From b89f81229ec706cdc9348bd871277181f06ad760 Mon Sep 17 00:00:00 2001
From: hailin <hailin@gdzx.xyz>
Date: Sun, 11 May 2025 12:06:06 +0800
Subject: [PATCH] .

---
 app/api/search.py | 96 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 96 insertions(+)

diff --git a/app/api/search.py b/app/api/search.py
index e69de29..3dc6612 100644
--- a/app/api/search.py
+++ b/app/api/search.py
@@ -0,0 +1,96 @@
+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 VectorStoreIndex 创建查询引擎
+        query_engine = index.as_query_engine()
+        logger.info(f"Query engine created successfully.")
+
+        # 执行查询
+        response = query_engine.query(request.query)
+        logger.info(f"Query response: {response}")
+
+        # 打印出每个结果的向量和文本
+        for i, node in enumerate(response):
+            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 response
+            ]
+        }
+
+        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))