.

app/api/search.py

@@ -6,29 +6,47 @@ from llama_index.vector_stores.faiss import FaissVectorStore
 from llama_index import VectorStoreIndex, ServiceContext
 from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 import os
+import logging
 
 router = APIRouter()
 
+# 设置日志记录器
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
 class QueryRequest(BaseModel):
     query: str
 
 @router.post("/search")
 def search_docs(request: QueryRequest, user_id: str = Query(..., description="用户ID")):
     try:
+        logger.info(f"Received search request from user: {user_id} with query: {request.query}")
+
+        # 构建索引路径
         index_path = os.path.join("index_data", f"{user_id}.index")
+        logger.info(f"Looking for index at path: {index_path}")
+
+        # 检查索引是否存在
         if not os.path.exists(index_path):
+            logger.error(f"Index not found for user: {user_id} at {index_path}")
             raise HTTPException(status_code=404, detail="用户索引不存在")
 
         # 构建 LlamaIndex 检索器
+        logger.info(f"Loading Faiss vector store from path: {index_path}")
         faiss_store = FaissVectorStore.from_persist_path(index_path)
         service_context = ServiceContext.from_defaults(embed_model=embedder)
+        logger.info("Service context created successfully.")
+
         index = VectorStoreIndex.from_vector_store(faiss_store, service_context=service_context)
+        logger.info("VectorStoreIndex created successfully.")
 
         # 检索结果（真实文本）
         retriever = index.as_retriever(similarity_top_k=settings.TOP_K)
+        logger.info(f"Retrieving top {settings.TOP_K} results for query: {request.query}")
         nodes = retriever.retrieve(request.query)
 
-        return {
+        # 返回检索结果
+        result = {
             "user_id": user_id,
             "query": request.query,
             "results": [
@@ -37,5 +55,9 @@ def search_docs(request: QueryRequest, user_id: str = Query(..., description="
             ]
         }
 
+        logger.info(f"Search results for user {user_id}: {result}")
+        return result
+
     except Exception as e:
+        logger.error(f"Error processing search request: {e}")
         raise HTTPException(status_code=500, detail=str(e))