CancelledError:
+ # 投机预发送被新消息抢占取消：不写错误日志、不重试
+ logger.info(
+ "[自动回复] 任务被取消（投机抢占）: group=%s sender=%s",
+ group_id,
+ sender_id,
+ )
+ raise
+ except Exception:
+ logger.exception("自动回复执行出错")
+ raise
+
+ def _is_at_bot(self, content: list[dict[str, Any]]) -> bool:
+ """检查消息内容中是否包含对机器人的 @ 提问"""
+ for seg in content:
+ if seg.get("type") == "at" and str(
+ seg.get("data", {}).get("qq", "")
+ ) == str(self.config.bot_qq):
+ return True
+ return False
+
+ async def _handle_injection_response(
+ self,
+ tid: int,
+ text: str,
+ is_private: bool = False,
+ sender_id: Optional[int] = None,
+ ) -> None:
+ """当检测到注入攻击时，生成并发送特定的防御性回复"""
+ reply = await self.security.generate_injection_response(text)
+ if is_private:
+ await self.sender.send_private_message(tid, reply, auto_history=False)
+ await self.history_manager.add_private_message(
+ tid, "<对注入消息的回复>", "Bot", "Bot"
+ )
+ else:
+ msg = f"[@{sender_id}] {reply}" if sender_id else reply
+ await self.sender.send_group_message(tid, msg, auto_history=False)
+ await self.history_manager.add_group_message(
+ tid, self.config.bot_qq, "<对注入消息的回复>", "Bot", ""
+ )
+
+ def _format_group_message_segment(self, item: BufferedMessage) -> str:
+ """格式化群聊单条 ```` 块。"""
+ time_str = datetime.fromtimestamp(item.arrival_time).strftime(
+ "%Y-%m-%d %H:%M:%S"
+ )
+ group_name = item.group_name or "未知群聊"
+ location = group_name if group_name.endswith("群") else f"{group_name}群"
+ safe_name = escape_xml_attr(item.sender_name or "未知用户")
+ safe_uid = escape_xml_attr(item.sender_id)
+ safe_gid = escape_xml_attr(item.group_id or 0)
+ safe_gname = escape_xml_attr(group_name)
+ safe_loc = escape_xml_attr(location)
+ safe_role = escape_xml_attr(item.sender_role or "member")
+ safe_title = escape_xml_attr(item.sender_title or "")
+ safe_time = escape_xml_attr(time_str)
+ safe_text = escape_xml_text(item.text)
+ message_id_attr = ""
+ if item.trigger_message_id is not None:
+ message_id_attr = (
+ f' message_id="{escape_xml_attr(item.trigger_message_id)}"'
+ )
+ level_attr = (
+ f' level="{escape_xml_attr(item.sender_level)}"'
+ if item.sender_level
+ else ""
+ )
+ attachment_xml = (
+ f"\n{attachment_refs_to_xml(item.attachments)}" if item.attachments else ""
+ )
+ return (
+ f'\n'
+ f" {safe_text}{attachment_xml}\n"
+ f" "
+ )
+
+ def _build_prompt(
+ self,
+ prefix: str,
+ name: str,
+ uid: int,
+ gid: int,
+ gname: str,
+ loc: str,
+ role: str,
+ title: str,
+ time_str: str,
+ text: str,
+ attachments: list[dict[str, str]] | None = None,
+ message_id: int | None = None,
+ level: str = "",
+ ) -> str:
+ """构建最终发送给 AI 的结构化 XML 消息 Prompt
+
+ 包含回复策略提示、用户信息和原始文本内容。
+ """
+ safe_name = escape_xml_attr(name)
+ safe_uid = escape_xml_attr(uid)
+ safe_gid = escape_xml_attr(gid)
+ safe_gname = escape_xml_attr(gname)
+ safe_loc = escape_xml_attr(loc)
+ safe_role = escape_xml_attr(role)
+ safe_title = escape_xml_attr(title)
+ safe_time = escape_xml_attr(time_str)
+ safe_text = escape_xml_text(text)
+ message_id_attr = ""
+ if message_id is not None:
+ message_id_attr = f' message_id="{escape_xml_attr(message_id)}"'
+ level_attr = f' level="{escape_xml_attr(level)}"' if level else ""
+ attachment_xml = (
+ f"\n{attachment_refs_to_xml(attachments)}" if attachments else ""
+ )
+ return f"""{prefix}
+ {safe_text}{attachment_xml}
+
+{_GROUP_STRATEGY_FOOTER}"""
diff --git a/src/Undefined/services/coordinator/private.py b/src/Undefined/services/coordinator/private.py
new file mode 100644
index 00000000..e3a6ec74
--- /dev/null
+++ b/src/Undefined/services/coordinator/private.py
@@ -0,0 +1,282 @@
+"""私聊回复与私聊 prompt 格式化。"""
+
+from __future__ import annotations
+
+
+import asyncio
+import logging
+import time
+from datetime import datetime
+from typing import TYPE_CHECKING, Any
+
+from Undefined.attachments import (
+ build_attachment_scope,
+ dispatch_pending_file_sends,
+ render_message_with_pic_placeholders,
+ attachment_refs_to_xml,
+)
+from Undefined.context import RequestContext
+from Undefined.context_resource_registry import collect_context_resources
+from Undefined.render import render_html_to_image, render_markdown_to_html
+from Undefined.services.message_batcher import BufferedMessage, make_scope
+from Undefined.utils.recent_messages import get_recent_messages_prefer_local
+from Undefined.utils.xml import escape_xml_attr, escape_xml_text
+
+if TYPE_CHECKING:
+ from Undefined.config import Config
+ from Undefined.services.message_batcher import BufferedMessage
+ from Undefined.services.security import SecurityService
+ from Undefined.utils.history import MessageHistoryManager
+ from Undefined.utils.scheduler import TaskScheduler
+ from Undefined.utils.sender import MessageSender
+
+logger = logging.getLogger(__name__)
+
+
+_PRIVATE_STRATEGY_FOOTER = """
+
+【私聊消息】
+这是私聊消息，用户专门来找你说话。你可以自由选择是否回复：
+- 如果想回复，先调用 send_message 工具发送回复内容，然后调用 end 结束对话
+- 只有明确纯表情包回复时，才先用 memes.search_memes 查表情包，再用 memes.send_meme_by_uid 单独发图；其他场景先把文字回复做好，表情包最后再搜或不搜
+- 如果不想回复，直接调用 end 结束对话即可"""
+
+
+class PrivateReplyMixin:
+ """私聊自动回复与私聊 prompt 格式化。"""
+
+ if TYPE_CHECKING:
+ ai: Any
+ config: Config
+ history_manager: MessageHistoryManager
+ onebot: Any
+ scheduler: TaskScheduler
+ security: SecurityService
+ sender: MessageSender
+
+ async def _dispatch_grouped_request(
+ self, items: list[BufferedMessage]
+ ) -> None: ...
+ async def _handle_injection_response(
+ self,
+ tid: int,
+ text: str,
+ is_private: bool = False,
+ sender_id: int | None = None,
+ ) -> None: ...
+ async def _send_image(self, tid: int, mtype: str, path: str) -> None: ...
+
+ async def handle_private_reply(
+ self,
+ user_id: int,
+ text: str,
+ message_content: list[dict[str, Any]],
+ attachments: list[dict[str, str]] | None = None,
+ is_poke: bool = False,
+ sender_name: str = "未知用户",
+ trigger_message_id: int | None = None,
+ ) -> None:
+ """处理私聊消息入口，决定回复策略并进行安全检测"""
+ logger.debug("[私聊回复] user=%s text_len=%s", user_id, len(text))
+ if user_id != self.config.superadmin_qq:
+ if await self.security.detect_injection(text, message_content):
+ logger.warning(f"[Security] 私聊注入攻击: user_id={user_id}")
+ await self.history_manager.modify_last_private_message(
+ user_id, "<这句话检测到用户进行注入，已删除>"
+ )
+ await self._handle_injection_response(user_id, text, is_private=True)
+ return
+
+ scope = make_scope(user_id=user_id)
+ item = BufferedMessage(
+ scope=scope,
+ sender_id=user_id,
+ text=text,
+ message_content=list(message_content),
+ attachments=list(attachments or []),
+ sender_name=sender_name,
+ arrival_time=time.time(),
+ is_private=True,
+ trigger_message_id=trigger_message_id,
+ is_poke=is_poke,
+ )
+
+ if is_poke:
+ # 拍一拍旁路 batcher，立即单条入队
+ await self._dispatch_grouped_request([item])
+ return
+
+ batcher = getattr(self, "_batcher", None)
+ if batcher is not None and batcher.is_enabled_for(is_group=False):
+ await batcher.submit(item)
+ return
+
+ await self._dispatch_grouped_request([item])
+
+ async def _execute_private_reply(self, request: dict[str, Any]) -> None:
+ user_id = request["user_id"]
+ sender_name = str(request.get("sender_name") or "未知用户")
+ full_question = request["full_question"]
+ trigger_message_id = request.get("trigger_message_id")
+ batcher_scope: str | None = make_scope(user_id=user_id)
+
+ async with RequestContext(
+ request_type="private",
+ user_id=user_id,
+ sender_id=user_id,
+ ) as ctx:
+
+ async def send_msg_cb(message: str, reply_to: int | None = None) -> None:
+ await self.sender.send_private_message(
+ user_id, message, reply_to=reply_to
+ )
+
+ async def get_recent_cb(
+ chat_id: str, msg_type: str, start: int, end: int
+ ) -> list[dict[str, Any]]:
+ return await get_recent_messages_prefer_local(
+ chat_id=chat_id,
+ msg_type=msg_type,
+ start=start,
+ end=end,
+ onebot_client=self.onebot,
+ history_manager=self.history_manager,
+ bot_qq=self.config.bot_qq,
+ attachment_registry=getattr(self.ai, "attachment_registry", None),
+ )
+
+ async def send_img_cb(tid: int, mtype: str, path: str) -> None:
+ await self._send_image(tid, mtype, path)
+
+ async def send_like_cb(uid: int, times: int = 1) -> None:
+ await self.onebot.send_like(uid, times)
+
+ async def send_private_cb(
+ uid: int, msg: str, reply_to: int | None = None
+ ) -> None:
+ await self.sender.send_private_message(uid, msg, reply_to=reply_to)
+
+ ai_client = self.ai
+ memory_storage = self.ai.memory_storage
+ runtime_config = self.ai.runtime_config
+ sender = self.sender
+ history_manager = self.history_manager
+ onebot_client = self.onebot
+ scheduler = self.scheduler
+ send_message_callback = send_msg_cb
+ get_recent_messages_callback = get_recent_cb
+ get_image_url_callback = self.onebot.get_image
+ get_forward_msg_callback = self.onebot.get_forward_msg
+ send_like_callback = send_like_cb
+ send_private_message_callback = send_private_cb
+ send_image_callback = send_img_cb
+ resource_vars = dict(globals())
+ resource_vars.update(locals())
+ resources = collect_context_resources(resource_vars)
+ for key, value in resources.items():
+ if value is not None:
+ ctx.set_resource(key, value)
+ if trigger_message_id is not None:
+ ctx.set_resource("trigger_message_id", trigger_message_id)
+ if request.get("_queue_lane"):
+ ctx.set_resource("queue_lane", request.get("_queue_lane"))
+ logger.debug(
+ "[上下文资源] private user=%s keys=%s",
+ user_id,
+ ", ".join(sorted(resources.keys())),
+ )
+
+ try:
+ batcher = getattr(self, "_batcher", None)
+ current_task = asyncio.current_task()
+ registered_task: asyncio.Task[Any] | None = None
+ if (
+ batcher is not None
+ and batcher_scope is not None
+ and current_task is not None
+ ):
+ batcher.register_inflight(batcher_scope, user_id, current_task, ctx)
+ registered_task = current_task
+ try:
+ result = await self.ai.ask(
+ full_question,
+ send_message_callback=send_msg_cb,
+ get_recent_messages_callback=get_recent_cb,
+ get_image_url_callback=self.onebot.get_image,
+ get_forward_msg_callback=self.onebot.get_forward_msg,
+ send_like_callback=send_like_cb,
+ sender=self.sender,
+ history_manager=self.history_manager,
+ onebot_client=self.onebot,
+ scheduler=self.scheduler,
+ extra_context={
+ "render_html_to_image": render_html_to_image,
+ "render_markdown_to_html": render_markdown_to_html,
+ "user_id": user_id,
+ "is_private_chat": True,
+ "sender_name": sender_name,
+ "selected_model_name": request.get("selected_model_name"),
+ },
+ )
+ finally:
+ if (
+ batcher is not None
+ and batcher_scope is not None
+ and registered_task is not None
+ ):
+ batcher.unregister_inflight(
+ batcher_scope, user_id, registered_task
+ )
+ if result:
+ scope_key = build_attachment_scope(
+ user_id=user_id,
+ request_type="private",
+ )
+ rendered = await render_message_with_pic_placeholders(
+ str(result),
+ registry=self.ai.attachment_registry,
+ scope_key=scope_key,
+ strict=False,
+ )
+ await self.sender.send_private_message(
+ user_id,
+ rendered.delivery_text,
+ history_message=rendered.history_text,
+ )
+ await dispatch_pending_file_sends(
+ rendered,
+ sender=self.sender,
+ target_type="private",
+ target_id=user_id,
+ registry=self.ai.attachment_registry,
+ )
+ except asyncio.CancelledError:
+ logger.info("[私聊回复] 任务被取消（投机抢占）: user=%s", user_id)
+ raise
+ except Exception:
+ logger.exception("私聊回复执行出错")
+ raise
+
+ def _format_private_message_segment(self, item: BufferedMessage) -> str:
+ """格式化私聊单条 ```` 块。"""
+ time_str = datetime.fromtimestamp(item.arrival_time).strftime(
+ "%Y-%m-%d %H:%M:%S"
+ )
+ safe_name = escape_xml_attr(item.sender_name or "未知用户")
+ safe_uid = escape_xml_attr(item.sender_id)
+ safe_time = escape_xml_attr(time_str)
+ safe_text = escape_xml_text(item.text)
+ message_id_attr = ""
+ if item.trigger_message_id is not None:
+ message_id_attr = (
+ f' message_id="{escape_xml_attr(item.trigger_message_id)}"'
+ )
+ attachment_xml = (
+ f"\n{attachment_refs_to_xml(item.attachments)}" if item.attachments else ""
+ )
+ return (
+ f'\n'
+ f" {safe_text}{attachment_xml}\n"
+ f" "
+ )
diff --git a/src/Undefined/services/message_batcher/__init__.py b/src/Undefined/services/message_batcher/__init__.py
new file mode 100644
index 00000000..c9195f1f
--- /dev/null
+++ b/src/Undefined/services/message_batcher/__init__.py
@@ -0,0 +1,48 @@
+"""同 sender 短时多消息合并器（MessageBatcher）。
+
+核心目标：把同一个 sender 在短时间内连续发出的消息合并到同一轮 AI 调用，
+让模型一次看到全部 ```` 块自行决定 "独立请求 / 修正 / 打断"，
+避免 N 条独立 LLM 调用造成的重复回复或行为打架。
+
+时序：每个 (scope, sender_id) 桶内有两条独立的"静默计时器"：
+
+- ``T1 = window_seconds`` —— "打字静默阈值"。静默达到 T1 视为用户写完，
+ 这一批 batch 结束。
+- ``T2 = pre_send_seconds`` —— "投机预发送阈值"，要求严格小于 T1。
+ 静默到 T2 时**先把当前 batch 提前发给 LLM 抢时间**（speculative pre-fire），
+ 但 batch 尚未结束；T1 才决定结束。
+
+新消息到来：
+
+- 若桶处于 ``TYPING``（尚未 pre-fire）：append 后重置 T1/T2。
+- 若桶处于 ``SPECULATING``（已 pre-fire，请求已入队或 inflight 在跑）：
+ - 检查 inflight 是否已经 "向用户发出过任何消息"
+ （来自 ``RequestContext.get_resource("message_sent_this_turn")``）。
+ - inflight 尚未发消息 → 调 ``inflight_task.cancel()``，桶回到 TYPING；
+ 新消息照常 append 到原有 items 后面，T1/T2 重置。
+ - inflight 已经发过消息且 ``allow_cancel_after_send=False``（默认安全）→
+ 保留旧 batch 让其自然走完，新消息开新 batch（即清空当前桶后立即重新作为首条入桶）。
+ - inflight 已经发过消息但开关 = True → 仍 cancel（可能造成重复发送，仅极端场景）。
+
+兼容回退：当 ``pre_send_seconds <= 0`` 或 ``>= window_seconds`` 时投机模式关闭，
+退化为旧版 "T1 静默到期才发车" 的行为。
+"""
+
+# 同 sender 短时合并：T1 结束 batch，T2 投机预发送
+from Undefined.services.message_batcher.scheduler import MessageBatcher
+from Undefined.services.message_batcher.state import (
+ BatchDispatchToken,
+ BatchPhase,
+ BufferedMessage,
+ FlushCallback,
+ make_scope,
+)
+
+__all__ = [
+ "BatchDispatchToken",
+ "BatchPhase",
+ "BufferedMessage",
+ "FlushCallback",
+ "MessageBatcher",
+ "make_scope",
+]
diff --git a/src/Undefined/services/message_batcher.py b/src/Undefined/services/message_batcher/scheduler.py
similarity index 85%
rename from src/Undefined/services/message_batcher.py
rename to src/Undefined/services/message_batcher/scheduler.py
index 4ad94e11..45b0cf93 100644
--- a/src/Undefined/services/message_batcher.py
+++ b/src/Undefined/services/message_batcher/scheduler.py
@@ -1,144 +1,28 @@
-"""同 sender 短时多消息合并器（MessageBatcher）。
-
-核心目标：把同一个 sender 在短时间内连续发出的消息合并到同一轮 AI 调用，
-让模型一次看到全部 ```` 块自行决定 "独立请求 / 修正 / 打断"，
-避免 N 条独立 LLM 调用造成的重复回复或行为打架。
-
-时序：每个 (scope, sender_id) 桶内有两条独立的"静默计时器"：
-
-- ``T1 = window_seconds`` —— "打字静默阈值"。静默达到 T1 视为用户写完，
- 这一批 batch 结束。
-- ``T2 = pre_send_seconds`` —— "投机预发送阈值"，要求严格小于 T1。
- 静默到 T2 时**先把当前 batch 提前发给 LLM 抢时间**（speculative pre-fire），
- 但 batch 尚未结束；T1 才决定结束。
-
-新消息到来：
-
-- 若桶处于 ``TYPING``（尚未 pre-fire）：append 后重置 T1/T2。
-- 若桶处于 ``SPECULATING``（已 pre-fire，请求已入队或 inflight 在跑）：
- - 检查 inflight 是否已经 "向用户发出过任何消息"
- （来自 ``RequestContext.get_resource("message_sent_this_turn")``）。
- - inflight 尚未发消息 → 调 ``inflight_task.cancel()``，桶回到 TYPING；
- 新消息照常 append 到原有 items 后面，T1/T2 重置。
- - inflight 已经发过消息且 ``allow_cancel_after_send=False``（默认安全）→
- 保留旧 batch 让其自然走完，新消息开新 batch（即清空当前桶后立即重新作为首条入桶）。
- - inflight 已经发过消息但开关 = True → 仍 cancel（可能造成重复发送，仅极端场景）。
-
-兼容回退：当 ``pre_send_seconds <= 0`` 或 ``>= window_seconds`` 时投机模式关闭，
-退化为旧版 "T1 静默到期才发车" 的行为。
-"""
+"""MessageBatcher 调度与 timer 逻辑。"""
from __future__ import annotations
+# 同 sender 短时合并：T1 结束 batch，T2 投机预发送
+
import asyncio
-import enum
import logging
import time
-from dataclasses import dataclass, field
-from typing import Any, Awaitable, Callable
+from typing import Any
from Undefined.config.models import MessageBatcherConfig
+from Undefined.services.message_batcher.state import (
+ BatchDispatchToken,
+ BatchPhase,
+ BufferedMessage,
+ FlushCallback,
+ _BatchState,
+ _InflightInfo,
+)
from Undefined.utils.coerce import was_message_sent
logger = logging.getLogger(__name__)
-@dataclass
-class BatchDispatchToken:
- """一次 batch 发车的身份令牌，用于取消已入队但尚未执行的投机请求。"""
-
- scope: str
- sender_id: int
- batch_id: int
- speculative: bool = False
- cancelled: bool = False
-
- def cancel(self) -> None:
- self.cancelled = True
-
-
-@dataclass
-class BufferedMessage:
- """缓冲中的单条消息上下文。"""
-
- scope: str
- sender_id: int
- text: str
- message_content: list[dict[str, Any]]
- attachments: list[dict[str, str]]
- sender_name: str
- arrival_time: float
- is_private: bool
- trigger_message_id: int | None = None
- is_poke: bool = False
- is_at_bot: bool = False
- is_fake_at: bool = False
- # 群聊扩展字段
- group_id: int | None = None
- group_name: str = ""
- sender_role: str = "member"
- sender_title: str = ""
- sender_level: str = ""
- batch_token: BatchDispatchToken | None = None
-
-
-FlushCallback = Callable[[list[BufferedMessage]], Awaitable[None]]
-"""``flush_callback(items)``：batcher 决定 fire 时调用，调用方负责拼装 prompt 并入队执行。
-
-调用约定：
-- batcher 的 ``flush_callback`` **不应** 立即 await LLM 的完成，
- 而是把请求扔进 QueueManager 后立即返回，真正的 LLM 任务由 coordinator 在 ``execute_reply``
- 开头调用 :meth:`MessageBatcher.register_inflight` 上报。
-- 若需要 batcher 关停时也等待 in-flight 收尾，由 :meth:`MessageBatcher.flush_all` 处理。
-"""
-
-
-class BatchPhase(enum.Enum):
- """桶状态机。"""
-
- TYPING = "typing" # 等待 T1/T2 静默
- SPECULATING = "speculating" # T2 已触发，请求已入队或 inflight 在跑；T1 仍未到
- FINALIZING = "finalizing" # T1 已到，等 inflight（若有）自然结束
-
-
-@dataclass
-class _InflightInfo:
- """inflight LLM 任务关联信息，由 coordinator 通过 ``register_inflight`` 上报。"""
-
- task: asyncio.Task[Any]
- # ``RequestContext`` 引用，用于判断 ``message_sent_this_turn`` 资源
- request_context: Any = None
-
-
-@dataclass
-class _BatchState:
- """单个 (scope, sender_id) 桶的状态。"""
-
- phase: BatchPhase = BatchPhase.TYPING
- items: list[BufferedMessage] = field(default_factory=list)
- first_arrival_monotonic: float = 0.0
- # T1 = window_seconds 静默 timer（决定 batch 结束）
- t1_handle: asyncio.TimerHandle | None = None
- # T2 = pre_send_seconds 静默 timer（决定 pre-fire）；投机关闭时为 None
- t2_handle: asyncio.TimerHandle | None = None
- # SPECULATING 阶段记录 inflight LLM 任务（由 coordinator 通过 register_inflight 注入）
- inflight: _InflightInfo | None = None
- # T2 fire 时由 batcher 创建的 flush task；inflight 还未上报前用于兜底取消
- speculative_flush_task: asyncio.Task[Any] | None = None
- # 当前 batch 的身份令牌；T2 入队后若又来新消息，可将旧 token 标记取消，
- # coordinator 在真正执行前会跳过它。
- dispatch_token: BatchDispatchToken | None = None
-
-
-def make_scope(*, group_id: int | None = None, user_id: int | None = None) -> str:
- """构造合并 key 的 scope 字符串。"""
- if group_id and group_id > 0:
- return f"group:{group_id}"
- if user_id is not None:
- return f"private:{user_id}"
- return "unknown"
-
-
class MessageBatcher:
"""同 sender 短时合并器（含 T2 投机预发送）。"""
@@ -187,17 +71,21 @@ def is_enabled_for(self, *, is_group: bool) -> bool:
return False
return cfg.group_enabled if is_group else cfg.private_enabled
+ # 立即触发 batch 发车
def has_buffer(self, scope: str, sender_id: int) -> bool:
return (scope, sender_id) in self._buckets
+ # 立即触发 batch 发车
async def flush_sender(self, scope: str, sender_id: int) -> bool:
return await self._handle_t1((scope, sender_id), raise_on_failure=False)
@property
def speculative_enabled(self) -> bool:
+ # 0 < pre_send < window 时启用 T2 投机预发送
cfg = self._config
return 0 < cfg.pre_send_seconds < cfg.window_seconds
+ # 提交消息进入 (scope,sender) 合并桶并重置 T1/T2 计时器
async def submit(self, item: BufferedMessage) -> None:
"""提交一条消息进入合并桶。
@@ -353,6 +241,7 @@ async def submit(self, item: BufferedMessage) -> None:
immediate_fire_items = self._pop_locked(key)
else:
# T1 delay
+ # fixed：从首条到达时刻起算绝对 T1；extend：每条新消息重置为 window_seconds
if cfg.strategy == "fixed":
target = state.first_arrival_monotonic + cfg.window_seconds
t1_delay = max(0.0, target - now_mono)
@@ -434,6 +323,7 @@ def register_inflight(
sender_id,
)
+ # 注销 inflight 任务
def unregister_inflight(
self, scope: str, sender_id: int, task: asyncio.Task[Any]
) -> None:
diff --git a/src/Undefined/services/message_batcher/state.py b/src/Undefined/services/message_batcher/state.py
new file mode 100644
index 00000000..6dbea7c4
--- /dev/null
+++ b/src/Undefined/services/message_batcher/state.py
@@ -0,0 +1,106 @@
+"""MessageBatcher 数据模型与 scope 工具。"""
+
+from __future__ import annotations
+
+# 同 sender 短时合并：T1 结束 batch，T2 投机预发送
+
+import asyncio
+import enum
+from dataclasses import dataclass, field
+from typing import Any, Awaitable, Callable
+
+
+@dataclass
+class BatchDispatchToken:
+ """一次 batch 发车的身份令牌，用于取消已入队但尚未执行的投机请求。"""
+
+ scope: str
+ sender_id: int
+ batch_id: int
+ speculative: bool = False
+ cancelled: bool = False
+
+ def cancel(self) -> None:
+ self.cancelled = True
+
+
+@dataclass
+class BufferedMessage:
+ """缓冲中的单条消息上下文。"""
+
+ scope: str
+ sender_id: int
+ text: str
+ message_content: list[dict[str, Any]]
+ attachments: list[dict[str, str]]
+ sender_name: str
+ arrival_time: float
+ is_private: bool
+ trigger_message_id: int | None = None
+ is_poke: bool = False
+ is_at_bot: bool = False
+ is_fake_at: bool = False
+ # 群聊扩展字段
+ group_id: int | None = None
+ group_name: str = ""
+ sender_role: str = "member"
+ sender_title: str = ""
+ sender_level: str = ""
+ batch_token: BatchDispatchToken | None = None
+
+
+FlushCallback = Callable[[list[BufferedMessage]], Awaitable[None]]
+"""``flush_callback(items)``：batcher 决定 fire 时调用，调用方负责拼装 prompt 并入队执行。
+
+调用约定：
+- batcher 的 ``flush_callback`` **不应** 立即 await LLM 的完成，
+ 而是把请求扔进 QueueManager 后立即返回，真正的 LLM 任务由 coordinator 在 ``execute_reply``
+ 开头调用 :meth:`MessageBatcher.register_inflight` 上报。
+- 若需要 batcher 关停时也等待 in-flight 收尾，由 :meth:`MessageBatcher.flush_all` 处理。
+"""
+
+
+class BatchPhase(enum.Enum):
+ """桶状态机：TYPING → SPECULATING(可选) → FINALIZING。"""
+
+ TYPING = "typing" # 等待 T1/T2 静默，用户仍在输入
+ SPECULATING = "speculating" # T2 已触发投机 pre-fire，T1 未到，batch 未结束
+ FINALIZING = "finalizing" # T1 已到，等待 inflight 自然结束后再释放桶
+
+
+@dataclass
+class _InflightInfo:
+ """inflight LLM 任务关联信息，由 coordinator 通过 ``register_inflight`` 上报。"""
+
+ task: asyncio.Task[Any]
+ # ``RequestContext`` 引用，用于判断 ``message_sent_this_turn`` 资源
+ request_context: Any = None
+
+
+@dataclass
+class _BatchState:
+ """单个 (scope, sender_id) 桶的状态。"""
+
+ phase: BatchPhase = BatchPhase.TYPING
+ items: list[BufferedMessage] = field(default_factory=list)
+ first_arrival_monotonic: float = 0.0
+ # T1 = window_seconds 静默 timer（决定 batch 结束）
+ t1_handle: asyncio.TimerHandle | None = None
+ # T2 = pre_send_seconds 静默 timer（决定 pre-fire）；投机关闭时为 None
+ t2_handle: asyncio.TimerHandle | None = None
+ # SPECULATING 阶段记录 inflight LLM 任务（由 coordinator 通过 register_inflight 注入）
+ inflight: _InflightInfo | None = None
+ # T2 fire 时由 batcher 创建的 flush task；inflight 还未上报前用于兜底取消
+ speculative_flush_task: asyncio.Task[Any] | None = None
+ # 当前 batch 的身份令牌；T2 入队后若又来新消息，可将旧 token 标记取消，
+ # coordinator 在真正执行前会跳过它。
+ dispatch_token: BatchDispatchToken | None = None
+
+
+def make_scope(*, group_id: int | None = None, user_id: int | None = None) -> str:
+ """构造合并 key 的 scope 字符串。"""
+ if group_id and group_id > 0:
+ return f"group:{group_id}"
+ if user_id is not None:
+ return f"private:{user_id}"
+ return "unknown"
diff --git a/src/Undefined/skills/agents/code_delivery_agent/tools/read/handler.py b/src/Undefined/skills/agents/code_delivery_agent/tools/read/handler.py
index aabb03ab..81ab2233 100644
--- a/src/Undefined/skills/agents/code_delivery_agent/tools/read/handler.py
+++ b/src/Undefined/skills/agents/code_delivery_agent/tools/read/handler.py
@@ -39,6 +39,7 @@ async def _read_single_file(
async with aiofiles.open(
full_path, "r", encoding="utf-8", errors="replace"
) as f:
+ # async for 循环
async for _ in f:
line_count += 1
diff --git a/src/Undefined/skills/agents/runner.py b/src/Undefined/skills/agents/runner.py
deleted file mode 100644
index 9e3cc326..00000000
--- a/src/Undefined/skills/agents/runner.py
+++ /dev/null
@@ -1,387 +0,0 @@
-from __future__ import annotations
-
-import asyncio
-import logging
-from pathlib import Path
-from typing import Any
-
-import aiofiles
-
-from Undefined.config.models import AgentModelConfig
-from Undefined.ai.transports.openai_transport import RESPONSES_OUTPUT_ITEMS_KEY
-from Undefined.skills.agents.agent_tool_registry import AgentToolRegistry
-from Undefined.skills.anthropic_skills import AnthropicSkillRegistry
-from Undefined.utils.tool_calls import parse_tool_arguments
-
-
-async def load_prompt_text(agent_dir: Path, default_prompt: str) -> str:
- """从 agent 目录加载 prompt.md，缺失时返回默认提示词。"""
-
- prompt_path = agent_dir / "prompt.md"
- if prompt_path.exists():
- async with aiofiles.open(prompt_path, "r", encoding="utf-8") as file:
- return await file.read()
- return default_prompt
-
-
-def _filter_tools_for_runtime_config(
- agent_name: str,
- tools: list[dict[str, Any]],
- runtime_config: Any | None,
-) -> list[dict[str, Any]]:
- if agent_name != "web_agent" or runtime_config is None:
- return tools
-
- if bool(getattr(runtime_config, "grok_search_enabled", False)):
- return tools
-
- filtered: list[dict[str, Any]] = []
- for tool in tools:
- function = tool.get("function") if isinstance(tool, dict) else None
- name = function.get("name") if isinstance(function, dict) else None
- if name == "grok_search":
- continue
- filtered.append(tool)
- return filtered
-
-
-async def run_agent_with_tools(
- *,
- agent_name: str,
- user_content: str,
- context_messages: list[dict[str, str]] | None = None,
- empty_user_content_message: str,
- default_prompt: str,
- context: dict[str, Any],
- agent_dir: Path,
- logger: logging.Logger,
- max_iterations: int = 20,
- tool_error_prefix: str = "错误",
-) -> str:
- """执行通用 Agent 循环。
-
- 该方法统一处理：
- - prompt 加载
- - LLM 迭代决策
- - tool call 并发执行
- - tool 结果回填 messages
- """
-
- if not user_content.strip():
- return empty_user_content_message
-
- tool_registry = AgentToolRegistry(
- agent_dir / "tools",
- current_agent_name=agent_name,
- is_main_agent=False,
- )
- tools = tool_registry.get_tools_schema()
- runtime_config = context.get("runtime_config")
- tools = _filter_tools_for_runtime_config(agent_name, tools, runtime_config)
-
- # 发现并加载 agent 私有 Anthropic Skills（可选）
- agent_skills_dir = agent_dir / "anthropic_skills"
- agent_skill_registry: AnthropicSkillRegistry | None = None
- if agent_skills_dir.exists() and agent_skills_dir.is_dir():
- agent_skill_registry = AnthropicSkillRegistry(agent_skills_dir)
- if agent_skill_registry.has_skills():
- # 将 anthropic skill tools 加入 agent 的可用工具列表
- tools = tools + agent_skill_registry.get_tools_schema()
- logger.info(
- "[Agent:%s] 加载了 %d 个私有 Anthropic Skills",
- agent_name,
- len(agent_skill_registry.get_all_skills()),
- )
-
- ai_client = context.get("ai_client")
- if not ai_client:
- return "AI client 未在上下文中提供"
-
- model_config_override = context.get("model_config_override")
- if isinstance(model_config_override, AgentModelConfig):
- agent_config = model_config_override
- else:
- agent_config = ai_client.agent_config
- # 动态选择 agent 模型
- group_id = context.get("group_id", 0) or 0
- user_id = context.get("user_id", 0) or 0
- global_enabled = runtime_config.model_pool_enabled if runtime_config else False
- agent_config = ai_client.model_selector.select_agent_config(
- agent_config,
- group_id=group_id,
- user_id=user_id,
- global_enabled=global_enabled,
- )
- system_prompt = await load_prompt_text(agent_dir, default_prompt)
-
- # 注入 agent 私有 Anthropic Skills 元数据到 system prompt
- if agent_skill_registry and agent_skill_registry.has_skills():
- skills_xml = agent_skill_registry.build_metadata_xml()
- if skills_xml:
- system_prompt = (
- f"{system_prompt}\n\n"
- f"【可用的 Anthropic Skills】\n"
- f"{skills_xml}\n\n"
- f"注意：以上是你可用的 Anthropic Agent Skills。"
- f"当任务与某个 skill 相关时，"
- f"可以调用对应的 skill tool（tool_name 字段）"
- f"来获取该领域的详细指令和知识。"
- )
-
- agent_history = context.get("agent_history", [])
-
- messages: list[dict[str, Any]] = [{"role": "system", "content": system_prompt}]
- if agent_history:
- messages.extend(agent_history)
- if context_messages:
- messages.extend(context_messages)
- messages.append({"role": "user", "content": user_content})
- transport_state: dict[str, Any] | None = None
- queue_lane = context.get("queue_lane")
- max_pre_tool_retries = max(
- 0, int(getattr(runtime_config, "ai_request_max_retries", 0) or 0)
- )
- pre_tool_failure_count = 0
-
- for iteration in range(1, max_iterations + 1):
- logger.debug("[Agent:%s] iteration=%s", agent_name, iteration)
- message_checkpoint_len = len(messages)
- transport_state_checkpoint = transport_state
- try:
- result = await ai_client.submit_queued_llm_call(
- model_config=agent_config,
- messages=messages,
- max_tokens=agent_config.max_tokens,
- call_type=f"agent:{agent_name}",
- tools=tools if tools else None,
- tool_choice="auto",
- transport_state=transport_state,
- queue_lane=queue_lane,
- )
- except Exception as exc:
- logger.exception(
- "[Agent:%s] queued LLM 调用失败: lane=%s iteration=%s error=%s",
- agent_name,
- queue_lane,
- iteration,
- exc,
- )
- raise RuntimeError("智能体模型请求失败") from exc
-
- try:
- tool_execution_started = False
- tool_name_map = (
- result.get("_tool_name_map") if isinstance(result, dict) else None
- )
- api_to_internal: dict[str, str] = {}
- if isinstance(tool_name_map, dict):
- raw_api_to_internal = tool_name_map.get("api_to_internal")
- if isinstance(raw_api_to_internal, dict):
- api_to_internal = {
- str(key): str(value)
- for key, value in raw_api_to_internal.items()
- }
-
- next_transport_state = (
- result.get("_transport_state") if isinstance(result, dict) else None
- )
- transport_state = (
- next_transport_state if isinstance(next_transport_state, dict) else None
- )
-
- choice: dict[str, Any] = result.get("choices", [{}])[0]
- message: dict[str, Any] = choice.get("message", {})
- content: str = message.get("content") or ""
- reasoning_content: str | None = message.get("reasoning_content")
- tool_calls: list[dict[str, Any]] = message.get("tool_calls", [])
-
- if content.strip() and tool_calls:
- content = ""
-
- if not tool_calls:
- return content
-
- assistant_message: dict[str, Any] = {
- "role": "assistant",
- "content": content,
- "tool_calls": tool_calls,
- }
- output_items = message.get(RESPONSES_OUTPUT_ITEMS_KEY)
- if isinstance(output_items, list):
- assistant_message[RESPONSES_OUTPUT_ITEMS_KEY] = output_items
- capture_reasoning = bool(
- getattr(agent_config, "thinking_tool_call_compat", False)
- ) or bool(getattr(agent_config, "reasoning_content_replay", False))
- if capture_reasoning and reasoning_content is not None:
- assistant_message["reasoning_content"] = reasoning_content
- messages.append(assistant_message)
-
- tool_tasks: list[asyncio.Future[Any]] = []
- tool_call_ids: list[str] = []
- tool_api_names: list[str] = []
- end_tool_call: dict[str, Any] | None = None
- end_tool_args: dict[str, Any] = {}
-
- for tool_call in tool_calls:
- call_id = str(tool_call.get("id", ""))
- function: dict[str, Any] = tool_call.get("function", {})
- api_function_name = str(function.get("name", ""))
- raw_args = function.get("arguments")
-
- internal_function_name = api_to_internal.get(
- api_function_name, api_function_name
- )
- logger.info(
- "[Agent:%s] preparing tool=%s",
- agent_name,
- internal_function_name,
- )
-
- function_args = parse_tool_arguments(
- raw_args,
- logger=logger,
- tool_name=api_function_name,
- )
-
- if not isinstance(function_args, dict):
- function_args = {}
-
- # 检测 end 工具，暂存后统一处理
- if internal_function_name == "end":
- if len(tool_calls) > 1:
- logger.warning(
- "[Agent:%s] end 与其他工具同时调用，"
- "将先执行其他工具，并回填 end 跳过结果",
- agent_name,
- )
- end_tool_call = tool_call
- end_tool_args = function_args
- continue
-
- tool_call_ids.append(call_id)
- tool_api_names.append(api_function_name)
-
- # Anthropic Skill tool 路由
- # 工具名格式: skills，如 skills-_-pdf-processing
- skill_delimiter = (
- agent_skill_registry.dot_delimiter
- if agent_skill_registry
- else "-_-"
- )
- is_agent_skill = internal_function_name.startswith(
- f"skills{skill_delimiter}"
- )
- if is_agent_skill and agent_skill_registry:
- tool_tasks.append(
- asyncio.ensure_future(
- agent_skill_registry.execute_skill_tool(
- internal_function_name,
- function_args,
- context,
- )
- )
- )
- else:
- tool_tasks.append(
- asyncio.ensure_future(
- tool_registry.execute_tool(
- internal_function_name,
- function_args,
- context,
- )
- )
- )
-
- if tool_tasks:
- tool_execution_started = True
- logger.info(
- "[Agent:%s] executing tools in parallel: count=%s",
- agent_name,
- len(tool_tasks),
- )
- results = await asyncio.gather(*tool_tasks, return_exceptions=True)
-
- for index, tool_result in enumerate(results):
- call_id = tool_call_ids[index]
- api_tool_name = tool_api_names[index]
- if isinstance(tool_result, Exception):
- content_str = f"{tool_error_prefix}: {tool_result}"
- else:
- content_str = str(tool_result)
-
- messages.append(
- {
- "role": "tool",
- "tool_call_id": call_id,
- "name": api_tool_name,
- "content": content_str,
- }
- )
-
- # 处理 end 工具调用
- if end_tool_call:
- end_call_id = str(end_tool_call.get("id", ""))
- end_api_name = end_tool_call.get("function", {}).get("name", "end")
- if tool_tasks:
- # end 与其他工具同时调用：跳过执行，但仍回填 tool 响应，
- # 避免 assistant.tool_calls 出现未配对的 tool_call_id。
- skip_content = (
- "end 与其他工具同轮调用，本轮未执行 end；"
- "请根据其他工具结果继续决策。"
- )
- messages.append(
- {
- "role": "tool",
- "tool_call_id": end_call_id,
- "name": end_api_name,
- "content": skip_content,
- }
- )
- logger.info(
- "[Agent:%s] end 与其他工具同时调用，已回填跳过响应",
- agent_name,
- )
- else:
- # end 单独调用，正常执行（参数已在循环中解析）
- tool_execution_started = True
- end_result = await tool_registry.execute_tool(
- "end", end_tool_args, context
- )
- messages.append(
- {
- "role": "tool",
- "tool_call_id": end_call_id,
- "name": end_api_name,
- "content": str(end_result),
- }
- )
- pre_tool_failure_count = 0
-
- except Exception as exc:
- if (
- not tool_execution_started
- and pre_tool_failure_count < max_pre_tool_retries
- ):
- pre_tool_failure_count += 1
- del messages[message_checkpoint_len:]
- transport_state = transport_state_checkpoint
- logger.warning(
- "[Agent:%s] pre-tool 本地失败，重试当前 LLM 轮次: lane=%s retry=%s/%s iteration=%s error=%s",
- agent_name,
- queue_lane,
- pre_tool_failure_count,
- max_pre_tool_retries,
- iteration,
- exc,
- )
- continue
- logger.exception(
- "[Agent:%s] 执行失败，已静默抑制: lane=%s iteration=%s error=%s",
- agent_name,
- queue_lane,
- iteration,
- exc,
- )
- return ""
-
- return "达到最大迭代次数"
diff --git a/src/Undefined/skills/agents/runner/__init__.py b/src/Undefined/skills/agents/runner/__init__.py
new file mode 100644
index 00000000..09676932
--- /dev/null
+++ b/src/Undefined/skills/agents/runner/__init__.py
@@ -0,0 +1,12 @@
+"""Agent runner 子包：context 准备、工具执行与 LLM 迭代循环。"""
+
+# 对外 re-export，兼容 `from Undefined.skills.agents.runner import run_agent_with_tools`
+from Undefined.skills.agents.runner.context import load_prompt_text
+from Undefined.skills.agents.runner.loop import run_agent_with_tools
+from Undefined.skills.agents.runner.tools import _filter_tools_for_runtime_config
+
+__all__ = [
+ "load_prompt_text",
+ "run_agent_with_tools",
+ "_filter_tools_for_runtime_config",
+]
diff --git a/src/Undefined/skills/agents/runner/context.py b/src/Undefined/skills/agents/runner/context.py
new file mode 100644
index 00000000..3e4e399e
--- /dev/null
+++ b/src/Undefined/skills/agents/runner/context.py
@@ -0,0 +1,133 @@
+# Agent 运行前上下文准备（工具注册表、模型、消息链）
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+import aiofiles
+
+from Undefined.config.models import AgentModelConfig
+from Undefined.skills.agents.agent_tool_registry import AgentToolRegistry
+from Undefined.skills.anthropic_skills import AnthropicSkillRegistry
+
+from Undefined.skills.agents.runner.tools import _filter_tools_for_runtime_config
+
+
+# 异步读取 agent 目录下的 prompt.md
+async def load_prompt_text(agent_dir: Path, default_prompt: str) -> str:
+ """从 agent 目录加载 prompt.md，缺失时返回默认提示词。"""
+
+ prompt_path = agent_dir / "prompt.md"
+ if prompt_path.exists():
+ async with aiofiles.open(prompt_path, "r", encoding="utf-8") as file:
+ return await file.read()
+ return default_prompt
+
+
+@dataclass
+# 类：PreparedAgentRun
+class PreparedAgentRun:
+ tool_registry: AgentToolRegistry
+ agent_skill_registry: AnthropicSkillRegistry | None
+ tools: list[dict[str, Any]]
+ agent_config: AgentModelConfig
+ messages: list[dict[str, Any]]
+ ai_client: Any
+ queue_lane: Any
+ max_pre_tool_retries: int
+
+
+# 准备 Agent 运行上下文：工具、模型、消息链
+async def prepare_agent_run(
+ *,
+ agent_name: str,
+ user_content: str,
+ context_messages: list[dict[str, str]] | None,
+ default_prompt: str,
+ context: dict[str, Any],
+ agent_dir: Path,
+ logger: Any,
+) -> PreparedAgentRun | str:
+ # 为当前 Agent 实例化私有工具注册表（含 callable agent 扫描）
+ tool_registry = AgentToolRegistry(
+ agent_dir / "tools",
+ current_agent_name=agent_name,
+ is_main_agent=False,
+ )
+ tools = tool_registry.get_tools_schema()
+ runtime_config = context.get("runtime_config")
+ tools = _filter_tools_for_runtime_config(agent_name, tools, runtime_config)
+
+ agent_skills_dir = agent_dir / "anthropic_skills"
+ agent_skill_registry: AnthropicSkillRegistry | None = None
+ # 可选：加载 Agent 目录下的 Anthropic Skills 并追加 tool schema
+ if agent_skills_dir.exists() and agent_skills_dir.is_dir():
+ agent_skill_registry = AnthropicSkillRegistry(agent_skills_dir)
+ if agent_skill_registry.has_skills():
+ tools = tools + agent_skill_registry.get_tools_schema()
+ logger.info(
+ "[Agent:%s] 加载了 %d 个私有 Anthropic Skills",
+ agent_name,
+ len(agent_skill_registry.get_all_skills()),
+ )
+
+ ai_client = context.get("ai_client")
+ if not ai_client:
+ return "AI client 未在上下文中提供"
+
+ model_config_override = context.get("model_config_override")
+ if isinstance(model_config_override, AgentModelConfig):
+ agent_config = model_config_override
+ else:
+ agent_config = ai_client.agent_config
+ group_id = context.get("group_id", 0) or 0
+ user_id = context.get("user_id", 0) or 0
+ global_enabled = runtime_config.model_pool_enabled if runtime_config else False
+ # 多模型池：按群/私聊上下文选择 Agent 专用模型配置
+ agent_config = ai_client.model_selector.select_agent_config(
+ agent_config,
+ group_id=group_id,
+ user_id=user_id,
+ global_enabled=global_enabled,
+ )
+ system_prompt = await load_prompt_text(agent_dir, default_prompt)
+
+ if agent_skill_registry and agent_skill_registry.has_skills():
+ skills_xml = agent_skill_registry.build_metadata_xml()
+ if skills_xml:
+ system_prompt = (
+ f"{system_prompt}\n\n"
+ f"【可用的 Anthropic Skills】\n"
+ f"{skills_xml}\n\n"
+ f"注意：以上是你可用的 Anthropic Agent Skills。"
+ f"当任务与某个 skill 相关时，"
+ f"可以调用对应的 skill tool（tool_name 字段）"
+ f"来获取该领域的详细指令和知识。"
+ )
+
+ agent_history = context.get("agent_history", [])
+
+ # 组装 LLM 消息链：system → agent 历史 → 上下文 → 当前用户输入
+ messages: list[dict[str, Any]] = [{"role": "system", "content": system_prompt}]
+ if agent_history:
+ messages.extend(agent_history)
+ if context_messages:
+ messages.extend(context_messages)
+ messages.append({"role": "user", "content": user_content})
+
+ queue_lane = context.get("queue_lane")
+ max_pre_tool_retries = max(
+ 0, int(getattr(runtime_config, "ai_request_max_retries", 0) or 0)
+ )
+
+ return PreparedAgentRun(
+ tool_registry=tool_registry,
+ agent_skill_registry=agent_skill_registry,
+ tools=tools,
+ agent_config=agent_config,
+ messages=messages,
+ ai_client=ai_client,
+ queue_lane=queue_lane,
+ max_pre_tool_retries=max_pre_tool_retries,
+ )
diff --git a/src/Undefined/skills/agents/runner/loop.py b/src/Undefined/skills/agents/runner/loop.py
new file mode 100644
index 00000000..bc215e8b
--- /dev/null
+++ b/src/Undefined/skills/agents/runner/loop.py
@@ -0,0 +1,182 @@
+# Agent LLM↔工具迭代循环核心
+from __future__ import annotations
+
+import logging
+from pathlib import Path
+from typing import Any
+
+from Undefined.ai.transports.openai_transport import RESPONSES_OUTPUT_ITEMS_KEY
+from Undefined.skills.agents.runner.context import prepare_agent_run
+from Undefined.skills.agents.runner.tools import execute_assistant_tool_calls
+
+
+# Agent 主循环：LLM 决策 → 工具执行 → 结果回填
+async def run_agent_with_tools(
+ *,
+ agent_name: str,
+ user_content: str,
+ context_messages: list[dict[str, str]] | None = None,
+ empty_user_content_message: str,
+ default_prompt: str,
+ context: dict[str, Any],
+ agent_dir: Path,
+ logger: logging.Logger,
+ max_iterations: int = 20,
+ tool_error_prefix: str = "错误",
+) -> str:
+ """执行通用 Agent 循环。
+
+ 该方法统一处理：
+ - prompt 加载
+ - LLM 迭代决策
+ - tool call 并发执行
+ - tool 结果回填 messages
+ """
+
+ # 空输入直接返回提示
+ if not user_content.strip():
+ return empty_user_content_message
+
+ prepared = await prepare_agent_run(
+ agent_name=agent_name,
+ user_content=user_content,
+ context_messages=context_messages,
+ default_prompt=default_prompt,
+ context=context,
+ agent_dir=agent_dir,
+ logger=logger,
+ )
+ # prepare 失败时 prepared 为错误字符串
+ if isinstance(prepared, str):
+ return prepared
+
+ messages = prepared.messages
+ transport_state: dict[str, Any] | None = None
+ pre_tool_failure_count = 0
+
+ # Agent 主循环：LLM 决策 → 工具执行 → 结果回填，直到无 tool_calls
+ # 迭代上限防止无限 tool 循环
+ for iteration in range(1, max_iterations + 1):
+ logger.debug("[Agent:%s] iteration=%s", agent_name, iteration)
+ # 记录 checkpoint，pre-tool 失败时可回滚 messages / transport_state
+ message_checkpoint_len = len(messages)
+ transport_state_checkpoint = transport_state
+ try:
+ # 通过队列提交 LLM 请求（含 tools 与 transport 多轮状态）
+ result = await prepared.ai_client.submit_queued_llm_call(
+ model_config=prepared.agent_config,
+ messages=messages,
+ max_tokens=prepared.agent_config.max_tokens,
+ call_type=f"agent:{agent_name}",
+ tools=prepared.tools if prepared.tools else None,
+ tool_choice="auto",
+ transport_state=transport_state,
+ queue_lane=prepared.queue_lane,
+ )
+ except Exception as exc:
+ logger.exception(
+ "[Agent:%s] queued LLM 调用失败: lane=%s iteration=%s error=%s",
+ agent_name,
+ prepared.queue_lane,
+ iteration,
+ exc,
+ )
+ raise RuntimeError("智能体模型请求失败") from exc
+
+ try:
+ tool_execution_started = False
+ tool_name_map = (
+ result.get("_tool_name_map") if isinstance(result, dict) else None
+ )
+ # API 工具名与内部 registry 名称的映射（含 dot 分隔符转换）
+ api_to_internal: dict[str, str] = {}
+ if isinstance(tool_name_map, dict):
+ raw_api_to_internal = tool_name_map.get("api_to_internal")
+ if isinstance(raw_api_to_internal, dict):
+ api_to_internal = {
+ str(key): str(value)
+ for key, value in raw_api_to_internal.items()
+ }
+
+ next_transport_state = (
+ result.get("_transport_state") if isinstance(result, dict) else None
+ )
+ # Responses API 等多轮 transport 状态，下一轮 LLM 调用需回传
+ transport_state = (
+ next_transport_state if isinstance(next_transport_state, dict) else None
+ )
+
+ choice: dict[str, Any] = result.get("choices", [{}])[0]
+ message: dict[str, Any] = choice.get("message", {})
+ content: str = message.get("content") or ""
+ reasoning_content: str | None = message.get("reasoning_content")
+ tool_calls: list[dict[str, Any]] = message.get("tool_calls", [])
+
+ # 模型同时返回文本与工具调用时，优先走工具路径
+ if content.strip() and tool_calls:
+ content = ""
+
+ # 无工具调用即视为最终回复
+ if not tool_calls:
+ return content
+
+ # 将 assistant 消息（含 tool_calls）追加到对话历史
+ assistant_message: dict[str, Any] = {
+ "role": "assistant",
+ "content": content,
+ "tool_calls": tool_calls,
+ }
+ output_items = message.get(RESPONSES_OUTPUT_ITEMS_KEY)
+ if isinstance(output_items, list):
+ assistant_message[RESPONSES_OUTPUT_ITEMS_KEY] = output_items
+ # 部分模型需回放 reasoning_content 以兼容 thinking + tool_call
+ capture_reasoning = bool(
+ getattr(prepared.agent_config, "thinking_tool_call_compat", False)
+ ) or bool(getattr(prepared.agent_config, "reasoning_content_replay", False))
+ if capture_reasoning and reasoning_content is not None:
+ assistant_message["reasoning_content"] = reasoning_content
+ messages.append(assistant_message)
+
+ # 并发执行 tool_calls，结果以 role=tool 消息回填
+ tool_execution_started = await execute_assistant_tool_calls(
+ agent_name=agent_name,
+ tool_calls=tool_calls,
+ api_to_internal=api_to_internal,
+ messages=messages,
+ tool_registry=prepared.tool_registry,
+ agent_skill_registry=prepared.agent_skill_registry,
+ context=context,
+ logger=logger,
+ tool_error_prefix=tool_error_prefix,
+ )
+ pre_tool_failure_count = 0
+
+ except Exception as exc:
+ # pre-tool 本地异常：在未开始执行工具前可重试当前 LLM 轮次
+ if (
+ not tool_execution_started
+ and pre_tool_failure_count < prepared.max_pre_tool_retries
+ ):
+ pre_tool_failure_count += 1
+ del messages[message_checkpoint_len:]
+ transport_state = transport_state_checkpoint
+ logger.warning(
+ "[Agent:%s] pre-tool 本地失败，重试当前 LLM 轮次: lane=%s retry=%s/%s iteration=%s error=%s",
+ agent_name,
+ prepared.queue_lane,
+ pre_tool_failure_count,
+ prepared.max_pre_tool_retries,
+ iteration,
+ exc,
+ )
+ continue
+ logger.exception(
+ "[Agent:%s] 执行失败，已静默抑制: lane=%s iteration=%s error=%s",
+ agent_name,
+ prepared.queue_lane,
+ iteration,
+ exc,
+ )
+ return ""
+
+ return "达到最大迭代次数"
diff --git a/src/Undefined/skills/agents/runner/tools.py b/src/Undefined/skills/agents/runner/tools.py
new file mode 100644
index 00000000..31d79177
--- /dev/null
+++ b/src/Undefined/skills/agents/runner/tools.py
@@ -0,0 +1,179 @@
+# Agent 工具并发调度与 end 工具特殊处理
+from __future__ import annotations
+
+import asyncio
+import logging
+from typing import Any
+
+from Undefined.ai.tooling import END_CO_CALL_REJECT_CONTENT
+from Undefined.skills.anthropic_skills import AnthropicSkillRegistry
+from Undefined.skills.agents.agent_tool_registry import AgentToolRegistry
+from Undefined.utils.tool_calls import parse_tool_arguments
+
+
+# 按运行时配置过滤不可用工具 schema
+def _filter_tools_for_runtime_config(
+ agent_name: str,
+ tools: list[dict[str, Any]],
+ runtime_config: Any | None,
+) -> list[dict[str, Any]]:
+ # web_agent 在 grok 未启用时从 schema 中剔除 grok_search
+ if agent_name != "web_agent" or runtime_config is None:
+ return tools
+
+ if bool(getattr(runtime_config, "grok_search_enabled", False)):
+ return tools
+
+ filtered: list[dict[str, Any]] = []
+ for tool in tools:
+ function = tool.get("function") if isinstance(tool, dict) else None
+ name = function.get("name") if isinstance(function, dict) else None
+ if name == "grok_search":
+ continue
+ filtered.append(tool)
+ return filtered
+
+
+# 并发执行 tool_calls 并回填 tool 消息
+async def execute_assistant_tool_calls(
+ *,
+ agent_name: str,
+ tool_calls: list[dict[str, Any]],
+ api_to_internal: dict[str, str],
+ messages: list[dict[str, Any]],
+ tool_registry: AgentToolRegistry,
+ agent_skill_registry: AnthropicSkillRegistry | None,
+ context: dict[str, Any],
+ logger: logging.Logger,
+ tool_error_prefix: str,
+) -> bool:
+ """并发执行 assistant 的 tool_calls，回填 tool 消息。返回是否已开始工具执行。"""
+
+ tool_tasks: list[asyncio.Future[Any]] = []
+ tool_call_ids: list[str] = []
+ tool_api_names: list[str] = []
+ end_tool_call: dict[str, Any] | None = None
+ end_tool_args: dict[str, Any] = {}
+ tool_execution_started = False
+
+ for tool_call in tool_calls:
+ call_id = str(tool_call.get("id", ""))
+ function: dict[str, Any] = tool_call.get("function", {})
+ api_function_name = str(function.get("name", ""))
+ raw_args = function.get("arguments")
+
+ internal_function_name = api_to_internal.get(
+ api_function_name, api_function_name
+ )
+ logger.info(
+ "[Agent:%s] preparing tool=%s",
+ agent_name,
+ internal_function_name,
+ )
+
+ function_args = parse_tool_arguments(
+ raw_args,
+ logger=logger,
+ tool_name=api_function_name,
+ )
+
+ if not isinstance(function_args, dict):
+ function_args = {}
+
+ # end 工具延后处理：若与其他工具同批调用则返回拒绝
+ if internal_function_name == "end":
+ if len(tool_calls) > 1:
+ logger.warning(
+ "[Agent:%s] end 与其他工具同时调用，"
+ "将先执行其他工具，end 将返回拒绝结果",
+ agent_name,
+ )
+ end_tool_call = tool_call
+ end_tool_args = function_args
+ continue
+
+ tool_call_ids.append(call_id)
+ tool_api_names.append(api_function_name)
+
+ skill_delimiter = (
+ agent_skill_registry.dot_delimiter if agent_skill_registry else "-_-"
+ )
+ # Anthropic Skill 走独立 registry，其余走 AgentToolRegistry
+ is_agent_skill = internal_function_name.startswith(f"skills{skill_delimiter}")
+ if is_agent_skill and agent_skill_registry:
+ tool_tasks.append(
+ asyncio.ensure_future(
+ agent_skill_registry.execute_skill_tool(
+ internal_function_name,
+ function_args,
+ context,
+ )
+ )
+ )
+ else:
+ tool_tasks.append(
+ asyncio.ensure_future(
+ tool_registry.execute_tool(
+ internal_function_name,
+ function_args,
+ context,
+ )
+ )
+ )
+
+ if tool_tasks:
+ tool_execution_started = True
+ logger.info(
+ "[Agent:%s] executing tools in parallel: count=%s",
+ agent_name,
+ len(tool_tasks),
+ )
+ # 同轮 tool_calls 并发执行，异常转为 tool 消息内容
+ results = await asyncio.gather(*tool_tasks, return_exceptions=True)
+
+ for index, tool_result in enumerate(results):
+ call_id = tool_call_ids[index]
+ api_tool_name = tool_api_names[index]
+ if isinstance(tool_result, Exception):
+ content_str = f"{tool_error_prefix}: {tool_result}"
+ else:
+ content_str = str(tool_result)
+
+ messages.append(
+ {
+ "role": "tool",
+ "tool_call_id": call_id,
+ "name": api_tool_name,
+ "content": content_str,
+ }
+ )
+
+ if end_tool_call:
+ end_call_id = str(end_tool_call.get("id", ""))
+ end_api_name = end_tool_call.get("function", {}).get("name", "end")
+ if tool_tasks:
+ messages.append(
+ {
+ "role": "tool",
+ "tool_call_id": end_call_id,
+ "name": end_api_name,
+ "content": END_CO_CALL_REJECT_CONTENT,
+ }
+ )
+ logger.info(
+ "[Agent:%s] end 与其他工具同时调用，其它工具已执行，end 已回填拒绝响应",
+ agent_name,
+ )
+ else:
+ tool_execution_started = True
+ end_result = await tool_registry.execute_tool("end", end_tool_args, context)
+ messages.append(
+ {
+ "role": "tool",
+ "tool_call_id": end_call_id,
+ "name": end_api_name,
+ "content": str(end_result),
+ }
+ )
+
+ return tool_execution_started
diff --git a/src/Undefined/skills/tools/__init__.py b/src/Undefined/skills/tools/__init__.py
index 2f23af88..4c316330 100644
--- a/src/Undefined/skills/tools/__init__.py
+++ b/src/Undefined/skills/tools/__init__.py
@@ -114,13 +114,9 @@ def _log_tools_summary(self, include_mcp: bool = True) -> None:
"""
tool_names = list(self._items.keys())
- # 分类工具
basic_tools, toolset_tools, mcp_tools = self._categorize_tools(tool_names)
-
- # 按类别分组工具集工具
toolset_by_category = self._group_toolsets_by_category(toolset_tools)
- # 输出统计信息
logger.info("=" * 60)
if include_mcp:
logger.info("工具加载完成统计 (包含 MCP)")
diff --git a/src/Undefined/skills/tools/bilibili_video/handler.py b/src/Undefined/skills/tools/bilibili_video/handler.py
index 2ff07a8e..2650f9ac 100644
--- a/src/Undefined/skills/tools/bilibili_video/handler.py
+++ b/src/Undefined/skills/tools/bilibili_video/handler.py
@@ -23,7 +23,6 @@ def _resolve_target(
return None, "target_id 必须是整数"
return (target_type, target_id), None # type: ignore[return-value]
- # 从上下文推断
request_type = context.get("request_type")
if request_type == "group":
group_id = context.get("group_id")
@@ -34,7 +33,6 @@ def _resolve_target(
if user_id:
return ("private", int(user_id)), None
- # 兜底
group_id = context.get("group_id")
if group_id:
return ("group", int(group_id)), None
@@ -51,13 +49,11 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
if not video_id:
return "video_id 不能为空"
- # 解析目标
target, error = _resolve_target(args, context)
if error or target is None:
return f"目标解析失败: {error or '参数错误'}"
target_type, target_id = target
- # 获取配置
runtime_config = context.get("runtime_config")
sender = context.get("sender")
onebot = context.get("onebot_client") or context.get("onebot")
@@ -67,7 +63,6 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
if not sender or not onebot:
return "缺少必要的运行时组件（sender/onebot）"
- # 读取 bilibili 配置
cookie = ""
prefer_quality = 80
max_duration = 600
diff --git a/src/Undefined/skills/tools/fetch_image_uid/handler.py b/src/Undefined/skills/tools/fetch_image_uid/handler.py
index 7986c811..cc84bbcc 100644
--- a/src/Undefined/skills/tools/fetch_image_uid/handler.py
+++ b/src/Undefined/skills/tools/fetch_image_uid/handler.py
@@ -38,7 +38,6 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
logger.exception("fetch_image_uid 注册失败: %s", exc)
return f"获取图片失败：{exc}"
- # 验证是否为图片类型
mime = str(getattr(record, "mime_type", "") or "").strip().lower()
if mime and not mime.startswith(_IMAGE_MIME_PREFIX):
return f"URL 内容不是图片类型（检测到 {mime}），仅支持图片"
diff --git a/src/Undefined/skills/tools/get_current_time/handler.py b/src/Undefined/skills/tools/get_current_time/handler.py
index 1c79700a..5996b743 100644
--- a/src/Undefined/skills/tools/get_current_time/handler.py
+++ b/src/Undefined/skills/tools/get_current_time/handler.py
@@ -37,7 +37,6 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
indent=2,
)
- # 默认返回 ISO 格式
return now.isoformat(timespec="seconds")
@@ -51,7 +50,6 @@ def _format_text(
"""生成人类可读的文本格式"""
lines = []
- # 公历信息
weekdays = ["星期一", "星期二", "星期三", "星期四", "星期五", "星期六", "星期日"]
weekday = weekdays[now.weekday()]
tz_offset = now.strftime("%z")
@@ -62,7 +60,6 @@ def _format_text(
f"{now.hour:02d}:{now.minute:02d}:{now.second:02d} ({tz_str})"
)
- # 农历信息
if include_lunar and lunar:
year_gz = lunar.getYearInGanZhi()
zodiac = lunar.getYearShengXiao()
@@ -70,19 +67,15 @@ def _format_text(
day_cn = lunar.getDayInChinese()
lines.append(f"农历：{year_gz}年({zodiac}年) {month_cn}{day_cn}")
- # 干支信息
month_gz = lunar.getMonthInGanZhi()
day_gz = lunar.getDayInGanZhi()
lines.append(f"干支：{year_gz}年 {month_gz}月 {day_gz}日")
- # 黄历信息
if include_almanac and lunar:
- # 节气
jieqi = lunar.getCurrentJieQi()
if jieqi:
lines.append(f"节气：{jieqi.getName()}")
- # 节日
festivals = []
if solar:
solar_festivals = solar.getFestivals()
@@ -92,7 +85,6 @@ def _format_text(
if festivals:
lines.append(f"节日：{' '.join(festivals)}")
- # 宜忌
yi = lunar.getDayYi()
if yi:
lines.append(f"宜：{' '.join(yi)}")
@@ -100,7 +92,6 @@ def _format_text(
if ji:
lines.append(f"忌：{' '.join(ji)}")
- # 冲煞
chong = lunar.getDayChongDesc()
sha = lunar.getDaySha()
if chong or sha:
@@ -109,7 +100,6 @@ def _format_text(
chong_sha += f"煞{sha}"
lines.append(f"冲煞：{chong_sha}")
- # 胎神
tai = lunar.getDayPositionTai()
if tai:
lines.append(f"胎神：{tai}")
@@ -127,7 +117,6 @@ def _format_json(
"""生成结构化 JSON 格式"""
result: Dict[str, Any] = {}
- # 公历信息
weekdays = ["星期一", "星期二", "星期三", "星期四", "星期五", "星期六", "星期日"]
weekday = weekdays[now.weekday()]
tz_offset = now.strftime("%z")
@@ -145,7 +134,6 @@ def _format_json(
"timezone": tz_str,
}
- # 农历信息
if include_lunar and lunar:
result["lunar"] = {
"year_cn": lunar.getYearInGanZhi(),
@@ -159,16 +147,13 @@ def _format_json(
},
}
- # 黄历信息
if include_almanac and lunar:
almanac: Dict[str, Any] = {}
- # 节气
jieqi = lunar.getCurrentJieQi()
if jieqi:
almanac["solar_term"] = {"current": jieqi.getName()}
- # 节日
festivals = []
if solar:
solar_festivals = solar.getFestivals()
@@ -178,7 +163,6 @@ def _format_json(
if festivals:
almanac["festivals"] = festivals
- # 宜忌
yi = lunar.getDayYi()
if yi:
almanac["yi"] = yi
@@ -186,7 +170,6 @@ def _format_json(
if ji:
almanac["ji"] = ji
- # 冲煞
chong = lunar.getDayChongDesc()
sha = lunar.getDaySha()
if chong or sha:
@@ -195,7 +178,6 @@ def _format_json(
chong_sha += f"煞{sha}"
almanac["chong"] = chong_sha
- # 胎神
tai = lunar.getDayPositionTai()
if tai:
almanac["fetal_god"] = tai
diff --git a/src/Undefined/skills/tools/get_picture/handler.py b/src/Undefined/skills/tools/get_picture/handler.py
index d580dce4..5fbb6893 100644
--- a/src/Undefined/skills/tools/get_picture/handler.py
+++ b/src/Undefined/skills/tools/get_picture/handler.py
@@ -98,7 +98,6 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
device = args.get("device", "pc")
fourk_type = args.get("fourk_type", "acg")
- # 参数验证
if delivery not in {"embed", "send"}:
return f"delivery 无效：{delivery}。仅支持 embed 或 send"
@@ -117,14 +116,12 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
if picture_type == "random4kPic" and fourk_type not in ("acg", "wallpaper"):
return "4K图片类型必须是 acg（二次元）或 wallpaper（风景）"
- # 构造请求参数
params: Dict[str, Any] = {"return": "json"}
if picture_type == "acg":
params["type"] = device
elif picture_type == "random4kPic":
params["type"] = fourk_type
- # 创建图片保存目录
from Undefined.utils.paths import IMAGE_CACHE_DIR, ensure_dir
img_dir = ensure_dir(IMAGE_CACHE_DIR)
@@ -133,7 +130,6 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
base_url = _get_xxapi_base_url()
api_url = f"{base_url}{API_PATHS[picture_type]}"
- # 获取图片
success_count = 0
fail_count = 0
local_image_paths: list[str] = []
@@ -153,14 +149,12 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
# 美腿类型直接返回 JPEG 图片，不需要解析 JSON
if picture_type == "meitui":
- # 验证响应内容类型
content_type = response.headers.get("content-type", "")
if "image" not in content_type.lower():
logger.error(f"响应不是图片格式: {content_type}")
fail_count += 1
continue
- # 保存图片
filename = f"{picture_type}_{uuid.uuid4().hex[:16]}.jpg"
filepath = img_dir / filename
filepath.write_bytes(response.content)
@@ -171,13 +165,11 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
else:
data = response.json()
- # 检查响应
if data.get("code") != 200:
logger.error(f"获取图片失败: {data.get('msg')}")
fail_count += 1
continue
- # 获取图片 URL
image_url = data.get("data")
if not image_url:
logger.error("响应中未找到图片 URL")
@@ -186,7 +178,6 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
logger.info(f"图片 URL: {image_url}")
- # 下载图片到本地
logger.info("正在下载图片到本地...")
image_response = await request_with_retry(
"GET",
@@ -195,7 +186,6 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
context=context,
)
- # 保存图片
filename = f"{picture_type}_{uuid.uuid4().hex[:16]}.jpg"
filepath = img_dir / filename
filepath.write_bytes(image_response.content)
@@ -214,7 +204,6 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
logger.exception(f"获取图片失败: {e}")
fail_count += 1
- # 如果没有获取到任何图片
if success_count == 0:
return f"获取 {TYPE_NAMES[picture_type]} 图片失败，请稍后重试"
diff --git a/src/Undefined/skills/tools/get_user_info/handler.py b/src/Undefined/skills/tools/get_user_info/handler.py
index e8521458..5bb34fea 100644
--- a/src/Undefined/skills/tools/get_user_info/handler.py
+++ b/src/Undefined/skills/tools/get_user_info/handler.py
@@ -32,7 +32,6 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
return "获取用户信息功能不可用（OneBot 客户端未设置）"
try:
- # 使用 get_stranger_info 获取详细信息
user_info = await onebot_client.get_stranger_info(user_id)
if not user_info:
@@ -40,12 +39,10 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
result_parts = ["【QQ用户信息】"]
- # 添加头像 URL (常用 API)
result_parts.append(
f"头像: http://q.qlogo.cn/headimg_dl?dst_uin={user_id}&spec=640"
)
- # 处理性别
sex = user_info.get("sex")
if sex == "male":
user_info["sex"] = "男"
@@ -59,8 +56,6 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
if value is not None and value != "":
result_parts.append(f"{display_name}: {value}")
- # 如果有其他字段（取决于 OneBot 实现，如 NapCat/Go-CQHttp 可能有更多）
- # 我们可以尝试输出一些常见的额外字段
extra_fields = {
"remark": "备注",
"signature": "签名",
diff --git a/src/Undefined/skills/tools/python_interpreter/handler.py b/src/Undefined/skills/tools/python_interpreter/handler.py
index 002bf560..f23365db 100644
--- a/src/Undefined/skills/tools/python_interpreter/handler.py
+++ b/src/Undefined/skills/tools/python_interpreter/handler.py
@@ -164,7 +164,6 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
libraries: list[str] = args.get("libraries") or []
send_files: list[str] = args.get("send_files") or []
- # 验证库名
for lib in libraries:
if not _SAFE_LIB_PATTERN.match(lib):
return f"错误: 无效的库名 '{lib}'。"
@@ -173,12 +172,10 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
memory = MEMORY_LIMIT_WITH_LIBS if has_libs else MEMORY_LIMIT
timeout = TIMEOUT_WITH_LIBS if has_libs else TIMEOUT
- # 创建宿主机临时目录，绑定挂载到容器 /tmp
host_tmpdir = tempfile.mkdtemp(prefix="pyinterp_")
defer_cleanup = False
try:
- # 验证文件路径必须绑定到容器 /tmp，并且不能逃逸宿主机临时目录
for fpath in send_files:
if _resolve_output_host_path(fpath, host_tmpdir) is None:
return f"错误: 输出文件路径必须位于容器 /tmp 目录内: '{fpath}'"
@@ -235,7 +232,6 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
timeout=exec_timeout,
)
- # 构建结果
parts: list[str] = []
if exit_code == 0:
parts.append(
@@ -246,7 +242,6 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
f"代码执行失败 (退出代码: {exit_code}):\n{error_output}\n{response}"
)
- # 执行成功时发送文件
if send_files and exit_code == 0:
file_result = await _send_output_files(send_files, host_tmpdir, context)
if file_result:
diff --git a/src/Undefined/skills/tools/qq_like/handler.py b/src/Undefined/skills/tools/qq_like/handler.py
index 87031020..3b635f4d 100644
--- a/src/Undefined/skills/tools/qq_like/handler.py
+++ b/src/Undefined/skills/tools/qq_like/handler.py
@@ -20,7 +20,6 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
if target_user_id is None:
return "请提供要点赞的目标QQ号（target_user_id参数）"
- # 验证参数类型
try:
target_user_id = int(target_user_id)
times = int(times)
@@ -37,7 +36,6 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
return "点赞功能不可用（回调函数未设置）"
try:
- # 调用点赞回调
await send_like_callback(target_user_id, times)
if times == 1:
@@ -49,7 +47,6 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
logger.exception(f"点赞失败: {e}")
error_msg = str(e)
- # 根据错误消息提供更友好的提示
if "SVIP 上限" in error_msg:
return "点赞失败：今日给同一好友的点赞数已达SVIP上限"
elif "点赞失败" in error_msg:
diff --git a/src/Undefined/skills/tools/task_progress/handler.py b/src/Undefined/skills/tools/task_progress/handler.py
index 668037a0..9327c70b 100644
--- a/src/Undefined/skills/tools/task_progress/handler.py
+++ b/src/Undefined/skills/tools/task_progress/handler.py
@@ -61,11 +61,9 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
if not isinstance(tasks_input, list) or len(tasks_input) == 0:
return "tasks 必须是非空数组"
- # 从 context 获取或初始化任务列表
task_store: list[dict[str, Any]] = context.get("_task_progress", [])
if action == "plan":
- # 创建/替换计划
new_tasks: list[dict[str, Any]] = []
for item in tasks_input:
tid_raw = item.get("id")
@@ -80,7 +78,6 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
status = "pending"
new_tasks.append({"id": tid, "description": desc, "status": status})
- # 按 id 排序
new_tasks.sort(key=lambda t: t.get("id", 0))
task_store = new_tasks
context["_task_progress"] = task_store
@@ -88,11 +85,9 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
logger.info(f"[task_progress] 创建计划，共 {len(task_store)} 个步骤")
return f"计划已创建\n{_format_task_list(task_store)}"
- # action == "update"
if not task_store:
return "还没有任务计划，请先用 plan 动作创建"
- # 构建 id -> index 映射
id_to_idx = {t["id"]: i for i, t in enumerate(task_store)}
updated: list[int] = []
@@ -112,7 +107,6 @@ async def execute(args: Dict[str, Any], context: Dict[str, Any]) -> str:
return f"不存在 id={tid} 的步骤"
task_store[idx]["status"] = new_status
- # 允许更新描述
new_desc = item.get("description")
if new_desc:
task_store[idx]["description"] = new_desc
diff --git a/src/Undefined/utils/paths.py b/src/Undefined/utils/paths.py
index 99124f9d..05254147 100644
--- a/src/Undefined/utils/paths.py
+++ b/src/Undefined/utils/paths.py
@@ -2,6 +2,8 @@
from pathlib import Path
+PACKAGE_ROOT = Path(__file__).resolve().parent.parent
+
DATA_DIR = Path("data")
CACHE_DIR = DATA_DIR / "cache"
RENDER_CACHE_DIR = CACHE_DIR / "render"
diff --git a/src/Undefined/utils/render_cache.py b/src/Undefined/utils/render_cache.py
index 2bf2afe8..12d7ed47 100644
--- a/src/Undefined/utils/render_cache.py
+++ b/src/Undefined/utils/render_cache.py
@@ -362,6 +362,7 @@ async def get_render_cache() -> HtmlRenderCache:
单例的 enabled / 容量由 ``[render.cache]`` 决定；
禁用时仍返回单例对象，但所有 get/put 立即短路。
"""
+ # global
global _cache
if _cache is not None:
await _cache.initialize()
@@ -387,6 +388,7 @@ async def get_render_cache() -> HtmlRenderCache:
async def close_render_cache() -> None:
"""关停时调用：刷盘并丢弃单例。"""
+ # global
global _cache
cache = _cache
if cache is None:
@@ -399,5 +401,6 @@ async def close_render_cache() -> None:
def reset_render_cache() -> None:
"""仅供测试使用：丢弃单例（不刷盘），下次调用重新加载。"""
+ # global
global _cache
_cache = None
diff --git a/src/Undefined/utils/resources.py b/src/Undefined/utils/resources.py
index b614032f..a8ce1186 100644
--- a/src/Undefined/utils/resources.py
+++ b/src/Undefined/utils/resources.py
@@ -8,10 +8,11 @@
import shutil
import tempfile
+from Undefined.utils.paths import PACKAGE_ROOT
+
def _candidate_paths(relative_path: str) -> list[Path]:
- module_path = Path(__file__).resolve()
- package_root = module_path.parents[1]
+ package_root = PACKAGE_ROOT
candidates = [
Path.cwd() / relative_path,
# If installed from wheel, extra files may live under site-packages/
diff --git a/src/Undefined/utils/sender_helpers.py b/src/Undefined/utils/sender_helpers.py
new file mode 100644
index 00000000..0138f289
--- /dev/null
+++ b/src/Undefined/utils/sender_helpers.py
@@ -0,0 +1,116 @@
+"""MessageSender 辅助函数。"""
+
+from pathlib import Path
+from typing import Any
+from urllib.parse import unquote, urlsplit
+
+from Undefined.attachments import attachment_refs_to_text
+
+
+def _extract_message_id(result: object) -> int | None:
+ if not isinstance(result, dict):
+ return None
+
+ message_id = result.get("message_id")
+ if message_id is None:
+ # OneBot 实现差异：message_id 可能在顶层或 data 子对象。
+ data = result.get("data")
+ if isinstance(data, dict):
+ message_id = data.get("message_id")
+
+ try:
+ return int(message_id) if message_id is not None else None
+ except (TypeError, ValueError):
+ return None
+
+
+def _format_size(size_bytes: int | None) -> str:
+ if size_bytes is None or size_bytes < 0:
+ return "未知大小"
+ if size_bytes < 1024:
+ return f"{size_bytes}B"
+ if size_bytes < 1024 * 1024:
+ return f"{size_bytes / 1024:.1f}KB"
+ if size_bytes < 1024 * 1024 * 1024:
+ return f"{size_bytes / 1024 / 1024:.2f}MB"
+ return f"{size_bytes / 1024 / 1024 / 1024:.2f}GB"
+
+
+def _build_file_history_message(file_name: str, size_bytes: int | None) -> str:
+ return f"[文件] {file_name} ({_format_size(size_bytes)})"
+
+
+def _append_attachment_refs(
+ history_content: str,
+ attachments: list[dict[str, str]] | None,
+) -> str:
+ refs_text = attachment_refs_to_text(attachments or [])
+ if not refs_text or refs_text in history_content:
+ return history_content
+ if not history_content:
+ return refs_text
+ return f"{history_content}\n{refs_text}"
+
+
+def _merge_attachment_refs(
+ *groups: list[dict[str, str]] | None,
+) -> list[dict[str, str]]:
+ merged: list[dict[str, str]] = []
+ seen_uids: set[str] = set()
+ for group in groups:
+ for item in group or []:
+ uid = str(item.get("uid", "") or "").strip()
+ if uid and uid in seen_uids:
+ continue
+ if uid:
+ seen_uids.add(uid)
+ merged.append(item)
+ return merged
+
+
+def _iter_segments_deep(value: object) -> list[dict[str, Any]]:
+ """递归收集消息段，用于合并转发中的本地媒体登记。"""
+ segments: list[dict[str, Any]] = []
+ if isinstance(value, dict):
+ type_value = value.get("type")
+ data = value.get("data")
+ if type_value is not None and isinstance(data, dict):
+ segments.append(value)
+ content = data.get("content")
+ if isinstance(content, (list, dict)):
+ segments.extend(_iter_segments_deep(content))
+ else:
+ for child in value.values():
+ if isinstance(child, (list, dict)):
+ segments.extend(_iter_segments_deep(child))
+ elif isinstance(value, list):
+ for child in value:
+ if isinstance(child, (list, dict)):
+ segments.extend(_iter_segments_deep(child))
+ return segments
+
+
+def _local_path_from_segment_source(source: Any) -> Path | None:
+ raw_source = str(source or "").strip()
+ if not raw_source:
+ return None
+ lowered = raw_source.lower()
+ if lowered.startswith(("http://", "https://", "base64://")):
+ return None
+ if lowered.startswith("file://"):
+ parsed = urlsplit(raw_source)
+ path = Path(unquote(parsed.path)).expanduser()
+ else:
+ path = Path(raw_source).expanduser()
+ if not path.is_absolute():
+ path = (Path.cwd() / path).resolve()
+ else:
+ path = path.resolve()
+ return path if path.is_file() else None
+
+
+def _get_file_size(file_path: str) -> int | None:
+ try:
+ return Path(file_path).stat().st_size
+ except OSError:
+ return None
diff --git a/src/Undefined/webui/routes/_runtime.py b/src/Undefined/webui/routes/_runtime.py
index 371c46b9..fa26c0d8 100644
--- a/src/Undefined/webui/routes/_runtime.py
+++ b/src/Undefined/webui/routes/_runtime.py
@@ -65,8 +65,9 @@ def _load_top_level_agent_names(root: Path) -> set[str]:
def _get_local_agent_tool_names() -> set[str]:
- skills_root = Path(__file__).resolve().parents[2] / "skills"
- return _load_top_level_agent_names(skills_root / "agents")
+ from Undefined.utils.paths import PACKAGE_ROOT
+
+ return _load_top_level_agent_names(PACKAGE_ROOT / "skills" / "agents")
def _tool_invoke_proxy_timeout_seconds(tool_name: str) -> float | None:
diff --git a/tests/test_ai_client_setup_paths.py b/tests/test_ai_client_setup_paths.py
new file mode 100644
index 00000000..baac7c81
--- /dev/null
+++ b/tests/test_ai_client_setup_paths.py
@@ -0,0 +1,115 @@
+from __future__ import annotations
+
+import asyncio
+from pathlib import Path
+
+import Undefined
+from Undefined.services.commands.registry import CommandRegistry
+from Undefined.skills.agents import AgentRegistry
+from Undefined.skills.pipelines.registry import PipelineRegistry
+from Undefined.skills.tools import ToolRegistry
+from Undefined.utils.paths import PACKAGE_ROOT
+
+
+def _skill_dirs(base: Path) -> set[str]:
+ if not base.is_dir():
+ return set()
+ return {
+ item.name
+ for item in base.iterdir()
+ if item.is_dir() and (item / "config.json").exists()
+ }
+
+
+def _toolset_tool_names(base: Path) -> set[str]:
+ if not base.is_dir():
+ return set()
+ names: set[str] = set()
+ for config_path in base.rglob("config.json"):
+ rel = config_path.parent.relative_to(base)
+ if len(rel.parts) == 2:
+ names.add(".".join(rel.parts))
+ return names
+
+
+def test_package_root_matches_undefined_package_directory() -> None:
+ assert PACKAGE_ROOT == Path(Undefined.__file__).resolve().parent
+
+
+def test_package_root_contains_skills_directories() -> None:
+ assert (PACKAGE_ROOT / "skills" / "tools").is_dir()
+ assert (PACKAGE_ROOT / "skills" / "agents").is_dir()
+ assert (PACKAGE_ROOT / "skills" / "anthropic_skills").is_dir()
+ assert (PACKAGE_ROOT / "skills" / "toolsets").is_dir()
+ assert (PACKAGE_ROOT / "skills" / "pipelines").is_dir()
+ assert (PACKAGE_ROOT / "skills" / "commands").is_dir()
+
+
+def test_setup_wrong_path_does_not_exist() -> None:
+ """Regression: ai/client/setup.py used parents[1] and pointed at ai/skills."""
+ wrong_root = Path(__file__).resolve().parents[2] / "src" / "Undefined" / "ai"
+ assert not (wrong_root / "skills" / "tools").exists()
+
+
+def test_tool_registry_loads_all_skill_directories() -> None:
+ tools_dir = PACKAGE_ROOT / "skills" / "tools"
+ toolsets_dir = PACKAGE_ROOT / "skills" / "toolsets"
+ registry = ToolRegistry(tools_dir)
+ basic = [name for name in registry._items if "." not in name]
+ toolsets = [
+ name for name in registry._items if "." in name and not name.startswith("mcp.")
+ ]
+
+ basic_dirs = _skill_dirs(tools_dir)
+ toolset_names = _toolset_tool_names(toolsets_dir)
+
+ assert len(basic) == len(basic_dirs)
+ assert len(toolsets) == len(toolset_names)
+ assert len(registry._items) == len(basic) + len(toolsets)
+ assert set(basic) == basic_dirs
+ assert set(toolsets) == toolset_names
+
+
+def test_all_registered_tools_import_handlers() -> None:
+ registry = ToolRegistry(PACKAGE_ROOT / "skills" / "tools")
+ errors: list[str] = []
+
+ for name, item in sorted(registry._items.items()):
+ try:
+ registry._load_handler_for_item(item)
+ if item.handler is None:
+ errors.append(f"{name}: handler is None")
+ except Exception as exc:
+ errors.append(f"{name}: {exc}")
+
+ assert errors == []
+
+
+def test_agent_registry_loads_expected_agents() -> None:
+ agents_dir = PACKAGE_ROOT / "skills" / "agents"
+ registry = AgentRegistry(agents_dir)
+ assert len(registry._items) == len(_skill_dirs(agents_dir))
+ assert set(registry._items) == _skill_dirs(agents_dir)
+
+
+def test_command_registry_loads_expected_commands() -> None:
+ commands_dir = PACKAGE_ROOT / "skills" / "commands"
+ registry = CommandRegistry(commands_dir)
+ registry.load_commands()
+ command_dirs = {
+ item.name
+ for item in commands_dir.iterdir()
+ if item.is_dir() and (item / "handler.py").exists()
+ }
+ assert len(registry._commands) == len(command_dirs)
+ assert set(registry._commands) == command_dirs
+
+
+def test_pipeline_registry_loads_expected_pipelines() -> None:
+ async def _load() -> PipelineRegistry:
+ registry = PipelineRegistry(PACKAGE_ROOT / "skills" / "pipelines")
+ await registry.load_items_async()
+ return registry
+
+ registry = asyncio.run(_load())
+ assert set(registry._items) == {"arxiv", "bilibili", "github"}
diff --git a/tests/test_ai_coordinator_queue_routing.py b/tests/test_ai_coordinator_queue_routing.py
index 194e67c7..7a78a73f 100644
--- a/tests/test_ai_coordinator_queue_routing.py
+++ b/tests/test_ai_coordinator_queue_routing.py
@@ -6,8 +6,8 @@
import pytest
-from Undefined.services import ai_coordinator as ai_coordinator_module
from Undefined.services.ai_coordinator import AICoordinator
+from Undefined.services.coordinator import group as coordinator_group_module
@pytest.mark.asyncio
@@ -245,7 +245,9 @@ async def _fake_ask(*_args: Any, **kwargs: Any) -> str:
coordinator.scheduler = SimpleNamespace()
monkeypatch.setattr(
- ai_coordinator_module, "collect_context_resources", lambda _vars: {}
+ coordinator_group_module,
+ "collect_context_resources",
+ lambda _vars: {},
)
await coordinator._execute_auto_reply(
diff --git a/tests/test_cli_startup_compat.py b/tests/test_cli_startup_compat.py
new file mode 100644
index 00000000..82f0f557
--- /dev/null
+++ b/tests/test_cli_startup_compat.py
@@ -0,0 +1,128 @@
+"""CLI 启动兼容性与根包 import 行为测试（Phase 0 起持续有效）。"""
+
+from __future__ import annotations
+
+import importlib
+import sys
+from pathlib import Path
+from typing import Iterator
+
+import pytest
+
+_MINIMAL_CONFIG = """
+[onebot]
+ws_url = "ws://127.0.0.1:3999"
+[models.chat]
+api_url = "https://api.example/v1"
+api_key = "sk-test"
+model_name = "gpt-test"
+"""
+
+
+@pytest.fixture
+def isolated_undefined_modules() -> Iterator[None]:
+ """测试前后清理 Undefined 相关 sys.modules 条目。"""
+ prefix = "Undefined"
+ saved = {
+ k: v
+ for k, v in sys.modules.items()
+ if k == prefix or k.startswith(f"{prefix}.")
+ }
+ for key in saved:
+ del sys.modules[key]
+ yield
+ for key in list(sys.modules):
+ if key == prefix or key.startswith(f"{prefix}."):
+ del sys.modules[key]
+ sys.modules.update(saved)
+
+
+@pytest.fixture
+def reset_config_singleton() -> Iterator[None]:
+ """重置 config 模块全局单例，避免测试间污染。"""
+ import Undefined.config as config_module
+
+ saved_config = config_module._config
+ saved_manager = config_module._config_manager
+ config_module._config = None
+ config_module._config_manager = None
+ yield
+ config_module._config = saved_config
+ config_module._config_manager = saved_manager
+
+
+def test_entry_point_undefined_main_run_importable() -> None:
+ from Undefined.main import run
+
+ assert callable(run)
+
+
+def test_entry_point_undefined_webui_run_importable() -> None:
+ from Undefined.webui import run
+
+ assert callable(run)
+
+
+def test_import_undefined_does_not_eagerly_load_onebot_or_handlers(
+ isolated_undefined_modules: None,
+) -> None:
+ import Undefined # noqa: F401
+
+ assert "Undefined.onebot" not in sys.modules
+ assert "Undefined.handlers" not in sys.modules
+ assert "Undefined.main" not in sys.modules
+
+
+def test_get_config_reads_config_toml_from_cwd(
+ monkeypatch: pytest.MonkeyPatch,
+ tmp_path: Path,
+ reset_config_singleton: None,
+) -> None:
+ monkeypatch.chdir(tmp_path)
+ (tmp_path / "config.toml").write_text(_MINIMAL_CONFIG, encoding="utf-8")
+
+ import Undefined.config as config_module
+
+ config_module._config = None
+ config_module._config_manager = None
+
+ cfg = config_module.get_config(strict=False)
+ assert cfg.onebot_ws_url == "ws://127.0.0.1:3999"
+ assert cfg.chat_model.model_name == "gpt-test"
+
+
+def test_root_get_config_same_as_subpackage(isolated_undefined_modules: None) -> None:
+ import Undefined
+
+ from Undefined.config import get_config as subpackage_get_config
+
+ assert Undefined.get_config is subpackage_get_config
+
+
+def test_import_undefined_does_not_import_webui_app(
+ isolated_undefined_modules: None,
+) -> None:
+ import Undefined # noqa: F401
+
+ assert "Undefined.webui.app" not in sys.modules
+
+
+def test_webui_run_deferred_import(monkeypatch: pytest.MonkeyPatch) -> None:
+ """webui.run 应延迟加载 app，import webui 包本身不拉起重型依赖。"""
+ import Undefined.webui as webui_module
+
+ original_import = importlib.import_module
+ app_imported = False
+
+ def tracking_import(name: str, package: object | None = None) -> object:
+ nonlocal app_imported
+ if name == "Undefined.webui.app":
+ app_imported = True
+ package_name = package if isinstance(package, str) else None
+ return original_import(name, package_name)
+
+ monkeypatch.setattr(importlib, "import_module", tracking_import)
+ importlib.reload(webui_module)
+
+ assert not app_imported
+ assert callable(webui_module.run)
diff --git a/tests/test_config_env_only.py b/tests/test_config_env_only.py
new file mode 100644
index 00000000..42049d2a
--- /dev/null
+++ b/tests/test_config_env_only.py
@@ -0,0 +1,61 @@
+from __future__ import annotations
+
+
+import pytest
+
+from Undefined.config import Config
+
+
+def test_env_only_chat_model_fields(monkeypatch: pytest.MonkeyPatch) -> None:
+ monkeypatch.setenv("ONEBOT_WS_URL", "ws://env-only:3001")
+ monkeypatch.setenv("CHAT_MODEL_API_URL", "https://env.example/v1")
+ monkeypatch.setenv("CHAT_MODEL_API_KEY", "env-key")
+ monkeypatch.setenv("CHAT_MODEL_NAME", "env-chat")
+ monkeypatch.setenv("VISION_MODEL_API_URL", "https://env.example/v1")
+ monkeypatch.setenv("VISION_MODEL_API_KEY", "env-key")
+ monkeypatch.setenv("VISION_MODEL_NAME", "env-vision")
+ monkeypatch.setenv("AGENT_MODEL_API_URL", "https://env.example/v1")
+ monkeypatch.setenv("AGENT_MODEL_API_KEY", "env-key")
+ monkeypatch.setenv("AGENT_MODEL_NAME", "env-agent")
+
+ cfg = Config.from_mapping({}, strict=False)
+ assert cfg.onebot_ws_url == "ws://env-only:3001"
+ assert cfg.chat_model.model_name == "env-chat"
+ assert cfg.vision_model.model_name == "env-vision"
+ assert cfg.agent_model.model_name == "env-agent"
+
+
+def test_env_overridden_by_mapping(monkeypatch: pytest.MonkeyPatch) -> None:
+ monkeypatch.setenv("CHAT_MODEL_NAME", "from-env")
+ cfg = Config.from_mapping(
+ {
+ "onebot": {"ws_url": "ws://x"},
+ "models": {
+ "chat": {
+ "api_url": "u",
+ "api_key": "k",
+ "model_name": "from-toml",
+ },
+ "vision": {"api_url": "u", "api_key": "k", "model_name": "v"},
+ "agent": {"api_url": "u", "api_key": "k", "model_name": "a"},
+ },
+ },
+ strict=False,
+ )
+ assert cfg.chat_model.model_name == "from-toml"
+
+
+def test_http_proxy_env_fallback(monkeypatch: pytest.MonkeyPatch) -> None:
+ monkeypatch.setenv("HTTP_PROXY", "http://127.0.0.1:7890")
+ cfg = Config.from_mapping(
+ {
+ "onebot": {"ws_url": "ws://x"},
+ "models": {
+ "chat": {"api_url": "u", "api_key": "k", "model_name": "m"},
+ "vision": {"api_url": "u", "api_key": "k", "model_name": "v"},
+ "agent": {"api_url": "u", "api_key": "k", "model_name": "a"},
+ },
+ },
+ strict=False,
+ )
+ assert cfg.http_proxy == "http://127.0.0.1:7890"
diff --git a/tests/test_config_env_registry.py b/tests/test_config_env_registry.py
new file mode 100644
index 00000000..385f3351
--- /dev/null
+++ b/tests/test_config_env_registry.py
@@ -0,0 +1,34 @@
+from __future__ import annotations
+
+from Undefined.config.env_registry import (
+ ENV_ALTERNATES,
+ ENV_REGISTRY,
+ all_env_mappings,
+ env_key_for_path,
+)
+
+
+def test_registry_contains_core_paths() -> None:
+ assert ENV_REGISTRY[("core", "bot_qq")] == "BOT_QQ"
+ assert ENV_REGISTRY[("onebot", "ws_url")] == "ONEBOT_WS_URL"
+ assert ENV_REGISTRY[("models", "chat", "api_url")] == "CHAT_MODEL_API_URL"
+
+
+def test_env_key_for_path_lookup() -> None:
+ assert env_key_for_path(("models", "agent", "model_name")) == "AGENT_MODEL_NAME"
+ assert env_key_for_path(("nonexistent",)) is None
+
+
+def test_all_env_mappings_is_copy() -> None:
+ snapshot = all_env_mappings()
+ snapshot[("fake",)] = "FAKE"
+ assert ("fake",) not in ENV_REGISTRY
+
+
+def test_alternate_env_keys_documented() -> None:
+ assert "HTTP_PROXY" in ENV_ALTERNATES
+ assert "EASTER_EGG_CALL_MESSAGE_MODE" in ENV_ALTERNATES
+
+
+def test_registry_has_model_context_window_entries() -> None:
+ assert ("models", "chat", "context_window_tokens") in ENV_REGISTRY
diff --git a/tests/test_config_from_mapping.py b/tests/test_config_from_mapping.py
new file mode 100644
index 00000000..2e28f806
--- /dev/null
+++ b/tests/test_config_from_mapping.py
@@ -0,0 +1,98 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import pytest
+
+from Undefined.config import Config, set_config
+
+
+_MINIMAL_MAPPING = {
+ "onebot": {"ws_url": "ws://127.0.0.1:3001"},
+ "models": {
+ "chat": {
+ "api_url": "https://api.example/v1",
+ "api_key": "sk-test",
+ "model_name": "gpt-test",
+ },
+ "vision": {
+ "api_url": "https://api.example/v1",
+ "api_key": "sk-test",
+ "model_name": "vision-test",
+ },
+ "agent": {
+ "api_url": "https://api.example/v1",
+ "api_key": "sk-test",
+ "model_name": "agent-test",
+ },
+ },
+}
+
+
+def test_from_mapping_builds_without_toml() -> None:
+ cfg = Config.from_mapping(_MINIMAL_MAPPING, strict=False)
+ assert cfg.onebot_ws_url == "ws://127.0.0.1:3001"
+ assert cfg.chat_model.model_name == "gpt-test"
+
+
+def test_builder_with_mapping() -> None:
+ cfg = Config.builder().with_mapping(_MINIMAL_MAPPING).build(strict=False)
+ assert cfg.agent_model.model_name == "agent-test"
+
+
+def test_set_config_injects_singleton(monkeypatch: pytest.MonkeyPatch) -> None:
+ import Undefined.config as config_pkg
+
+ monkeypatch.setattr(config_pkg, "_config", None)
+ monkeypatch.setattr(config_pkg, "_config_manager", None)
+ cfg = Config.from_mapping(_MINIMAL_MAPPING, strict=False)
+ set_config(cfg)
+ assert config_pkg.get_config(strict=False) is cfg
+ assert config_pkg.get_config_manager().load(strict=False) is cfg
+
+
+def test_from_mapping_matches_load(tmp_path: Path) -> None:
+ toml = """
+[onebot]
+ws_url = "ws://127.0.0.1:3001"
+[models.chat]
+api_url = "https://api.example/v1"
+api_key = "sk-test"
+model_name = "gpt-test"
+[models.vision]
+api_url = "https://api.example/v1"
+api_key = "sk-test"
+model_name = "vision-test"
+[models.agent]
+api_url = "https://api.example/v1"
+api_key = "sk-test"
+model_name = "agent-test"
+"""
+ path = tmp_path / "config.toml"
+ path.write_text(toml, encoding="utf-8")
+ from_file = Config.load(path, strict=False)
+ from_map = Config.from_mapping(
+ {
+ "onebot": {"ws_url": "ws://127.0.0.1:3001"},
+ "models": {
+ "chat": {
+ "api_url": "https://api.example/v1",
+ "api_key": "sk-test",
+ "model_name": "gpt-test",
+ },
+ "vision": {
+ "api_url": "https://api.example/v1",
+ "api_key": "sk-test",
+ "model_name": "vision-test",
+ },
+ "agent": {
+ "api_url": "https://api.example/v1",
+ "api_key": "sk-test",
+ "model_name": "agent-test",
+ },
+ },
+ },
+ strict=False,
+ )
+ assert from_file.chat_model.model_name == from_map.chat_model.model_name
+ assert from_file.onebot_ws_url == from_map.onebot_ws_url
diff --git a/tests/test_end_defer_co_call.py b/tests/test_end_defer_co_call.py
new file mode 100644
index 00000000..70730f13
--- /dev/null
+++ b/tests/test_end_defer_co_call.py
@@ -0,0 +1,241 @@
+from __future__ import annotations
+
+from types import SimpleNamespace
+from typing import Any, cast
+from unittest.mock import AsyncMock
+
+import pytest
+
+from Undefined.ai.client import AIClient
+from Undefined.ai.tooling import END_CO_CALL_REJECT_CONTENT
+from Undefined.config.models import ChatModelConfig
+
+
+def _build_minimal_ai_client(
+ *,
+ execute_tool: Any,
+ llm_responses: list[dict[str, Any]],
+) -> Any:
+ client: Any = object.__new__(AIClient)
+ client.runtime_config = cast(
+ Any,
+ SimpleNamespace(
+ log_thinking=False,
+ ai_request_max_retries=0,
+ missing_tool_call_retries=0,
+ ),
+ )
+ client._prompt_builder = cast(
+ Any,
+ SimpleNamespace(
+ build_messages=AsyncMock(
+ return_value=[{"role": "user", "content": "hello"}]
+ ),
+ end_summaries=[],
+ ),
+ )
+ client.tool_manager = cast(
+ Any,
+ SimpleNamespace(
+ get_openai_tools=lambda: [],
+ execute_tool=execute_tool,
+ ),
+ )
+ client._filter_tools_for_runtime_config = lambda tools: tools
+ client._get_runtime_config = cast(Any, lambda: client.runtime_config)
+ client.model_selector = cast(Any, SimpleNamespace(wait_ready=AsyncMock()))
+ client.chat_config = ChatModelConfig(
+ api_url="https://api.openai.com/v1",
+ api_key="sk-test",
+ model_name="chat-model",
+ max_tokens=1024,
+ )
+ client._find_chat_config_by_name = lambda _name: client.chat_config
+ client._search_wrapper = None
+ client._end_summary_storage = cast(Any, None)
+ client._send_private_message_callback = None
+ client._send_image_callback = None
+ client.memory_storage = None
+ client._knowledge_manager = None
+ client._cognitive_service = None
+ client._meme_service = None
+ client._crawl4ai_capabilities = SimpleNamespace(
+ available=False,
+ error=None,
+ proxy_config_available=False,
+ )
+
+ submit_calls: list[list[dict[str, Any]]] = []
+
+ async def _submit_queued_llm_call(
+ *,
+ messages: list[dict[str, Any]],
+ **kwargs: Any,
+ ) -> dict[str, Any]:
+ submit_calls.append(list(messages))
+ index = len(submit_calls) - 1
+ if index >= len(llm_responses):
+ raise RuntimeError("unexpected extra llm call")
+ return llm_responses[index]
+
+ client.submit_queued_llm_call = AsyncMock(side_effect=_submit_queued_llm_call)
+ client._submit_calls = submit_calls
+ return client
+
+
+@pytest.mark.asyncio
+async def test_ai_ask_rejects_end_when_co_called_with_send_message() -> None:
+ execute_calls: list[str] = []
+
+ async def _execute_tool(
+ name: str, args: dict[str, Any], ctx: dict[str, Any]
+ ) -> str:
+ execute_calls.append(name)
+ if name == "send_message":
+ ctx["message_sent_this_turn"] = True
+ return "消息已发送（message_id=1）"
+ if name == "end":
+ ctx["conversation_ended"] = True
+ return "对话已结束"
+ return "ok"
+
+ client = _build_minimal_ai_client(
+ execute_tool=_execute_tool,
+ llm_responses=[
+ {
+ "choices": [
+ {
+ "message": {
+ "content": "",
+ "tool_calls": [
+ {
+ "id": "call_send",
+ "function": {
+ "name": "send_message",
+ "arguments": '{"message":"喵"}',
+ },
+ },
+ {
+ "id": "call_end",
+ "function": {
+ "name": "end",
+ "arguments": "{}",
+ },
+ },
+ ],
+ }
+ }
+ ],
+ },
+ {
+ "choices": [
+ {
+ "message": {
+ "content": "",
+ "tool_calls": [
+ {
+ "id": "call_end_only",
+ "function": {
+ "name": "end",
+ "arguments": "{}",
+ },
+ }
+ ],
+ }
+ }
+ ],
+ },
+ ],
+ )
+
+ result = await AIClient.ask(client, "hello")
+
+ assert result == ""
+ assert execute_calls == ["send_message", "end"]
+ assert len(client._submit_calls) == 2
+
+ end_tool_messages = [
+ m
+ for m in client._submit_calls[1]
+ if m.get("role") == "tool" and m.get("tool_call_id") == "call_end"
+ ]
+ assert len(end_tool_messages) == 1
+ assert end_tool_messages[0]["content"] == END_CO_CALL_REJECT_CONTENT
+
+
+@pytest.mark.asyncio
+async def test_ai_ask_rejects_end_when_other_tool_failed() -> None:
+ execute_calls: list[str] = []
+
+ async def _execute_tool(
+ name: str, args: dict[str, Any], ctx: dict[str, Any]
+ ) -> str:
+ execute_calls.append(name)
+ if name == "send_message":
+ raise RuntimeError("send failed")
+ if name == "end":
+ ctx["conversation_ended"] = True
+ return "对话已结束"
+ return "ok"
+
+ client = _build_minimal_ai_client(
+ execute_tool=_execute_tool,
+ llm_responses=[
+ {
+ "choices": [
+ {
+ "message": {
+ "content": "",
+ "tool_calls": [
+ {
+ "id": "call_send",
+ "function": {
+ "name": "send_message",
+ "arguments": '{"message":"喵"}',
+ },
+ },
+ {
+ "id": "call_end",
+ "function": {
+ "name": "end",
+ "arguments": "{}",
+ },
+ },
+ ],
+ }
+ }
+ ],
+ },
+ {
+ "choices": [
+ {
+ "message": {
+ "content": "",
+ "tool_calls": [
+ {
+ "id": "call_end_only",
+ "function": {
+ "name": "end",
+ "arguments": "{}",
+ },
+ }
+ ],
+ }
+ }
+ ],
+ },
+ ],
+ )
+
+ result = await AIClient.ask(client, "hello")
+
+ assert result == ""
+ assert execute_calls == ["send_message", "end"]
+ assert len(client._submit_calls) == 2
+
+ end_tool_messages = [
+ m
+ for m in client._submit_calls[1]
+ if m.get("role") == "tool" and m.get("tool_call_id") == "call_end"
+ ]
+ assert end_tool_messages[0]["content"] == END_CO_CALL_REJECT_CONTENT
diff --git a/tests/test_handlers_meme_annotation.py b/tests/test_handlers_meme_annotation.py
index a8ff4d62..1b35b322 100644
--- a/tests/test_handlers_meme_annotation.py
+++ b/tests/test_handlers_meme_annotation.py
@@ -1,4 +1,4 @@
-"""测试 handlers.py 中的表情包自动匹配功能"""
+"""测试 handlers/message_flow 中的表情包自动匹配功能"""
from __future__ import annotations
diff --git a/tests/test_llm_request_params.py b/tests/test_llm_request_params.py
index e34227b6..a917e6fa 100644
--- a/tests/test_llm_request_params.py
+++ b/tests/test_llm_request_params.py
@@ -1,5 +1,6 @@
from __future__ import annotations
+import hashlib
from types import SimpleNamespace
from typing import Any, cast
from unittest.mock import AsyncMock
@@ -339,10 +340,11 @@ async def test_chat_request_auto_sets_prompt_cache_key_from_request_context() ->
call_type="chat",
)
+ group_scope = hashlib.sha1(b"12345", usedforsecurity=False).hexdigest()[:8]
assert fake_client.chat.completions.last_kwargs is not None
assert (
fake_client.chat.completions.last_kwargs["prompt_cache_key"]
- == "pc:gpt-test:chat:group:12345"
+ == f"pc:gpt-test:chat:group:{group_scope}"
)
await requester._http_client.aclose()
@@ -1130,6 +1132,7 @@ async def test_ai_client_request_model_prefetch_keeps_transport_count_from_calle
"tool_manager",
SimpleNamespace(maybe_merge_agent_tools=lambda _call_type, tools: tools),
)
+ setattr(client, "_filter_tools_for_runtime_config", lambda tools: tools)
monkeypatch.setattr(client, "_maybe_prefetch_tools", prefetch_mock)
cfg = ChatModelConfig(
diff --git a/tests/test_llm_retry_suppression.py b/tests/test_llm_retry_suppression.py
index 2012a693..c58a1746 100644
--- a/tests/test_llm_retry_suppression.py
+++ b/tests/test_llm_retry_suppression.py
@@ -8,7 +8,7 @@
import pytest
-from Undefined.ai.client import AIClient
+from Undefined.ai.client import AIClient, MISSING_TOOL_CALL_RETRY_HINT
from Undefined.ai.queue_budget import compute_queued_llm_timeout_seconds
from Undefined.config.models import (
AgentModelConfig,
@@ -21,8 +21,11 @@
@pytest.mark.asyncio
-async def test_ai_ask_reraises_queued_llm_error() -> None:
+async def test_ai_ask_suppresses_queued_llm_error_when_retries_exhausted() -> None:
client: Any = object.__new__(AIClient)
+ client.runtime_config = cast(
+ Any, SimpleNamespace(log_thinking=False, ai_request_max_retries=0)
+ )
client._prompt_builder = cast(
Any,
SimpleNamespace(
@@ -34,9 +37,7 @@ async def test_ai_ask_reraises_queued_llm_error() -> None:
)
client.tool_manager = cast(Any, SimpleNamespace(get_openai_tools=lambda: []))
client._filter_tools_for_runtime_config = lambda tools: tools
- client._get_runtime_config = cast(
- Any, lambda: cast(Any, SimpleNamespace(log_thinking=False))
- )
+ client._get_runtime_config = cast(Any, lambda: client.runtime_config)
client.model_selector = cast(Any, SimpleNamespace(wait_ready=AsyncMock()))
client.chat_config = ChatModelConfig(
api_url="https://api.openai.com/v1",
@@ -60,8 +61,9 @@ async def test_ai_ask_reraises_queued_llm_error() -> None:
proxy_config_available=False,
)
- with pytest.raises(RuntimeError, match="boom"):
- await AIClient.ask(client, "hello")
+ result = await AIClient.ask(client, "hello")
+
+ assert result == ""
@pytest.mark.asyncio
@@ -185,13 +187,22 @@ async def test_ai_ask_limits_missing_tool_call_retries() -> None:
max_tokens=1024,
)
client._find_chat_config_by_name = lambda _name: client.chat_config
- client.submit_queued_llm_call = AsyncMock(
- side_effect=[
- {"choices": [{"message": {"content": "plain 1", "tool_calls": []}}]},
- {"choices": [{"message": {"content": "plain 2", "tool_calls": []}}]},
- {"choices": [{"message": {"content": "plain 3", "tool_calls": []}}]},
- ]
- )
+ llm_responses = [
+ {"choices": [{"message": {"content": "plain 1", "tool_calls": []}}]},
+ {"choices": [{"message": {"content": "plain 2", "tool_calls": []}}]},
+ {"choices": [{"message": {"content": "plain 3", "tool_calls": []}}]},
+ ]
+ submit_calls: list[list[dict[str, Any]]] = []
+
+ async def _submit_queued_llm_call(
+ *,
+ messages: list[dict[str, Any]],
+ **kwargs: Any,
+ ) -> dict[str, Any]:
+ submit_calls.append(list(messages))
+ return llm_responses[len(submit_calls) - 1]
+
+ client.submit_queued_llm_call = AsyncMock(side_effect=_submit_queued_llm_call)
client._search_wrapper = None
client._end_summary_storage = cast(Any, None)
client._send_private_message_callback = None
@@ -213,6 +224,13 @@ async def test_ai_ask_limits_missing_tool_call_retries() -> None:
assert cast(AsyncMock, client.submit_queued_llm_call).await_count == 3
send_message.assert_awaited_once_with("plain 3")
+ second_call_messages = submit_calls[1]
+ assert second_call_messages[-2:] == [
+ {"role": "assistant", "content": "plain 1"},
+ {"role": "user", "content": MISSING_TOOL_CALL_RETRY_HINT},
+ ]
+ assert "send_message" not in MISSING_TOOL_CALL_RETRY_HINT
+
@pytest.mark.asyncio
async def test_agent_runner_reraises_queued_llm_error(tmp_path: Path) -> None:
diff --git a/tests/test_package_layout.py b/tests/test_package_layout.py
new file mode 100644
index 00000000..66833a0f
--- /dev/null
+++ b/tests/test_package_layout.py
@@ -0,0 +1,32 @@
+"""打包布局与模块结构回归测试。"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import Undefined
+
+
+def test_py_typed_marker_exists() -> None:
+ pkg_root = Path(Undefined.__file__).resolve().parent
+ marker = pkg_root / "py.typed"
+ assert marker.is_file(), "src/Undefined/py.typed must exist for PEP 561"
+
+
+def test_py_typed_declared_in_pyproject() -> None:
+ repo_root = Path(__file__).resolve().parents[1]
+ pyproject = (repo_root / "pyproject.toml").read_text(encoding="utf-8")
+ assert 'src/Undefined/py.typed" = "Undefined/py.typed"' in pyproject
+
+
+def test_no_shadowed_monolith_modules() -> None:
+ """禁止 foo.py 与 foo/ 包目录并存（会导致一份实现成为不可达死代码）。"""
+ pkg_root = Path(Undefined.__file__).resolve().parent
+ violations: list[str] = []
+ for path in pkg_root.rglob("*.py"):
+ if path.name == "__init__.py":
+ continue
+ package_dir = path.with_suffix("")
+ if package_dir.is_dir() and (package_dir / "__init__.py").is_file():
+ violations.append(str(path.relative_to(pkg_root)))
+ assert violations == [], f"shadowed monolith modules: {violations}"
diff --git a/tests/test_public_api_imports.py b/tests/test_public_api_imports.py
new file mode 100644
index 00000000..c1b97d99
--- /dev/null
+++ b/tests/test_public_api_imports.py
@@ -0,0 +1,150 @@
+"""根包公共 API 与向后兼容 import 路径测试（Phase 3 API-FACADE 启用）。"""
+
+from __future__ import annotations
+
+import importlib
+from typing import Any
+
+import pytest
+
+# 根包 lazy re-export 符号（见 docs/python-api.md）
+_ROOT_EXPORTS: tuple[str, ...] = (
+ "Config",
+ "get_config",
+ "set_config",
+ "AIClient",
+ "ToolRegistry",
+ "AgentRegistry",
+ "PipelineRegistry",
+ "BaseRegistry",
+ "AnthropicSkillRegistry",
+ "CognitiveService",
+ "KnowledgeManager",
+ "MemeService",
+ "AttachmentRegistry",
+ "RuntimeAPIServer",
+ "RuntimeAPIContext",
+)
+
+# 拆分后须继续可用的 shim / 深层 import 路径
+_BACKWARD_COMPAT_PATHS: tuple[tuple[str, str], ...] = (
+ ("Undefined.config.loader", "Config"),
+ ("Undefined.ai.client", "AIClient"),
+ ("Undefined.attachments", "AttachmentRegistry"),
+ ("Undefined.handlers", "MessageHandler"),
+ ("Undefined.onebot", "OneBotClient"),
+ ("Undefined.skills.tools", "ToolRegistry"),
+ ("Undefined.skills.agents", "AgentRegistry"),
+ ("Undefined.skills.pipelines.registry", "PipelineRegistry"),
+ ("Undefined.skills.registry", "BaseRegistry"),
+ ("Undefined.skills.anthropic_skills", "AnthropicSkillRegistry"),
+ ("Undefined.cognitive.service", "CognitiveService"),
+ ("Undefined.knowledge.manager", "KnowledgeManager"),
+ ("Undefined.memes.service", "MemeService"),
+ ("Undefined.api.app", "RuntimeAPIServer"),
+ ("Undefined.api._context", "RuntimeAPIContext"),
+)
+
+
+@pytest.mark.parametrize("symbol", _ROOT_EXPORTS)
+def test_root_package_exports(symbol: str) -> None:
+ import Undefined
+
+ assert hasattr(Undefined, symbol), f"Undefined.{symbol} missing from root exports"
+ getattr(Undefined, symbol)
+
+
+def test_root_package_all_matches_exports() -> None:
+ import Undefined
+
+ expected = {"__version__", *_ROOT_EXPORTS}
+ assert set(Undefined.__all__) == expected
+
+
+def test_root_package_lazy_import_does_not_load_cli_modules() -> None:
+ import sys
+
+ saved_modules = {
+ name: module
+ for name, module in sys.modules.items()
+ if name == "Undefined" or name.startswith("Undefined.")
+ }
+ try:
+ for name in saved_modules:
+ del sys.modules[name]
+
+ import Undefined # noqa: F401
+
+ assert "Undefined.onebot" not in sys.modules
+ assert "Undefined.handlers" not in sys.modules
+ assert "Undefined.main" not in sys.modules
+ finally:
+ for name in list(sys.modules):
+ if (
+ name == "Undefined" or name.startswith("Undefined.")
+ ) and name not in saved_modules:
+ del sys.modules[name]
+ sys.modules.update(saved_modules)
+
+
+@pytest.mark.parametrize(("module_path", "symbol"), _BACKWARD_COMPAT_PATHS)
+def test_backward_compat_import_path(module_path: str, symbol: str) -> None:
+ module = importlib.import_module(module_path)
+ assert hasattr(module, symbol), f"{module_path}.{symbol} missing"
+ getattr(module, symbol)
+
+
+def test_set_config_not_used_by_default_get_config(
+ monkeypatch: pytest.MonkeyPatch,
+ tmp_path: Any,
+) -> None:
+ """库嵌入 set_config() 与 CLI get_config() 路径隔离（CONFIG Phase 2）。"""
+ import Undefined.config as config_module
+
+ from Undefined.config import Config, set_config
+
+ config_module._config = None
+ config_module._config_manager = None
+
+ monkeypatch.chdir(tmp_path)
+ (tmp_path / "config.toml").write_text(
+ """
+[onebot]
+ws_url = "ws://127.0.0.1:3999"
+[models.chat]
+api_url = "https://api.example/v1"
+api_key = "sk-test"
+model_name = "from-file"
+""",
+ encoding="utf-8",
+ )
+
+ cfg = config_module.get_config(strict=False)
+ assert cfg.chat_model.model_name == "from-file"
+
+ injected = Config.from_mapping(
+ {
+ "onebot": {"ws_url": "ws://127.0.0.1:3001"},
+ "models": {
+ "chat": {
+ "api_url": "https://api.example/v1",
+ "api_key": "sk-test",
+ "model_name": "injected",
+ },
+ "vision": {
+ "api_url": "https://api.example/v1",
+ "api_key": "sk-test",
+ "model_name": "vision-test",
+ },
+ "agent": {
+ "api_url": "https://api.example/v1",
+ "api_key": "sk-test",
+ "model_name": "agent-test",
+ },
+ },
+ },
+ strict=False,
+ )
+ set_config(injected)
+ assert config_module.get_config(strict=False) is injected
+ assert config_module.get_config_manager().load(strict=False) is injected
diff --git a/uv.lock b/uv.lock
index 3459b092..dd09c6b9 100644
--- a/uv.lock
+++ b/uv.lock
@@ -4626,7 +4626,7 @@ wheels = [
[[package]]
name = "undefined-bot"
-version = "3.4.2"
+version = "3.5.0"
source = { editable = "." }
dependencies = [
{ name = "aiofiles" },