.

app/api/search.py

@@ -6,6 +6,7 @@ from llama_index.vector_stores.faiss import FaissVectorStore
 from llama_index import VectorStoreIndex, ServiceContext, StorageContext
 import os
 import logging
+import chardet
 
 router = APIRouter()
 
@@ -16,6 +17,15 @@ logger = logging.getLogger(__name__)
 class QueryRequest(BaseModel):
     query: str
 
+# 自动检测文件编码并加载
+def read_file_with_detected_encoding(file_path: str):
+    with open(file_path, "rb") as f:
+        raw_data = f.read()
+        result = chardet.detect(raw_data)
+        encoding = result['encoding']  # 获取检测到的编码
+    with open(file_path, "r", encoding=encoding) as f:
+        return f.read()
+
 @router.post("/search")
 def search_docs(request: QueryRequest, user_id: str = Query(..., description="用户ID")):
     try:
@@ -33,7 +43,16 @@ def search_docs(request: QueryRequest, user_id: str = Query(..., description="
         # 创建 StorageContext 并加载 Faiss 向量存储目录
         logger.info(f"Loading Faiss vector store from path: {index_path}")
         storage_context = StorageContext.from_defaults(persist_dir=index_path)
-        
+
+        # 自动读取文件内容并解码
+        vector_store_file = os.path.join(index_path, "vector_store.json")
+        if os.path.exists(vector_store_file):
+            file_content = read_file_with_detected_encoding(vector_store_file)
+            logger.info(f"Successfully read vector_store.json with detected encoding.")
+        else:
+            logger.error(f"vector_store.json not found at {vector_store_file}")
+            raise HTTPException(status_code=404, detail="vector_store.json not found")
+
         # 加载 Faiss 向量存储
         faiss_store = FaissVectorStore.from_persist_path(storage_context)
         service_context = ServiceContext.from_defaults(embed_model=embedder, llm=None)