.

app/api/search.py

@@ -3,7 +3,7 @@ from pydantic import BaseModel
 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 import VectorStoreIndex, ServiceContext, StorageContext
 import os
 import logging
 import faiss  # 引入faiss
@@ -44,15 +44,19 @@ def search_docs(request: QueryRequest, user_id: str = Query(..., description="
         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.")
 
-        # 创建 VectorStoreIndex
-        index = VectorStoreIndex.from_vector_store(vector_store, service_context=service_context)
+        # 创建 VectorStoreIndex 并加载文本与向量
+        index = VectorStoreIndex.from_storage_context(storage_context)
         logger.info("VectorStoreIndex created successfully.")
 
-        # 检索结果（真实文本）
+        # 检索结果（包含文本）
         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)
@@ -62,7 +66,7 @@ def search_docs(request: QueryRequest, user_id: str = Query(..., description="
             "user_id": user_id,
             "query": request.query,
             "results": [
-                {"score": float(node.score or 0), "text": node.get_content()}
+                {"score": float(node.score or 0), "text": node.get_content()}  # 确保从 Node 中获取文本
                 for node in nodes
             ]
         }