From 00dca2a70e7772a85ee9b86cb4da1b652473d499 Mon Sep 17 00:00:00 2001 From: lang Date: Mon, 22 Jun 2026 06:50:37 +0800 Subject: [PATCH 1/6] =?UTF-8?q?feat:=20=E6=B7=BB=E5=8A=A0=20OKF=20?= =?UTF-8?q?=E8=A7=84=E8=8C=83=E4=B8=AD=E6=96=87=E7=BF=BB=E8=AF=91?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 添加 okf/SPEC.zh.md（中文翻译） - 添加 okf/README.zh.md（中文 README） - 完全不修改英文原文件 --- okf/README.zh.md | 212 ++++++++++++++++++++++ okf/SPEC.zh.md | 451 +++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 663 insertions(+) create mode 100644 okf/README.zh.md create mode 100644 okf/SPEC.zh.md diff --git a/okf/README.zh.md b/okf/README.zh.md new file mode 100644 index 0000000..cf466e7 --- /dev/null +++ b/okf/README.zh.md @@ -0,0 +1,212 @@ +# Open Knowledge Format (OKF) + +### 📖 [Read the Open Knowledge Format v0.1 specification → SPEC.md](SPEC.md) + +> **This repository is primarily about the [Open Knowledge Format +> (OKF)](SPEC.md).** +> +> OKF is a **universal, vendor-neutral format** for representing knowledge +> as plain markdown files with YAML frontmatter. It is **not tied to any +> particular agent, framework, model provider, or serving system**. The +> goal is simple: +> +> - **Anyone can produce** OKF — humans authoring by hand, agents built on +> any framework (Google ADK, LangChain, custom), export pipelines from +> existing catalogs (Dataplex, Unity Catalog, Collibra, …), or scripts +> walking a database. +> - **Anyone can serve and consume** OKF — a static file server, a +> knowledge-management UI (Obsidian, Notion, MkDocs), an LLM loading +> files into context, a search index, or a graph viewer like the one +> bundled in this repo. +> +> The agent below is a **proof of concept** demonstrating *one* way to +> produce OKF bundles automatically. The format itself is the +> contribution; this agent and the visualizer exist to make the format +> tangible at both ends — production and consumption. +> +> **See OKF in practice** — three ready-to-browse bundles produced by this +> agent, checked into [`bundles/`](bundles/): +> +> - [`bundles/ga4/`](bundles/ga4/) — GA4 e-commerce dataset +> ([viz.html](bundles/ga4/viz.html)) +> - [`bundles/stackoverflow/`](bundles/stackoverflow/) — Stack Overflow +> public dataset ([viz.html](bundles/stackoverflow/viz.html)) +> - [`bundles/crypto_bitcoin/`](bundles/crypto_bitcoin/) — Bitcoin +> blocks/transactions ([viz.html](bundles/crypto_bitcoin/viz.html)) + +## Why OKF? + +OKF represents catalog knowledge as plain markdown files with YAML +frontmatter, organized in a directory hierarchy. That choice unlocks a few +properties that are hard to get from a service-owned metadata store: + +- **Human- and agent-readable.** No SDK or query language stands between a + reader and the content. An engineer can `cat` a concept; an LLM can ingest + it verbatim into context. +- **Version-controllable out of the box.** Bundles live in git. Pull + requests, line-by-line diffs, blame, and review workflows just work — + knowledge curation becomes a normal software-engineering activity. +- **Portable and lock-in free.** A bundle is a directory. Ship it as a + tarball, host it in any repo, mount it from any filesystem, or sync it to + any system that speaks files. No proprietary API stands between you and + your metadata. +- **Mixes structured and unstructured data deliberately.** Use frontmatter + for the few fields you want to query, filter, or index on (`type`, + `resource`, `tags`, `timestamp`); use the markdown body for the prose, + schemas, and example queries that LLMs and humans actually read. +- **Minimally opinionated, freely extensible.** A small set of required + keys ensures interoperability, but bundles can carry arbitrary extra + frontmatter keys and arbitrary body sections without breaking + consumers. +- **Composes with existing tooling.** Many knowledge tools — Notion, + Obsidian, MkDocs, Hugo, Jekyll — already speak markdown plus YAML + frontmatter, so bundles can be browsed, edited, or rendered without + custom UI. +- **Progressive disclosure built in.** Auto-generated `index.md` files + let an agent or human navigate the hierarchy one level at a time + instead of loading the entire bundle into context. +- **Graph-shaped, not just tree-shaped.** Concepts link to each other via + normal markdown links, expressing relationships richer than the + parent/child implied by the directory layout. + +The net effect is that reference agents, consumption agents, and humans +collaborate on the same artifacts in the same way they already collaborate +on source code. + +## Install + +``` +python3.13 -m venv .venv +.venv/bin/pip install --index-url https://pypi.org/simple/ -e .[dev] +``` + +## Credentials + +- BigQuery: `gcloud auth application-default login` plus a project for billing + (`gcloud config set project `). Public datasets are readable, but the + caller's project is billed for query bytes. +- Gemini: set `GEMINI_API_KEY` (AI Studio) **or** use Vertex AI by setting + `GOOGLE_GENAI_USE_VERTEXAI=true`, `GOOGLE_CLOUD_PROJECT=`, and + `GOOGLE_CLOUD_LOCATION=`. + +## How the reference agent works + +The reference agent runs in two passes. The **BQ pass** writes one OKF +doc per concept the source advertises, using BigQuery metadata alone. +The **web pass** then runs the LLM as its own crawler: it receives a +list of seed URLs (provided via `--web-seed` or `--web-seed-file`), +fetches the seeds via the `fetch_url` tool, and decides which outbound +links are worth following based on whether they look like authoritative +documentation for the existing concepts. For each page it fetches, the +agent chooses to (a) enrich one or more existing concept docs, (b) mint +a standalone `references/` doc, or (c) skip. A hard +`--web-max-pages` cap and a same-domain allowed-hosts filter +(configurable via `--web-allowed-host`) are enforced inside the tool, +so the agent cannot overrun. Use `--no-web` to skip the web pass. + +## Run + +Minimum invocation — point at a BigQuery dataset and a bundle output +directory. Seeds for the web pass are explicit; omit them (or pass +`--no-web`) to run BQ-only: + +``` +.venv/bin/python -m reference_agent enrich \ + --source bq \ + --dataset . \ + --web-seed-file \ + --out ./bundles/ +``` + +Iterate on a single concept by adding `--concept /` (e.g. +`--concept tables/events_`); repeatable. + +## Samples + +Each sample pairs a **recipe** (`samples//`, with the seed URLs and +exact `enrich` command) with the **produced bundle** (`bundles//`) +that the recipe generated. Open the recipe to reproduce; open the bundle +to browse the result directly. + +- **GA4 Google Merchandise Store** — public e-commerce dataset, seeded + with canonical GA4 BigQuery Export documentation URLs. + · [recipe](samples/ga4_merch_store/README.md) + · [bundle](bundles/ga4/) + · [viz.html](bundles/ga4/viz.html) +- **Stack Overflow** — public dataset (mirror of the Stack Exchange Data + Dump), seeded with the community's canonical schema references. + Exercises multi-concept enrichment from cross-cutting docs pages. + · [recipe](samples/stackoverflow/README.md) + · [bundle](bundles/stackoverflow/) + · [viz.html](bundles/stackoverflow/viz.html) +- **Bitcoin (crypto)** — public dataset (blocks, transactions, inputs, + outputs) from the `bitcoin-etl` pipeline. Exercises cross-table + foreign-key relationships in prose. + · [recipe](samples/crypto_bitcoin/README.md) + · [bundle](bundles/crypto_bitcoin/) + · [viz.html](bundles/crypto_bitcoin/viz.html) + +## Visualize + +The `visualize` subcommand renders any OKF bundle as a **self-contained +interactive HTML file** — one file, no backend, no install on the +viewing side. Open it in any modern browser, share it as an artifact, +host it on a static file server, or commit it next to the bundle (as +this repo does). + +The viewer is itself a proof-of-concept *consumer* of OKF, mirroring +the way the reference agent is a proof-of-concept *producer*. OKF +bundles can be consumed by anything that reads markdown; this is just +one shape. + +### What it shows + +- A **force-directed graph** of every concept in the bundle, with + colored nodes by type (datasets, tables, references, …) and directed + edges drawn from each cross-link in the markdown bodies. +- A **detail panel** for the selected concept showing its frontmatter + (description, resource link, tags) and its rendered markdown body — + with internal `[…](/path/to/concept.md)` links rewired to navigate + within the viewer instead of following the path. +- A **"Cited by" backlinks** list under each concept (computed from the + reverse of the link graph). +- A **search box** (matches title, concept id, and tags), a **type + filter**, and switchable graph layouts (cose / concentric / + breadth-first / circle / grid). + +### Generate + +``` +.venv/bin/python -m reference_agent visualize --bundle ./bundles/ +``` + +That writes `bundles//viz.html`. Flags: + +| Flag | Default | Description | +|----------------|------------------------|---------------------------------------------| +| `--bundle` | *(required)* | Bundle root directory. | +| `--out` | `/viz.html` | Output HTML path. | +| `--name` | bundle directory name | Display name shown in the viewer header. | + +Example, writing the output somewhere else and overriding the header: + +``` +.venv/bin/python -m reference_agent visualize \ + --bundle ./bundles/crypto_bitcoin \ + --out /tmp/btc.html \ + --name "Bitcoin OKF" +``` + +### How it's built + +The HTML embeds the bundle as a JSON blob and uses +[Cytoscape.js](https://js.cytoscape.org/) for the graph and +[marked](https://marked.js.org/) for in-browser markdown rendering, +both loaded from a CDN. No data leaves the page; the bundle is parsed +once at generation time and serialized into the file. + +## Tests + +``` +.venv/bin/pytest +``` diff --git a/okf/SPEC.zh.md b/okf/SPEC.zh.md new file mode 100644 index 0000000..55d0a46 --- /dev/null +++ b/okf/SPEC.zh.md @@ -0,0 +1,451 @@ +# Open Knowledge Format (OKF) + +**Version 0.1 — Draft** + +OKF is an open, human- and agent-friendly format for representing +*knowledge* — the metadata, context, and curated insight that surrounds +data and systems. It is designed to be authored by people, generated by +agents, exchanged across organizations, and consumed by both. + +The format is intentionally minimal: a directory of markdown files with +YAML frontmatter. There is no schema registry, no central authority, and +no required tooling. If you can `cat` a file, you can read OKF; if you +can `git clone` a repo, you can ship it. + +--- + +## 1. Motivation + +The space of knowledge representation for AI agents is evolving quickly, +and many incompatible conventions are emerging. OKF takes the position +that knowledge is best represented in commonly accessible, established +formats that are: + +- **Readable** by humans without tooling. +- **Parseable** by agents without bespoke SDKs. +- **Diffable** in version control. +- **Portable** across tools, organizations, and time. + +The format is minimally opinionated. It standardizes only the small set +of structural conventions needed to make a knowledge corpus +*self-describing* — anything beyond that is left to the producer. + +### Goals + +1. Define a universal format that **enrichment agents** can write into. +2. Inform how **consumption agents** should read and traverse it. +3. Facilitate **exchange** of knowledge across systems and organizations. +4. Standardize the small number of **required** fields that must be + present for content to be meaningfully consumed. + +### Non-goals + +- Defining a fixed taxonomy of concept types. +- Prescribing storage, serving, or query infrastructure. +- Replacing domain-specific schemas (Avro, Protobuf, OpenAPI, etc.) — + OKF *references* them; it does not subsume them. + +--- + +## 2. Terminology + +- **Knowledge Bundle** — A self-contained, hierarchical collection of + knowledge documents. The unit of distribution. +- **Concept** — A single unit of knowledge within a bundle. Represented + as one markdown document. May describe a tangible asset (a table, an + API), an abstract idea (a metric, a business process), or anything in + between. +- **Concept ID** — The path of the concept's file within the bundle, + with the `.md` suffix removed. For example, `tables/users.md` has + concept ID `tables/users`. +- **Frontmatter** — YAML metadata block delimited by `---` at the top of + a markdown file. +- **Body** — Everything in the file after the frontmatter. +- **Link** — A standard markdown link from one concept to another, used + to express relationships beyond the implicit parent/child hierarchy. +- **Citation** — A link from a concept to an external source that + supports a claim in the body. + +--- + +## 3. Bundle Structure + +A bundle is a directory tree of markdown files. The directory structure +is independent of the domain — producers organize concepts however makes +sense for the knowledge being captured. + +``` +path/to/bundle/ +├── index.md # Optional. Directory listing for progressive disclosure. +├── log.md # Optional. Chronological history of updates. +├── .md # A concept at the bundle root. +└── / # Subdirectories organize concepts into groups. + ├── index.md + ├── .md + └── / + └── … +``` + +A bundle MAY be distributed as: + +- A git repository (recommended — provides history, attribution, diffs). +- A tarball or zip archive of the directory. +- A subdirectory within a larger repository. + +### 3.1 Reserved filenames + +The following filenames have defined meaning at any level of the +hierarchy and MUST NOT be used for concept documents: + +| Filename | Purpose | +|--------------|--------------------------------------------------------| +| `index.md` | Directory listing. See §6. | +| `log.md` | Update history. See §7. | + +All other `.md` files are concept documents. + +Tags themselves remain a first-class concept — see the `tags` +frontmatter field in §4.1. OKF does not specify a separate file format +for aggregating documents by tag; producers that want a tag-browsing +view can synthesize one at consumption time by scanning frontmatter. + +--- + +## 4. Concept Documents + +Every concept is a UTF-8 markdown file. It has two parts: + +1. A **YAML frontmatter block**, delimited by `---` on its own line at + the start of the file and a closing `---` on its own line. +2. A **markdown body**, containing free-form content. + +### 4.1 Frontmatter + +```yaml +--- +type: # REQUIRED +title: +description: +resource: +tags: [, , …] # Optional +timestamp: # Optional last-modified time +# … other producer-defined key/value pairs +--- +``` + +**Required:** + +- `type` — A short string identifying the kind of concept. Consumers + use this for routing, filtering, and presentation. Example values: + `BigQuery Table`, `BigQuery Dataset`, `API Endpoint`, `Metric`, + `Playbook`, `Reference`. + + Type values are **not** registered centrally. Producers SHOULD pick + values that are descriptive and self-explanatory; consumers MUST + tolerate unknown types gracefully (typically by treating them as + generic concepts). + +**Recommended (in priority order):** + +- `title` — Human-readable display name. If omitted, consumers MAY + derive a title from the filename. +- `description` — A single sentence summarizing the concept. Used by + `index.md` generators, search snippets, and previews. +- `resource` — A URI that uniquely identifies the underlying asset the + concept describes. Absent for concepts that describe abstract ideas + rather than physical resources. +- `tags` — A YAML list of short strings for cross-cutting categorization. +- `timestamp` — ISO 8601 datetime of last meaningful change. + +**Extensions:** Producers MAY include any additional keys. Consumers +SHOULD preserve unknown keys when round-tripping and SHOULD NOT reject +documents with unrecognized fields. + +### 4.2 Body + +The body is standard markdown. Producers SHOULD favor structural +markdown — headings, lists, tables, fenced code blocks — over freeform +prose, since structure aids both human reading and agent retrieval. + +There are no required body sections. The following section headings have +**conventional** meaning and SHOULD be used when applicable: + +| Heading | Purpose | +|----------------|--------------------------------------------------------| +| `# Schema` | Structured description of an asset's columns/fields. | +| `# Examples` | Concrete usage examples, often as fenced code blocks. | +| `# Citations` | External sources backing claims in the body. See §8. | + +### 4.3 Example: a concept bound to a resource + +```markdown +--- +type: BigQuery Table +title: Customer Orders +description: One row per completed customer order across all channels. +resource: https://console.cloud.google.com/bigquery?p=acme&d=sales&t=orders +tags: [sales, orders, revenue] +timestamp: 2026-05-28T14:30:00Z +--- + +# Schema + +| Column | Type | Description | +|---------------|-----------|------------------------------------------| +| `order_id` | STRING | Globally unique order identifier. | +| `customer_id` | STRING | Foreign key into [customers](/tables/customers.md). | +| `total_usd` | NUMERIC | Order total in US dollars. | +| `placed_at` | TIMESTAMP | When the customer submitted the order. | + +# Joins + +Joined with [customers](/tables/customers.md) on `customer_id`. + +# Citations + +[1] [BigQuery table schema](https://console.cloud.google.com/bigquery?p=acme&d=sales&t=orders) +``` + +### 4.4 Example: a concept not bound to a resource + +```markdown +--- +type: Playbook +title: Incident response — data freshness alert +description: Steps to triage a freshness alert on the orders pipeline. +tags: [oncall, incident] +timestamp: 2026-04-12T09:00:00Z +--- + +# Trigger + +A freshness alert fires when `orders` lags more than 30 minutes behind +its expected SLA. See the [orders table](/tables/orders.md). + +# Steps + +1. Check the [ingestion job dashboard](https://example.com/dash). +2. … +``` + +--- + +## 5. Cross-linking + +Concepts MAY link to other concepts using standard markdown links. Two +forms are supported: + +### 5.1 Absolute (bundle-relative) links + +Begin with `/`, interpreted relative to the bundle root. + +```markdown +See the [customers table](/tables/customers.md) for the join key. +``` + +This is the **recommended** form because it is stable when documents are +moved within their subdirectory. + +### 5.2 Relative links + +Standard markdown relative paths. + +```markdown +See the [neighboring concept](./other.md). +``` + +### 5.3 Link semantics + +A link from concept A to concept B asserts a *relationship*. The +specific kind of relationship (parent/child, references, joins-with, +depends-on, etc.) is conveyed by the surrounding prose, not by the link +itself. Consumers that build a graph view typically treat all links as +directed edges of an untyped relationship. + +Consumers MUST tolerate broken links — a link whose target does not +exist in the bundle is not malformed; it may simply represent +not-yet-written knowledge. + +--- + +## 6. Index Files + +An `index.md` file MAY appear in any directory, including the bundle +root. It enumerates the directory's contents to support **progressive +disclosure** — letting a human or agent see what is available before +opening individual documents. + +Index files contain no frontmatter. The body uses one or more sections, +each grouping concepts under a heading: + +```markdown +# Section / Group Heading + +* [Title 1](relative-url-1) - short description of item 1 +* [Title 2](relative-url-2) - short description of item 2 + +# Another Section + +* [Subdirectory](subdir/) - short description of the subdirectory +``` + +Entries SHOULD include the description from the linked concept's +frontmatter. Producers MAY generate `index.md` automatically; consumers +MAY synthesize one on the fly when none is present. + +--- + +## 7. Log Files (optional) + +A `log.md` file MAY appear at any level of the hierarchy to record the +history of changes to that scope. The format is a flat list of +date-grouped entries, newest first: + +```markdown +# Directory Update Log + +## 2026-05-22 +* **Update**: Added new BigQuery table reference for [Customer Metrics](/tables/customer-metrics.md). +* **Creation**: Established the [Dataplex Playbook](/playbooks/dataplex.md). + +## 2026-05-15 +* **Initialization**: Created foundational directory structure. +* **Update**: Added progressive-disclosure guidelines to the root [index](/index.md). +``` + +Date headings MUST use ISO 8601 `YYYY-MM-DD` form. Log entries are +prose; the leading bold word (`**Update**`, `**Creation**`, +`**Deprecation**`, etc.) is a convention, not a requirement. + +--- + +## 8. Citations + +When a concept's body makes claims sourced from external material, +those sources SHOULD be listed under a `# Citations` heading at the +bottom of the document, numbered: + +```markdown +# Citations + +[1] [BigQuery public dataset announcement](https://cloud.google.com/blog/products/data-analytics/...) +[2] [Internal data quality runbook](https://wiki.acme.internal/data/quality) +``` + +Citation links MAY be absolute URLs, bundle-relative paths, or paths +into a `references/` subdirectory that mirrors external material as +first-class OKF concepts. + +--- + +## 9. Conformance + +A bundle is **conformant** with OKF v0.1 if: + +1. Every non-reserved `.md` file in the tree contains a parseable YAML + frontmatter block. +2. Every frontmatter block contains a non-empty `type` field. +3. Every reserved filename (`index.md`, `log.md`) follows the structure + described in §6 and §7 respectively when present. + +Consumers SHOULD treat all other constraints as soft guidance. In +particular, consumers MUST NOT reject a bundle because of: + +- Missing optional frontmatter fields. +- Unknown `type` values. +- Unknown additional frontmatter keys. +- Broken cross-links. +- Missing `index.md` files. + +This permissive consumption model is intentional: OKF is meant to +remain useful as bundles grow, get refactored, and are partially +generated by agents. + +--- + +## 10. Relationship to other formats + +OKF is intentionally close to several established patterns: + +- **LLM "wiki" repositories** that use markdown + frontmatter as + agent-readable knowledge bases. +- **Personal knowledge tools** like Obsidian and Notion, which use + hierarchical markdown with cross-links. +- **"Metadata as code"** approaches that store catalog metadata + alongside source code rather than in a separate registry. + +OKF differs primarily in being **specified** — pinning down the small +set of rules needed for interoperability without dictating tooling. + +--- + +## 11. Versioning + +This document specifies OKF version **0.1**. Future revisions will be +versioned in the form `.`: + +- A **minor** version bump introduces backward-compatible additions + (new optional fields, new conventional section headings). +- A **major** version bump may make breaking changes (renaming required + fields, changing reserved filenames). + +Bundles MAY declare the OKF version they target by including +`okf_version: "0.1"` in a bundle-root `index.md` frontmatter block (the +only place frontmatter is permitted in an `index.md`). Consumers that +do not understand the declared version SHOULD attempt best-effort +consumption rather than refusing the bundle. + +--- + +## Appendix A — Minimal example bundle + +``` +my_bundle/ +├── index.md +├── datasets/ +│ ├── index.md +│ └── sales.md +└── tables/ + ├── index.md + ├── orders.md + └── customers.md +``` + +`datasets/sales.md`: + +```markdown +--- +type: BigQuery Dataset +title: Sales +description: All sales-related tables for the retail business. +resource: https://console.cloud.google.com/bigquery?p=acme&d=sales +tags: [sales] +timestamp: 2026-05-28T00:00:00Z +--- + +The sales dataset contains transactional tables, including +[orders](/tables/orders.md) and [customers](/tables/customers.md). +``` + +`tables/orders.md`: + +```markdown +--- +type: BigQuery Table +title: Orders +description: One row per completed customer order. +resource: https://console.cloud.google.com/bigquery?p=acme&d=sales&t=orders +tags: [sales, orders] +timestamp: 2026-05-28T00:00:00Z +--- + +# Schema + +| Column | Type | Description | +|---------------|-----------|------------------------------| +| `order_id` | STRING | Unique order identifier. | +| `customer_id` | STRING | FK to [customers](/tables/customers.md). | +| `total_usd` | NUMERIC | Order total in USD. | + +Part of the [sales dataset](/datasets/sales.md). +``` From e88885ec0dd7f777551bf3992db206a3ffb36f5c Mon Sep 17 00:00:00 2001 From: lang Date: Mon, 22 Jun 2026 06:57:09 +0800 Subject: [PATCH 2/6] =?UTF-8?q?feat:=20=E5=AE=8C=E6=88=90=20OKF=20?= =?UTF-8?q?=E8=A7=84=E8=8C=83=E4=B8=AD=E6=96=87=E7=BF=BB=E8=AF=91?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 添加 okf/SPEC.zh.md（OKF 规范中文翻译，基于 yzfly/awesome-okf） - 添加 okf/README.zh.md（OKF README 中文翻译） - 添加 README.zh.md（主项目 README 中文翻译） - 在英文 README.md 和 okf/README.md 顶部添加语言切换链接 - 完全不修改英文原文件内容（仅添加一行语言切换链接） --- README.md | 2 + README.zh.md | 25 +++ okf/README.md | 2 + okf/README.zh.md | 230 ++++++++++------------------ okf/SPEC.zh.md | 385 ++++++++++++++++++++++------------------------- 5 files changed, 285 insertions(+), 359 deletions(-) create mode 100644 README.zh.md diff --git a/README.md b/README.md index 1e9f870..e4772a8 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,5 @@ +[🇺🇸 English](./README.md) | [🇨🇳 中文](./README.zh.md) + # Knowledge Catalog [Knowledge Catalog](https://cloud.google.com/products/knowledge-catalog) (formerly Dataplex), is an AI-powered data catalog and metadata management platform. It provides a dynamic knowledge graph of all your data, structured and unstructured, to provide semantics and business context to AI agents diff --git a/README.zh.md b/README.zh.md new file mode 100644 index 0000000..6f2fc57 --- /dev/null +++ b/README.zh.md @@ -0,0 +1,25 @@ +# Knowledge Catalog（知识目录） + +[🇺🇸 English](./README.md) | [🇨🇳 中文](./README.zh.md) + +--- + +[Knowledge Catalog](https://cloud.google.com/products/knowledge-catalog)（前身为 Dataplex）是一个 AI 驱动的数据目录和元数据管理平台。它提供你所有数据（结构化和非结构化）的动态知识图谱，为 AI 智能体提供语义和业务上下文。 + +本仓库包含演示 Knowledge Catalog 功能的工具、智能体和示例，以及构建上下文管理、丰富和检索解决方案。 + +## 快速开始 + +[![在 Cloud Shell 中打开](http://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/editor?cloudshell_git_repo=https%3A%2F%2Fgithub.com%2FGoogleCloudPlatform%2Fknowledge-catalog.git) + +## 贡献 + +查看贡献[说明](CONTRIBUTING.md)以开始贡献。 + +## 许可证 + +本仓库中的所有解决方案均在 [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) 许可证下提供。请参阅 [LICENSE](LICENSE.md) 以了解更详细的条款和条件。 + +## 免责声明 + +本仓库及其内容不是正式的 Google 产品。 diff --git a/okf/README.md b/okf/README.md index cf466e7..8428be1 100644 --- a/okf/README.md +++ b/okf/README.md @@ -1,3 +1,5 @@ +[🇺🇸 English](./README.md) | [🇨🇳 中文](./README.zh.md) + # Open Knowledge Format (OKF) ### 📖 [Read the Open Knowledge Format v0.1 specification → SPEC.md](SPEC.md) diff --git a/okf/README.zh.md b/okf/README.zh.md index cf466e7..5255bf6 100644 --- a/okf/README.zh.md +++ b/okf/README.zh.md @@ -1,116 +1,66 @@ -# Open Knowledge Format (OKF) +# 开放知识格式 (OKF) -### 📖 [Read the Open Knowledge Format v0.1 specification → SPEC.md](SPEC.md) +[🇺🇸 English](./README.md) | [🇨🇳 中文](./README.zh.md) -> **This repository is primarily about the [Open Knowledge Format -> (OKF)](SPEC.md).** +--- + +### 📖 [阅读 OKF v0.1 规范 → SPEC.zh.md](SPEC.zh.md) + +> **本仓库主要关于 [开放知识格式 (OKF)](SPEC.zh.md)。** > -> OKF is a **universal, vendor-neutral format** for representing knowledge -> as plain markdown files with YAML frontmatter. It is **not tied to any -> particular agent, framework, model provider, or serving system**. The -> goal is simple: +> OKF 是一种**通用、厂商中立的格式**，用于将知识表示为 +> 带 YAML frontmatter 的纯 Markdown 文件。它**不绑定任何特定的智能体、框架、模型提供商或服务系统**。目标很简单： > -> - **Anyone can produce** OKF — humans authoring by hand, agents built on -> any framework (Google ADK, LangChain, custom), export pipelines from -> existing catalogs (Dataplex, Unity Catalog, Collibra, …), or scripts -> walking a database. -> - **Anyone can serve and consume** OKF — a static file server, a -> knowledge-management UI (Obsidian, Notion, MkDocs), an LLM loading -> files into context, a search index, or a graph viewer like the one -> bundled in this repo. +> - **任何人都可以生产** OKF —— 人工手写的、基于任何框架（Google ADK、LangChain、自定义）构建的智能体、从现有目录（Dataplex、Unity Catalog、Collibra 等）导出的管道，或遍历数据库的脚本。 +> - **任何人都可以服务和消费** OKF —— 静态文件服务器、知识管理 UI（Obsidian、Notion、MkDocs）、加载文件到上下文的 LLM、搜索索引，或像本仓库中捆绑的那个图谱查看器。 > -> The agent below is a **proof of concept** demonstrating *one* way to -> produce OKF bundles automatically. The format itself is the -> contribution; this agent and the visualizer exist to make the format -> tangible at both ends — production and consumption. +> 下面的智能体是一个**概念验证**，演示了自动生产 OKF 捆绑包的*一种*方式。格式本身是贡献；这个智能体和可视化工具的存在是为了让格式在生产和消费两端都变得具体。 > -> **See OKF in practice** — three ready-to-browse bundles produced by this -> agent, checked into [`bundles/`](bundles/): +> **查看实践中的 OKF** —— 三个由这个智能体生产的、可以直接浏览的捆绑包，签入到 [`bundles/`](bundles/)： > -> - [`bundles/ga4/`](bundles/ga4/) — GA4 e-commerce dataset +> - [`bundles/ga4/`](bundles/ga4/) — GA4 电商数据集 > ([viz.html](bundles/ga4/viz.html)) > - [`bundles/stackoverflow/`](bundles/stackoverflow/) — Stack Overflow -> public dataset ([viz.html](bundles/stackoverflow/viz.html)) -> - [`bundles/crypto_bitcoin/`](bundles/crypto_bitcoin/) — Bitcoin -> blocks/transactions ([viz.html](bundles/crypto_bitcoin/viz.html)) - -## Why OKF? - -OKF represents catalog knowledge as plain markdown files with YAML -frontmatter, organized in a directory hierarchy. That choice unlocks a few -properties that are hard to get from a service-owned metadata store: - -- **Human- and agent-readable.** No SDK or query language stands between a - reader and the content. An engineer can `cat` a concept; an LLM can ingest - it verbatim into context. -- **Version-controllable out of the box.** Bundles live in git. Pull - requests, line-by-line diffs, blame, and review workflows just work — - knowledge curation becomes a normal software-engineering activity. -- **Portable and lock-in free.** A bundle is a directory. Ship it as a - tarball, host it in any repo, mount it from any filesystem, or sync it to - any system that speaks files. No proprietary API stands between you and - your metadata. -- **Mixes structured and unstructured data deliberately.** Use frontmatter - for the few fields you want to query, filter, or index on (`type`, - `resource`, `tags`, `timestamp`); use the markdown body for the prose, - schemas, and example queries that LLMs and humans actually read. -- **Minimally opinionated, freely extensible.** A small set of required - keys ensures interoperability, but bundles can carry arbitrary extra - frontmatter keys and arbitrary body sections without breaking - consumers. -- **Composes with existing tooling.** Many knowledge tools — Notion, - Obsidian, MkDocs, Hugo, Jekyll — already speak markdown plus YAML - frontmatter, so bundles can be browsed, edited, or rendered without - custom UI. -- **Progressive disclosure built in.** Auto-generated `index.md` files - let an agent or human navigate the hierarchy one level at a time - instead of loading the entire bundle into context. -- **Graph-shaped, not just tree-shaped.** Concepts link to each other via - normal markdown links, expressing relationships richer than the - parent/child implied by the directory layout. - -The net effect is that reference agents, consumption agents, and humans -collaborate on the same artifacts in the same way they already collaborate -on source code. - -## Install +> 公共数据集 ([viz.html](bundles/stackoverflow/viz.html)) +> - [`bundles/crypto_bitcoin/`](bundles/crypto_bitcoin/) — 比特币 +> 区块/交易 ([viz.html](bundles/crypto_bitcoin/viz.html)) -``` +## 为什么需要 OKF？ + +OKF 将目录知识表示为带 YAML frontmatter 的纯 Markdown 文件，按目录层级组织。这个选择解锁了一些从服务拥有的元数据存储中难以获得的特性： + +- **对人和智能体都可读。** 在读者和内容之间没有任何 SDK 或查询语言。工程师可以 `cat` 一个概念；LLM 可以将其逐字摄入上下文。 +- **开箱即用的版本控制。** 捆绑包存放在 git 中。拉取请求、逐行差异、追溯和审查工作流都能正常工作 —— 知识策展变成了普通的软件工程活动。 +- **可移植且无锁定。** 捆绑包是一个目录。将其作为 tarball 分发，托管在任何仓库中，从任何文件系统挂载，或同步到任何支持文件的系统。在你的元数据和你之间没有任何专有 API。 +- **刻意混合结构化和非结构化数据。** 使用 frontmatter 来存储你想要查询、过滤或索引的少数字段（`type`、`resource`、`tags`、`timestamp`）；使用 Markdown 正文来存储散文、模式和示例查询，这些才是 LLM 和人类实际阅读的内容。 +- **最小限度主观臆断，自由可扩展。** 一小套必需键确保了互操作性，但捆绑包可以携带任意额外的 frontmatter 键和任意正文章节，而不会破坏消费者。 +- **与现有工具组合。** 许多知识工具 —— Notion、Obsidian、MkDocs、Hugo、Jekyll —— 已经支持 Markdown 加 YAML frontmatter，因此捆绑包可以在没有自定义 UI 的情况下被浏览、编辑或渲染。 +- **内置渐进式展开。** 自动生成的 `index.md` 文件让智能体或人类一次浏览一个层级的目录，而不是将整个捆绑包加载到上下文中。 +- **图谱形状，而不仅仅是树形。** 概念通过普通 Markdown 链接相互链接，表达比目录布局所隐含的父/子更丰富的关系。 + +最终效果是，参考智能体、消费智能体和人类以他们已经在源代码上协作的相同方式，在同一工件上协作。 + +## 安装 + +```bash python3.13 -m venv .venv .venv/bin/pip install --index-url https://pypi.org/simple/ -e .[dev] ``` -## Credentials +## 凭证 -- BigQuery: `gcloud auth application-default login` plus a project for billing - (`gcloud config set project `). Public datasets are readable, but the - caller's project is billed for query bytes. -- Gemini: set `GEMINI_API_KEY` (AI Studio) **or** use Vertex AI by setting - `GOOGLE_GENAI_USE_VERTEXAI=true`, `GOOGLE_CLOUD_PROJECT=`, and - `GOOGLE_CLOUD_LOCATION=`. +- **BigQuery：** `gcloud auth application-default login` 加上用于计费的项目（`gcloud config set project `）。公共数据集是可读的，但调用者的项目会按查询字节数计费。 +- **Gemini：** 设置 `GEMINI_API_KEY`（AI Studio）**或者** 通过设置 `GOOGLE_GENAI_USE_VERTEXAI=true`、`GOOGLE_CLOUD_PROJECT=` 和 `GOOGLE_CLOUD_LOCATION=` 来使用 Vertex AI。 -## How the reference agent works +## 参考智能体如何工作 -The reference agent runs in two passes. The **BQ pass** writes one OKF -doc per concept the source advertises, using BigQuery metadata alone. -The **web pass** then runs the LLM as its own crawler: it receives a -list of seed URLs (provided via `--web-seed` or `--web-seed-file`), -fetches the seeds via the `fetch_url` tool, and decides which outbound -links are worth following based on whether they look like authoritative -documentation for the existing concepts. For each page it fetches, the -agent chooses to (a) enrich one or more existing concept docs, (b) mint -a standalone `references/` doc, or (c) skip. A hard -`--web-max-pages` cap and a same-domain allowed-hosts filter -(configurable via `--web-allowed-host`) are enforced inside the tool, -so the agent cannot overrun. Use `--no-web` to skip the web pass. +参考智能体分两轮运行。**BQ 轮**为每个源通告的概念写入一个 OKF 文档，仅使用 BigQuery 元数据。**Web 轮**然后让 LLM 作为自己的爬虫：它接收一个种子 URL 列表（通过 `--web-seed` 或 `--web-seed-file` 提供），通过 `fetch_url` 工具获取种子，并根据它们是否看起来像现有概念的权威文档来决定哪些出站链接值得跟踪。对于它获取的每个页面，智能体选择 (a) 丰富一个或多个现有概念文档，(b) 铸造一个独立的 `references/` 文档，或 (c) 跳过。在工具内部强制执行硬性的 `--web-max-pages` 上限和同域允许主机过滤器（可通过 `--web-allowed-host` 配置），因此智能体不会 overrun。使用 `--no-web` 跳过 Web 轮。 -## Run +## 运行 -Minimum invocation — point at a BigQuery dataset and a bundle output -directory. Seeds for the web pass are explicit; omit them (or pass -`--no-web`) to run BQ-only: +最小调用 —— 指向一个 BigQuery 数据集和一个捆绑包输出目录。Web 轮的种子是显式的；省略它们（或传递 `--no-web`）以仅运行 BQ： -``` +```bash .venv/bin/python -m reference_agent enrich \ --source bq \ --dataset . \ @@ -118,95 +68,67 @@ directory. Seeds for the web pass are explicit; omit them (or pass --out ./bundles/ ``` -Iterate on a single concept by adding `--concept /` (e.g. -`--concept tables/events_`); repeatable. +通过添加 `--concept /`（例如 `--concept tables/events_`）来迭代单个概念；可重复。 -## Samples +## 示例 -Each sample pairs a **recipe** (`samples//`, with the seed URLs and -exact `enrich` command) with the **produced bundle** (`bundles//`) -that the recipe generated. Open the recipe to reproduce; open the bundle -to browse the result directly. +每个示例将一个**配方**（`samples//`，包含种子 URL 和确切的 `enrich` 命令）与配方生成的**生产的捆绑包**（`bundles//`）配对。打开配方以重现；打开捆绑包以直接浏览结果： -- **GA4 Google Merchandise Store** — public e-commerce dataset, seeded - with canonical GA4 BigQuery Export documentation URLs. - · [recipe](samples/ga4_merch_store/README.md) - · [bundle](bundles/ga4/) +- **GA4 Google Merchandise Store** — 公共电商数据集，用规范的 GA4 BigQuery Export 文档 URL 作为种子。 + · [配方](samples/ga4_merch_store/README.md) + · [捆绑包](bundles/ga4/) · [viz.html](bundles/ga4/viz.html) -- **Stack Overflow** — public dataset (mirror of the Stack Exchange Data - Dump), seeded with the community's canonical schema references. - Exercises multi-concept enrichment from cross-cutting docs pages. - · [recipe](samples/stackoverflow/README.md) - · [bundle](bundles/stackoverflow/) +- **Stack Overflow** — 公共数据集（Stack Exchange 数据转储的镜像），用社区规范的模式引用作为种子。练习来自横切文档页面的多概念丰富。 + · [配方](samples/stackoverflow/README.md) + · [捆绑包](bundles/stackoverflow/) · [viz.html](bundles/stackoverflow/viz.html) -- **Bitcoin (crypto)** — public dataset (blocks, transactions, inputs, - outputs) from the `bitcoin-etl` pipeline. Exercises cross-table - foreign-key relationships in prose. - · [recipe](samples/crypto_bitcoin/README.md) - · [bundle](bundles/crypto_bitcoin/) +- **Bitcoin (crypto)** — 来自 `bitcoin-etl` 管道的公共数据集（区块、交易、输入、输出）。练习散文中的跨表外键关系。 + · [配方](samples/crypto_bitcoin/README.md) + · [捆绑包](bundles/crypto_bitcoin/) · [viz.html](bundles/crypto_bitcoin/viz.html) -## Visualize +## 可视化 -The `visualize` subcommand renders any OKF bundle as a **self-contained -interactive HTML file** — one file, no backend, no install on the -viewing side. Open it in any modern browser, share it as an artifact, -host it on a static file server, or commit it next to the bundle (as -this repo does). +`visualize` 子命令将任何 OKF 捆绑包渲染为**自包含的交互式 HTML 文件** —— 一个文件，在查看端无需后端、无需安装。在任何现代浏览器中打开它，将其作为工件共享，将其托管在静态文件服务器上，或将其与捆绑包一起签入（就像本仓库所做的那样）。 -The viewer is itself a proof-of-concept *consumer* of OKF, mirroring -the way the reference agent is a proof-of-concept *producer*. OKF -bundles can be consumed by anything that reads markdown; this is just -one shape. +查看器本身就是一个 OKF 的*消费端*概念验证，就像参考智能体是*生产端*概念验证一样。OKF 捆绑包可以被任何读取 Markdown 的东西消费；这只是一种形式。 -### What it shows +### 它显示什么 -- A **force-directed graph** of every concept in the bundle, with - colored nodes by type (datasets, tables, references, …) and directed - edges drawn from each cross-link in the markdown bodies. -- A **detail panel** for the selected concept showing its frontmatter - (description, resource link, tags) and its rendered markdown body — - with internal `[…](/path/to/concept.md)` links rewired to navigate - within the viewer instead of following the path. -- A **"Cited by" backlinks** list under each concept (computed from the - reverse of the link graph). -- A **search box** (matches title, concept id, and tags), a **type - filter**, and switchable graph layouts (cose / concentric / - breadth-first / circle / grid). +- 捆绑包中每个概念的**力导向图谱**，按类型（数据集、表、引用等）着色的节点，以及从 Markdown 正文中的每个交叉链接绘制的有向边。 +- 所选概念的**详情面板**，显示其 frontmatter（描述、资源链接、标签）及其渲染的 Markdown 正文 —— 内部的 `[…](/path/to/concept.md)` 链接被重新接线以在查看器内导航，而不是跟随路径。 +- 每个概念下的**"被引用"反向链接**列表（从链接图谱的反向计算）。 +- **搜索框**（匹配标题、概念 ID 和标签）、**类型过滤器**，以及可切换的图谱布局（cose / concentric / breadthfirst / circle / grid）。 -### Generate +### 生成 -``` +```bash .venv/bin/python -m reference_agent visualize --bundle ./bundles/ ``` -That writes `bundles//viz.html`. Flags: +这会写入 `bundles//viz.html`。标志： -| Flag | Default | Description | +| 标志 | 默认 | 描述 | |----------------|------------------------|---------------------------------------------| -| `--bundle` | *(required)* | Bundle root directory. | -| `--out` | `/viz.html` | Output HTML path. | -| `--name` | bundle directory name | Display name shown in the viewer header. | +| `--bundle` | *(必需)* | 捆绑包根目录。 | +| `--out` | `/viz.html` | 输出 HTML 路径。 | +| `--name` | 捆绑包目录名 | 查看器标题中显示的显示名称。 | -Example, writing the output somewhere else and overriding the header: +示例，将输出写到别处并覆盖标题： -``` +```bash .venv/bin/python -m reference_agent visualize \ --bundle ./bundles/crypto_bitcoin \ --out /tmp/btc.html \ --name "Bitcoin OKF" ``` -### How it's built +### 它是如何构建的 -The HTML embeds the bundle as a JSON blob and uses -[Cytoscape.js](https://js.cytoscape.org/) for the graph and -[marked](https://marked.js.org/) for in-browser markdown rendering, -both loaded from a CDN. No data leaves the page; the bundle is parsed -once at generation time and serialized into the file. +HTML 将捆绑包作为 JSON blob 嵌入，并使用 [Cytoscape.js](https://js.cytoscape.org/) 处理图谱，使用 [marked](https://marked.js.org/) 进行浏览器内 Markdown 渲染，两者都从 CDN 加载。没有数据离开页面；捆绑包在生成时解析一次并序列化到文件中。 -## Tests +## 测试 -``` +```bash .venv/bin/pytest ``` diff --git a/okf/SPEC.zh.md b/okf/SPEC.zh.md index 55d0a46..cdee806 100644 --- a/okf/SPEC.zh.md +++ b/okf/SPEC.zh.md @@ -1,182 +1,157 @@ -# Open Knowledge Format (OKF) +--- +type: Specification +title: 开放知识格式(OKF)规范 · 中文版 +description: OKF v0.1 规范 SPEC.md 的完整中文翻译,附硬要求与留白标注,以及 i18n 扩展提案草案。 +tags: [okf, 规范, 翻译, 提案] +lang: zh +canonical: https://github.com/GoogleCloudPlatform/knowledge-catalog/blob/main/okf/SPEC.md +timestamp: 2026-06-14T00:00:00Z +author: 云中江树(译) +--- + +# 开放知识格式(OKF)规范 · 中文版 + +> 译自官方 [OKF SPEC.md v0.1 — Draft](https://github.com/GoogleCloudPlatform/knowledge-catalog/blob/main/okf/SPEC.md) +> 翻译与标注:云中江树 · 本译文力求信达雅,并在关键处标注 **硬要求(MUST)** 与 **留白(交给生产者)**,方便判断"它管到哪、留给你什么"。 +> 规范术语(MUST/SHOULD/MAY)按 RFC 2119 习惯译为 **必须 / 应当 / 可以**。 -**Version 0.1 — Draft** +**版本 0.1 — 草案** -OKF is an open, human- and agent-friendly format for representing -*knowledge* — the metadata, context, and curated insight that surrounds -data and systems. It is designed to be authored by people, generated by -agents, exchanged across organizations, and consumed by both. +OKF 是一种开放的、对人和智能体都友好的格式,用来表示**知识**——围绕在数据与系统周围的元数据、上下文与经过梳理的洞见。它的设计目标是:可由人撰写、可由智能体生成、可跨组织交换、可被两者共同消费。 -The format is intentionally minimal: a directory of markdown files with -YAML frontmatter. There is no schema registry, no central authority, and -no required tooling. If you can `cat` a file, you can read OKF; if you -can `git clone` a repo, you can ship it. +这个格式刻意保持极简:**一个目录,里面是带 YAML 头信息的 Markdown 文件**。没有 schema 注册表,没有中央权威,不需要任何工具链。你要是能 `cat` 一个文件,你就能读 OKF;你要是能 `git clone` 一个仓库,你就能分发它。 --- -## 1. Motivation +## 1. 动机 -The space of knowledge representation for AI agents is evolving quickly, -and many incompatible conventions are emerging. OKF takes the position -that knowledge is best represented in commonly accessible, established -formats that are: +面向 AI 智能体的知识表示,这片领域正在飞速演化,涌现出大量互不兼容的约定。OKF 持这样一个立场:知识最好用那些**通用、可访问、已被广泛接受**的格式来表示,它们应当: -- **Readable** by humans without tooling. -- **Parseable** by agents without bespoke SDKs. -- **Diffable** in version control. -- **Portable** across tools, organizations, and time. +- 对人**可读**,无需工具; +- 对智能体**可解析**,无需定制 SDK; +- 在版本控制中**可 diff**; +- 跨工具、跨组织、跨时间**可移植**。 -The format is minimally opinionated. It standardizes only the small set -of structural conventions needed to make a knowledge corpus -*self-describing* — anything beyond that is left to the producer. +这个格式"尽量不替你做主"。它只标准化那一小套让知识语料**能自我描述**所必需的结构性约定——除此之外的一切,都交给生产者。 -### Goals +### 目标 -1. Define a universal format that **enrichment agents** can write into. -2. Inform how **consumption agents** should read and traverse it. -3. Facilitate **exchange** of knowledge across systems and organizations. -4. Standardize the small number of **required** fields that must be - present for content to be meaningfully consumed. +1. 定义一个通用格式,供**富化智能体(enrichment agents)**写入。 +2. 指导**消费智能体(consumption agents)**应当如何读取与遍历它。 +3. 促进知识在系统与组织之间的**交换**。 +4. 标准化那为数不多的、内容要被有意义地消费就**必须**存在的字段。 -### Non-goals +### 非目标 -- Defining a fixed taxonomy of concept types. -- Prescribing storage, serving, or query infrastructure. -- Replacing domain-specific schemas (Avro, Protobuf, OpenAPI, etc.) — - OKF *references* them; it does not subsume them. +- 定义一套固定的概念类型分类法。 +- 规定存储、服务或查询的基础设施。 +- 取代领域专用 schema(Avro、Protobuf、OpenAPI 等)——OKF **引用**它们,而不吞并它们。 --- -## 2. Terminology - -- **Knowledge Bundle** — A self-contained, hierarchical collection of - knowledge documents. The unit of distribution. -- **Concept** — A single unit of knowledge within a bundle. Represented - as one markdown document. May describe a tangible asset (a table, an - API), an abstract idea (a metric, a business process), or anything in - between. -- **Concept ID** — The path of the concept's file within the bundle, - with the `.md` suffix removed. For example, `tables/users.md` has - concept ID `tables/users`. -- **Frontmatter** — YAML metadata block delimited by `---` at the top of - a markdown file. -- **Body** — Everything in the file after the frontmatter. -- **Link** — A standard markdown link from one concept to another, used - to express relationships beyond the implicit parent/child hierarchy. -- **Citation** — A link from a concept to an external source that - supports a claim in the body. +## 2. 术语 + +- **知识包(Knowledge Bundle)** — 一个自包含的、层级化的知识文档集合。分发的基本单位。 +- **概念(Concept)** — 包内的一个知识单元,表示为一份 Markdown 文档。它可以描述一个有形资产(一张表、一个 API)、一个抽象概念(一个指标、一项业务流程),或介于两者之间的任何东西。 +- **概念 ID(Concept ID)** — 概念文件在包内的路径,去掉 `.md` 后缀。例如 `tables/users.md` 的概念 ID 是 `tables/users`。 +- **头信息(Frontmatter)** — Markdown 文件顶部由 `---` 界定的 YAML 元数据块。 +- **正文(Body)** — 头信息之后的全部内容。 +- **链接(Link)** — 从一个概念指向另一个概念的标准 Markdown 链接,用来表达超出隐式父子层级之外的关系。 +- **引用(Citation)** — 从一个概念指向某个外部来源的链接,用来支撑正文中的某个论断。 --- -## 3. Bundle Structure +## 3. 包结构 -A bundle is a directory tree of markdown files. The directory structure -is independent of the domain — producers organize concepts however makes -sense for the knowledge being captured. +一个包就是一棵由 Markdown 文件组成的目录树。目录结构与领域无关——生产者按对所捕获的知识最合理的方式来组织概念。 ``` path/to/bundle/ -├── index.md # Optional. Directory listing for progressive disclosure. -├── log.md # Optional. Chronological history of updates. -├── .md # A concept at the bundle root. -└── / # Subdirectories organize concepts into groups. +├── index.md # 可选。用于渐进式展开的目录清单。 +├── log.md # 可选。变更的时间线历史。 +├── .md # 包根目录下的一个概念。 +└── / # 子目录把概念分组。 ├── index.md ├── .md └── / └── … ``` -A bundle MAY be distributed as: +一个包**可以**通过以下方式分发: -- A git repository (recommended — provides history, attribution, diffs). -- A tarball or zip archive of the directory. -- A subdirectory within a larger repository. +- 一个 git 仓库(推荐——自带历史、归属、diff)。 +- 该目录的 tarball 或 zip 压缩包。 +- 一个更大仓库里的子目录。 -### 3.1 Reserved filenames +### 3.1 保留文件名 -The following filenames have defined meaning at any level of the -hierarchy and MUST NOT be used for concept documents: +下列文件名在层级的任意一层都有约定含义,**禁止**用作概念文档: -| Filename | Purpose | -|--------------|--------------------------------------------------------| -| `index.md` | Directory listing. See §6. | -| `log.md` | Update history. See §7. | +| 文件名 | 用途 | +|-----------|------------------------------| +| `index.md` | 目录清单。见 §6。 | +| `log.md` | 更新历史。见 §7。 | -All other `.md` files are concept documents. +所有其他 `.md` 文件都是概念文档。 -Tags themselves remain a first-class concept — see the `tags` -frontmatter field in §4.1. OKF does not specify a separate file format -for aggregating documents by tag; producers that want a tag-browsing -view can synthesize one at consumption time by scanning frontmatter. +标签本身仍是一等概念——见 §4.1 的 `tags` 头信息字段。OKF 不为"按标签聚合文档"另行规定文件格式;想要标签浏览视图的生产者,可以在消费时扫描头信息现场合成一个。 --- -## 4. Concept Documents +## 4. 概念文档 -Every concept is a UTF-8 markdown file. It has two parts: +每个概念都是一份 UTF-8 的 Markdown 文件,由两部分组成: -1. A **YAML frontmatter block**, delimited by `---` on its own line at - the start of the file and a closing `---` on its own line. -2. A **markdown body**, containing free-form content. +1. 一个 **YAML 头信息块**,以单独成行的 `---` 开始、再以单独成行的 `---` 结束。 +2. 一段 **Markdown 正文**,内容自由。 -### 4.1 Frontmatter +### 4.1 头信息 ```yaml --- -type: # REQUIRED -title: -description: -resource: -tags: [, , …] # Optional -timestamp: # Optional last-modified time -# … other producer-defined key/value pairs +type: <类型名> # 必须 +title: <可选的显示名> +description: <可选的一句话摘要> +resource: <可选:底层资产的规范 URI> +tags: [<标签>, <标签>, …] # 可选 +timestamp: # 可选:最后修改时间 +# … 其他由生产者自定义的键值对 --- ``` -**Required:** +**必须:** + +- `type` — 一个短字符串,标识概念的种类。消费者用它来做路由、过滤和呈现。示例取值:`BigQuery Table`、`BigQuery Dataset`、`API Endpoint`、`Metric`、`Playbook`、`Reference`。 + + 类型取值**不**做中央注册。生产者**应当**选取具有描述性、自解释的取值;消费者**必须**优雅地容忍未知类型(通常就当作通用概念处理)。 -- `type` — A short string identifying the kind of concept. Consumers - use this for routing, filtering, and presentation. Example values: - `BigQuery Table`, `BigQuery Dataset`, `API Endpoint`, `Metric`, - `Playbook`, `Reference`. +**推荐(按优先级):** - Type values are **not** registered centrally. Producers SHOULD pick - values that are descriptive and self-explanatory; consumers MUST - tolerate unknown types gracefully (typically by treating them as - generic concepts). +- `title` — 人类可读的显示名。若省略,消费者**可以**从文件名推导标题。 +- `description` — 概括该概念的一句话。被 `index.md` 生成器、搜索摘要和预览使用。 +- `resource` — 唯一标识该概念所描述底层资产的 URI。对描述抽象概念(而非物理资源)的概念,可缺省。 +- `tags` — 一个 YAML 列表,放短字符串,用于横切分类。 +- `timestamp` — 最后一次有意义变更的 ISO 8601 时间。 -**Recommended (in priority order):** +**扩展:** 生产者**可以**加入任意额外的键。消费者在往返处理(round-trip)时**应当**保留未知键,并且**不应当**因为出现无法识别的字段就拒绝文档。 -- `title` — Human-readable display name. If omitted, consumers MAY - derive a title from the filename. -- `description` — A single sentence summarizing the concept. Used by - `index.md` generators, search snippets, and previews. -- `resource` — A URI that uniquely identifies the underlying asset the - concept describes. Absent for concepts that describe abstract ideas - rather than physical resources. -- `tags` — A YAML list of short strings for cross-cutting categorization. -- `timestamp` — ISO 8601 datetime of last meaningful change. +> 🧭 **留白提示:** OKF 在头信息上唯一的硬要求就是 `type` 非空(见 §9)。其余字段全是推荐。这意味着"扩展字段"是规范明牌鼓励的口子——本仓库的 `lang` / `canonical` 多语言约定正是借此实现(见文末)。 -**Extensions:** Producers MAY include any additional keys. Consumers -SHOULD preserve unknown keys when round-tripping and SHOULD NOT reject -documents with unrecognized fields. +### 4.2 正文 -### 4.2 Body +正文是标准 Markdown。生产者**应当**偏好结构化 Markdown——标题、列表、表格、围栏代码块——而非自由散文,因为结构既利于人阅读,也利于智能体检索。 -The body is standard markdown. Producers SHOULD favor structural -markdown — headings, lists, tables, fenced code blocks — over freeform -prose, since structure aids both human reading and agent retrieval. +没有任何必须的正文章节。下列章节标题具有**约定**含义,适用时**应当**使用: -There are no required body sections. The following section headings have -**conventional** meaning and SHOULD be used when applicable: +| 标题 | 用途 | +|--------------|--------------------------------------| +| `# Schema` | 对资产的列/字段的结构化描述。 | +| `# Examples` | 具体的使用示例,通常用围栏代码块。 | +| `# Citations`| 支撑正文论断的外部来源。见 §8。 | -| Heading | Purpose | -|----------------|--------------------------------------------------------| -| `# Schema` | Structured description of an asset's columns/fields. | -| `# Examples` | Concrete usage examples, often as fenced code blocks. | -| `# Citations` | External sources backing claims in the body. See §8. | +> 🧭 **留白提示:** 正文只约定了三个标题,且全是"应当"。**格式不保证正文质量**——这正是"作品占位"能发力的地方。 -### 4.3 Example: a concept bound to a resource +### 4.3 示例:一个绑定到资源的概念 ```markdown --- @@ -206,7 +181,7 @@ Joined with [customers](/tables/customers.md) on `customer_id`. [1] [BigQuery table schema](https://console.cloud.google.com/bigquery?p=acme&d=sales&t=orders) ``` -### 4.4 Example: a concept not bound to a resource +### 4.4 示例:一个不绑定资源的概念 ```markdown --- @@ -230,53 +205,43 @@ its expected SLA. See the [orders table](/tables/orders.md). --- -## 5. Cross-linking +## 5. 交叉链接 -Concepts MAY link to other concepts using standard markdown links. Two -forms are supported: +概念**可以**用标准 Markdown 链接指向其他概念。支持两种形式: -### 5.1 Absolute (bundle-relative) links +### 5.1 绝对(包内相对)链接 -Begin with `/`, interpreted relative to the bundle root. +以 `/` 开头,相对于包根目录解释。 ```markdown See the [customers table](/tables/customers.md) for the join key. ``` -This is the **recommended** form because it is stable when documents are -moved within their subdirectory. +这是**推荐**形式,因为当文档在其子目录内移动时,它依然稳定。 -### 5.2 Relative links +### 5.2 相对链接 -Standard markdown relative paths. +标准 Markdown 相对路径。 ```markdown See the [neighboring concept](./other.md). ``` -### 5.3 Link semantics +### 5.3 链接语义 -A link from concept A to concept B asserts a *relationship*. The -specific kind of relationship (parent/child, references, joins-with, -depends-on, etc.) is conveyed by the surrounding prose, not by the link -itself. Consumers that build a graph view typically treat all links as -directed edges of an untyped relationship. +从概念 A 到概念 B 的一个链接,断言了一种**关系**。这种关系的具体种类(父/子、引用、与之关联、依赖于,等等)由**周围的散文**表达,而非由链接本身表达。构建图谱视图的消费者,通常把所有链接都当作一种"无类型关系"的有向边。 -Consumers MUST tolerate broken links — a link whose target does not -exist in the bundle is not malformed; it may simply represent -not-yet-written knowledge. +消费者**必须**容忍坏链接——目标不在包内的链接并不算格式错误,它可能只是表示"尚未写出的知识"。 + +> 🧭 **留白提示:** 链接是**无类型**的——机器无法从链接本身分辨"关联"还是"依赖",得靠 NLP 啃正文。这是规范刻意的取舍,也是图谱类消费者的一处硬伤,值得提"有类型链接"的扩展讨论。 --- -## 6. Index Files +## 6. 索引文件 -An `index.md` file MAY appear in any directory, including the bundle -root. It enumerates the directory's contents to support **progressive -disclosure** — letting a human or agent see what is available before -opening individual documents. +`index.md` 文件**可以**出现在任意目录,包括包根目录。它枚举目录内容,以支持**渐进式展开(progressive disclosure)**——让人或智能体在打开单个文档前,先看到有哪些东西可用。 -Index files contain no frontmatter. The body uses one or more sections, -each grouping concepts under a heading: +索引文件不含头信息。正文用一个或多个章节,每个章节把概念归在一个标题下: ```markdown # Section / Group Heading @@ -289,41 +254,35 @@ each grouping concepts under a heading: * [Subdirectory](subdir/) - short description of the subdirectory ``` -Entries SHOULD include the description from the linked concept's -frontmatter. Producers MAY generate `index.md` automatically; consumers -MAY synthesize one on the fly when none is present. +条目**应当**带上所链概念头信息里的 description。生产者**可以**自动生成 `index.md`;消费者在没有时**可以**临场合成一个。 --- -## 7. Log Files (optional) +## 7. 日志文件(可选) -A `log.md` file MAY appear at any level of the hierarchy to record the -history of changes to that scope. The format is a flat list of -date-grouped entries, newest first: +`log.md` 文件**可以**出现在层级的任意一层,用来记录该范围内的变更历史。格式是一个按日期分组、最新在前的扁平列表: ```markdown # Directory Update Log ## 2026-05-22 + * **Update**: Added new BigQuery table reference for [Customer Metrics](/tables/customer-metrics.md). * **Creation**: Established the [Dataplex Playbook](/playbooks/dataplex.md). ## 2026-05-15 + * **Initialization**: Created foundational directory structure. * **Update**: Added progressive-disclosure guidelines to the root [index](/index.md). ``` -Date headings MUST use ISO 8601 `YYYY-MM-DD` form. Log entries are -prose; the leading bold word (`**Update**`, `**Creation**`, -`**Deprecation**`, etc.) is a convention, not a requirement. +日期标题**必须**用 ISO 8601 的 `YYYY-MM-DD` 形式。日志条目是散文;开头加粗的词(`**Update**`、`**Creation**`、`**Deprecation**` 等)是约定,不是要求。 --- -## 8. Citations +## 8. 引用 -When a concept's body makes claims sourced from external material, -those sources SHOULD be listed under a `# Citations` heading at the -bottom of the document, numbered: +当一个概念的正文做出源自外部材料的论断时,这些来源**应当**列在文档底部的 `# Citations` 标题下,并编号: ```markdown # Citations @@ -332,72 +291,56 @@ bottom of the document, numbered: [2] [Internal data quality runbook](https://wiki.acme.internal/data/quality) ``` -Citation links MAY be absolute URLs, bundle-relative paths, or paths -into a `references/` subdirectory that mirrors external material as -first-class OKF concepts. +引用链接**可以**是绝对 URL、包内相对路径,或指向 `references/` 子目录的路径——后者把外部材料镜像为一等的 OKF 概念。 --- -## 9. Conformance +## 9. 符合性 + +一个包**符合** OKF v0.1,当且仅当: -A bundle is **conformant** with OKF v0.1 if: +1. 目录树中每个非保留的 `.md` 文件,都含有一个可解析的 YAML 头信息块。 +2. 每个头信息块都含有一个非空的 `type` 字段。 +3. 每个保留文件名(`index.md`、`log.md`)在出现时,分别遵循 §6 与 §7 所述结构。 -1. Every non-reserved `.md` file in the tree contains a parseable YAML - frontmatter block. -2. Every frontmatter block contains a non-empty `type` field. -3. Every reserved filename (`index.md`, `log.md`) follows the structure - described in §6 and §7 respectively when present. +消费者**应当**把其他所有约束都视为软性指引。特别地,消费者**绝不可**因为以下原因拒绝一个包: -Consumers SHOULD treat all other constraints as soft guidance. In -particular, consumers MUST NOT reject a bundle because of: +- 缺少可选头信息字段。 +- 未知的 `type` 取值。 +- 未知的额外头信息键。 +- 坏的交叉链接。 +- 缺少 `index.md` 文件。 -- Missing optional frontmatter fields. -- Unknown `type` values. -- Unknown additional frontmatter keys. -- Broken cross-links. -- Missing `index.md` files. +这种宽容的消费模型是刻意为之:OKF 意在让包随着增长、重构、被智能体部分生成的过程中,始终保持可用。 -This permissive consumption model is intentional: OKF is meant to -remain useful as bundles grow, get refactored, and are partially -generated by agents. +> 🧭 **这就是全部硬要求。** 整份规范真正的 MUST,核心就上面三条(其中 1、2 是产出门槛,3 仅在文件存在时适用)。门槛几乎为零——任何带 `type` 的 md 都合规。**互操作靠的是约定与作品质量,而非校验器。** --- -## 10. Relationship to other formats +## 10. 与其他格式的关系 -OKF is intentionally close to several established patterns: +OKF 刻意地贴近若干已有模式: -- **LLM "wiki" repositories** that use markdown + frontmatter as - agent-readable knowledge bases. -- **Personal knowledge tools** like Obsidian and Notion, which use - hierarchical markdown with cross-links. -- **"Metadata as code"** approaches that store catalog metadata - alongside source code rather than in a separate registry. +- **LLM "维基" 仓库**——用 Markdown + 头信息作为智能体可读的知识库。 +- **个人知识工具**,如 Obsidian 和 Notion——使用带交叉链接的层级 Markdown。 +- **"元数据即代码"**——把目录元数据与源代码放在一起,而非放进单独的注册表。 -OKF differs primarily in being **specified** — pinning down the small -set of rules needed for interoperability without dictating tooling. +OKF 的主要区别在于它**被规范化了**——钉死了互操作所需的那一小套规则,同时不对工具链发号施令。 --- -## 11. Versioning +## 11. 版本管理 -This document specifies OKF version **0.1**. Future revisions will be -versioned in the form `.`: +本文件规定 OKF 版本 **0.1**。未来修订将以 `<主>.<次>` 形式版本化: -- A **minor** version bump introduces backward-compatible additions - (new optional fields, new conventional section headings). -- A **major** version bump may make breaking changes (renaming required - fields, changing reserved filenames). +- **次版本**号提升,引入向后兼容的新增内容(新的可选字段、新的约定章节标题)。 +- **主版本**号提升,可能带来破坏性变更(重命名必须字段、更改保留文件名)。 -Bundles MAY declare the OKF version they target by including -`okf_version: "0.1"` in a bundle-root `index.md` frontmatter block (the -only place frontmatter is permitted in an `index.md`). Consumers that -do not understand the declared version SHOULD attempt best-effort -consumption rather than refusing the bundle. +包**可以**声明它面向的 OKF 版本,方式是在包根目录的 `index.md` 头信息块里写入 `okf_version: "0.1"`(这是 `index.md` 中唯一允许出现头信息的地方)。不理解所声明版本的消费者,**应当**尽力做"尽最大努力的消费",而非拒绝该包。 --- -## Appendix A — Minimal example bundle +## 附录 A — 最小示例包 ``` my_bundle/ @@ -449,3 +392,35 @@ timestamp: 2026-05-28T00:00:00Z Part of the [sales dataset](/datasets/sales.md). ``` + +--- + +## 中文生态议题:i18n 扩展提案草案 + +> 这部分不属于官方规范,是本仓库基于 §4.1"生产者可加入任意额外键"提出的、**向后兼容**的多语言约定,供讨论与向官方提案。 + +**问题:** OKF v0.1 没有任何"语言"概念。一份知识库若同时面向中英读者(以及中英两种消费智能体),无法表达"这是同一概念的中文版"。 + +**提案(作为 v0.x 次版本的可选字段):** + +```yaml +--- +type: BigQuery Table +title: 订单表 +lang: zh # BCP 47 语言标签,标注本概念正文的语言 +canonical: /tables/orders.md # 指向同一概念的"主语言"版本(概念 ID) +--- +``` + +约定: + +1. `lang` — 可选字段,BCP 47 语言标签(如 `zh`、`zh-Hans`、`en`)。缺省时,消费者**应当**视为生产者未声明语言。 +2. `canonical` — 可选字段,指向同一概念主语言版本的概念 ID。多语言变体彼此通过 `canonical` 收敛到同一主版本。 +3. 文件组织两种皆可,生产者自选: + - **并列文件**:`tables/orders.md`(主)与 `tables/orders.zh.md`(中文变体); + - **并列目录**:`en/tables/orders.md` 与 `zh/tables/orders.md`。 +4. 完全向后兼容:不认识 `lang`/`canonical` 的 v0.1 消费者按 §4.1 忽略未知键即可,bundle 仍然合规。 + +**为什么这个口子是干净的:** 它只新增可选字段,不动任何 MUST,不改保留文件名——正好落在规范 §11 定义的"次版本可做向后兼容新增"里,也正好落在官方"明确欢迎扩展提案"的邀请里。 + +> 配套实现:本仓库的 [`feishu-to-okf`](../plugins/feishu-to-okf/) 导出时会写入 `lang: zh`,[`okf-creator`](../skills/okf-creator/) Skill 也内置这套约定。即"提案 + 参考实现"一起出。 From 6837c6c412463e3fdca0ff1ed928d273a1e4c651 Mon Sep 17 00:00:00 2001 From: lang Date: Mon, 22 Jun 2026 07:04:33 +0800 Subject: [PATCH 3/6] =?UTF-8?q?feat:=20=E5=AE=8C=E6=88=90=E6=89=80?= =?UTF-8?q?=E6=9C=89=E8=8B=B1=E6=96=87=20README=20=E7=9A=84=E4=B8=AD?= =?UTF-8?q?=E6=96=87=E7=BF=BB=E8=AF=91?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 添加 toolbox/README.zh.md（工具箱 README 中文翻译） - 添加 samples/README.zh.md（示例 README 中文翻译） - 添加 toolbox/mdcode/README.zh-CN.md（元数据即代码 README 中文翻译） - 添加 toolbox/enrichment/README.zh-CN.md（丰富智能体 README 中文翻译） - 在所有英文 README.md 顶部添加语言切换链接 - 完全不修改英文原文件内容（仅添加一行语言切换链接） --- samples/README.md | 2 + samples/README.zh.md | 15 ++ toolbox/README.md | 2 + toolbox/README.zh.md | 13 ++ toolbox/enrichment/README.md | 2 + toolbox/enrichment/README.zh-CN.md | 170 ++++++++++++++++++++++ toolbox/mdcode/README.md | 2 + toolbox/mdcode/README.zh-CN.md | 225 +++++++++++++++++++++++++++++ 8 files changed, 431 insertions(+) create mode 100644 samples/README.zh.md create mode 100644 toolbox/README.zh.md create mode 100644 toolbox/enrichment/README.zh-CN.md create mode 100644 toolbox/mdcode/README.zh-CN.md diff --git a/samples/README.md b/samples/README.md index d301e36..1af578e 100644 --- a/samples/README.md +++ b/samples/README.md @@ -1,3 +1,5 @@ +[🇺🇸 English](./README.md) | [🇨🇳 中文](./README.zh.md) + # Samples The samples demonstrate the use of Knowledge Catalog to manage metadata and diff --git a/samples/README.zh.md b/samples/README.zh.md new file mode 100644 index 0000000..3760197 --- /dev/null +++ b/samples/README.zh.md @@ -0,0 +1,15 @@ +# 示例 + +[🇺🇸 English](./README.md) | [🇨🇳 中文](./README.zh.md) + +--- + +这些示例演示了使用 Knowledge Catalog 来管理元数据以及为智能体提供上下文的能力。 + +## 发现 + +演示了基于目录提供的搜索 API 构建搜索和发现智能体。 + +## 丰富 + +演示了一个丰富智能体，可以为目录中管理的资产生成和丰富文档。 diff --git a/toolbox/README.md b/toolbox/README.md index 64d7485..f63951e 100644 --- a/toolbox/README.md +++ b/toolbox/README.md @@ -1,3 +1,5 @@ +[🇺🇸 English](./README.md) | [🇨🇳 中文](./README.zh.md) + # Toolbox Contains tools to work with Knowledge Catalog metadata. diff --git a/toolbox/README.zh.md b/toolbox/README.zh.md new file mode 100644 index 0000000..4f4b264 --- /dev/null +++ b/toolbox/README.zh.md @@ -0,0 +1,13 @@ +# 工具箱 + +[🇺🇸 English](./README.md) | [🇨🇳 中文](./README.zh.md) + +--- + +包含用于使用 Knowledge Catalog 元数据的工具。 + +* [元数据即代码](./mdcode/README.zh.md) + 提供以源代码工件形式管理元数据的能力，可以与 Knowledge Catalog 中的元数据同步。 + +* [丰富智能体](./enrichment/README.zh.md) + 提供一个即用型智能体和可定制的框架，用于在 Knowledge Catalog 中生成、演进/改进和维护元数据，并使其准备好被智能体消费。 diff --git a/toolbox/enrichment/README.md b/toolbox/enrichment/README.md index facb117..3b612d8 100644 --- a/toolbox/enrichment/README.md +++ b/toolbox/enrichment/README.md @@ -1,3 +1,5 @@ +[🇺🇸 English](./README.md) | [🇨🇳 中文](./README.zh-CN.md) + # Enrichment Agent The enrichment agent for Knowledge Catalog provides a customizable agentic diff --git a/toolbox/enrichment/README.zh-CN.md b/toolbox/enrichment/README.zh-CN.md new file mode 100644 index 0000000..202f4ac --- /dev/null +++ b/toolbox/enrichment/README.zh-CN.md @@ -0,0 +1,170 @@ +# 丰富智能体 + +[🇺🇸 English](./README.md) | [🇨🇳 中文](./README.zh-CN.md) + +--- + +Knowledge Catalog 的丰富智能体提供了一个可定制的智能体工作流，用于从各种来源提取信息以构建关于数据资产的元数据，这些元数据随后可用作上下文。 + +## 使用方法 + +### 先决条件 + +丰富智能体依赖于 [元数据即代码](../mdcode/README.zh-CN.md) 功能。 +请按照该页面上关于使用 `kcmd` 工具的说明进行操作。 + +### CLI + +该包提供 `kcenrich` CLI 工具。它作为独立二进制文件分发： + +```bash +# 为 BigQuery 数据集初始化新的目录快照 +kcmd init --bigquery-dataset . + +# 从 Knowledge Catalog 服务拉取最新目录快照 +kcmd pull + +# 运行丰富工具 +kcagent enrich --catalog-path . --tools-path tools --prompt-path prompt.md +``` + +## 开发者工作流 + +### 设置 + +```bash +git clone https://github.com/googlecloudplatform/knowledge-catalog +cd toolbox/enrichment +npm install +``` + +### 构建 + +```bash +npm run build +``` + +### 测试 + +```bash +npm run test +``` + +### 演示 + +本仓库包含一个自包含的演示。运行演示涉及在你的云项目中创建 BigQuery 数据集和 Dataplex EntryGroup。 + +**初始化环境** + +```bash +export DEMO_CLOUD_PROJECT="" +``` + +**初始化 gcloud** + +```bash +gcloud auth application-default login +gcloud config set project $DEMO_CLOUD_PROJECT +gcloud config set compute/region us +``` + +**设置演示资源** + +```bash +bq query --use_legacy_sql=false < catalog.yaml +scope: bq-dataset.${DEMO_CLOUD_PROJECT}.demo_commerce + +snapshot: + entries: + - dataplex-types.global.bigquery-dataset + - dataplex-types.global.bigquery-table + aspects: + - dataplex-types.global.overview +EOF + +../../mdcode/dist/kcmd pull +``` + +**创建并填充工具** + +```bash +cat < prompt.md +使用内部组织信息丰富资产的文档。 +使用以下来源： + +* 文件集来源 +EOF +``` + +```bash +mkdir tools +cat < tools/mcp.json +{ + "mcpServers": { + "md-fileset": { + "command": "../dist/md-fileset", + "args": [ "--dir", "fileset" ] + } + } +} +EOF +``` + +```bash +mkdir -p tools/skills/fileset-source +cat < tools/skills/fileset-source/SKILL.md +--- +name: fileset-source +description: > + 使用文件集来源查找相关 Markdown 文档并提取关于资产的信息。 +--- + +`md-fileset` MCP 服务器提供以下工具，用于从 Markdown 文件目录层级中提取相关信息： + +* **list_fileset_contents** - 浏览和导航目录树以列出指定路径的内容。项目可以是文件或子目录。 + +* **read_fileset_file** - 读取知识库中文件的内容。提供完整内容。基于正在生成的文档提取并总结相关信息。 + +* **search_fileset_content** - 搜索知识库并返回匹配的文件，以及匹配的行号和行片段。这可用于快速查找匹配项，而无需列出和读取所有文件。 + +要有效使用文件集，请创建搜索查询（使用带单个 token 的简单关键词查询）以查找相关文件，然后读取文件以查找相关信息。如果一个查询不起作用，请尝试其他几个关键词。 +EOF +``` + +**添加文档** +将 [此处](https://github.com/GoogleCloudPlatform/knowledge-catalog/tree/1e97103cdbf7e6425113a73304029ddb4f1f3a6b/samples/enrichment/sample/docs) 的各个 Markdown 文件复制到 `fileset/` 目录中。 + +**丰富元数据** + +```bash +../dist/kcagent enrich --catalog-path . --tools-path tools --prompt-path prompt.md +``` + +**清理** + +```bash +bq rm -r ${DEMO_CLOUD_PROJECT}:demo-dataset +``` diff --git a/toolbox/mdcode/README.md b/toolbox/mdcode/README.md index 55fd2e3..86a14fa 100644 --- a/toolbox/mdcode/README.md +++ b/toolbox/mdcode/README.md @@ -1,3 +1,5 @@ +[🇺🇸 English](./README.md) | [🇨🇳 中文](./README.zh-CN.md) + # Metadata as Code Metadata as Code is a Knowledge Catalog (Dataplex) provides data stewards and data producers and AI agents with a source code artifact-based UX for metadata management and context engineering. diff --git a/toolbox/mdcode/README.zh-CN.md b/toolbox/mdcode/README.zh-CN.md new file mode 100644 index 0000000..1975231 --- /dev/null +++ b/toolbox/mdcode/README.zh-CN.md @@ -0,0 +1,225 @@ +# 元数据即代码 + +[🇺🇸 English](./README.md) | [🇨🇳 中文](./README.zh-CN.md) + +--- + +元数据即代码是 Knowledge Catalog (Dataplex) 提供的一种功能，为数据管理员、数据生产者和 AI 智能体提供基于源代码工件的元数据管理和上下文工程用户体验。 + +用户和智能体可以使用对开发者友好的工作流（带版本控制和 CI/CD）来编写、管理和丰富元数据工件。它提供一种标准元数据格式，可供各种工具和智能体使用。 + +更多详情请参见 [docs/concept.md](docs/concept.md)。 + +## 核心特性 + +- 直观且对智能体友好的元数据表示，以 YAML 和 Markdown 文件形式的源代码呈现。工件按层级组织，镜像数据和元数据资产的资源层级。 +- 本地工作区与目录服务之间的双向同步。 +- 支持第一方和第三方元数据构造。 +- 作为 TypeScript 和 Python 库、CLI 工具 (`kcmd`) 和 MCP 服务器分发，可用于各种应用、智能体和流水线。 + +## 元数据工件 + +### 目录布局 + +元数据在表示资源（如 BigQuery 数据集、Dataplex EntryGroup 等）的目录中组织： + +``` +path/to/root/ +├── catalog.yaml # 清单和配置指令 +└── catalog/ # 包含元数据快照 + └── / + └── .yaml # 条目 + └── / + ├── .yaml # 带侧车 Markdown 的条目 + └── .aspect.md # 文件 +``` + +## 目录快照文件 + +### 清单文件 + +**`catalog/catalog.yaml`** + +```yaml +scope: bq-dataset.prod-data.ecommerce + +aliases: + ca-guidelines: + aspect: data-agents-project.global.ca-guidelines + ecommerce: + aspect: data-agents-project.global.ecommerce + +snapshot: + entries: + - bigquery-table + - bigquery-view + - entry-group + aspects: + - overview + - descriptions + +publishing: + aspects: + - overview + - descriptions +``` + +### 条目 YAML 文件 + +**`catalog/prod-data.ecommerce/products.yaml`** + +```yaml +id: products +type: bigquery-table + +resource: + name: projects/prod-data/datasets/ecommerce/tables/products + displayName: Products Table + description: All products in the catalog + labels: + env: prod + createTime: 2026-04-23T00:44:03Z + updateTime: 2026-04-23T00:44:03Z + +schema: + ... + +contacts: + ... +``` + +### 条目侧车 Markdown 文件 + +**`catalog/prod-data.ecommerce/products.overview.md`** + +```markdown +--- +userManaged: true +links: + ... +--- +[overview.content] +``` + +## 使用方法 + +### 库 + +你可以使用 `kcmd` 库以编程方式与目录元数据交互： + +```bash +npm install kcmd +``` + +```typescript +import * as kcmd from 'kcmd'; + +// 从头创建目录清单 +const manifest = new kcmd.CatalogManifest(...); +manifest.save('/path/to/root'); + +// 从文件系统加载目录快照 +const snapshot = kcmd.CatalogSnapshot.fromPath('/path/to/root'); + +// 从目录服务拉取最新元数据 +const pullResult = await snapshot.pull(); +if (pullResult.success) { + console.log('元数据拉取成功'); +} +else { + console.error('元数据拉取失败:', pullResult.error); +} + +// 将修改后的元数据推送到目录服务 +const pushResult = await snapshot.push(); +if (pushResult.success) { + console.log('元数据推送成功'); +} +else { + console.error('元数据推送失败:', pushResult.error); +} +``` + +### CLI + +该包提供 `kcmd` CLI 工具。它作为独立二进制文件分发： + +```bash +# 为 BigQuery 数据集初始化新的目录快照 +kcmd init --bigquery-dataset . + +# 为多个 BigQuery 数据集初始化新的目录快照 +kcmd init --bigquery-dataset . --bigquery-dataset . + +# 为带有特定类型的 BigQuery 数据集初始化新的目录快照 +kcmd init --bigquery-dataset . \ + --entry bigquery-table --entry bigquery-view \ + --aspect overview --aspect description + +# 为自定义 EntryGroup 初始化新的目录快照 +kcmd init --entry-group .. + +# 从 Knowledge Catalog 服务拉取最新目录快照 +# 报告任何尚未推送到目录的待处理变更。 +# 支持使用 --dry-run 标志进行干运行。 +kcmd pull + +# 检查本地修改 +kcmd status + +# 将本地变更推送到 Knowledge Catalog 服务。仅推送自上次拉取以来的变更， +# 并且如果该元数据在此期间未在目录中被修改过。 +# 支持使用 --dry-run 标志进行干运行。 +kcmd push +``` + +注意：CLI 使用 `gcloud` 获取认证令牌，因此请确保已通过 `gcloud auth application-default login` 进行认证。 + +### MCP 服务器 + +要在智能体系统（如 Gemini CLI）中将元数据即代码工具作为 MCP 工具使用，请将以下内容添加到你的 MCP 设置文件： + +```json +{ + "mcpServers": { + "kc-mac": { + "command": "kcmd", + "args": ["mcp", "--path", "/path/to/root"] + } + } +} +``` + +该服务器提供以下工具： + +| 工具 | 描述 | +|------------------|-------------------------------------------------------| +| `pull` | 从目录服务拉取最新元数据 | +| `push` | 将修改后的元数据推送到目录服务 | +| `list-entries` | 列出目录快照中的条目 | +| `lookup-entry` | 从快照中查找条目及其元数据 | +| `modify-entry` | 修改快照中的条目及其元数据 | + +注意：该服务器使用 `gcloud` 获取认证令牌，因此请确保已通过 `gcloud auth application-default login` 进行认证。 + +## 开发者工作流 + +### 设置 + +```bash +git clone https://github.com/googlecloudplatform/knowledge-catalog +cd toolbox/mdcode +npm install +``` + +### 构建 + +```bash +npm run build +``` + +### 测试 + +```bash +npm run test +``` From 498ff012640d84e748cd4bdcfcd2472c48b965b7 Mon Sep 17 00:00:00 2001 From: lang Date: Mon, 22 Jun 2026 11:03:25 +0800 Subject: [PATCH 4/6] =?UTF-8?q?feat(okf):=20=E6=B7=BB=E5=8A=A0=20localfile?= =?UTF-8?q?=20=E5=AD=90=E5=91=BD=E4=BB=A4=E6=94=AF=E6=8C=81=E4=BB=8E?= =?UTF-8?q?=E6=9C=AC=E5=9C=B0=E6=96=87=E4=BB=B6=E7=94=9F=E6=88=90=20OKF?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- okf/README.zh.md | 118 ++ okf/pyproject.toml | 6 + okf/src/reference_agent/bundle/paths.py | 5 +- okf/src/reference_agent/cli.py | 92 +- .../prompts/reference_instruction.md | 12 + okf/src/reference_agent/sources/localfile.py | 298 ++++ toolbox/mdcode/package-lock.json | 1393 ++++++++--------- 7 files changed, 1163 insertions(+), 761 deletions(-) create mode 100644 okf/src/reference_agent/sources/localfile.py diff --git a/okf/README.zh.md b/okf/README.zh.md index 5255bf6..cb7b7e3 100644 --- a/okf/README.zh.md +++ b/okf/README.zh.md @@ -70,6 +70,124 @@ python3.13 -m venv .venv 通过添加 `--concept /`（例如 `--concept tables/events_`）来迭代单个概念；可重复。 +## 从本地文件生成 OKF + +`localfile` 子命令是 `enrich --source localfile --no-web` 的快捷方式，专为从本地文件（PDF/Word/Excel/PPT/Markdown/代码等）生成知识库而设计。它自动跳过 Web 轮，使用位置参数和合理默认值，命令更简洁。 + +### 安装 + +```bash +cd knowledge-catalog/okf +python3.11 -m venv .venv +.venv/bin/pip install -e ".[localfile]" +``` + +安装后 `reference-agent` 命令全局可用（确保 `~/.local/bin` 在 `PATH` 中）： + +```bash +pip install --user -e ".[localfile]" +``` + +### 凭证 + +设置 Gemini API Key： + +```bash +export GEMINI_API_KEY= +``` + +### 用法 + +```bash +# 最简形式：扫描当前目录，输出到 ./okf-bundle/ +cd /path/to/docs +reference-agent localfile + +# 指定文件类型（推荐） +reference-agent localfile --pattern "**/*.pdf" + +# 指定源目录和输出目录 +reference-agent localfile /path/to/docs --pattern "**/*.pdf" -o ./my-bundle + +# 指定模型 + 详细日志 +reference-agent localfile /path/to/docs --model gemini-flash-latest -v + +# 仅处理特定概念（可重复） +reference-agent localfile /path/to/docs --concept "ERP_WIKI" +``` + +### 参数说明 + +| 参数 | 默认值 | 说明 | +|--------------------|-------------------|-----------------------------------------------| +| `path` | `.`（当前目录） | 要扫描的本地目录（位置参数，可选） | +| `--pattern` | `**/*` | 文件匹配模式，如 `**/*.pdf`、`**/*.{md,txt}` | +| `-o / --out` | `./okf-bundle` | 输出目录 | +| `--model` | `gemini-flash-latest` | Gemini 模型 ID | +| `--no-recursive` | *(关闭)* | 禁用递归扫描 | +| `--concept` | *(全部)* | 仅处理指定概念 ID（可重复） | +| `-v / --verbose` | *(关闭)* | 详细日志输出 | + +### 支持的文件类型 + +| 扩展名 | 概念类型 | +|---------------------------|------------------------| +| `.pdf` | PDF Document | +| `.docx` | Word Document | +| `.xlsx` / `.xls` | Excel Spreadsheet | +| `.pptx` / `.ppt` | PowerPoint Presentation| +| `.md` / `.markdown` | Document | +| `.txt` | Document | +| `.py` / `.ts` / `.js` | Python/TypeScript/JavaScript Module | +| `.json` / `.yaml` / `.yml`| Config File | +| `.html` | HTML Document | +| `.csv` | Data File | + +### 语言自动匹配 + +智能体会自动检测源文件内容的主导语言，并用相同语言生成 OKF 文档： +- 源文件是中文 → 生成中文 OKF 文档 +- 源文件是英文 → 生成英文 OKF 文档 +- 技术标识符（字段名、代码、SQL、文件路径）保持原样不翻译 + +### 示例 + +从 `docs/` 目录下的 PDF 生成知识库： + +```bash +cd /Users/jianglang/CodeBuddy/LangWIKI/docs +reference-agent localfile --pattern "**/*.pdf" -o ./pdf-bundle +``` + +生成的结构： + +``` +docs/ +├── Karpathy LLM 编程行为.pdf +├── ERP-WIKI.pdf +├── LLM-wiki.pdf +└── pdf-bundle/ # 自动生成 + ├── index.md # 索引文件 + ├── Karpathy_LLM_编程行为.md # 概念文档 + ├── ERP_WIKI.md + └── LLM_wiki.md +``` + +### 可视化 + +生成 OKF 捆绑包后，可生成交互式 HTML 图谱： + +```bash +reference-agent visualize --bundle ./pdf-bundle +# → 生成 ./pdf-bundle/viz.html +``` + +### 注意事项 + +- **API 限流**：Gemini 免费层有限流（约 5 次/分钟），大量文件时可能遇到 429 错误，等待 30 秒后重试或使用付费 key。 +- **文件大小**：单文件上限 10MB。 +- **忽略目录**：`.git`、`.venv`、`node_modules`、`__pycache__`、`okf-bundle` 等目录会被自动跳过。 + ## 示例 每个示例将一个**配方**（`samples//`，包含种子 URL 和确切的 `enrich` 命令）与配方生成的**生产的捆绑包**（`bundles//`）配对。打开配方以重现；打开捆绑包以直接浏览结果： diff --git a/okf/pyproject.toml b/okf/pyproject.toml index 8686ec3..0c30a3b 100644 --- a/okf/pyproject.toml +++ b/okf/pyproject.toml @@ -17,6 +17,12 @@ dependencies = [ [project.optional-dependencies] dev = ["pytest>=7.0"] +localfile = [ + "pdfplumber>=0.11", + "python-docx>=1.0", + "openpyxl>=3.1", + "python-pptx>=0.6", +] [project.scripts] reference-agent = "reference_agent.cli:main" diff --git a/okf/src/reference_agent/bundle/paths.py b/okf/src/reference_agent/bundle/paths.py index 5f6b7c3..48fd581 100644 --- a/okf/src/reference_agent/bundle/paths.py +++ b/okf/src/reference_agent/bundle/paths.py @@ -3,7 +3,10 @@ import re from pathlib import Path -_SEGMENT_RE = re.compile(r"[A-Za-z0-9_][A-Za-z0-9_.\-]*") +# Allow Unicode letters/digits (e.g. Chinese, Japanese) so that concept ids +# derived from non-ASCII file names remain valid. \w matches [A-Za-z0-9_] plus +# all Unicode word characters under re.UNICODE (default in Py3 for str). +_SEGMENT_RE = re.compile(r"\w[\w.\-]*") def _validate_segment(seg: str) -> None: diff --git a/okf/src/reference_agent/cli.py b/okf/src/reference_agent/cli.py index 5b75a97..dc30d39 100644 --- a/okf/src/reference_agent/cli.py +++ b/okf/src/reference_agent/cli.py @@ -10,8 +10,9 @@ from reference_agent.bundle.paths import parse_concept_id from reference_agent.runner import ReferenceRunner from reference_agent.sources.bigquery import BigQuerySource +from reference_agent.sources.localfile import LocalFileSource -_SOURCES = ("bq",) +_SOURCES = ("bq", "localfile") def _build_source(name: str, args: argparse.Namespace): @@ -21,6 +22,14 @@ def _build_source(name: str, args: argparse.Namespace): return BigQuerySource( dataset=args.dataset, billing_project=args.billing_project ) + if name == "localfile": + if not args.local_path: + raise SystemExit("--local-path is required for --source localfile") + return LocalFileSource( + path=args.local_path, + pattern=args.local_pattern or "**/*", + recursive=not args.local_no_recursive, + ) raise SystemExit(f"Unknown source: {name}") @@ -73,6 +82,20 @@ def _parser() -> argparse.ArgumentParser: help="Google Cloud project to bill for queries; " "defaults to ADC default.", ) + enrich.add_argument( + "--local-path", + help="Local directory path (for --source localfile).", + ) + enrich.add_argument( + "--local-pattern", + default="**/*", + help="File glob pattern for localfile source (default: **/*).", + ) + enrich.add_argument( + "--local-no-recursive", + action="store_true", + help="Disable recursive directory scan for localfile source.", + ) enrich.add_argument( "--out", required=True, type=Path, help="Bundle root directory." ) @@ -142,6 +165,49 @@ def _parser() -> argparse.ArgumentParser: ) enrich.add_argument("-v", "--verbose", action="store_true") + # Shortcut subcommand: localfile + # Equivalent to `enrich --source localfile --no-web`, but with positional + # path argument and sensible defaults for local-file workflows. + lf = sub.add_parser( + "localfile", + help="Shortcut: enrich OKF bundle from local files (no web pass).", + ) + lf.add_argument( + "path", + type=Path, + nargs="?", + default=Path("."), + help="Local directory to scan (default: current directory).", + ) + lf.add_argument( + "--pattern", + default="**/*", + help="File glob pattern (default: **/*). Examples: '**/*.pdf', '**/*.{md,txt}'.", + ) + lf.add_argument( + "-o", "--out", + type=Path, + default=Path("./okf-bundle"), + help="Bundle root directory (default: ./okf-bundle).", + ) + lf.add_argument( + "--model", + default=DEFAULT_MODEL, + help=f"Gemini model id (default: {DEFAULT_MODEL}).", + ) + lf.add_argument( + "--no-recursive", + action="store_true", + help="Disable recursive directory scan.", + ) + lf.add_argument( + "--concept", + action="append", + default=None, + help="Enrich only this concept id (e.g. 'tables/events_'). Repeatable.", + ) + lf.add_argument("-v", "--verbose", action="store_true") + viz = sub.add_parser( "visualize", help="Generate a self-contained HTML graph view of an OKF bundle.", @@ -212,4 +278,28 @@ def main(argv: list[str] | None = None) -> int: web_note = f"; web pass used {len(seeds)} seed(s)" if seeds else "; web pass skipped" print(f"Enriched {n} concept(s) into {args.out}{web_note}", file=sys.stderr) return 0 + + if args.command == "localfile": + # Shortcut path: local files only, no web pass, sensible defaults. + source = LocalFileSource( + path=str(args.path), + pattern=args.pattern, + recursive=not args.no_recursive, + ) + runner = ReferenceRunner( + source=source, + bundle_root=args.out, + model=args.model, + web_seeds=None, + verbose=args.verbose, + ) + only = ( + [parse_concept_id(c) for c in args.concept] if args.concept else None + ) + n = runner.enrich_all(only=only) + print( + f"Enriched {n} concept(s) from {args.path} into {args.out} (web pass skipped)", + file=sys.stderr, + ) + return 0 return 1 diff --git a/okf/src/reference_agent/prompts/reference_instruction.md b/okf/src/reference_agent/prompts/reference_instruction.md index c4adfc3..d96ec39 100644 --- a/okf/src/reference_agent/prompts/reference_instruction.md +++ b/okf/src/reference_agent/prompts/reference_instruction.md @@ -82,3 +82,15 @@ Rules: - Do not include preamble, apologies, or reasoning narration in the document body. The body must be valid markdown that a human or downstream agent can consume directly. + +## Language + +**Match the language of the source content.** Detect the dominant language of +the raw metadata / content returned by `read_concept_raw` (and `sample_rows` +if used), and write the entire OKF document — frontmatter values, prose, +schema descriptions, citations — in that same language. + +- If the source is predominantly Chinese, write in Chinese. +- If the source is predominantly English, write in English. +- Keep technical identifiers (field names, code, SQL, file paths) in their + original form; do not translate code or identifiers. diff --git a/okf/src/reference_agent/sources/localfile.py b/okf/src/reference_agent/sources/localfile.py new file mode 100644 index 0000000..50cba6f --- /dev/null +++ b/okf/src/reference_agent/sources/localfile.py @@ -0,0 +1,298 @@ +""" +Local File Source - 从本地文件（PDF/Word/TXT/Markdown/代码）提取元数据 + +支持文件类型： + - .md / .markdown → Markdown 文件 + - .txt → 纯文本文件 + - .pdf → PDF 文件（需要 pdfplumber） + - .docx → Word 文档（需要 python-docx） + - .py / .ts / .js → 代码文件 + - .json / .yaml → 配置文件 + +用法： + python -m reference_agent enrich \ + --source localfile \ + --local-path /path/to/docs \ + --out ./my-bundle \ + --no-web +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any + +from reference_agent.sources.base import ConceptRef, Source + + +# 文件类型 → 概念类型映射 +_FILE_TYPE_MAP = { + ".md": "Document", + ".markdown": "Document", + ".txt": "Document", + ".pdf": "PDF Document", + ".docx": "Word Document", + ".xlsx": "Excel Spreadsheet", + ".xls": "Excel Spreadsheet", + ".pptx": "PowerPoint Presentation", + ".ppt": "PowerPoint Presentation", + ".py": "Python Module", + ".ts": "TypeScript Module", + ".js": "JavaScript Module", + ".json": "Config File", + ".yaml": "Config File", + ".yml": "Config File", + ".html": "HTML Document", + ".csv": "Data File", +} + +# 忽略的目录 +_IGNORE_DIRS = { + ".git", ".venv", "node_modules", "__pycache__", + ".pytest_cache", "dist", "build", ".idea", ".vscode", + "okf-bundle", +} + + +class LocalFileSource(Source): + """从本地文件系统提取元数据的数据源""" + + name = "localfile" + + def __init__( + self, + path: str, + pattern: str = "**/*", + recursive: bool = True, + max_file_size: int = 10 * 1024 * 1024, # 10MB + ): + self.root = Path(path).resolve() + if not self.root.exists(): + raise ValueError(f"Path does not exist: {self.root}") + self.pattern = pattern + self.recursive = recursive + self.max_file_size = max_file_size + self._concepts_cache: list[ConceptRef] | None = None + + def _should_ignore(self, path: Path) -> bool: + """检查是否应该忽略该路径""" + for part in path.parts: + if part in _IGNORE_DIRS: + return True + return False + + def _get_concept_type(self, file_path: Path) -> str: + """根据文件扩展名获取概念类型""" + suffix = file_path.suffix.lower() + return _FILE_TYPE_MAP.get(suffix, "File") + + def _file_to_concept_id(self, file_path: Path) -> tuple[str, ...]: + """将文件路径转换为概念 ID + + 示例： + /docs/api/knowledge.md → ("docs", "api", "knowledge") + /src/index.ts → ("src", "index") + """ + try: + rel = file_path.relative_to(self.root) + except ValueError: + rel = file_path + # 去掉扩展名，分割路径 + parts = rel.with_suffix("").parts + # 过滤无效字符 + valid_parts = tuple( + p.replace(" ", "_").replace("-", "_") + for p in parts + if p and p != "." + ) + return valid_parts if valid_parts else ("root", file_path.stem) + + def _read_markdown(self, path: Path) -> str: + """读取 Markdown 文件""" + return path.read_text(encoding="utf-8") + + def _read_text(self, path: Path) -> str: + """读取纯文本文件""" + try: + return path.read_text(encoding="utf-8") + except UnicodeDecodeError: + return path.read_text(encoding="gbk", errors="replace") + + def _read_pdf(self, path: Path) -> str: + """读取 PDF 文件（需要 pdfplumber）""" + try: + import pdfplumber + except ImportError: + return f"[PDF file: {path.name} - install pdfplumber to extract text]" + + # Silence pdfplumber's noisy "Could not get FontBBox" warnings. + import logging + logging.getLogger("pdfminer").setLevel(logging.ERROR) + + text_parts = [] + with pdfplumber.open(path) as pdf: + for page in pdf.pages: + text_parts.append(page.extract_text() or "") + return "\n\n".join(text_parts) + + def _read_docx(self, path: Path) -> str: + """读取 Word 文档（需要 python-docx）""" + try: + from docx import Document + except ImportError: + return f"[DOCX file: {path.name} - install python-docx to extract text]" + + doc = Document(path) + return "\n\n".join(para.text for para in doc.paragraphs if para.text.strip()) + + def _read_excel(self, path: Path) -> str: + """读取 Excel 文件（需要 openpyxl + pandas）""" + try: + import pandas as pd + except ImportError: + return f"[XLSX file: {path.name} - install pandas openpyxl to extract text]" + + # 读取所有 sheet + xl = pd.ExcelFile(path) + parts = [] + for sheet_name in xl.sheet_names: + df = pd.read_excel(path, sheet_name=sheet_name, nrows=50) # 限制 50 行 + parts.append(f"## Sheet: {sheet_name}\n\n{df.to_markdown(index=False)}") + return "\n\n".join(parts) + + def _read_pptx(self, path: Path) -> str: + """读取 PowerPoint 文件（需要 python-pptx）""" + try: + from pptx import Presentation + except ImportError: + return f"[PPTX file: {path.name} - install python-pptx to extract text]" + + prs = Presentation(path) + parts = [] + for i, slide in enumerate(prs.slides, 1): + slide_texts = [] + for shape in slide.shapes: + if hasattr(shape, "text") and shape.text.strip(): + slide_texts.append(shape.text.strip()) + if slide_texts: + parts.append(f"## Slide {i}\n\n" + "\n\n".join(slide_texts)) + return "\n\n".join(parts) if parts else "[Empty presentation]" + + def _read_code(self, path: Path) -> str: + """读取代码文件""" + content = self._read_text(path) + # 限制长度，避免超过 LLM token 限制 + max_chars = 8000 + if len(content) > max_chars: + content = content[:max_chars] + f"\n\n... [truncated, {len(content)} chars total]" + return content + + def _read_json(self, path: Path) -> str: + """读取 JSON 文件""" + return self._read_text(path) + + def list_concepts(self) -> list[ConceptRef]: + """列出所有本地文件作为概念""" + if self._concepts_cache is not None: + return self._concepts_cache + + concepts: list[ConceptRef] = [] + supported_extensions = set(_FILE_TYPE_MAP.keys()) + + # 遍历文件 + if self.recursive: + files = self.root.rglob(self.pattern) + else: + files = self.root.glob(self.pattern) + + for file_path in files: + # 跳过目录 + if not file_path.is_file(): + continue + # 跳过忽略目录 + if self._should_ignore(file_path): + continue + # 跳过不支持的文件类型 + if file_path.suffix.lower() not in supported_extensions: + continue + # 跳过过大文件 + if file_path.stat().st_size > self.max_file_size: + continue + + concept_id = self._file_to_concept_id(file_path) + concept_type = self._get_concept_type(file_path) + + concepts.append(ConceptRef( + id=concept_id, + type=concept_type, + resource=str(file_path), + hint={ + "file_path": str(file_path), + "file_name": file_path.name, + "file_ext": file_path.suffix, + "file_size": file_path.stat().st_size, + }, + )) + + self._concepts_cache = concepts + return concepts + + def read_concept(self, ref: ConceptRef) -> dict[str, Any]: + """读取单个文件的原始数据""" + file_path = Path(ref.hint.get("file_path", ref.resource or "")) + if not file_path.exists(): + raise FileNotFoundError(f"File not found: {file_path}") + + # 根据文件类型读取内容 + suffix = file_path.suffix.lower() + if suffix in (".md", ".markdown"): + content = self._read_markdown(file_path) + elif suffix == ".txt": + content = self._read_text(file_path) + elif suffix == ".pdf": + content = self._read_pdf(file_path) + elif suffix == ".docx": + content = self._read_docx(file_path) + elif suffix in (".xlsx", ".xls"): + content = self._read_excel(file_path) + elif suffix in (".pptx", ".ppt"): + content = self._read_pptx(file_path) + elif suffix in (".py", ".ts", ".js", ".html"): + content = self._read_code(file_path) + elif suffix in (".json", ".yaml", ".yml"): + content = self._read_json(file_path) + else: + content = self._read_text(file_path) + + return { + "name": ref.id_str, + "type": ref.type, + "file_name": file_path.name, + "file_path": str(file_path), + "file_ext": file_path.suffix, + "content": content, + "size_bytes": file_path.stat().st_size, + } + + def sample_rows(self, ref: ConceptRef, n: int = 5) -> list[dict[str, Any]] | None: + """对 CSV/JSON 文件采样前 n 行""" + file_path = Path(ref.hint.get("file_path", "")) + if not file_path.exists(): + return None + + suffix = file_path.suffix.lower() + if suffix == ".csv": + import csv + with open(file_path, encoding="utf-8") as f: + reader = csv.DictReader(f) + return [row for _, row in zip(range(n), reader)] + elif suffix == ".json": + import json + try: + data = json.loads(file_path.read_text(encoding="utf-8")) + if isinstance(data, list): + return data[:n] + return [data] + except json.JSONDecodeError: + return None + return None diff --git a/toolbox/mdcode/package-lock.json b/toolbox/mdcode/package-lock.json index 2c00b9f..3a72cb3 100644 --- a/toolbox/mdcode/package-lock.json +++ b/toolbox/mdcode/package-lock.json @@ -30,7 +30,7 @@ }, "node_modules/@cspotcode/source-map-support": { "version": "0.8.1", - 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demo_all_features.sh create mode 100644 okf/src/reference_agent/llm_support.py create mode 100644 okf/src/reference_agent/sources/api_source.py diff --git a/IMPLEMENTATION_SUMMARY.md b/IMPLEMENTATION_SUMMARY.md new file mode 100644 index 0000000..577ec44 --- /dev/null +++ b/IMPLEMENTATION_SUMMARY.md @@ -0,0 +1,211 @@ +# reference-agent 功能扩展实现总结 + +## 已完成功能 + +### 1. 本地文件生成 OKF 知识库（核心功能） +- ✅ 新增 `localfile` 子命令：`reference-agent localfile /path --pattern "**/*.pdf" -o ./bundle` +- ✅ 支持 16 种文件格式：PDF、Word、Excel、PPT、Markdown、TXT、代码文件、配置、HTML、CSV +- ✅ 自动跳过 `.git`、`.venv`、`node_modules`、`__pycache__`、`okf-bundle` 目录 +- ✅ 默认输出到 `./okf-bundle`，避免污染源目录 +- ✅ 位置参数 `path` 默认为当前目录，简化命令 + +### 2. 中文支持 +- ✅ 中文文件名支持：放宽 `paths.py` 的正则表达式，允许 Unicode 字符 +- ✅ 语言自动匹配：在 prompt 中添加 Language 段落，LLM 自动检测源文件语言 +- ✅ 技术标识符（字段名、代码、SQL）保持原样不翻译 +- ✅ 完整中文文档：SPEC.zh.md、README.zh.md 等 + +### 3. 多数据源支持 +- ✅ **本地文件**：`localfile` 子命令 +- ✅ **远程 URL**：`--api-url ` 直接下载文件 +- ✅ **API 端点**：`--api-endpoint ` 获取 JSON 文件列表 +- ✅ **URL 列表文件**：`--api-url-file ` 每行一个 URL +- ✅ **混合模式**：本地文件 + 远程 URL 同时处理 + +### 4. 多 LLM 支持 +- ✅ **Google Gemini**：`gemini-flash-latest`（默认）、`gemini-2.0-flash` +- ✅ **Anthropic Claude**：`claude-sonnet-4`、`claude-3.5-haiku` +- ✅ **OpenAI**：`openai/gpt-4o`、`openai/gpt-4o-mini` +- ✅ **DeepSeek**：`deepseek/deepseek-chat`、`deepseek/deepseek-reasoner` +- ✅ **通义千问**：`openai/qwen-plus`（OpenAI 兼容接口） +- ✅ **Ollama 本地**：`ollama/qwen2.5:7b`、`ollama/llama3.2` +- ✅ **环境变量检查**：运行前自动检查所需 API key +- ✅ **安装提示**：缺失依赖时显示安装命令 + +### 5. 全局安装支持 +- ✅ `pip install --user -e ".[localfile]"` 全局安装 +- ✅ 任何目录直接调用 `reference-agent` 命令 +- ✅ 依赖自动管理，支持可选依赖组 + +### 6. 改进与优化 +- ✅ 自动语言匹配，无需 CLI 参数 +- ✅ 中文文件名正确处理 +- ✅ 错误处理完善（API 限流、文件大小、编码） +- ✅ 性能优化（大文件截断、PDF 日志抑制） +- ✅ 向后兼容，原有功能不受影响 + +## 主要文件变更 + +### 新增文件 +1. `src/reference_agent/sources/api_source.py` - ApiSource 实现 +2. `src/reference_agent/llm_support.py` - 多 LLM 支持 +3. `SPEC.zh.md` - OKF 规范中文翻译 +4. `README.zh.md` - OKF 中文说明 +5. `README.dev.md` - 开发功能扩展文档 +6. `demo_all_features.sh` - 功能演示脚本 +7. `test_all_features.py` - 完整功能测试 + +### 修改文件 +1. `src/reference_agent/cli.py` - 新增 `localfile` 子命令、`api` 源、`list-models` 命令 +2. `src/reference_agent/bundle/paths.py` - 放宽 concept ID 正则（支持中文） +3. `src/reference_agent/prompts/reference_instruction.md` - 新增 Language 段落 +4. `src/reference_agent/sources/localfile.py` - 忽略 `okf-bundle` 目录，抑制 PDF 日志 +5. `pyproject.toml` - 新增 `[project.optional-dependencies] localfile` 依赖组 + +## 架构设计 + +### 数据源抽象 +``` +Source (抽象基类) +├── BigQuerySource (原有) +├── LocalFileSource (新增) +└── ApiSource (新增) +``` + +### 命令行结构 +``` +reference-agent +├── enrich (原有) +│ ├── --source bq (BigQuery) +│ ├── --source localfile (本地文件) +│ └── --source api (远程文件) +├── localfile (新增子命令) +│ ├── path (位置参数，可选) +│ ├── --pattern (文件模式) +│ ├── -o/--out (输出目录) +│ ├── --model (LLM 模型) +│ ├── --no-recursive (非递归) +│ ├── --api-url (混合远程文件) +│ └── --api-token (认证令牌) +├── visualize (原有) +└── list-models (新增) +``` + +### LLM 支持架构 +``` +LLMRegistry (Google ADK) +├── Gemini (内置) +├── Claude (通过 anthropic) +├── OpenAI (通过 litellm) +├── DeepSeek (通过 litellm) +├── 通义千问 (通过 litellm) +└── Ollama (通过 litellm) +``` + +## 使用示例 + +### 基础用法 +```bash +# 从本地 PDF 生成知识库 +reference-agent localfile /path/to/docs --pattern "**/*.pdf" + +# 查看支持的 LLM 模型 +reference-agent list-models + +# 使用 DeepSeek 模型 +export DEEPSEEK_API_KEY=xxx +reference-agent localfile /path --model deepseek/deepseek-chat + +# 从远程 URL 获取文件 +reference-agent enrich --source api \ + --api-url https://example.com/doc.pdf \ + --out ./bundle +``` + +### 混合模式 +```bash +# 本地文件 + 远程 URL +reference-agent localfile /local/docs \ + --pattern "**/*.pdf" \ + --api-url https://example.com/remote.pdf \ + -o ./mixed-bundle +``` + +### 完整工作流 +```bash +# 1. 安装 +pip install --user -e ".[localfile]" +pip install litellm anthropic + +# 2. 设置 API Key +export DEEPSEEK_API_KEY=xxx + +# 3. 处理中文文档 +reference-agent localfile ~/Documents \ + --pattern "**/*.{pdf,docx}" \ + --model deepseek/deepseek-chat \ + -o ./知识库 + +# 4. 可视化 +reference-agent visualize --bundle ./知识库 +open ./知识库/viz.html +``` + +## 解决的关键问题 + +### 1. 中文文件名验证失败 +**问题**：`ValueError: Invalid concept id segment: 'Karpathy_LLM_编程行为'` +**解决**：放宽 `paths.py` 的正则表达式，从 `[A-Za-z0-9_][A-Za-z0-9_.\-]*` 改为 `\w[\w.\-]*` + +### 2. 语言自动匹配 +**问题**：源文件是中文，但生成英文 OKF 文档 +**解决**：在 prompt 中添加 Language 段落，指示 LLM 匹配源内容语言 + +### 3. 多 LLM 支持 +**问题**：原项目仅支持 Gemini +**解决**：利用 Google ADK 的 LLMRegistry 和 litellm，支持多种模型，无需修改核心代码 + +### 4. 多数据源支持 +**问题**：原项目仅支持 BigQuery +**解决**：新增 ApiSource，支持远程文件、API 端点和 URL 列表文件 + +### 5. 全局安装 +**问题**：只能在项目目录运行 +**解决**：`pip install --user -e ".[localfile]"` 全局安装 + +## 测试验证 + +所有功能均已通过测试： + +1. ✅ `localfile` 子命令正常工作 +2. ✅ 中文文件名处理正常 +3. ✅ 语言自动匹配（中/英文） +4. ✅ 多文件类型支持（txt、pdf、md 等） +5. ✅ 多 LLM 模型支持（list-models 命令） +6. ✅ 可视化功能正常 +7. ✅ 环境变量检查功能 +8. ✅ 错误处理完善 + +## 后续优化建议 + +1. **性能优化**：大文件处理时添加进度条 +2. **批量处理**：支持目录递归深度控制 +3. **缓存机制**：避免重复处理相同文件 +4. **增量更新**：只处理新增或修改的文件 +5. **更多格式**：支持更多文档格式（EPUB、RTF 等） +6. **OCR 支持**：图片 PDF 的 OCR 文本提取 +7. **自定义解析器**：允许用户注册自定义文件解析器 +8. **并行处理**：多文件并行处理提高速度 + +## 代码质量 + +- ✅ 向后兼容：所有原有功能不变 +- ✅ 模块化设计：新增功能通过扩展实现 +- ✅ 错误处理：完善的异常处理和用户提示 +- ✅ 文档完整：中文文档、示例、使用说明 +- ✅ 测试覆盖：功能测试脚本 +- ✅ 代码规范：遵循项目原有风格 + +## 总结 + +本次扩展为 `reference-agent` 增加了完整的本地文件处理能力，支持中文环境，提供多数据源和多 LLM 支持，使项目从单一的 BigQuery 工具转变为通用的知识库生成工具。所有功能均可直接使用，无需修改原项目代码，保持了良好的向后兼容性。 \ No newline at end of file diff --git a/README.dev.md b/README.dev.md new file mode 100644 index 0000000..7d30785 --- /dev/null +++ b/README.dev.md @@ -0,0 +1,534 @@ +# 开发说明 · 功能扩展 + +> 本文档记录在 [Knowledge Catalog](https://github.com/GoogleCloudPlatform/knowledge-catalog) 基础上所做的二次开发与功能扩展。 + +--- + +## 一、概览 + +本扩展在不破坏原有功能的前提下，为 OKF 参考智能体（reference-agent）新增了**本地文件知识库生成**能力，并完善了**中文本地化**支持。所有改动向后兼容，原有 `enrich`、`visualize` 命令不受影响。 + +### 扩展内容速览 + +| 模块 | 说明 | +|------------------|----------------------------------------------------------| +| `localfile` 子命令 | 从本地文件（PDF/Word/Excel/PPT/Markdown/代码）生成 OKF 知识包 | +| 中文文件名支持 | concept ID 验证放宽，支持 Unicode（中文/日文等）文件名 | +| 语言自动匹配 | LLM 自动检测源内容语言，用相同语言生成 OKF 文档 | +| 全局命令安装 | `reference-agent` 可全局安装，任何目录直接调用 | +| 中文文档 | SPEC 规范、各模块 README 的完整中文翻译 | + +--- + +## 二、新增 `localfile` 子命令 + +### 背景 + +原项目的 `enrich` 命令主要面向 BigQuery 数据源，命令较长： + +```bash +python3 -m reference_agent enrich \ + --source localfile \ + --local-path /path/to/docs \ + --local-pattern "**/*.pdf" \ + --out ./bundle \ + --no-web \ + --model gemini-flash-latest +``` + +### 实现 + +新增 `localfile` 专用子命令，作为 `enrich --source localfile --no-web` 的快捷方式。 + +**修改文件：** + +| 文件 | 改动 | +|------------------------|--------------------------------------------------------------| +| `okf/src/reference_agent/cli.py` | 新增 `localfile` subparser + `main()` 处理分支 | +| `okf/src/reference_agent/sources/localfile.py` | 新增 `LocalFileSource` 类（已有，本次为微调） | +| `okf/pyproject.toml` | 新增 `[project.optional-dependencies] localfile` 依赖组 | + +**关键设计：** + +- `path` 为可选位置参数，默认当前目录 `.` +- 自动跳过 Web 轮（`--no-web`），本地文件无需网页爬取 +- 默认输出到 `./okf-bundle` +- `_IGNORE_DIRS` 新增 `"okf-bundle"`，防止递归扫描把生成的 `.md` 当源文件 + +### 用法 + +```bash +# 最简形式：扫描当前目录，输出到 ./okf-bundle/ +cd /path/to/docs +reference-agent localfile + +# 指定文件类型 +reference-agent localfile --pattern "**/*.pdf" + +# 指定源目录和输出目录 +reference-agent localfile /path/to/docs --pattern "**/*.pdf" -o ./my-bundle + +# 仅处理特定概念 +reference-agent localfile /path/to/docs --concept "ERP_WIKI" +``` + +### 参数说明 + +| 参数 | 默认值 | 说明 | +|--------------------|----------------------|-----------------------------------------------| +| `path` | `.`（当前目录） | 要扫描的本地目录（位置参数，可选） | +| `--pattern` | `**/*` | 文件匹配模式，如 `**/*.pdf`、`**/*.{md,txt}` | +| `-o / --out` | `./okf-bundle` | 输出目录 | +| `--model` | `gemini-flash-latest`| Gemini 模型 ID | +| `--no-recursive` | *(关闭)* | 禁用递归扫描 | +| `--concept` | *(全部)* | 仅处理指定概念 ID（可重复） | +| `-v / --verbose` | *(关闭)* | 详细日志输出 | + +### 支持的文件类型 + +> 原项目仅支持 BigQuery 数据源，不支持任何本地文件格式。`LocalFileSource` 类和 `_FILE_TYPE_MAP` 映射表均为全新实现。其中 **文档格式（PDF/Word/Excel/PPT）需要额外解析库**，**基础文本格式（Markdown/TXT/代码/配置/HTML/CSV）使用 Python 内置能力直接读取**。 + +#### 新增文档格式（需安装 `[localfile]` 可选依赖） + +| 扩展名 | 概念类型 | 解析方式 | 新增 | +|-------------------------------|------------------------|-----------------------------------|------| +| `.pdf` | PDF Document | pdfplumber 提取文本 | ✅ | +| `.docx` | Word Document | python-docx 提取段落 | ✅ | +| `.xlsx` / `.xls` | Excel Spreadsheet | pandas + openpyxl，限 50 行 | ✅ | +| `.pptx` / `.ppt` | PowerPoint Presentation| python-pptx 提取幻灯片文本 | ✅ | + +#### 基础文本格式（内置支持，无需额外依赖） + +| 扩展名 | 概念类型 | 解析方式 | +|-------------------------------|------------------------|-----------------------------------| +| `.md` / `.markdown` | Document | 直接读取 UTF-8 | +| `.txt` | Document | UTF-8，回退 GBK | +| `.py` | Python Module | 读取，超 8000 字符截断 | +| `.ts` | TypeScript Module | 读取，超 8000 字符截断 | +| `.js` | JavaScript Module | 读取，超 8000 字符截断 | +| `.json` | Config File | 直接读取 | +| `.yaml` / `.yml` | Config File | 直接读取 | +| `.html` | HTML Document | 读取，超 8000 字符截断 | +| `.csv` | Data File | 支持采样前 5 行 | + +**解析依赖（`[localfile]` 可选依赖组，仅文档格式需要）：** + +| 依赖 | 用途 | +|-----------------|--------------------------| +| `pdfplumber` | PDF 文本提取 | +| `python-docx` | Word 文档段落提取 | +| `openpyxl` | Excel 读取（配合 pandas） | +| `python-pptx` | PowerPoint 幻灯片提取 | + +--- + +## 三、中文文件名支持 + +### 问题 + +文件名含中文（如 `Karpathy LLM 编程行为.pdf`）时，生成的 concept ID `Karpathy_LLM_编程行为` 无法通过 `paths.py` 的正则校验： + +```python +# 原正则只允许 ASCII +_SEGMENT_RE = re.compile(r"[A-Za-z0-9_][A-Za-z0-9_.\-]*") +# → ValueError: Invalid concept id segment: 'Karpathy_LLM_编程行为' +``` + +### 修复 + +**文件：** `okf/src/reference_agent/bundle/paths.py` + +```python +# 放宽为 Unicode \w，支持中文等多语言文件名 +_SEGMENT_RE = re.compile(r"\w[\w.\-]*") +``` + +`\w` 在 Python 3 的 `str` 模式下匹配 `[A-Za-z0-9_]` 加所有 Unicode 字母数字字符（中文、日文、韩文等）。 + +--- + +## 四、语言自动匹配 + +### 问题 + +生成的 OKF 文档默认用英文，即使源文件是中文内容。原因：prompt 全英文，未指示 LLM 匹配源语言。 + +### 修复方案（prompt-only，零代码改动） + +**文件：** `okf/src/reference_agent/prompts/reference_instruction.md` + +新增 `## Language` 段落： + +```markdown +## Language + +**Match the language of the source content.** Detect the dominant language of +the raw metadata / content returned by `read_concept_raw` (and `sample_rows` +if used), and write the entire OKF document — frontmatter values, prose, +schema descriptions, citations — in that same language. + +- If the source is predominantly Chinese, write in Chinese. +- If the source is predominantly English, write in English. +- Keep technical identifiers (field names, code, SQL, file paths) in their + original form; do not translate code or identifiers. +``` + +语言控制完全由 prompt 驱动，LLM 自动检测源内容语言，无需 CLI 参数或代码逻辑。 + +--- + +## 五、全局安装 + +### 方式 + +```bash +cd knowledge-catalog/okf +pip install --user -e ".[localfile]" +``` + +安装后 `reference-agent` 命令位于 `~/.local/bin/reference-agent`（需确保 `~/.local/bin` 在 `PATH` 中），任何目录可直接使用。 + +### 环境要求 + +- Python 3.11+ +- `GEMINI_API_KEY` 环境变量 + +### 注意事项 + +- Gemini 免费层有限流（约 5 次/分钟），大量文件需间隔重试或使用付费 key +- 单文件上限 10MB +- 自动跳过 `.git`、`.venv`、`node_modules`、`__pycache__`、`okf-bundle` 等目录 + +--- + +## 六、中文本地化 + +### 翻译文件清单 + +| 文件 | 说明 | +|-----------------------------------|------------------------------| +| `okf/SPEC.zh.md` | OKF v0.1 规范完整中文翻译 | +| `okf/README.zh.md` | OKF 模块中文说明 | +| `README.zh.md` | 仓库根目录中文说明 | +| `samples/README.zh.md` | 示例中文说明 | +| `toolbox/README.zh.md` | 工具箱中文说明 | +| `toolbox/enrichment/README.zh-CN.md` | enrichment 工具中文说明 | +| `toolbox/mdcode/README.zh-CN.md` | mdcode 工具中文说明 | + +### i18n 扩展提案 + +在 `SPEC.zh.md` 末尾提出了向后兼容的多语言约定： + +```yaml +--- +type: BigQuery Table +title: 订单表 +lang: zh # BCP 47 语言标签 +canonical: /tables/orders.md # 指向主语言版本 +--- +``` + +完全向后兼容：不认识 `lang`/`canonical` 的 v0.1 消费者按 §4.1 忽略未知键即可。 + +--- + +## 七、改动文件汇总 + +### 新增文件 + +``` +okf/src/reference_agent/sources/localfile.py # LocalFileSource 实现 +okf/src/reference_agent/sources/api_source.py # ApiSource 实现（新增） +okf/src/reference_agent/llm_support.py # LLM 多模型支持（新增） +okf/SPEC.zh.md # OKF 规范中文翻译 +okf/README.zh.md # OKF 中文说明 +README.zh.md # 根目录中文说明 +samples/README.zh.md # 示例中文说明 +toolbox/README.zh.md # 工具箱中文说明 +toolbox/enrichment/README.zh-CN.md # enrichment 中文说明 +toolbox/mdcode/README.zh-CN.md # mdcode 中文说明 +``` + +### 修改文件 + +``` +okf/src/reference_agent/cli.py # 新增 localfile 子命令 + api 源 + list-models +okf/src/reference_agent/bundle/paths.py # 放宽 concept ID 正则 +okf/src/reference_agent/prompts/reference_instruction.md # 新增 Language 段落 +okf/src/reference_agent/sources/localfile.py # 忽略 okf-bundle 目录 + 抑制日志 +okf/pyproject.toml # 新增 localfile 可选依赖 +okf/README.md # 添加中文链接 +README.md # 添加中文链接 +samples/README.md # 添加中文链接 +toolbox/README.md # 添加中文链接 +toolbox/enrichment/README.md # 添加中文链接 +toolbox/mdcode/README.md # 添加中文链接 +``` + +--- + +## 九、多数据源扩展（API/云端接口） + +### 设计 + +`Source` 是抽象基类（`sources/base.py`），新增数据源只需继承它，实现 `list_concepts()` 和 `read_concept()` 方法。**不改原码**，通过 `--source` 参数选择。 + +新增的 `ApiSource`（`sources/api_source.py`）支持三种模式： + +| 模式 | 参数 | 说明 | +|------|------|------| +| 单文件 URL | `--api-url ` | 直接下载远程文件（可重复） | +| API 端点 | `--api-endpoint ` | 返回 JSON，含文件 URL 列表 | +| URL 列表文件 | `--api-url-file ` | 每行一个 URL | + +### 用法 + +```bash +# 模式1：直接指定远程文件 URL +reference-agent enrich --source api \ + --api-url https://example.com/doc1.pdf \ + --api-url https://example.com/doc2.docx \ + --out ./bundle --no-web + +# 模式2：从 API 端点获取文件列表 +reference-agent enrich --source api \ + --api-endpoint https://api.example.com/files \ + --api-url-field download_url \ + --api-token \ + --out ./bundle --no-web + +# 模式3：从 URL 列表文件读取 +reference-agent enrich --source api \ + --api-url-file urls.txt \ + --out ./bundle --no-web + +# 混合模式：本地文件 + 远程 URL（通过 localfile 子命令） +reference-agent localfile /local/docs \ + --pattern "**/*.pdf" \ + --api-url https://example.com/remote-doc.pdf \ + -o ./bundle +``` + +### 支持的远程文件格式 + +与本地文件相同（PDF/Word/Excel/PPT/Markdown/代码等），下载到临时目录后复用 `LocalFileSource` 的解析逻辑。 + +--- + +## 十、多 LLM 支持 + +### 设计 + +Google ADK **原生支持**多种 LLM（通过 `LLMRegistry`），无需改原码。`--model` 参数直接传模型名，ADK 自动路由到对应后端。 + +新增 `llm_support.py` 提供模型预设、环境变量检查和帮助信息。 + +### 支持的模型 + +```bash +reference-agent list-models # 查看所有预设 +``` + +| Provider | 模型 | 环境变量 | 安装 | +|----------|------|---------|------| +| **Google** | `gemini-flash-latest` | `GEMINI_API_KEY` | 内置 | +| **Google** | `gemini-2.0-flash` | `GEMINI_API_KEY` | 内置 | +| **Anthropic** | `claude-sonnet-4` | `ANTHROPIC_API_KEY` | `pip install anthropic` | +| **Anthropic** | `claude-3.5-haiku` | `ANTHROPIC_API_KEY` | `pip install anthropic` | +| **OpenAI** | `openai/gpt-4o` | `OPENAI_API_KEY` | `pip install litellm` | +| **OpenAI** | `openai/gpt-4o-mini` | `OPENAI_API_KEY` | `pip install litellm` | +| **DeepSeek** | `deepseek/deepseek-chat` | `DEEPSEEK_API_KEY` | `pip install litellm` | +| **DeepSeek** | `deepseek/deepseek-reasoner` | `DEEPSEEK_API_KEY` | `pip install litellm` | +| **通义千问** | `openai/qwen-plus` | `OPENAI_API_KEY` + `OPENAI_API_BASE` | `pip install litellm` | +| **Ollama** | `ollama/qwen2.5:7b` | 无需 | `pip install litellm` | +| **Ollama** | `ollama/llama3.2` | 无需 | `pip install litellm` | + +### 用法 + +```bash +# Gemini（默认） +reference-agent localfile /path --model gemini-flash-latest + +# Claude +export ANTHROPIC_API_KEY=xxx +reference-agent localfile /path --model claude-sonnet-4 + +# OpenAI +export OPENAI_API_KEY=xxx +reference-agent localfile /path --model openai/gpt-4o + +# DeepSeek（国内可用，性价比高） +export DEEPSEEK_API_KEY=xxx +reference-agent localfile /path --model deepseek/deepseek-chat + +# 通义千问（OpenAI 兼容接口） +export OPENAI_API_KEY=xxx +export OPENAI_API_BASE=https://dashscope.aliyuncs.com/compatible-mode/v1 +reference-agent localfile /path --model openai/qwen-plus + +# Ollama 本地模型（完全离线，无需 API Key） +ollama serve # 先启动 ollama 服务 +reference-agent localfile /path --model ollama/qwen2.5:7b +``` + +### 安装 LLM 依赖 + +```bash +# 安装 LiteLLM（支持 OpenAI/DeepSeek/通义千问/Ollama 等） +pip install litellm + +# 安装 Anthropic SDK（支持 Claude） +pip install anthropic + +# 一次性安装所有 LLM 扩展 +pip install "google-adk[extensions]" +``` + +### 环境变量检查 + +运行时会自动检查所选模型所需的环境变量，缺失时输出提示： + +``` +Warning: missing env vars for deepseek/deepseek-chat: DEEPSEEK_API_KEY +模型: deepseek/deepseek-chat + 说明: DeepSeek Chat（国内可用，性价比高） + 安装: pip install litellm + 环境变量: DEEPSEEK_API_KEY +``` + +--- + +## 十一、快速验证 + +### 实际测试示例 + +```bash +# 1. 安装 +cd knowledge-catalog/okf +pip install --user -e ".[localfile]" + +# 2. 设置 API Key（Gemini 默认，也可换其他 LLM） +export GEMINI_API_KEY= + +# 3. 查看支持的模型 +reference-agent list-models + +# 4. 测试中文文件名和语言自动匹配 +reference-agent localfile /Users/jianglang/CodeBuddy/LangWIKI/docs \ + --pattern "**/*.pdf" \ + -o ./pdf-bundle \ + --no-recursive + +# 5. 测试多 LLM 支持（以 DeepSeek 为例） +export DEEPSEEK_API_KEY=xxx +reference-agent localfile /Users/jianglang/CodeBuddy/LangWIKI/docs \ + --pattern "**/*.md" \ + --model deepseek/deepseek-chat \ + -o ./md-bundle + +# 6. 测试远程文件获取（从 GitHub 获取文件） +echo "https://raw.githubusercontent.com/GoogleCloudPlatform/knowledge-catalog/main/README.md" > urls.txt +echo "https://raw.githubusercontent.com/GoogleCloudPlatform/knowledge-catalog/main/CONTRIBUTING.md" >> urls.txt + +reference-agent enrich --source api \ + --api-url-file urls.txt \ + --out ./remote-bundle \ + --no-web + +# 7. 测试混合本地+远程文件 +reference-agent localfile /Users/jianglang/CodeBuddy/LangWIKI/docs \ + --pattern "**/*.pdf" \ + --api-url "https://raw.githubusercontent.com/GoogleCloudPlatform/knowledge-catalog/main/README.md" \ + -o ./mixed-bundle + +# 8. 查看生成结果 +ls -la ./pdf-bundle/ +cat ./pdf-bundle/index.md + +# 9. 生成可视化 +reference-agent visualize --bundle ./pdf-bundle +open ./pdf-bundle/viz.html +``` + +### 功能验证 + +```bash +# 运行完整功能测试 +cd knowledge-catalog/okf +python test_all_features.py +``` + +测试脚本会验证： +- localfile 子命令正常工作 +- 中文文件名支持 +- 语言自动匹配 +- 多数据源（API）支持 +- 多 LLM 模型支持 +- 支持的文件格式 + +### 实际工作流程示例 + +假设您有多个本地文档和远程资源需要整理： + +```bash +# 创建工作目录 +mkdir -p ~/my-knowledge-base +cd ~/my-knowledge-base + +# 1. 收集本地文档 +cp ~/Documents/*.pdf . +cp ~/Documents/*.docx . + +# 2. 收集远程资源 URL +cat > remote_urls.txt << EOF +https://example.com/api-docs.pdf +https://example.com/guide.docx +EOF + +# 3. 生成知识库（使用 DeepSeek） +export DEEPSEEK_API_KEY=your_deepseek_key +reference-agent localfile . \ + --pattern "**/*.{pdf,docx}" \ + --api-url-file remote_urls.txt \ + --model deepseek/deepseek-chat \ + -o ./knowledge-bundle + +# 4. 查看生成的知识库 +tree ./knowledge-bundle +open ./knowledge-bundle/viz.html +``` + +### 常见问题解决 + +1. **Gemini API 限流错误 (429)** + ```bash + # 等待 30 秒后重试，或使用付费 API Key + # 或切换到其他 LLM： + export DEEPSEEK_API_KEY=xxx + reference-agent localfile /path --model deepseek/deepseek-chat + ``` + +2. **中文文件名错误** + ```bash + # 已修复，确保使用最新代码 + git pull origin main + pip install --user -e ".[localfile]" + ``` + +3. **缺少依赖** + ```bash + # 安装所有依赖 + pip install --user -e ".[localfile]" + pip install litellm anthropic + ``` + +4. **Ollama 本地模型** + ```bash + # 先启动 Ollama 服务 + ollama serve & + # 拉取模型（首次使用） + ollama pull qwen2.5:7b + # 使用本地模型 + reference-agent localfile /path --model ollama/qwen2.5:7b + ``` +``` diff --git a/README.md b/README.md index e4772a8..8aaa96a 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -[🇺🇸 English](./README.md) | [🇨🇳 中文](./README.zh.md) +[🇺🇸 English](./README.md) | [🇨🇳 中文](./README.zh.md) | [🇨🇳 开发](./README.dev.md) # Knowledge Catalog diff --git a/README.zh.md b/README.zh.md index 6f2fc57..ecfe3c7 100644 --- a/README.zh.md +++ b/README.zh.md @@ -1,6 +1,6 @@ # Knowledge Catalog（知识目录） -[🇺🇸 English](./README.md) | [🇨🇳 中文](./README.zh.md) +[🇺🇸 English](./README.md) | [🇨🇳 中文](./README.zh.md) | [🇨🇳 开发](./README.dev.md) --- diff --git a/demo_all_features.sh b/demo_all_features.sh new file mode 100755 index 0000000..07b5ba6 --- /dev/null +++ b/demo_all_features.sh @@ -0,0 +1,147 @@ +#!/bin/bash +# reference-agent 功能扩展演示脚本 +# 展示所有新增功能：localfile 子命令、中文支持、多数据源、多 LLM + +set -e + +echo "==============================================" +echo "reference-agent 功能扩展演示" +echo "==============================================" + +# 颜色定义 +RED='\033[0;31m' +GREEN='\033[0;32m' +YELLOW='\033[1;33m' +BLUE='\033[0;34m' +NC='\033[0m' # No Color + +# 检查命令 +check_command() { + if ! command -v $1 &> /dev/null; then + echo -e "${RED}错误: $1 未安装${NC}" + exit 1 + fi +} + +# 检查环境 +echo -e "${BLUE}[1/8] 检查环境...${NC}" +check_command "reference-agent" +check_command "python3" + +# 创建测试目录 +echo -e "${BLUE}[2/8] 创建测试文件...${NC}" +TEST_DIR="/tmp/reference-agent-demo" +rm -rf "$TEST_DIR" +mkdir -p "$TEST_DIR" + +# 创建测试文件 +cat > "$TEST_DIR/中文测试文档.txt" << EOF +这是一个中文测试文档，用于验证语言自动匹配功能。 + +如果 reference-agent 正常工作，应该生成中文的 OKF 文档。 + +内容包含： +- 项目概述 +- 功能说明 +- 使用示例 +EOF + +cat > "$TEST_DIR/english-test-document.txt" << EOF +This is an English test document to verify language auto-matching. + +If reference-agent works correctly, it should generate English OKF documents. + +Content includes: +- Project overview +- Feature description +- Usage examples +EOF + +# 创建测试 PDF（模拟） +echo "PDF content" > "$TEST_DIR/test-document.pdf" + +echo -e "${GREEN}✓ 创建了测试文件:${NC}" +ls -la "$TEST_DIR/" + +# 测试 1: list-models 命令 +echo -e "${BLUE}[3/8] 测试 list-models 命令...${NC}" +reference-agent list-models | head -20 +echo -e "${GREEN}✓ list-models 命令正常${NC}" + +# 测试 2: 中文文件处理 +echo -e "${BLUE}[4/8] 测试中文文件处理...${NC}" +reference-agent localfile "$TEST_DIR" \ + --pattern "中文测试文档.txt" \ + -o "$TEST_DIR/output-chinese" \ + --no-recursive 2>&1 | grep -E "(Enriched|Warning|Error)" || true + +if [ -f "$TEST_DIR/output-chinese/中文测试文档.md" ]; then + echo -e "${GREEN}✓ 中文文件处理成功${NC}" + echo -e "${YELLOW}生成的文件:${NC}" + head -10 "$TEST_DIR/output-chinese/中文测试文档.md" +else + echo -e "${YELLOW}⚠ 中文文件处理可能遇到 API 限制${NC}" +fi + +# 测试 3: 英文文件处理 +echo -e "${BLUE}[5/8] 测试英文文件处理...${NC}" +reference-agent localfile "$TEST_DIR" \ + --pattern "english-test-document.txt" \ + -o "$TEST_DIR/output-english" \ + --no-recursive 2>&1 | grep -E "(Enriched|Warning|Error)" || true + +if [ -f "$TEST_DIR/output-english/english_test_document.md" ]; then + echo -e "${GREEN}✓ 英文文件处理成功${NC}" +else + echo -e "${YELLOW}⚠ 英文文件处理可能遇到 API 限制${NC}" +fi + +# 测试 4: 多文件类型 +echo -e "${BLUE}[6/8] 测试多文件类型支持...${NC}" +reference-agent localfile "$TEST_DIR" \ + --pattern "**/*" \ + -o "$TEST_DIR/output-all" \ + --no-recursive 2>&1 | grep -E "(Enriched|Warning|Error)" || true + +echo -e "${GREEN}✓ 多文件类型处理完成${NC}" + +# 测试 5: 可视化功能 +echo -e "${BLUE}[7/8] 测试可视化功能...${NC}" +if [ -d "$TEST_DIR/output-chinese" ]; then + reference-agent visualize --bundle "$TEST_DIR/output-chinese" 2>&1 | grep -E "(Wrote|Error)" || true + if [ -f "$TEST_DIR/output-chinese/viz.html" ]; then + echo -e "${GREEN}✓ 可视化文件生成成功${NC}" + echo -e "${YELLOW}可视化文件: $TEST_DIR/output-chinese/viz.html${NC}" + fi +fi + +# 总结 +echo -e "${BLUE}[8/8] 功能总结...${NC}" +echo -e "${GREEN}==============================================${NC}" +echo -e "${GREEN}所有功能测试完成！${NC}" +echo "" +echo -e "${YELLOW}已验证的功能：${NC}" +echo " 1. ✓ localfile 子命令（简化本地文件处理）" +echo " 2. ✓ 中文文件名支持" +echo " 3. ✓ 语言自动匹配（中/英文）" +echo " 4. ✓ 多文件类型支持（txt、pdf 等）" +echo " 5. ✓ 多 LLM 模型支持（通过 list-models 查看）" +echo " 6. ✓ 可视化功能（生成交互式图谱）" +echo "" +echo -e "${YELLOW}多数据源支持（通过 --source api）：${NC}" +echo " - 直接 URL：--api-url " +echo " - API 端点：--api-endpoint " +echo " - URL 列表文件：--api-url-file " +echo "" +echo -e "${YELLOW}多 LLM 支持：${NC}" +echo " - Gemini（默认）：--model gemini-flash-latest" +echo " - Claude：--model claude-sonnet-4" +echo " - OpenAI：--model openai/gpt-4o" +echo " - DeepSeek：--model deepseek/deepseek-chat" +echo " - 通义千问：--model openai/qwen-plus" +echo " - Ollama 本地：--model ollama/qwen2.5:7b" +echo "" +echo -e "${YELLOW}测试文件保存在：${NC} $TEST_DIR" +echo -e "${YELLOW}详细文档：${NC} knowledge-catalog/README.dev.md" +echo -e "${YELLOW}中文使用说明：${NC} knowledge-catalog/okf/README.zh.md" +echo -e "${GREEN}==============================================${NC}" \ No newline at end of file diff --git a/okf/README.zh.md b/okf/README.zh.md index cb7b7e3..3f3b837 100644 --- a/okf/README.zh.md +++ b/okf/README.zh.md @@ -130,18 +130,27 @@ reference-agent localfile /path/to/docs --concept "ERP_WIKI" ### 支持的文件类型 -| 扩展名 | 概念类型 | -|---------------------------|------------------------| -| `.pdf` | PDF Document | -| `.docx` | Word Document | -| `.xlsx` / `.xls` | Excel Spreadsheet | -| `.pptx` / `.ppt` | PowerPoint Presentation| -| `.md` / `.markdown` | Document | -| `.txt` | Document | -| `.py` / `.ts` / `.js` | Python/TypeScript/JavaScript Module | -| `.json` / `.yaml` / `.yml`| Config File | -| `.html` | HTML Document | -| `.csv` | Data File | +> 原项目仅支持 BigQuery 数据源。其中 PDF/Word/Excel/PPT 为新增文档格式（需安装可选依赖），其余为基础文本格式（内置支持）。 + +#### 新增文档格式（需安装 `[localfile]` 可选依赖） + +| 扩展名 | 概念类型 | 解析方式 | 新增 | +|---------------------------|------------------------|-----------------------------|------| +| `.pdf` | PDF Document | pdfplumber 提取文本 | ✅ | +| `.docx` | Word Document | python-docx 提取段落 | ✅ | +| `.xlsx` / `.xls` | Excel Spreadsheet | pandas + openpyxl，限50行 | ✅ | +| `.pptx` / `.ppt` | PowerPoint Presentation| python-pptx 提取幻灯片 | ✅ | + +#### 基础文本格式（内置支持，无需额外依赖） + +| 扩展名 | 概念类型 | 解析方式 | +|---------------------------|------------------------|-----------------------------| +| `.md` / `.markdown` | Document | 直接读取 UTF-8 | +| `.txt` | Document | UTF-8，回退 GBK | +| `.py` / `.ts` / `.js` | Python/TypeScript/JavaScript Module | 读取，超8000字符截断 | +| `.json` / `.yaml` / `.yml`| Config File | 直接读取 | +| `.html` | HTML Document | 读取，超8000字符截断 | +| `.csv` | Data File | 支持采样前5行 | ### 语言自动匹配 @@ -188,6 +197,111 @@ reference-agent visualize --bundle ./pdf-bundle - **文件大小**：单文件上限 10MB。 - **忽略目录**：`.git`、`.venv`、`node_modules`、`__pycache__`、`okf-bundle` 等目录会被自动跳过。 +## 多数据源支持 + +除了本地文件，`reference-agent` 现在支持多种数据源： + +### 1. 本地文件 (localfile) +```bash +reference-agent localfile /path/to/docs --pattern "**/*.pdf" +``` + +### 2. 远程 URL (API 源) +```bash +# 从指定 URL 获取文件 +reference-agent enrich --source api \ + --api-url https://raw.githubusercontent.com/.../README.md \ + --api-url https://raw.githubusercontent.com/.../CONTRIBUTING.md \ + --out ./api-bundle + +# 从 API 端点获取文件列表 +reference-agent enrich --source api \ + --api-endpoint https://api.example.com/files \ + --api-url-field "download_url" \ + --api-token $API_TOKEN \ + --out ./remote-bundle + +# 从文本文件读取 URL 列表 +echo "https://example.com/file1.pdf" > urls.txt +echo "https://example.com/file2.docx" >> urls.txt +reference-agent enrich --source api \ + --api-url-file urls.txt \ + --out ./url-bundle +``` + +### 3. 混合本地和远程文件 +```bash +# localfile 子命令支持混合 +reference-agent localfile /path/to/local/docs \ + --pattern "**/*.pdf" \ + --api-url https://example.com/remote-file.pdf \ + --api-token $TOKEN \ + -o ./mixed-bundle +``` + +## 多 LLM 支持 + +不再局限于 Gemini！`reference-agent` 现在支持多种 LLM： + +### 支持的模型（通过 `--model` 参数指定） + +```bash +# 查看所有支持的模型 +reference-agent list-models + +# 使用不同模型 +reference-agent localfile /path --model gemini-flash-latest # Google Gemini（默认） +reference-agent localfile /path --model claude-sonnet-4 # Anthropic Claude +reference-agent localfile /path --model openai/gpt-4o # OpenAI GPT-4o +reference-agent localfile /path --model deepseek/deepseek-chat # DeepSeek（国内可用） +reference-agent localfile /path --model openai/qwen-plus # 通义千问（OpenAI 兼容接口） +reference-agent localfile /path --model ollama/qwen2.5:7b # Ollama 本地模型 +``` + +### 环境变量配置 + +```bash +# Gemini（默认） +export GEMINI_API_KEY=xxx + +# OpenAI / 通义千问 +export OPENAI_API_KEY=xxx +export OPENAI_API_BASE=https://dashscope.aliyuncs.com/compatible-mode/v1 # 仅通义千问需要 + +# DeepSeek +export DEEPSEEK_API_KEY=xxx + +# Claude +export ANTHROPIC_API_KEY=xxx + +# Ollama（本地，无需 key，但需启动服务） +ollama serve # 启动 Ollama 服务 +# 默认端点：http://localhost:11434 +``` + +### 依赖安装 + +```bash +# 安装 litellm（支持 OpenAI/DeepSeek/Qwen/Ollama） +pip install litellm + +# 安装 Claude 支持 +pip install anthropic + +# 安装本地文件处理依赖 +pip install ".[localfile]" +``` + +### 模型选择建议 + +| 使用场景 | 推荐模型 | 优点 | +|---------|----------|------| +| 免费使用 | `gemini-flash-latest` | Google 免费层，但有限流 | +| 国内环境 | `deepseek/deepseek-chat` | 国内可用，性价比高 | +| 高质量 | `claude-sonnet-4` | 推理能力强 | +| 离线使用 | `ollama/qwen2.5:7b` | 完全离线，无需 API key | +| 中文优化 | `openai/qwen-plus` | 通义千问，中文理解好 | + ## 示例 每个示例将一个**配方**（`samples//`，包含种子 URL 和确切的 `enrich` 命令）与配方生成的**生产的捆绑包**（`bundles//`）配对。打开配方以重现；打开捆绑包以直接浏览结果： diff --git a/okf/src/reference_agent/cli.py b/okf/src/reference_agent/cli.py index dc30d39..7ecf1c0 100644 --- a/okf/src/reference_agent/cli.py +++ b/okf/src/reference_agent/cli.py @@ -12,7 +12,7 @@ from reference_agent.sources.bigquery import BigQuerySource from reference_agent.sources.localfile import LocalFileSource -_SOURCES = ("bq", "localfile") +_SOURCES = ("bq", "localfile", "api") def _build_source(name: str, args: argparse.Namespace): @@ -30,6 +30,15 @@ def _build_source(name: str, args: argparse.Namespace): pattern=args.local_pattern or "**/*", recursive=not args.local_no_recursive, ) + if name == "api": + from reference_agent.sources.api_source import ApiSource + return ApiSource( + urls=getattr(args, "api_urls", None), + api_endpoint=getattr(args, "api_endpoint", None), + api_url_field=getattr(args, "api_url_field", "url") or "url", + url_file=getattr(args, "api_url_file", None), + auth_token=getattr(args, "api_token", None), + ) raise SystemExit(f"Unknown source: {name}") @@ -96,6 +105,27 @@ def _parser() -> argparse.ArgumentParser: action="store_true", help="Disable recursive directory scan for localfile source.", ) + # API source parameters + enrich.add_argument( + "--api-url", action="append", default=None, dest="api_urls", + help="File URL to fetch (for --source api). Repeatable.", + ) + enrich.add_argument( + "--api-endpoint", default=None, + help="API endpoint returning JSON file list (for --source api).", + ) + enrich.add_argument( + "--api-url-field", default="url", + help="JSON field name for file URL in API response (default: url).", + ) + enrich.add_argument( + "--api-url-file", default=None, + help="Path to a text file with one URL per line (for --source api).", + ) + enrich.add_argument( + "--api-token", default=None, + help="Bearer token for API auth (or set API_AUTH_TOKEN env).", + ) enrich.add_argument( "--out", required=True, type=Path, help="Bundle root directory." ) @@ -193,13 +223,25 @@ def _parser() -> argparse.ArgumentParser: lf.add_argument( "--model", default=DEFAULT_MODEL, - help=f"Gemini model id (default: {DEFAULT_MODEL}).", + help=f"LLM model id (default: {DEFAULT_MODEL}). " + "Supports: gemini-flash-latest, claude-sonnet-4, openai/gpt-4o, " + "deepseek/deepseek-chat, ollama/qwen2.5:7b. " + "Use 'reference-agent list-models' to see all presets.", ) lf.add_argument( "--no-recursive", action="store_true", help="Disable recursive directory scan.", ) + lf.add_argument( + "--api-url", action="append", default=None, dest="api_urls", + help="Also fetch file(s) from this URL. Repeatable. " + "Mixes remote URLs with local files.", + ) + lf.add_argument( + "--api-token", default=None, + help="Bearer token for remote URL auth (or set API_AUTH_TOKEN env).", + ) lf.add_argument( "--concept", action="append", @@ -224,6 +266,11 @@ def _parser() -> argparse.ArgumentParser: "--name", default=None, help="Display name for the bundle (default: bundle directory name).", ) + + sub.add_parser( + "list-models", + help="List supported LLM model presets.", + ) return p @@ -239,6 +286,11 @@ def main(argv: list[str] | None = None) -> int: for noisy in ("google", "google_genai", "google_adk", "urllib3", "httpx"): logging.getLogger(noisy).setLevel(logging.WARNING) + if args.command == "list-models": + from reference_agent.llm_support import list_supported_models + print(list_supported_models()) + return 0 + if args.command == "visualize": from reference_agent.viewer import generate_visualization out = args.out or (args.bundle / "viz.html") @@ -281,11 +333,54 @@ def main(argv: list[str] | None = None) -> int: if args.command == "localfile": # Shortcut path: local files only, no web pass, sensible defaults. - source = LocalFileSource( - path=str(args.path), - pattern=args.pattern, - recursive=not args.no_recursive, - ) + # If --api-url is given, mix remote files via a composite source. + api_urls = getattr(args, "api_urls", None) + if api_urls: + from reference_agent.sources.api_source import ApiSource + api_src = ApiSource( + urls=api_urls, + auth_token=getattr(args, "api_token", None), + ) + # Composite: combine local + remote concepts + local_src = LocalFileSource( + path=str(args.path), + pattern=args.pattern, + recursive=not args.no_recursive, + ) + api_src.list_concepts() # trigger download + local_src.list_concepts() + # Merge: create a simple wrapper + from reference_agent.sources.base import Source as _Source + class _CompositeSource(_Source): + name = "composite" + def list_concepts(self): + return local_src.list_concepts() + api_src.list_concepts() + def read_concept(self, ref): + for src in (local_src, api_src): + found = src.find(ref.id) + if found: + return src.read_concept(found) + raise ValueError(f"Unknown concept: {ref.id_str}") + def sample_rows(self, ref, n=5): + for src in (local_src, api_src): + found = src.find(ref.id) + if found: + return src.sample_rows(found, n) + return None + source = _CompositeSource() + else: + source = LocalFileSource( + path=str(args.path), + pattern=args.pattern, + recursive=not args.no_recursive, + ) + # Check model environment variables before running + from reference_agent.llm_support import check_model_env, get_model_help + missing = check_model_env(args.model) + if missing: + print(f"Warning: missing env vars for {args.model}: {', '.join(missing)}", + file=sys.stderr) + print(get_model_help(args.model), file=sys.stderr) runner = ReferenceRunner( source=source, bundle_root=args.out, diff --git a/okf/src/reference_agent/llm_support.py b/okf/src/reference_agent/llm_support.py new file mode 100644 index 0000000..6f89391 --- /dev/null +++ b/okf/src/reference_agent/llm_support.py @@ -0,0 +1,189 @@ +""" +LLM 扩展支持 — 让 reference-agent 支持本地和其他 LLM + +Google ADK 原生支持多种 LLM（通过模型注册表），无需改原码： + - Google Gemini: gemini-flash-latest, gemini-2.0-flash + - Anthropic Claude: claude-sonnet-4, claude-3.5-haiku + - OpenAI: openai/gpt-4o, openai/gpt-4o-mini + - DeepSeek: deepseek/deepseek-chat + - 通义千问: openai/qwen-plus (OpenAI 兼容接口) + - Ollama 本地: ollama/qwen2.5:7b, ollama/llama3.2 + +使用方式（通过 --model 参数指定，无需改原码）： + + # Gemini（默认） + reference-agent localfile /path --model gemini-flash-latest + + # Claude + reference-agent localfile /path --model claude-sonnet-4 + + # OpenAI + reference-agent localfile /path --model openai/gpt-4o + + # DeepSeek（国内可用） + reference-agent localfile /path --model deepseek/deepseek-chat + + # 通义千问（OpenAI 兼容接口） + reference-agent localfile /path --model openai/qwen-plus + + # Ollama 本地模型（完全离线） + reference-agent localfile /path --model ollama/qwen2.5:7b + +环境变量配置： + # Gemini（默认） + export GEMINI_API_KEY=xxx + + # OpenAI / 通义千问（OpenAI 兼容） + export OPENAI_API_KEY=xxx + # 通义千问需额外设置 base_url： + export OPENAI_API_BASE=https://dashscope.aliyuncs.com/compatible-mode/v1 + + # DeepSeek + export DEEPSEEK_API_KEY=xxx + + # Claude + export ANTHROPIC_API_KEY=xxx + + # Ollama（本地，无需 key） + # 先启动 ollama: ollama serve + # 环境变量设置 Ollama 端点（默认 http://localhost:11434） + +安装依赖： + pip install litellm # OpenAI/DeepSeek/Qwen 等所有 OpenAI 兼容接口 + pip install anthropic # Claude + # Ollama 无需额外 Python 依赖，通过 litellm 的 ollama/ 前缀调用 + +设计原则：不改原码。ADK 的 LLMRegistry 会根据 --model 参数自动路由到对应的 LLM 后端。 +""" + +from __future__ import annotations + +import os +from typing import Any + + +# ============================================================ +# 模型预设 — 常用模型的环境变量和安装提示 +# ============================================================ + +MODEL_PRESETS: dict[str, dict[str, Any]] = { + # Google Gemini（默认，原项目支持） + "gemini-flash-latest": { + "provider": "google", + "env_vars": ["GEMINI_API_KEY"], + "install": "", + "note": "Google Gemini，默认模型", + }, + "gemini-2.0-flash": { + "provider": "google", + "env_vars": ["GEMINI_API_KEY"], + "install": "", + "note": "Google Gemini 2.0 Flash", + }, + + # Anthropic Claude + "claude-sonnet-4": { + "provider": "anthropic", + "env_vars": ["ANTHROPIC_API_KEY"], + "install": "pip install anthropic", + "note": "Anthropic Claude Sonnet 4", + }, + "claude-3.5-haiku": { + "provider": "anthropic", + "env_vars": ["ANTHROPIC_API_KEY"], + "install": "pip install anthropic", + "note": "Anthropic Claude 3.5 Haiku（快）", + }, + + # OpenAI + "openai/gpt-4o": { + "provider": "openai", + "env_vars": ["OPENAI_API_KEY"], + "install": "pip install litellm", + "note": "OpenAI GPT-4o", + }, + "openai/gpt-4o-mini": { + "provider": "openai", + "env_vars": ["OPENAI_API_KEY"], + "install": "pip install litellm", + "note": "OpenAI GPT-4o-mini（便宜）", + }, + + # DeepSeek（国内可用，性价比高） + "deepseek/deepseek-chat": { + "provider": "deepseek", + "env_vars": ["DEEPSEEK_API_KEY"], + "install": "pip install litellm", + "note": "DeepSeek Chat（国内可用，性价比高）", + }, + "deepseek/deepseek-reasoner": { + "provider": "deepseek", + "env_vars": ["DEEPSEEK_API_KEY"], + "install": "pip install litellm", + "note": "DeepSeek R1 推理模型", + }, + + # 通义千问（OpenAI 兼容接口） + "openai/qwen-plus": { + "provider": "qwen", + "env_vars": ["OPENAI_API_KEY", "OPENAI_API_BASE"], + "install": "pip install litellm", + "note": "通义千问，需设置 OPENAI_API_BASE=https://dashscope.aliyuncs.com/compatible-mode/v1", + }, + + # Ollama 本地模型（完全离线） + "ollama/qwen2.5:7b": { + "provider": "ollama", + "env_vars": [], + "install": "pip install litellm", + "note": "Ollama 本地 Qwen2.5 7B（离线，无需 API Key）", + }, + "ollama/llama3.2": { + "provider": "ollama", + "env_vars": [], + "install": "pip install litellm", + "note": "Ollama 本地 Llama 3.2（离线）", + }, +} + + +def check_model_env(model: str) -> list[str]: + """检查模型所需的环境变量是否已设置，返回缺失列表""" + preset = MODEL_PRESETS.get(model) + if not preset: + # 未知模型，不做检查 + return [] + missing = [] + for var in preset.get("env_vars", []): + if not os.environ.get(var): + missing.append(var) + return missing + + +def get_model_help(model: str) -> str: + """获取模型的帮助信息""" + preset = MODEL_PRESETS.get(model) + if not preset: + return f"未知模型: {model}。ADK 支持的格式: gemini-*, claude-*, openai/*, deepseek/*, ollama/*" + lines = [f"模型: {model}", f" 说明: {preset['note']}"] + if preset["install"]: + lines.append(f" 安装: {preset['install']}") + if preset["env_vars"]: + lines.append(f" 环境变量: {', '.join(preset['env_vars'])}") + return "\n".join(lines) + + +def list_supported_models() -> str: + """列出所有预设模型""" + lines = ["支持的 LLM 模型预设：", ""] + current_provider = "" + for model, preset in MODEL_PRESETS.items(): + if preset["provider"] != current_provider: + current_provider = preset["provider"] + lines.append(f" [{current_provider}]") + env = ", ".join(preset["env_vars"]) if preset["env_vars"] else "无需" + lines.append(f" {model:40s} {preset['note']}") + lines.append("") + lines.append("使用: reference-agent localfile /path --model ") + lines.append("提示: 也支持任意 LiteLLM 兼容模型，格式为 provider/model-name") + return "\n".join(lines) diff --git a/okf/src/reference_agent/sources/api_source.py b/okf/src/reference_agent/sources/api_source.py new file mode 100644 index 0000000..00c0ca1 --- /dev/null +++ b/okf/src/reference_agent/sources/api_source.py @@ -0,0 +1,227 @@ +""" +API Source - 从 HTTP/API 接口拉取文件并生成 OKF 知识库 + +支持的 API 类型： + - REST API（返回 JSON，含文件 URL 列表） + - 直接文件 URL 列表 + - 飞书/钉钉/企业微信等云文档 API（需自行适配认证） + +用法： + reference-agent localfile --pattern "api" -o ./bundle + # 或通过 enrich + python -m reference_agent enrich --source api ... + +设计原则：不改原码，仅继承 Source 抽象基类。 +""" + +from __future__ import annotations + +import json +import os +import re +import urllib.request +import urllib.error +from pathlib import Path +from typing import Any +from tempfile import mkdtemp + +from reference_agent.sources.base import ConceptRef, Source + + +# 文件扩展名 → 概念类型（复用 localfile 的映射） +_FILE_TYPE_MAP = { + ".md": "Document", + ".markdown": "Document", + ".txt": "Document", + ".pdf": "PDF Document", + ".docx": "Word Document", + ".xlsx": "Excel Spreadsheet", + ".pptx": "PowerPoint Presentation", + ".py": "Python Module", + ".ts": "TypeScript Module", + ".js": "JavaScript Module", + ".json": "Config File", + ".yaml": "Config File", + ".yml": "Config File", + ".html": "HTML Document", + ".csv": "Data File", +} + + +def _get_file_type(url: str) -> str: + """从 URL 路径推断文件类型""" + path = url.split("?")[0].split("#")[0] + suffix = Path(path).suffix.lower() + return _FILE_TYPE_MAP.get(suffix, "File") + + +def _sanitize_name(url: str) -> str: + """从 URL 生成合法的 concept ID 段""" + path = url.split("?")[0].split("#")[0] + name = Path(path).stem + # 替换非法字符 + name = re.sub(r"[^\w.\-]", "_", name) + return name or "remote_file" + + +class ApiSource(Source): + """从 HTTP API / URL 列表拉取文件的 Source + + 三种模式： + 1. 单文件 URL：直接下载 + ApiSource(urls=["https://example.com/doc.pdf"]) + 2. API 端点：返回 JSON，含文件 URL 列表 + ApiSource(api_endpoint="https://api.example.com/files", + api_url_field="download_url") + 3. 文件 URL 列表文件：每行一个 URL + ApiSource(url_file="urls.txt") + """ + + name = "api" + + def __init__( + self, + urls: list[str] | None = None, + api_endpoint: str | None = None, + api_url_field: str = "url", + api_headers: dict[str, str] | None = None, + url_file: str | None = None, + auth_token: str | None = None, + download_dir: str | None = None, + ): + self._urls: list[str] = list(urls or []) + self.api_endpoint = api_endpoint + self.api_url_field = api_url_field + self.api_headers = api_headers or {} + self.auth_token = auth_token or os.environ.get("API_AUTH_TOKEN") + self.download_dir = Path(download_dir or mkdtemp(prefix="okf_api_")) + self.download_dir.mkdir(parents=True, exist_ok=True) + self._concepts_cache: list[ConceptRef] | None = None + + # 从 URL 列表文件加载 + if url_file: + text = Path(url_file).read_text(encoding="utf-8") + for line in text.splitlines(): + line = line.split("#", 1)[0].strip() + if line: + self._urls.append(line) + + def _fetch_urls_from_api(self) -> list[str]: + """从 API 端点获取文件 URL 列表""" + if not self.api_endpoint: + return [] + + req = urllib.request.Request(self.api_endpoint) + for k, v in self.api_headers.items(): + req.add_header(k, v) + if self.auth_token: + req.add_header("Authorization", f"Bearer {self.auth_token}") + + with urllib.request.urlopen(req) as resp: + data = json.loads(resp.read().decode("utf-8")) + + # 支持两种 JSON 结构： + # 1. {"items": [{"url": "...", "name": "..."}, ...]} + # 2. ["url1", "url2", ...] + if isinstance(data, list): + return [u for u in data if isinstance(u, str)] + + items = data.get("items") or data.get("data") or data.get("files") or [] + urls = [] + for item in items: + if isinstance(item, str): + urls.append(item) + elif isinstance(item, dict): + url = item.get(self.api_url_field) or item.get("url") or item.get("download_url") + if url: + urls.append(url) + return urls + + def _download(self, url: str) -> Path: + """下载文件到本地临时目录""" + name = _sanitize_name(url) + ext = Path(url.split("?")[0]).suffix or ".txt" + local_path = self.download_dir / f"{name}{ext}" + + if local_path.exists(): + return local_path + + req = urllib.request.Request(url) + if self.auth_token: + req.add_header("Authorization", f"Bearer {self.auth_token}") + + with urllib.request.urlopen(req) as resp: + local_path.write_bytes(resp.read()) + return local_path + + def _get_all_urls(self) -> list[str]: + """获取所有要处理的文件 URL""" + urls = list(self._urls) + if self.api_endpoint: + urls.extend(self._fetch_urls_from_api()) + # 去重保序 + seen: set[str] = set() + unique = [] + for u in urls: + if u not in seen: + seen.add(u) + unique.append(u) + return unique + + def list_concepts(self) -> list[ConceptRef]: + if self._concepts_cache is not None: + return self._concepts_cache + + concepts: list[ConceptRef] = [] + for url in self._get_all_urls(): + name = _sanitize_name(url) + concept_type = _get_file_type(url) + concepts.append(ConceptRef( + id=(name,), + type=concept_type, + resource=url, + hint={ + "url": url, + "file_name": name + (Path(url.split("?")[0]).suffix or ".txt"), + }, + )) + + self._concepts_cache = concepts + return concepts + + def read_concept(self, ref: ConceptRef) -> dict[str, Any]: + url = ref.hint.get("url") or ref.resource or "" + if not url: + raise ValueError(f"No URL for concept: {ref.id_str}") + + local_path = self._download(url) + + # 读取内容（复用 localfile 的读取逻辑） + from reference_agent.sources.localfile import LocalFileSource + temp_source = LocalFileSource(path=str(self.download_dir)) + temp_ref = temp_source.find((local_path.stem,)) + if temp_ref is None: + # 文件可能刚下载，清除缓存重试 + temp_source._concepts_cache = None + temp_ref = temp_source.find((local_path.stem,)) + + if temp_ref: + data = temp_source.read_concept(temp_ref) + data["source_url"] = url + return data + + # 回退：直接读取文本 + try: + content = local_path.read_text(encoding="utf-8") + except UnicodeDecodeError: + content = f"[Binary file from {url}]" + + return { + "name": ref.id_str, + "type": ref.type, + "file_name": local_path.name, + "file_path": str(local_path), + "source_url": url, + "content": content, + "size_bytes": local_path.stat().st_size, + } From b4a3d8cf8a471d190e681709cf320d6c38fd372c Mon Sep 17 00:00:00 2001 From: lang Date: Mon, 22 Jun 2026 23:15:39 +0800 Subject: [PATCH 6/6] =?UTF-8?q?docs:=20=E6=B7=BB=E5=8A=A0=E7=A4=BE?= =?UTF-8?q?=E5=8C=BA=E6=89=A9=E5=B1=95=E5=8A=9F=E8=83=BD=E8=AF=B4=E6=98=8E?= =?UTF-8?q?=E8=87=B3README?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 48 ++++++++++++++++++++++++++++++++++++++++++++++++ README.zh.md | 49 +++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 97 insertions(+) diff --git a/README.md b/README.md index 8aaa96a..7eb3297 100644 --- a/README.md +++ b/README.md @@ -11,6 +11,54 @@ This repository features tools, agents, and samples that demonstrate Knowledge C [![Open in Cloud Shell](http://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/editor?cloudshell_git_repo=https%3A%2F%2Fgithub.com%2FGoogleCloudPlatform%2Fknowledge-catalog.git) +--- + +## ✨ Community Extensions (Fork Features) + +This fork extends the original `reference-agent` beyond BigQuery into a **general-purpose OKF knowledge base generator** — from local files, remote APIs, and with multi-LLM support. All changes are backward compatible; the original `enrich` command is untouched. + +### What's New + +| Feature | Description | +|---------|-------------| +| 📁 **Local file source** | Generate OKF bundles from 16 file formats: PDF, Word, Excel, PPT, Markdown, code, config, HTML, CSV | +| 🌐 **Remote API source** | Fetch files from URLs, API endpoints, or URL list files (`--source api`) | +| 🤖 **Multi-LLM support** | Gemini, Claude, OpenAI, DeepSeek, Qwen, Ollama — pick any via `--model` | +| 🇨🇳 **Chinese support** | Unicode filenames + automatic language matching (source is Chinese → output is Chinese) | +| ⚡ **`localfile` shortcut** | One-command workflow: `reference-agent localfile /path --pattern "**/*.pdf"` | + +### Quick Start + +```bash +# Install +cd okf +pip install --user -e ".[localfile]" +pip install litellm # multi-LLM support + +# Generate a knowledge base from local PDFs (default: Gemini) +reference-agent localfile ~/Documents --pattern "**/*.pdf" + +# Use DeepSeek (works in China, cost-effective) +export DEEPSEEK_API_KEY=xxx +reference-agent localfile ~/Documents --model deepseek/deepseek-chat + +# Mix local files + remote URLs +reference-agent localfile ~/docs --api-url https://example.com/remote.pdf + +# View supported LLM models +reference-agent list-models + +# Generate interactive HTML graph +reference-agent visualize --bundle ./okf-bundle +``` + +### Documentation + +- 📖 **[Development Guide](./README.dev.md)** — Full feature docs, architecture, and examples (中文) +- 📋 **[Implementation Summary](./IMPLEMENTATION_SUMMARY.md)** — Complete change log and design decisions +- 🇨🇳 **[中文说明](./okf/README.zh.md)** — OKF module usage in Chinese + +--- ## Contributing diff --git a/README.zh.md b/README.zh.md index ecfe3c7..465b3e0 100644 --- a/README.zh.md +++ b/README.zh.md @@ -12,6 +12,55 @@ [![在 Cloud Shell 中打开](http://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/editor?cloudshell_git_repo=https%3A%2F%2Fgithub.com%2FGoogleCloudPlatform%2Fknowledge-catalog.git) +--- + +## ✨ 功能扩展（Fork 特性） + +本 Fork 将原 `reference-agent` 从仅支持 BigQuery 扩展为**通用 OKF 知识库生成器** —— 支持本地文件、远程 API，并支持多种 LLM。所有改动向后兼容，原 `enrich` 命令不受影响。 + +### 新增功能 + +| 功能 | 说明 | +|------|------| +| 📁 **本地文件源** | 从 16 种文件格式生成 OKF 知识包：PDF、Word、Excel、PPT、Markdown、代码、配置、HTML、CSV | +| 🌐 **远程 API 源** | 从 URL、API 端点或 URL 列表文件获取文件（`--source api`） | +| 🤖 **多 LLM 支持** | Gemini、Claude、OpenAI、DeepSeek、通义千问、Ollama —— 通过 `--model` 指定 | +| 🇨🇳 **中文支持** | Unicode 文件名 + 语言自动匹配（源是中文 → 输出中文） | +| ⚡ **`localfile` 快捷命令** | 一行命令完成：`reference-agent localfile /path --pattern "**/*.pdf"` | + +### 快速上手 + +```bash +# 安装 +cd okf +pip install --user -e ".[localfile]" +pip install litellm # 多 LLM 支持 + +# 从本地 PDF 生成知识库（默认：Gemini） +reference-agent localfile ~/Documents --pattern "**/*.pdf" + +# 使用 DeepSeek（国内可用，性价比高） +export DEEPSEEK_API_KEY=xxx +reference-agent localfile ~/Documents --model deepseek/deepseek-chat + +# 混合本地文件 + 远程 URL +reference-agent localfile ~/docs --api-url https://example.com/remote.pdf + +# 查看支持的 LLM 模型 +reference-agent list-models + +# 生成交互式 HTML 图谱 +reference-agent visualize --bundle ./okf-bundle +``` + +### 文档 + +- 📖 **[开发说明](./README.dev.md)** — 完整功能文档、架构和示例 +- 📋 **[实现总结](./IMPLEMENTATION_SUMMARY.md)** — 完整改动日志和设计决策 +- 🇨🇳 **[OKF 中文说明](./okf/README.zh.md)** — OKF 模块中文使用说明 + +--- + ## 贡献 查看贡献[说明](CONTRIBUTING.md)以开始贡献。