.

app/api/search.py

@@ -4,7 +4,6 @@ from app.core.embedding import embedder
 from app.core.config import settings
 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
 
@@ -22,18 +21,18 @@ def search_docs(request: QueryRequest, user_id: str = Query(..., description="
     try:
         logger.info(f"Received search request from user: {user_id} with query: {request.query}")
 
-        # 修正后的索引路径，确保指向具体的 index.faiss 文件
-        index_path = os.path.join("index_data", user_id, "index.faiss")  # 指向文件，而非目录
+        # 修正后的索引路径，确保指向整个目录，而不是单个文件
+        index_path = os.path.join("index_data", user_id)  # 使用整个目录路径
         logger.info(f"Looking for index at path: {index_path}")
 
-        # 检查索引是否存在
+        # 检查索引目录是否存在
         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
+        # 加载整个 Faiss 向量存储目录
         logger.info(f"Loading Faiss vector store from path: {index_path}")
-        faiss_store = FaissVectorStore.from_persist_path(index_path)
+        faiss_store = FaissVectorStore.from_persist_dir(index_path)  # 从目录加载
         service_context = ServiceContext.from_defaults(embed_model=embedder, llm=None)
         logger.info("Service context created successfully.")