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

app/core/retriever.py

@@ -16,7 +16,7 @@ from qdrant_client import models
 from app.config import settings
 from app.core.embeddings import EmbeddingService, create_embedding_service
 from app.core.query_parser import QueryParser, RouteDecision
-from app.core.ranker import finalize, rrf_fuse
+from app.core.ranker import rrf_fuse
 from app.core.result_summarizer import ResultSummarizer
 from app.core.sparse import SparseEncoder
 from app.models.knowledge import load_taxonomy
@@ -30,6 +30,7 @@ from app.services.qdrant import (
     SPARSE_COLLECTIONS,
     QdrantService,
 )
+from app.services.reranker import RerankerService, create_reranker_service
 
 logger = structlog.get_logger()
 
@@ -49,6 +50,7 @@ class Retriever:
         embedding: EmbeddingService | None = None,
         sparse_encoder: SparseEncoder | None = None,
         result_summarizer: ResultSummarizer | None = None,
+        reranker: RerankerService | None = None,
     ) -> None:
         self.qdrant = qdrant or QdrantService()
         self.query_parser = query_parser or QueryParser(
@@ -58,6 +60,7 @@ class Retriever:
         self.embedding = embedding or create_embedding_service()
         self.sparse_encoder = sparse_encoder or SparseEncoder()
         self.result_summarizer = result_summarizer or ResultSummarizer()
+        self.reranker = reranker if reranker is not None else create_reranker_service()
 
     async def search(self, request: SearchRequest) -> SearchResponse:
         """分层检索主流程"""
@@ -77,7 +80,7 @@ class Retriever:
             # L1 无候选文档 → 全库 chunk 兜底
             chunk_hits = await self._search_collection(COLLECTION_CHUNKS, dense, sparse, settings.retrieval_top_k, None)
             logger.info("L1 无命中，全库 chunk 兜底", hits=len(chunk_hits))
-            hits = self._to_hits(finalize(chunk_hits, self._final_k(request)))
+            hits = self._to_hits(await self._rerank(query_text, chunk_hits, self._final_k(request)))
             return await self._build_response(request, route, hits, fallback=True)
 
         doc_ids = _unique((p.payload or {}).get("doc_id") for p in l1_hits)
@@ -123,7 +126,7 @@ class Retriever:
         )
         logger.info("chunk 检索完成", hits=len(chunk_hits))
 
-        final_points = finalize(rrf_fuse([chunk_hits]), self._final_k(request))
+        final_points = await self._rerank(query_text, chunk_hits, self._final_k(request))
         hits = self._to_hits(final_points)
         return await self._build_response(request, route, hits, fallback=route.fallback)
 
@@ -145,6 +148,26 @@ class Retriever:
         """最终返回数：请求指定优先，否则用配置默认值"""
         return request.top_k or settings.retrieval_final_k
 
+    async def _rerank(
+        self, query_text: str, points: list[models.ScoredPoint], final_k: int
+    ) -> list[models.ScoredPoint]:
+        """对 chunk 候选池做语义精排（cross-encoder）
+
+        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），
+        保证检索链路在任何情况下都不因重排失败而中断。
+        """
+        if self.reranker is None or not points:
+            return points[:final_k]
+        documents = [(p.payload or {}).get("text", "") for p in points]
+        try:
+            scores = await self.reranker.rerank(query_text, documents)
+        except Exception:
+            logger.warning("重排调用失败，退化为 RRF 融合顺序", exc_info=True)
+            return points[:final_k]
+        # 按相关性分数降序重排，写回 score 字段为相关性分数
+        ordered = sorted(range(len(points)), key=lambda i: scores[i], reverse=True)
+        return [points[i].model_copy(update={"score": scores[i]}) for i in ordered[:final_k]]
+
     @staticmethod
     def _to_hits(points: list[models.ScoredPoint]) -> list[SearchHit]:
         """chunk 点组装为 SearchHit，字段取自 chunk payload（doc_summary 仅上下文标注）"""