From 95b170809407464c9a80b84a614f099d4e9529d1 Mon Sep 17 00:00:00 2001 From: Yara Date: Wed, 8 Jul 2026 22:53:14 +0300 Subject: [PATCH 1/3] Use RAGU MixSearchEngine for backend search --- back/.env.example | 4 + back/README.md | 9 + back/openapi.json | 4 +- back/pyproject.toml | 2 + back/src/ragu_web_api/schemas/agent.py | 2 +- back/src/ragu_web_api/schemas/datasets.py | 2 +- .../ragu_web_api/services/index_repository.py | 279 +- .../ragu_web_api/services/mock_repository.py | 2 +- back/uv.lock | 3263 ++++++++++++++++- front/api/openapi_ragu_web.json | 2 +- front/chat/chat.view.ts | 2 +- 11 files changed, 3541 insertions(+), 30 deletions(-) diff --git a/back/.env.example b/back/.env.example index 680d568..7dd3dbf 100644 --- a/back/.env.example +++ b/back/.env.example @@ -4,3 +4,7 @@ YANDEX_FOLDER_ID=your-folder-id YANDEX_API_KEY=your-api-key YANDEX_LLM_MODEL=yandexgpt-5-pro YANDEX_BASE_URL=https://ai.api.cloud.yandex.net/v1 + +LOCAL_EMBEDDER_URL=http://localhost:8001/v1 +EMBEDDER_API_KEY=unused +EMBEDDER_MODEL_NAME=intfloat/multilingual-e5-large diff --git a/back/README.md b/back/README.md index a4b5ef7..7fc5100 100644 --- a/back/README.md +++ b/back/README.md @@ -42,12 +42,21 @@ export YANDEX_FOLDER_ID=... export YANDEX_API_KEY=... export YANDEX_LLM_MODEL=yandexgpt-5-pro export YANDEX_BASE_URL=https://ai.api.cloud.yandex.net/v1 + +export LOCAL_EMBEDDER_URL=http://localhost:8001/v1 +export EMBEDDER_API_KEY=unused +export EMBEDDER_MODEL_NAME=intfloat/multilingual-e5-large ``` The LLM variables are optional. If they are missing, `/agent/messages` still returns graph retrieval results with a clear fallback message instead of calling an external model. +Search uses RAGU `MixSearchEngine` over the selected prebuilt index when +`EMBEDDER_MODEL_NAME` and an OpenAI-compatible embedding endpoint are configured. +The endpoint can be provided through `LOCAL_EMBEDDER_URL` or `EMBEDDER_BASE_URL`; +without it the API falls back to the lightweight local keyword retrieval. + ## API All application endpoints are under `/api/v1`. diff --git a/back/openapi.json b/back/openapi.json index d6d54df..0f39a1e 100644 --- a/back/openapi.json +++ b/back/openapi.json @@ -691,7 +691,7 @@ "query_plan" ], "title": "Engine", - "default": "local" + "default": "mix" }, "top_k": { "type": "integer", @@ -1162,7 +1162,7 @@ "query_plan" ], "title": "Default Engine", - "default": "local" + "default": "mix" }, "available_engines": { "items": { diff --git a/back/pyproject.toml b/back/pyproject.toml index 86accc3..47c84cf 100644 --- a/back/pyproject.toml +++ b/back/pyproject.toml @@ -9,8 +9,10 @@ description = "FastAPI gateway contract for the RAGU web demo" requires-python = ">=3.10" dependencies = [ "fastapi>=0.115.0", + "graph-ragu>=0.0.3", "openai>=2.32.0", "pydantic>=2.8.0", + "ragu>=0.1.56", "uvicorn[standard]>=0.30.0", ] diff --git a/back/src/ragu_web_api/schemas/agent.py b/back/src/ragu_web_api/schemas/agent.py index ae557bb..14933a8 100644 --- a/back/src/ragu_web_api/schemas/agent.py +++ b/back/src/ragu_web_api/schemas/agent.py @@ -16,7 +16,7 @@ class ChatMessage(APIModel): class AgentRequest(APIModel): message: str = Field(min_length=1, examples=["Who wrote the Norwegian anthem?"]) history: list[ChatMessage] = Field(default_factory=list) - engine: SearchEngine = "local" + engine: SearchEngine = "mix" top_k: int = Field(default=8, ge=1, le=50) rerank: bool = True include_trace: bool = True diff --git a/back/src/ragu_web_api/schemas/datasets.py b/back/src/ragu_web_api/schemas/datasets.py index b22c0ce..1197590 100644 --- a/back/src/ragu_web_api/schemas/datasets.py +++ b/back/src/ragu_web_api/schemas/datasets.py @@ -43,7 +43,7 @@ class DatasetCard(APIModel): class DatasetDetail(DatasetCard): - default_engine: SearchEngine = "local" + default_engine: SearchEngine = "mix" available_engines: list[SearchEngine] = Field( default_factory=lambda: ["local", "global", "naive", "mix", "query_plan"] ) diff --git a/back/src/ragu_web_api/services/index_repository.py b/back/src/ragu_web_api/services/index_repository.py index efc0aa0..665bd6c 100644 --- a/back/src/ragu_web_api/services/index_repository.py +++ b/back/src/ragu_web_api/services/index_repository.py @@ -1,6 +1,7 @@ from __future__ import annotations import json +import logging import math import os import re @@ -54,6 +55,7 @@ REQUIRED_INDEX_FILES = ("knowledge_graph.gml", "kv_chunks.json") TOKEN_RE = re.compile(r"[A-Za-zА-Яа-яЁё0-9_]{3,}") +LOGGER = logging.getLogger(__name__) @dataclass(frozen=True) @@ -76,6 +78,18 @@ class RetrievalResult: communities: list[tuple[CommunitySummary, float]] +@dataclass(frozen=True) +class RaguEmbedderConfig: + api_key: str | None + base_url: str | None + model_name: str | None + provider: str + + @property + def is_configured(self) -> bool: + return bool(self.model_name and (self.api_key or self.base_url)) + + @dataclass class LoadedIndex: definition: IndexDefinition @@ -139,6 +153,90 @@ async def complete(self, messages: list[dict[str, str]]) -> str | None: return response.choices[0].message.content or "" +class RaguMixSearchAdapter: + def __init__(self, config: RaguEmbedderConfig) -> None: + self.config = config + self._engines: dict[str, Any] = {} + + async def search(self, index: LoadedIndex, query: str, top_k: int) -> RetrievalResult | None: + if not self.config.is_configured: + return None + if not _is_ragu_vector_index(index.definition.path): + return None + + engine = self._engines.get(index.definition.id) + if engine is None: + engine = self._build_engine(index.definition) + self._engines[index.definition.id] = engine + + result = await engine.a_search(query, top_k=top_k) + return _retrieval_from_ragu_mix(index, result, top_k) + + def _build_engine(self, definition: IndexDefinition) -> Any: + try: + from ragu import KnowledgeGraph, LocalSearchEngine, MixSearchEngine, NaiveSearchEngine, Settings + from ragu.graph.index import StorageArguments + from ragu.models.embedder import EmbedderOpenAI + from ragu.models.openai import CachedAsyncOpenAI + except Exception as exc: + raise RuntimeError(f"RAGU search package is unavailable: {exc}") from exc + + embedding_dim = _ragu_embedding_dim(definition.path) + if embedding_dim is None: + raise RuntimeError(f"RAGU vector index at '{definition.path}' has no embedding_dim.") + + Settings.storage_folder = str(definition.path) + Settings.language = _ragu_language(definition.language) + + client = CachedAsyncOpenAI( + base_url=self.config.base_url, + api_key=self.config.api_key or "unused", + rate_max_simultaneous=4, + rate_max_per_minute=240, + retry_times_sec=None, + embed_timeout=30.0, + ) + embedder = EmbedderOpenAI( + client=client, + model_name=self.config.model_name, + dim=embedding_dim, + batch_size=32, + max_concurrent_batches=2, + ) + + knowledge_graph = KnowledgeGraph( + llm=_RaguSearchOnlyLLM(), + embedder=embedder, + storage_settings=StorageArguments(), + language=Settings.language, + ) + engines = [ + LocalSearchEngine( + llm=_RaguSearchOnlyLLM(), + knowledge_graph=knowledge_graph, + embedder=embedder, + language=Settings.language, + ), + NaiveSearchEngine( + llm=_RaguSearchOnlyLLM(), + knowledge_graph=knowledge_graph, + embedder=embedder, + language=Settings.language, + ), + ] + return MixSearchEngine( + llm=_RaguSearchOnlyLLM(), + engines=engines, + allow_partial_failures=True, + language=Settings.language, + ) + + +class _RaguSearchOnlyLLM: + async def chat_completion(self, *_args: Any, **_kwargs: Any) -> str: + raise RuntimeError("RAGU LLM generation is disabled in the web API search adapter.") + + class IndexRepository: def __init__( self, @@ -150,6 +248,7 @@ def __init__( self._definitions = self._discover_indexes() self._loaded: dict[str, LoadedIndex] = {} self._llm = OpenAICompatibleLLM(llm_config or _llm_config_from_env(env)) + self._ragu_search = RaguMixSearchAdapter(_ragu_embedder_config_from_env(env)) def list_datasets(self, locale: Locale = "ru") -> list[DatasetCard]: return [self._dataset_card(item, locale) for item in self._definitions.values()] @@ -159,7 +258,7 @@ def get_dataset(self, dataset_id: str, locale: Locale = "ru") -> DatasetDetail: dataset = self._dataset_card(definition, locale) return DatasetDetail( **dataset.model_dump(), - default_engine="naive", + default_engine="mix", available_engines=["naive", "local", "global", "mix", "query_plan"], created_at=definition.created_at, updated_at=definition.updated_at, @@ -301,7 +400,7 @@ async def answer(self, dataset_id: str, request: AgentRequest) -> AgentResponse: index = self._load_index(dataset_id) retrieval_start = time.perf_counter() - retrieval = self._retrieve(index, request.message, request.top_k) + retrieval = await self._retrieve_with_ragu(index, request) retrieval_ms = int((time.perf_counter() - retrieval_start) * 1000) generation_start = time.perf_counter() @@ -605,6 +704,20 @@ def _node_relation(self, edge: GraphEdge, direction: str, other_node: GraphNode) other_node_label=other_node.label, ) + async def _retrieve_with_ragu(self, index: LoadedIndex, request: AgentRequest) -> RetrievalResult: + try: + retrieval = await self._ragu_search.search(index, request.message, request.top_k) + except Exception as exc: + LOGGER.warning( + "RAGU MixSearchEngine failed for dataset '%s'; falling back to local retrieval: %s", + index.definition.id, + exc, + ) + retrieval = None + if retrieval is None or not (retrieval.nodes or retrieval.chunks): + return self._retrieve(index, request.message, request.top_k) + return retrieval + def _retrieve(self, index: LoadedIndex, query: str, top_k: int) -> RetrievalResult: terms = _query_terms(query) chunk_scores = _rank_items( @@ -856,6 +969,161 @@ def _llm_config_from_env(env: dict[str, str]) -> LLMConfig: ) +def _ragu_embedder_config_from_env(env: dict[str, str]) -> RaguEmbedderConfig: + base_url = ( + env.get("EMBEDDER_BASE_URL") + or env.get("LOCAL_EMBEDDER_URL") + or env.get("OPENAI_BASE_URL") + or env.get("LLM_BASE_URL") + ) + api_key = ( + env.get("EMBEDDER_API_KEY") + or env.get("OPENAI_API_KEY") + or env.get("LLM_API_KEY") + ) + model_name = ( + env.get("EMBEDDER_MODEL_NAME") + or env.get("OPENAI_EMBEDDING_MODEL") + or env.get("OPENAI_EMBEDDER_MODEL") + or env.get("LLM_EMBEDDER_MODEL") + ) + return RaguEmbedderConfig( + api_key=api_key, + base_url=base_url, + model_name=model_name, + provider="OpenAI-compatible embeddings" if model_name else "not configured", + ) + + +def _is_ragu_vector_index(path: Path) -> bool: + return all( + (path / filename).exists() + for filename in ("knowledge_graph.gml", "kv_chunks.json", "vdb_entity.json", "vdb_chunk.json") + ) + + +def _ragu_embedding_dim(path: Path) -> int | None: + for filename in ("vdb_entity.json", "vdb_chunk.json"): + try: + value = _read_json_object(path / filename).get("embedding_dim") + except FileNotFoundError: + continue + dim = _safe_int(value, 0) + if dim > 0: + return dim + return None + + +def _ragu_language(language: str) -> str: + return "russian" if language == "ru" else "english" + + +def _retrieval_from_ragu_mix(index: LoadedIndex, ragu_retrieve: Any, top_k: int) -> RetrievalResult: + edge_by_id = {edge.id: edge for edge in index.edges} + nodes_by_id: dict[str, tuple[GraphNode, float]] = {} + edges_by_id: dict[str, tuple[GraphEdge, float]] = {} + chunks_by_id: dict[str, tuple[ProvenanceChunk, float]] = {} + communities_by_id: dict[str, tuple[CommunitySummary, float]] = {} + + for child_retrieve in _ragu_child_retrieves(ragu_retrieve): + child_result = getattr(child_retrieve, "result", None) + if child_result is None: + continue + + entity_scores = _ragu_metric_scores(child_retrieve, "entities", "relevance_score") + for entity in getattr(child_result, "entities", []) or []: + node_id = str(getattr(entity, "id", "")) + node = index.node_by_id.get(node_id) + if node is not None: + _put_scored(nodes_by_id, node.id, node, entity_scores.get(node_id, 1.0)) + + for relation in getattr(child_result, "relations", []) or []: + edge_id = str(getattr(relation, "id", "")) + edge = edge_by_id.get(edge_id) + if edge is not None: + _put_scored(edges_by_id, edge.id, edge, _safe_float(getattr(relation, "relation_strength", 1.0), 1.0)) + + chunk_scores = _ragu_metric_scores(child_retrieve, "chunks", "score") + result_chunks = getattr(child_result, "chunks", []) or [] + result_scores = list(getattr(child_result, "scores", []) or []) + for idx, chunk in enumerate(result_chunks): + chunk_id = str(getattr(chunk, "id", "")) + provenance_chunk = index.chunk_by_id.get(chunk_id) + if provenance_chunk is None: + continue + score = chunk_scores.get(chunk_id) + if score is None and idx < len(result_scores): + score = _safe_float(result_scores[idx], 1.0) + _put_scored(chunks_by_id, provenance_chunk.id, provenance_chunk, score or 1.0) + + for summary in getattr(child_result, "summaries", []) or []: + community_id = str(getattr(summary, "id", "")) + community = index.community_by_id.get(community_id) + if community is not None: + _put_scored(communities_by_id, community.id, community, 1.0) + + for chunk, score in list(chunks_by_id.values())[:top_k]: + for node in index.nodes_by_chunk.get(chunk.id, [])[:top_k]: + _put_scored(nodes_by_id, node.id, node, score * 0.9) + for edge in index.edges_by_chunk.get(chunk.id, [])[:top_k]: + _put_scored(edges_by_id, edge.id, edge, score * 0.9) + + if not communities_by_id: + for node, score in nodes_by_id.values(): + if node.community_id and node.community_id in index.community_by_id: + community = index.community_by_id[node.community_id] + _put_scored(communities_by_id, community.id, community, score) + + return RetrievalResult( + nodes=sorted( + nodes_by_id.values(), + key=lambda item: (-item[1], -item[0].degree, item[0].label.casefold()), + )[:top_k], + edges=sorted( + edges_by_id.values(), + key=lambda item: (-item[1], -item[0].strength, item[0].id), + )[:top_k], + chunks=sorted( + chunks_by_id.values(), + key=lambda item: (-item[1], item[0].id), + )[:top_k], + communities=sorted( + communities_by_id.values(), + key=lambda item: (-item[1], -item[0].size, item[0].id), + )[:top_k], + ) + + +def _ragu_child_retrieves(ragu_retrieve: Any) -> list[Any]: + result = getattr(ragu_retrieve, "result", None) + children = getattr(result, "results", None) + if isinstance(children, list): + return children + return [ragu_retrieve] + + +def _ragu_metric_scores(retrieve: Any, key: str, score_key: str) -> dict[str, float]: + metrics = getattr(retrieve, "metrics", {}) or {} + items = metrics.get(key, []) if isinstance(metrics, dict) else [] + scores: dict[str, float] = {} + if not isinstance(items, list): + return scores + for item in items: + if not isinstance(item, dict): + continue + item_id = item.get("id") + if item_id is None: + continue + scores[str(item_id)] = _safe_float(item.get(score_key), 1.0) + return scores + + +def _put_scored(target: dict[str, tuple[Any, float]], key: str, item: Any, score: float) -> None: + current = target.get(key) + if current is None or score > current[1]: + target[key] = (item, score) + + def _index_candidates(root: Path) -> list[Path]: if _is_index_dir(root): return [root] @@ -1041,6 +1309,13 @@ def _safe_int(value: Any, default: int = 0) -> int: return default +def _safe_float(value: Any, default: float = 0.0) -> float: + try: + return float(value) + except (TypeError, ValueError): + return default + + def _as_string_list(value: Any) -> list[str]: if value is None: return [] diff --git 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4e4fd09..b098afa 100644 --- a/.github/workflows/deploy.yml +++ b/.github/workflows/deploy.yml @@ -19,10 +19,25 @@ jobs: runs-on: ubuntu-latest steps: - - uses: hyoo-ru/mam_build@master2 + - name: Checkout MAM + uses: actions/checkout@v4 with: - package: "raggu/web" - modules: 'front/app' + repository: hyoo-ru/mam + ref: master + + - name: Checkout package + uses: actions/checkout@v4 + with: + path: raggu/web + + - name: Install MAM dependencies + run: | + npm install --omit=optional + # MAM calls convertCompilerOptionsFromJson; keep TypeScript on a version that exposes it. + npm install --save-exact --omit=optional typescript@6.0.3 + + - name: Build front app + run: npm start raggu/web/front/app - name: Deploy main if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/master' From 4a57e4fa54627963f3098c1e8b657ece82062721 Mon Sep 17 00:00:00 2001 From: Yara Date: Thu, 9 Jul 2026 09:11:58 +0300 Subject: [PATCH 3/3] docs: document environment variables --- back/.env.example | 60 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 60 insertions(+) diff --git a/back/.env.example b/back/.env.example index 7dd3dbf..5d5efc5 100644 --- a/back/.env.example +++ b/back/.env.example @@ -1,10 +1,70 @@ +# RAGU indexes +# Path to prebuilt RAGU indexes. Use either a directory with many indexes or one index directory. RAGU_INDEXES_DIR=../../RAGU/indexes +# YandexGPT LLM +# Used for agent answers when YANDEX_API_KEY and YANDEX_FOLDER_ID are set. +# Takes priority over the OpenAI-compatible LLM fallback below. + +# Yandex Cloud folder ID that owns the YandexGPT model. YANDEX_FOLDER_ID=your-folder-id + +# Yandex Cloud API key with access to Foundation Models. YANDEX_API_KEY=your-api-key + +# YandexGPT model name. Plain names are expanded to gpt:///. +# A full gpt://... model URI can be used here too. YANDEX_LLM_MODEL=yandexgpt-5-pro + +# OpenAI-compatible Yandex Foundation Models endpoint. YANDEX_BASE_URL=https://ai.api.cloud.yandex.net/v1 +# OpenAI-compatible LLM fallback +# Used only when YandexGPT is not configured. Works with OpenAI, compatible gateways, +# or a local OpenAI-compatible chat endpoint. + +# LLM API key. Prefer OPENAI_API_KEY; use LLM_API_KEY for non-OpenAI providers. +OPENAI_API_KEY= + +# Alternative LLM API key name for OpenAI-compatible providers. +LLM_API_KEY= + +# Chat model name. Prefer LLM_MODEL_NAME for provider-neutral configs. +LLM_MODEL_NAME= + +# Alternative chat model name for OpenAI-style configs, for example gpt-4.1-mini. +OPENAI_MODEL= + +# Base URL for the OpenAI-compatible LLM API, for example https://api.openai.com/v1. +OPENAI_BASE_URL= + +# Alternative provider-neutral base URL for the OpenAI-compatible LLM API. +LLM_BASE_URL= + +# Optional OpenAI project ID. Leave empty when the provider does not use projects. +OPENAI_PROJECT= + +# Embeddings / vector search +# Search uses RAGU MixSearchEngine when EMBEDDER_MODEL_NAME and an OpenAI-compatible +# embedding endpoint are configured. Otherwise it falls back to local keyword search. + +# Local OpenAI-compatible embedding endpoint. LOCAL_EMBEDDER_URL=http://localhost:8001/v1 + +# Explicit embedding endpoint. Takes priority over LOCAL_EMBEDDER_URL. +EMBEDDER_BASE_URL= + +# Embedding API key. Use "unused" for local endpoints that ignore API keys. EMBEDDER_API_KEY=unused + +# Embedding model served by the configured endpoint. EMBEDDER_MODEL_NAME=intfloat/multilingual-e5-large + +# Alternative embedding model name for OpenAI configs, for example text-embedding-3-large. +OPENAI_EMBEDDING_MODEL= + +# Alternative embedding model name used by some OpenAI-compatible configs. +OPENAI_EMBEDDER_MODEL= + +# Provider-neutral fallback embedding model name. +LLM_EMBEDDER_MODEL=