diff --git a/app/api/search copy.py b/app/api/search copy.py
index cb3c4ce..ae12f5f 100644
--- a/app/api/search copy.py	
+++ b/app/api/search copy.py	
@@ -1,6 +1,6 @@
 from fastapi import APIRouter, HTTPException, Query
 from pydantic import BaseModel
-from app.core.embedding import embedder
+from app.core.embedding import embedder  # 使用已加载的本地嵌入模型（BGEEmbedding）
 from app.core.config import settings
 from llama_index.vector_stores.faiss import FaissVectorStore
 from llama_index import VectorStoreIndex, ServiceContext, StorageContext, load_index_from_storage
@@ -56,15 +56,15 @@ def search_docs(request: QueryRequest, user_id: str = Query(..., description="
         index = load_index_from_storage(storage_context)
         logger.info("VectorStoreIndex loaded successfully.")
 
-        # 检索结果（包含文本）
-        retriever = index.as_retriever(similarity_top_k=settings.TOP_K)
+        # 将用户查询通过本地模型生成嵌入向量
+        query_vector = embedder.encode([request.query])  # 使用本地模型生成查询的嵌入向量
+        logger.info(f"Generated query embedding: {query_vector}")
+
+        # 使用 FaissVectorStore 检索最相似的节点
+        retriever = vector_store.as_retriever(similarity_top_k=settings.TOP_K, query_embedding=query_vector)
         logger.info(f"Retrieving top {settings.TOP_K} results for query: {request.query}")
         nodes = retriever.retrieve(request.query)
 
-
-
-
-
         # 打印出每个结果的向量和文本
         for i, node in enumerate(nodes):
             # 打印文本
@@ -76,19 +76,6 @@ def search_docs(request: QueryRequest, user_id: str = Query(..., description="
             logger.info(f"  Embedding (Vector): {embedding}")  # 打印向量
             logger.info(f"  Embedding Length: {len(embedding)}")  # 打印向量的长度（即向量的维度）
 
-
-
-
-
-
-
-
-
-
-
-
-
-
         # 返回检索结果
         result = {
             "user_id": user_id,
diff --git a/app/api/search.py b/app/api/search.py
index ae12f5f..e69de29 100644
--- a/app/api/search.py
+++ b/app/api/search.py
@@ -1,94 +0,0 @@
-from fastapi import APIRouter, HTTPException, Query
-from pydantic import BaseModel
-from app.core.embedding import embedder  # 使用已加载的本地嵌入模型（BGEEmbedding）
-from app.core.config import settings
-from llama_index.vector_stores.faiss import FaissVectorStore
-from llama_index import VectorStoreIndex, ServiceContext, StorageContext, load_index_from_storage
-import os
-import logging
-import faiss  # 引入faiss
-
-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}")
-
-        # 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")
-
-        # 加载 Faiss 索引
-        faiss_index_file = os.path.join(index_path, "index.faiss")  # 指定faiss索引文件路径
-        if not os.path.exists(faiss_index_file):
-            logger.error(f"Faiss index not found at {faiss_index_file}")
-            raise HTTPException(status_code=404, detail="Faiss索引文件未找到")
-
-        faiss_index = faiss.read_index(faiss_index_file)  # 使用faiss加载索引文件
-        logger.info("Faiss index loaded successfully.")
-
-        # 创建 FaissVectorStore 实例
-        vector_store = FaissVectorStore(faiss_index=faiss_index)
-        logger.info("FaissVectorStore created successfully.")
-
-        # 创建 StorageContext 实例（确保同时加载文本和向量）
-        storage_context = StorageContext.from_defaults(persist_dir=index_path, vector_store=vector_store)
-        logger.info("Storage context created successfully.")
-
-        # 创建 ServiceContext 实例
-        service_context = ServiceContext.from_defaults(embed_model=embedder, llm=None)
-        logger.info("Service context created successfully.")
-
-        # 使用 load_index_from_storage 加载索引
-        index = load_index_from_storage(storage_context)
-        logger.info("VectorStoreIndex loaded successfully.")
-
-        # 将用户查询通过本地模型生成嵌入向量
-        query_vector = embedder.encode([request.query])  # 使用本地模型生成查询的嵌入向量
-        logger.info(f"Generated query embedding: {query_vector}")
-
-        # 使用 FaissVectorStore 检索最相似的节点
-        retriever = vector_store.as_retriever(similarity_top_k=settings.TOP_K, query_embedding=query_vector)
-        logger.info(f"Retrieving top {settings.TOP_K} results for query: {request.query}")
-        nodes = retriever.retrieve(request.query)
-
-        # 打印出每个结果的向量和文本
-        for i, node in enumerate(nodes):
-            # 打印文本
-            logger.info(f"Result {i+1}:")
-            logger.info(f"  Text: {node.get_content()}")  # 打印文本
-
-            # 打印向量及其长度
-            embedding = node.embedding
-            logger.info(f"  Embedding (Vector): {embedding}")  # 打印向量
-            logger.info(f"  Embedding Length: {len(embedding)}")  # 打印向量的长度（即向量的维度）
-
-        # 返回检索结果
-        result = {
-            "user_id": user_id,
-            "query": request.query,
-            "results": [
-                {"score": float(node.score or 0), "text": node.get_content()}  # 确保从 Node 中获取文本
-                for node in nodes
-            ]
-        }
-
-        logger.info(f"Search results for user {user_id}: {result}")
-        return result
-
-    except Exception as e:
-        logger.error(f"Error processing search request: {e}", exc_info=True)
-        raise HTTPException(status_code=500, detail=str(e))