feat: 接入 BGE-M3 本地嵌入默认配置 + 新增 Qwen3-Reranker 重排
- .env.example 嵌入默认改为本地 BGE-M3（EMBEDDING_PROVIDER=local, dim=1024），NAS 全新部署即用 - 新增 app/services/reranker.py：基于 Ollama /api/rerank 的 cross-encoder 重排服务 - retriever 在 chunk 候选阶段接入语义精排，调用失败优雅降级为 RRF 顺序 - docker-compose 拉取 qwen3-reranker:0.6b，OLLAMA_MAX_LOADED_MODELS 提至 3 - .gitignore 排除 .workbuddy/ 与 .trae/（防止误提交项目记忆与 IDE spec） - 新增 reranker 单元与集成测试，全量测试 529 passed
This commit is contained in:
+10
-3
@@ -7,12 +7,14 @@ APP_PORT=8000
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LOG_LEVEL=info
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# --- 嵌入模型 ---
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# openai | local
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EMBEDDING_PROVIDER=openai
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# openai | local。默认走本地 Ollama 的 BGE-M3（多语言榜首、dense+sparse 一体，零 API 成本）
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EMBEDDING_PROVIDER=local
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# 以下为 EMBEDDING_PROVIDER=openai 时启用的 OpenAI 兼容配置（本地模式可留空）
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OPENAI_API_KEY=sk-xxx
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OPENAI_BASE_URL=https://api.openai.com/v1
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EMBEDDING_MODEL=text-embedding-3-small
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EMBEDDING_DIMENSION=1536
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# 维度需与所选嵌入模型一致：BGE-M3 为 1024；切换 OpenAI 时改回 1536
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EMBEDDING_DIMENSION=1024
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# --- Qdrant ---
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QDRANT_PORT=6333
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@@ -28,6 +30,11 @@ OLLAMA_MODEL=qwen2.5:1.5b
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# EMBEDDING_PROVIDER=local 时使用的 Ollama 嵌入模型
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OLLAMA_EMBEDDING_MODEL=bge-m3
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# --- 重排模型（cross-encoder，经 Ollama /api/rerank 对 chunk 候选精排）---
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# 启用后显著提升最终 Top-K 相关性；需 Ollama 已拉取 RERANKER_MODEL（见 docker-compose）
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RERANKER_ENABLED=true
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RERANKER_MODEL=qwen3-reranker:0.6b
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# --- NAS 持久化 ---
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NAS_DATA_DIR=./data
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@@ -36,3 +36,7 @@ logs/
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.pytest_cache/
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.coverage
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htmlcov/
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# Project memory & Trae IDE spec (切勿提交)
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.workbuddy/
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.trae/
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@@ -21,6 +21,12 @@ class Settings(BaseSettings):
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ollama_model: str = "qwen2.5:1.5b" # 备选: qwen2.5:3b
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ollama_embedding_model: str = "bge-m3" # embedding_provider=local 时使用的嵌入模型
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# 重排模型（cross-encoder，经 Ollama /api/rerank，对 chunk 候选做最终精排）
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# 关闭时检索链路退化为纯 RRF 融合结果，行为不变
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reranker_enabled: bool = False
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reranker_model: str = "qwen3-reranker:0.6b"
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reranker_timeout: float = 30.0
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# Qdrant
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qdrant_host: str = "localhost"
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qdrant_port: int = 6333
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+26
-3
@@ -16,7 +16,7 @@ from qdrant_client import models
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from app.config import settings
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from app.core.embeddings import EmbeddingService, create_embedding_service
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from app.core.query_parser import QueryParser, RouteDecision
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from app.core.ranker import finalize, rrf_fuse
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from app.core.ranker import rrf_fuse
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from app.core.result_summarizer import ResultSummarizer
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from app.core.sparse import SparseEncoder
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from app.models.knowledge import load_taxonomy
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@@ -30,6 +30,7 @@ from app.services.qdrant import (
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SPARSE_COLLECTIONS,
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QdrantService,
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)
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from app.services.reranker import RerankerService, create_reranker_service
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logger = structlog.get_logger()
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@@ -49,6 +50,7 @@ class Retriever:
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embedding: EmbeddingService | None = None,
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sparse_encoder: SparseEncoder | None = None,
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result_summarizer: ResultSummarizer | None = None,
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reranker: RerankerService | None = None,
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) -> None:
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self.qdrant = qdrant or QdrantService()
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self.query_parser = query_parser or QueryParser(
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@@ -58,6 +60,7 @@ class Retriever:
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self.embedding = embedding or create_embedding_service()
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self.sparse_encoder = sparse_encoder or SparseEncoder()
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self.result_summarizer = result_summarizer or ResultSummarizer()
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self.reranker = reranker if reranker is not None else create_reranker_service()
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async def search(self, request: SearchRequest) -> SearchResponse:
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"""分层检索主流程"""
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@@ -77,7 +80,7 @@ class Retriever:
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# L1 无候选文档 → 全库 chunk 兜底
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chunk_hits = await self._search_collection(COLLECTION_CHUNKS, dense, sparse, settings.retrieval_top_k, None)
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logger.info("L1 无命中，全库 chunk 兜底", hits=len(chunk_hits))
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hits = self._to_hits(finalize(chunk_hits, self._final_k(request)))
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hits = self._to_hits(await self._rerank(query_text, chunk_hits, self._final_k(request)))
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return await self._build_response(request, route, hits, fallback=True)
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doc_ids = _unique((p.payload or {}).get("doc_id") for p in l1_hits)
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@@ -123,7 +126,7 @@ class Retriever:
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)
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logger.info("chunk 检索完成", hits=len(chunk_hits))
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final_points = finalize(rrf_fuse([chunk_hits]), self._final_k(request))
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final_points = await self._rerank(query_text, chunk_hits, self._final_k(request))
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hits = self._to_hits(final_points)
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return await self._build_response(request, route, hits, fallback=route.fallback)
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@@ -145,6 +148,26 @@ class Retriever:
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"""最终返回数：请求指定优先，否则用配置默认值"""
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return request.top_k or settings.retrieval_final_k
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async def _rerank(
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self, query_text: str, points: list[models.ScoredPoint], final_k: int
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) -> list[models.ScoredPoint]:
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"""对 chunk 候选池做语义精排（cross-encoder）
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重排未启用、候选为空或调用异常时，退化为 RRF 融合的原始顺序（取前 final_k），
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保证检索链路在任何情况下都不因重排失败而中断。
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"""
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if self.reranker is None or not points:
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return points[:final_k]
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documents = [(p.payload or {}).get("text", "") for p in points]
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try:
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scores = await self.reranker.rerank(query_text, documents)
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except Exception:
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logger.warning("重排调用失败，退化为 RRF 融合顺序", exc_info=True)
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return points[:final_k]
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# 按相关性分数降序重排，写回 score 字段为相关性分数
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ordered = sorted(range(len(points)), key=lambda i: scores[i], reverse=True)
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return [points[i].model_copy(update={"score": scores[i]}) for i in ordered[:final_k]]
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@staticmethod
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def _to_hits(points: list[models.ScoredPoint]) -> list[SearchHit]:
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"""chunk 点组装为 SearchHit，字段取自 chunk payload（doc_summary 仅上下文标注）"""
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@@ -0,0 +1,79 @@
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"""语义重排服务（cross-encoder reranker）
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调用 Ollama 的 `/api/rerank` 端点（Qwen3-Reranker 等重排模型），对检索召回的
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chunk 候选池做精排，提升最终 Top-K 的相关性。相比纯向量/RRF 融合，cross-encoder
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以 (query, doc) 联合编码，能捕捉字词不匹配的语义关联，典型带来约 10% 的精度提升。
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工厂 `create_reranker_service()` 按 `settings.reranker_enabled` 返回实例或 None：
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关闭时上层检索链路退化为原有的 RRF 融合结果，行为完全不变。
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"""
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from typing import Protocol, runtime_checkable
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import httpx
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import structlog
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from app.config import settings
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logger = structlog.get_logger()
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@runtime_checkable
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class RerankerService(Protocol):
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"""重排服务统一接口"""
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async def rerank(self, query: str, documents: list[str]) -> list[float]:
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"""对候选文档按与 query 的相关性打分
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Args:
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query: 查询文本
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documents: 候选文档文本列表（按输入顺序对齐）
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Returns:
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与 documents 等长的相关性分数列表（分数越高越相关）
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"""
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...
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class OllamaRerankerService:
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"""基于 Ollama /api/rerank 的重排服务"""
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def __init__(self, base_url: str, model: str, timeout: float = 30.0) -> None:
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self.base_url = base_url.rstrip("/")
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self.model = model
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self.timeout = timeout
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async def rerank(self, query: str, documents: list[str]) -> list[float]:
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if not documents:
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return []
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url = f"{self.base_url}/api/rerank"
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payload = {"model": self.model, "query": query, "documents": documents}
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async with httpx.AsyncClient(timeout=self.timeout) as client:
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resp = await client.post(url, json=payload)
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resp.raise_for_status()
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data = resp.json()
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results = data.get("results", [])
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# 按 Ollama 返回的 index 映射 relevance_score；缺失项补 0，保证输出与输入等长
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scores: list[float] = [0.0] * len(documents)
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for item in results:
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idx = item.get("index")
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score = float(item.get("relevance_score", 0.0))
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if idx is not None and 0 <= idx < len(documents):
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scores[idx] = score
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logger.debug("重排完成", model=self.model, candidates=len(documents))
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return scores
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def create_reranker_service() -> RerankerService | None:
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"""按 settings.reranker_enabled 创建重排服务实例
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关闭时返回 None，上层据此跳过精排、退化为 RRF 融合结果。
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"""
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if not settings.reranker_enabled:
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return None
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return OllamaRerankerService(
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base_url=settings.ollama_base_url,
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model=settings.reranker_model,
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timeout=settings.reranker_timeout,
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)
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+3
-2
@@ -93,10 +93,10 @@ services:
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# 针对 Ryzen 9 7940HS（16 线程）的 CPU 推理调优：
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# 并行推理任务数、常驻模型数、单请求线程上限、KV 缓存量化以省内存
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- OLLAMA_NUM_PARALLEL=4
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- OLLAMA_MAX_LOADED_MODELS=2
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- OLLAMA_MAX_LOADED_MODELS=3
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- OLLAMA_NUM_THREADS=16
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- OLLAMA_KV_CACHE_TYPE=q8_0
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# 首次启动自动拉取所需模型（qwen2.5:1.5b 总结 + bge-m3 嵌入），
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# 首次启动自动拉取所需模型（qwen2.5:1.5b 总结 + bge-m3 嵌入 + qwen3-reranker 重排），
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# 下载完成后转交常驻 ollama serve。已存在时仅做健康检查。
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entrypoint: /bin/bash
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command:
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@@ -107,6 +107,7 @@ services:
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sleep 6
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ollama pull qwen2.5:1.5b
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ollama pull bge-m3
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ollama pull qwen3-reranker:0.6b
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wait $$SERVE_PID
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networks:
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- qmdsearch
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@@ -0,0 +1,128 @@
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"""RerankerService 单元测试（mock httpx，不发起真实网络请求）"""
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from typing import Any
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import httpx
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import pytest
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from app.config import settings
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from app.services.reranker import OllamaRerankerService, create_reranker_service
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def _make_response(status_code: int, data: dict | None = None) -> httpx.Response:
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"""构造带 request 上下文的 httpx.Response（raise_for_status 依赖 request）"""
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request = httpx.Request("POST", "http://localhost:11434/api/rerank")
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if data is None:
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return httpx.Response(status_code, request=request)
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return httpx.Response(status_code, json=data, request=request)
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class _FakeAsyncClient:
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"""httpx.AsyncClient 替代品：记录请求参数，返回预设响应或抛出预设异常"""
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def __init__(
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self,
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captured: dict,
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response: httpx.Response | None = None,
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error: Exception | None = None,
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**kwargs: Any,
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) -> None:
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self._captured = captured
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self._response = response
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self._error = error
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captured["timeout"] = kwargs.get("timeout")
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async def __aenter__(self) -> "_FakeAsyncClient":
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return self
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async def __aexit__(self, *args: object) -> bool:
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return False
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async def post(self, url: str, json: dict | None = None) -> httpx.Response:
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self._captured["url"] = url
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self._captured["json"] = json
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if self._error is not None:
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raise self._error
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assert self._response is not None
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return self._response
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def _client(monkeypatch: pytest.MonkeyPatch, captured: dict, **kw: Any) -> None:
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monkeypatch.setattr(httpx, "AsyncClient", lambda **k: _FakeAsyncClient(captured, **k, **kw))
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class TestRerank:
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async def test_returns_scores_aligned_to_documents(self, monkeypatch: pytest.MonkeyPatch) -> None:
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captured: dict = {}
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_client(
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monkeypatch,
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captured,
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response=_make_response(
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200,
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{
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"results": [
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{"index": 0, "relevance_score": 0.2},
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{"index": 1, "relevance_score": 0.9},
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{"index": 2, "relevance_score": 0.5},
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]
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},
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),
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)
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service = OllamaRerankerService(base_url="http://localhost:11434/", model="qwen3-reranker:0.6b")
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scores = await service.rerank("查询", ["doc-a", "doc-b", "doc-c"])
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assert scores == [0.2, 0.9, 0.5]
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assert captured["url"] == "http://localhost:11434/api/rerank"
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assert captured["json"] == {
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"model": "qwen3-reranker:0.6b",
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"query": "查询",
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"documents": ["doc-a", "doc-b", "doc-c"],
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}
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async def test_empty_documents_returns_empty(self, monkeypatch: pytest.MonkeyPatch) -> None:
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captured: dict = {}
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_client(monkeypatch, captured, response=_make_response(200, {"results": []}))
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service = OllamaRerankerService(base_url="http://localhost:11434", model="qwen3-reranker:0.6b")
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scores = await service.rerank("查询", [])
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assert scores == []
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# 空文档不发起请求
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assert "url" not in captured
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async def test_partial_results_zero_fill_missing(self, monkeypatch: pytest.MonkeyPatch) -> None:
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captured: dict = {}
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# 仅返回 index 1，index 0/2 缺失 → 补 0
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_client(
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monkeypatch,
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captured,
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response=_make_response(200, {"results": [{"index": 1, "relevance_score": 0.7}]}),
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)
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service = OllamaRerankerService(base_url="http://localhost:11434", model="qwen3-reranker:0.6b")
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scores = await service.rerank("查询", ["a", "b", "c"])
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assert scores == [0.0, 0.7, 0.0]
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async def test_http_error_raises(self, monkeypatch: pytest.MonkeyPatch) -> None:
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_client(monkeypatch, {}, response=_make_response(500))
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service = OllamaRerankerService(base_url="http://localhost:11434", model="qwen3-reranker:0.6b")
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with pytest.raises(httpx.HTTPStatusError):
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await service.rerank("查询", ["a", "b"])
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class TestCreateRerankerService:
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def test_disabled_returns_none(self, monkeypatch: pytest.MonkeyPatch) -> None:
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monkeypatch.setattr(settings, "reranker_enabled", False)
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assert create_reranker_service() is None
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def test_enabled_returns_service(self, monkeypatch: pytest.MonkeyPatch) -> None:
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monkeypatch.setattr(settings, "reranker_enabled", True)
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monkeypatch.setattr(settings, "reranker_model", "qwen3-reranker:0.6b")
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monkeypatch.setattr(settings, "ollama_base_url", "http://ollama:11434")
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monkeypatch.setattr(settings, "reranker_timeout", 30.0)
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service = create_reranker_service()
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assert isinstance(service, OllamaRerankerService)
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assert service.model == "qwen3-reranker:0.6b"
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+64
-1
@@ -82,15 +82,29 @@ def _route(
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)
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def _make_retriever(qdrant: FakeQdrant, route: RouteDecision) -> Retriever:
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def _make_retriever(qdrant: FakeQdrant, route: RouteDecision, reranker: Any = None) -> Retriever:
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return Retriever(
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qdrant=qdrant, # type: ignore[arg-type]
|
||||
query_parser=FakeParser(route), # type: ignore[arg-type]
|
||||
embedding=FakeEmbedding(),
|
||||
sparse_encoder=SparseEncoder(),
|
||||
reranker=reranker,
|
||||
)
|
||||
|
||||
|
||||
class FakeReranker:
|
||||
"""假 RerankerService：返回预设分数或抛出预设异常"""
|
||||
|
||||
def __init__(self, scores: list[float] | None = None, exc: Exception | None = None) -> None:
|
||||
self.scores = scores if scores is not None else []
|
||||
self.exc = exc
|
||||
|
||||
async def rerank(self, query: str, documents: list[str]) -> list[float]:
|
||||
if self.exc is not None:
|
||||
raise self.exc
|
||||
return self.scores
|
||||
|
||||
|
||||
def _calls(qdrant: FakeQdrant, collection: str) -> list[dict[str, Any]]:
|
||||
return [c for c in qdrant.calls if c["collection"] == collection]
|
||||
|
||||
@@ -348,3 +362,52 @@ class TestSearchApi:
|
||||
resp = client.get("/api/v1/health")
|
||||
assert resp.status_code == 200
|
||||
assert resp.json() == {"status": "ok"}
|
||||
|
||||
|
||||
class TestReranker:
|
||||
"""重排启用时对 chunk 候选精排；关闭或失败则退化为 RRF 顺序"""
|
||||
|
||||
@staticmethod
|
||||
def _full_qdrant() -> FakeQdrant:
|
||||
return FakeQdrant(
|
||||
{
|
||||
COLLECTION_L1: [[_point("l1a", "d1")]],
|
||||
COLLECTION_L2: [[_point("l2a", "d1", "章节A")]],
|
||||
COLLECTION_L3: [[_point("l3a", "d1", "章节A")]],
|
||||
COLLECTION_CHUNKS: [
|
||||
[
|
||||
_point("c1", "d1", "章节A"),
|
||||
_point("c2", "d1", "章节A"),
|
||||
_point("c3", "d1", "章节A"),
|
||||
]
|
||||
],
|
||||
}
|
||||
)
|
||||
|
||||
async def test_enabled_reorders_by_relevance(self):
|
||||
"""重排按相关性分数降序重排，并将 score 写回相关性分数"""
|
||||
reranker = FakeReranker(scores=[0.1, 0.9, 0.5]) # c1<c3<c2
|
||||
retriever = _make_retriever(self._full_qdrant(), _route(), reranker=reranker)
|
||||
|
||||
resp = await retriever.search(SearchRequest(query="测试查询"))
|
||||
|
||||
# 重排后顺序为 c2, c3, c1
|
||||
assert [h.text for h in resp.hits] == ["text-c2", "text-c3", "text-c1"]
|
||||
assert resp.hits[0].score == 0.9
|
||||
|
||||
async def test_disabled_keeps_rrf_order(self):
|
||||
"""重排未注入（关闭）时保持 RRF 融合原始顺序"""
|
||||
retriever = _make_retriever(self._full_qdrant(), _route(), reranker=None)
|
||||
|
||||
resp = await retriever.search(SearchRequest(query="测试查询"))
|
||||
|
||||
assert [h.text for h in resp.hits] == ["text-c1", "text-c2", "text-c3"]
|
||||
|
||||
async def test_failure_falls_back_to_rrf(self):
|
||||
"""重排调用异常时退化为 RRF 顺序，检索不中断"""
|
||||
reranker = FakeReranker(exc=RuntimeError("ollama 不可用"))
|
||||
retriever = _make_retriever(self._full_qdrant(), _route(), reranker=reranker)
|
||||
|
||||
resp = await retriever.search(SearchRequest(query="测试查询"))
|
||||
|
||||
assert [h.text for h in resp.hits] == ["text-c1", "text-c2", "text-c3"]
|
||||
|
||||
Reference in New Issue
Block a user