256 lines
9.0 KiB
Python
256 lines
9.0 KiB
Python
"""双索引检索引擎 — embedding/keywords/blend 三模式。
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- embedding: pgvector 语义检索（LlamaIndex PGVectorStore.aquery）
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- keywords: PG 全文检索（jieba 分词 + tsquery_rank）
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- blend: 并行执行两种检索，按分数归一化后合并去重排序
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参考 LlamaIndex hybrid retriever 模式（VectorStoreRetriever + 全文检索）。
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ACL 过滤由调用方在传入 `kb_ids` 前完成（见 acl.filter_kb_by_user_acl）。
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"""
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from __future__ import annotations
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import asyncio
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import logging
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from typing import TYPE_CHECKING
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from sqlalchemy import text
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from agentkit.rag_platform.fulltext import KB_CHUNKS_TABLE, build_tsquery
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from agentkit.rag_platform.models import QueryMode, QueryResult
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if TYPE_CHECKING:
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from llama_index.core.embeddings import BaseEmbedding
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from llama_index.core.vector_stores.types import VectorStoreQuery
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from llama_index.vector_stores.postgres import PGVectorStore
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logger = logging.getLogger(__name__)
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# ponytail: 默认归一化策略 — min-max，将每种检索的分数映射到 [0,1]
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# 升级路径：若需更精细的融合权重，可引入 RRF (Reciprocal Rank Fusion) 或学习权重
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_BLEND_WEIGHT_EMBEDDING = 0.6
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_BLEND_WEIGHT_KEYWORDS = 0.4
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def _normalize_scores(scores: list[float]) -> list[float]:
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"""min-max 归一化分数到 [0, 1]。
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空列表返回空。常数列表（含单元素）返回全 1.0 — 所有结果等价于最高相关度。
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"""
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if not scores:
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return []
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lo, hi = min(scores), max(scores)
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if hi - lo < 1e-9:
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return [1.0 for _ in scores]
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return [(s - lo) / (hi - lo) for s in scores]
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class RetrievalEngine:
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"""双索引检索引擎 — embedding/keywords/blend 三模式。
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Args:
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vector_store: LlamaIndex PGVectorStore 实例（embedding 模式使用）
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session_factory: SQLAlchemy async session factory（keywords 模式使用）
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embed_model: LlamaIndex BaseEmbedding（embedding 模式使用）
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"""
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def __init__(
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self,
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vector_store: "PGVectorStore",
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session_factory,
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embed_model: "BaseEmbedding | None" = None,
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) -> None:
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self._vector_store = vector_store
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self._sf = session_factory
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self._embed_model = embed_model
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async def retrieve(
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self,
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query: str,
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kb_ids: list[str],
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mode: QueryMode,
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top_k: int = 5,
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) -> list[QueryResult]:
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"""检索入口 — 根据 mode 分发到具体实现。
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Args:
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query: 查询文本
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kb_ids: 限定检索的知识库 ID 列表（已通过 ACL 过滤）
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mode: 检索模式
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top_k: 返回结果数
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Returns:
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QueryResult 列表（按分数降序）。无结果时返回空列表。
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"""
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if not kb_ids:
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return []
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if mode == QueryMode.embedding:
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return await self._retrieve_embedding(query, kb_ids, top_k)
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elif mode == QueryMode.keywords:
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return await self._retrieve_keywords(query, kb_ids, top_k)
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elif mode == QueryMode.blend:
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return await self._retrieve_blend(query, kb_ids, top_k)
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else: # pragma: no cover — 枚举已穷尽
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raise ValueError(f"Unsupported query mode: {mode}")
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async def _retrieve_embedding(
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self,
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query: str,
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kb_ids: list[str],
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top_k: int,
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) -> list[QueryResult]:
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"""pgvector 语义检索。
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使用 LlamaIndex vector_store.aquery 执行向量检索，结果按 metadata.kb_id
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过滤到 kb_ids 子集。
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"""
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if self._embed_model is None:
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raise ValueError("embed_model is required for embedding retrieval mode")
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from llama_index.core.vector_stores.types import VectorStoreQuery
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query_embedding = await self._embed_model.aget_text_embedding(query)
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vs_query: VectorStoreQuery = VectorStoreQuery(
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query_embedding=query_embedding,
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similarity_top_k=top_k,
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)
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result = await self._vector_store.aquery(vs_query)
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kb_set = set(kb_ids)
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out: list[QueryResult] = []
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for node, score in zip(result.nodes, result.similarities):
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meta = node.metadata or {}
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node_kb = meta.get("kb_id", "")
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if node_kb not in kb_set:
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continue
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out.append(
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QueryResult(
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chunk_id=node.node_id,
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content=node.get_content(),
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score=float(score) if score is not None else 0.0,
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metadata=meta,
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document_id=meta.get("document_id", ""),
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kb_id=node_kb,
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)
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)
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if len(out) >= top_k:
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break
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return out
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async def _retrieve_keywords(
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self,
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query: str,
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kb_ids: list[str],
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top_k: int,
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) -> list[QueryResult]:
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"""PG 全文检索（jieba 分词 + tsquery）。
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使用 `ts_rank(search_vector, tsquery)` 排序，按 kb_id 过滤。
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kb_id 存储在 metadata_ JSON 列中（LlamaIndex PGVectorStore schema），
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用 `metadata_->>'kb_id'` 提取。
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"""
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tsquery = build_tsquery(query)
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if not tsquery:
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return []
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# ponytail: 用 ANY(%s) 传 kb_ids 列表，避免字符串拼接
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# 升级路径：若 kb_ids 数量超过 PG 参数限制（32k），需分批查询
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# 列名参考 LlamaIndex PGVectorStore 默认 schema：
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# id / embedding / text / metadata_ (JSON) / document_id / search_vector
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sql = text(
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f"""
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SELECT id, text, metadata_, document_id,
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ts_rank(search_vector, to_tsquery('simple', :tsquery)) AS score
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FROM {KB_CHUNKS_TABLE}
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WHERE search_vector @@ to_tsquery('simple', :tsquery)
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AND metadata_->>'kb_id' = ANY(:kb_ids)
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ORDER BY score DESC
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LIMIT :top_k
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""" # noqa: S608 — 表名为常量
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)
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async with self._sf() as db:
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result = await db.execute(
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sql,
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{
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"tsquery": tsquery,
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"kb_ids": list(kb_ids),
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"top_k": top_k,
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},
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)
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rows = result.all()
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out: list[QueryResult] = []
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for row in rows:
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chunk_id, content, metadata, document_id, score = row
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meta: dict[str, object] = metadata if isinstance(metadata, dict) else {}
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kb_id = meta.get("kb_id", "")
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out.append(
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QueryResult(
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chunk_id=str(chunk_id),
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content=content or "",
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score=float(score) if score is not None else 0.0,
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metadata=meta,
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document_id=str(document_id) if document_id is not None else "",
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kb_id=str(kb_id) if kb_id is not None else "",
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)
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)
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return out
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async def _retrieve_blend(
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self,
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query: str,
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kb_ids: list[str],
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top_k: int,
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) -> list[QueryResult]:
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"""双索引合并 — 语义 + 全文结果去重排序。
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并行执行两种检索（各取 top_k），按 chunk_id 去重，分数归一化后加权融合。
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若任一检索失败（如 embed_model 未配置），降级为另一种。
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"""
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# 并行执行两种检索
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embed_task = self._safe_retrieve_embedding(query, kb_ids, top_k)
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kw_task = self._retrieve_keywords(query, kb_ids, top_k)
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embed_results, kw_results = await asyncio.gather(
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embed_task, kw_task, return_exceptions=False
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)
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# 归一化分数
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embed_scores = _normalize_scores([r.score for r in embed_results])
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for r, s in zip(embed_results, embed_scores):
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r.score = s * _BLEND_WEIGHT_EMBEDDING
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kw_scores = _normalize_scores([r.score for r in kw_results])
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for r, s in zip(kw_results, kw_scores):
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r.score = s * _BLEND_WEIGHT_KEYWORDS
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# 合并去重 — 同 chunk_id 取最高分
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merged: dict[str, QueryResult] = {}
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for r in (*embed_results, *kw_results):
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existing = merged.get(r.chunk_id)
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if existing is None or r.score > existing.score:
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merged[r.chunk_id] = r
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# 按分数降序，取 top_k
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results = sorted(merged.values(), key=lambda x: x.score, reverse=True)
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return results[:top_k]
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async def _safe_retrieve_embedding(
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self,
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query: str,
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kb_ids: list[str],
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top_k: int,
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) -> list[QueryResult]:
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"""embedding 检索的容错包装 — 失败时返回空列表（降级为纯关键词）。"""
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if self._embed_model is None:
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return []
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try:
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return await self._retrieve_embedding(query, kb_ids, top_k)
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except Exception as e:
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logger.warning("Embedding retrieval failed, falling back to keywords only: %s", e)
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return []
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__all__ = ["RetrievalEngine"]
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