推理优化

backend/app/rag/pipeline.py

@@ -36,6 +36,8 @@ class RAGPipeline:
         self.rerank_top_n = rerank_top_n
         self.use_rerank = use_rerank
         self.return_parent_docs = return_parent_docs
+        self._last_docs = []  # 保存最后一次检索的文档
+        self._last_scores = []  # 保存最后一次检索的分数
 
         if llm == "default_small":
             try:
@@ -49,6 +51,16 @@ class RAGPipeline:
         self.reranker = create_document_reranker() if use_rerank else None
         logger.info(f"[Pipeline] init: rerank={use_rerank}, return_parent={return_parent_docs}")
 
+    @property
+    def last_docs(self) -> List[Document]:
+        """获取最后一次检索的文档"""
+        return self._last_docs
+
+    @property
+    def last_scores(self) -> List[dict]:
+        """获取最后一次检索的分数信息"""
+        return self._last_scores
+
     async def aretrieve(self, query: str) -> List[Document]:
         # Step 1: 检索
         child_docs = await self._retrieve(query)
@@ -69,9 +81,24 @@ class RAGPipeline:
 
         # Step 3: 获取父文档
         if self.return_parent_docs:
-            return await self._get_parents(child_docs)
+            parent_docs = await self._get_parents(child_docs)
+            # 保存分数信息到 last_scores 供外部访问
+            self._last_scores = self._extract_scores(parent_docs)
+            return parent_docs
+
+        self._last_scores = self._extract_scores(child_docs)
         return child_docs
 
+    def _extract_scores(self, docs: List[Document]) -> List[dict]:
+        """提取文档的分数信息"""
+        scores = []
+        for doc in docs:
+            scores.append({
+                "embedding_score": doc.metadata.get("embedding_score", doc.metadata.get("score", 0.0)),
+                "rerank_score": doc.metadata.get("rerank_score", 0.0),
+            })
+        return scores
+
     async def _retrieve(self, query: str) -> List[Document]:
         if self.query_generator:
             queries = await self.query_generator.agenerate(query)
@@ -100,7 +127,7 @@ class RAGPipeline:
         try:
             from backend.rag_core import create_docstore
             docstore, _ = create_docstore()
-            parent_docs = docstore.mget(list(parent_map.keys()))
+            parent_docs =await docstore.amget(list(parent_map.keys()))
 
             # 构建结果，保持分数信息
             result = []