.

app/api/search.py

@@ -3,10 +3,10 @@ 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, StorageContext
+from llama_index import VectorStoreIndex, ServiceContext
 import os
 import logging
-import chardet
+import faiss  # 引入faiss
 
 router = APIRouter()
 
@@ -17,19 +17,6 @@ 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
-    try:
-        with open(file_path, "r", encoding=encoding, errors='ignore') as f:
-            return f.read()
-    except UnicodeDecodeError:
-        logger.error(f"UnicodeDecodeError: Unable to decode the file using {encoding}.")
-        raise HTTPException(status_code=500, detail="Error reading the file due to encoding issues.")
-
 @router.post("/search")
 def search_docs(request: QueryRequest, user_id: str = Query(..., description="用户ID")):
     try:
@@ -44,25 +31,25 @@ def search_docs(request: QueryRequest, user_id: str = Query(..., description="
             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")
 
-        # 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)
+        # 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")
 
-        # 自动读取 docstore.json 文件内容并解码
-        docstore_file = os.path.join(index_path, "docstore.json")
-        if os.path.exists(docstore_file):
-            file_content = read_file_with_detected_encoding(docstore_file)
-            logger.info(f"Successfully read docstore.json with detected encoding.")
-        else:
-            logger.error(f"docstore.json not found at {docstore_file}")
-            raise HTTPException(status_code=404, detail="docstore.json not found")
+        faiss_index = faiss.read_index(faiss_index_file)  # 使用faiss加载索引文件
+        logger.info("Faiss index loaded successfully.")
 
-        # 加载 Faiss 向量存储
-        faiss_store = FaissVectorStore.from_persist_path(storage_context)
+        # 创建 FaissVectorStore 实例
+        vector_store = FaissVectorStore(faiss_index=faiss_index)
+        logger.info("FaissVectorStore created successfully.")
+
+        # 创建 ServiceContext 实例
         service_context = ServiceContext.from_defaults(embed_model=embedder, llm=None)
         logger.info("Service context created successfully.")
 
-        index = VectorStoreIndex.from_vector_store(faiss_store, service_context=service_context)
+        # 创建 VectorStoreIndex
+        index = VectorStoreIndex.from_vector_store(vector_store, service_context=service_context)
         logger.info("VectorStoreIndex created successfully.")
 
         # 检索结果（真实文本）