80 lines
3.3 KiB
Python
80 lines
3.3 KiB
Python
from fastapi import APIRouter, HTTPException, Query
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from pydantic import BaseModel
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from app.core.embedding import embedder
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from app.core.config import settings
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from llama_index.vector_stores.faiss import FaissVectorStore
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from llama_index import VectorStoreIndex, ServiceContext, StorageContext, load_index_from_storage
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import os
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import logging
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import faiss # 引入faiss
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router = APIRouter()
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# 设置日志记录器
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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class QueryRequest(BaseModel):
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query: str
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@router.post("/search")
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def search_docs(request: QueryRequest, user_id: str = Query(..., description="用户ID")):
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try:
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logger.info(f"Received search request from user: {user_id} with query: {request.query}")
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# 修正后的索引路径，确保指向整个目录，而不是单个文件
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index_path = os.path.join("index_data", user_id) # 使用整个目录路径
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logger.info(f"Looking for index at path: {index_path}")
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# 检查索引目录是否存在
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if not os.path.exists(index_path):
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logger.error(f"Index not found for user: {user_id} at {index_path}")
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raise HTTPException(status_code=404, detail="用户索引不存在")
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# 加载 Faiss 索引
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faiss_index_file = os.path.join(index_path, "index.faiss") # 指定faiss索引文件路径
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if not os.path.exists(faiss_index_file):
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logger.error(f"Faiss index not found at {faiss_index_file}")
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raise HTTPException(status_code=404, detail="Faiss索引文件未找到")
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faiss_index = faiss.read_index(faiss_index_file) # 使用faiss加载索引文件
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logger.info("Faiss index loaded successfully.")
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# 创建 FaissVectorStore 实例
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vector_store = FaissVectorStore(faiss_index=faiss_index)
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logger.info("FaissVectorStore created successfully.")
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# 创建 StorageContext 实例（确保同时加载文本和向量）
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storage_context = StorageContext.from_defaults(persist_dir=index_path, vector_store=vector_store)
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logger.info("Storage context created successfully.")
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# 创建 ServiceContext 实例
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service_context = ServiceContext.from_defaults(embed_model=embedder, llm=None)
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logger.info("Service context created successfully.")
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# 使用 load_index_from_storage 加载索引
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index = load_index_from_storage(storage_context)
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logger.info("VectorStoreIndex loaded successfully.")
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# 检索结果（包含文本）
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retriever = index.as_retriever(similarity_top_k=settings.TOP_K)
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logger.info(f"Retrieving top {settings.TOP_K} results for query: {request.query}")
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nodes = retriever.retrieve(request.query)
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# 返回检索结果
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result = {
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"user_id": user_id,
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"query": request.query,
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"results": [
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{"score": float(node.score or 0), "text": node.get_content()} # 确保从 Node 中获取文本
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for node in nodes
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]
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}
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logger.info(f"Search results for user {user_id}: {result}")
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return result
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except Exception as e:
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logger.error(f"Error processing search request: {e}", exc_info=True)
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raise HTTPException(status_code=500, detail=str(e))
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