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import os
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import subprocess
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import tempfile
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import urllib.parse
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import uuid
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from contextlib import asynccontextmanager
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from io import BytesIO
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from logging.handlers import RotatingFileHandler
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from typing import List
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import jieba # 导入 jieba 分词库
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import uvicorn
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from fastapi import FastAPI, Request, HTTPException
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from fastapi.staticfiles import StaticFiles
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from gensim.models import KeyedVectors
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from pydantic import BaseModel, Field, ValidationError
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from starlette.responses import StreamingResponse
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from Config.Config import MS_MODEL_PATH, MS_MODEL_LIMIT, MS_HOST, MS_PORT, MS_MAX_CONNECTIONS, MS_NPROBE, \
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MS_COLLECTION_NAME, ES_CONFIG
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from Milvus.Utils.MilvusCollectionManager import MilvusCollectionManager
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from Milvus.Utils.MilvusConnectionPool import *
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from Milvus.Utils.MilvusConnectionPool import MilvusConnectionPool
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from Util.ALiYunUtil import ALiYunUtil
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from Util.EsSearchUtil import EsSearchUtil
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# 初始化日志
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.INFO)
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handler = RotatingFileHandler('Logs/start.log', maxBytes=1024 * 1024, backupCount=5)
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handler.setFormatter(logging.Formatter('%(asctime)s - %(levelname)s - %(message)s'))
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logger.addHandler(handler)
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# 将HTML文件转换为Word文件
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def html_to_word_pandoc(html_file, output_file):
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subprocess.run(['pandoc', html_file, '-o', output_file])
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async def lifespan(app: FastAPI):
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# 初始化阿里云大模型工具
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app.state.aliyun_util = ALiYunUtil()
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yield
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app = FastAPI(lifespan=lifespan)
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# 挂载静态文件目录
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app.mount("/static", StaticFiles(directory="Static"), name="static")
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class QueryRequest(BaseModel):
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query: str = Field(..., description="用户查询的问题")
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documents: List[str] = Field(..., description="用户上传的文档")
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class SaveWordRequest(BaseModel):
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html: str = Field(..., description="要保存为Word的HTML内容")
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@app.post("/api/save-word")
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async def save_to_word(request: Request):
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temp_html = None
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output_file = None
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try:
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# Parse request data
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try:
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data = await request.json()
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html_content = data.get('html_content', '')
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if not html_content:
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raise ValueError("Empty HTML content")
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except Exception as e:
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logger.error(f"Request parsing failed: {str(e)}")
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raise HTTPException(status_code=400, detail=f"Invalid request: {str(e)}")
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# 创建临时HTML文件
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temp_html = os.path.join(tempfile.gettempdir(), uuid.uuid4().hex + ".html")
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with open(temp_html, "w", encoding="utf-8") as f:
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f.write(html_content)
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# 使用pandoc转换
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output_file = os.path.join(tempfile.gettempdir(), "【理想大模型】问答.docx")
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subprocess.run(['pandoc', temp_html, '-o', output_file], check=True)
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# 读取生成的Word文件
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with open(output_file, "rb") as f:
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stream = BytesIO(f.read())
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# 返回响应
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encoded_filename = urllib.parse.quote("【理想大模型】问答.docx")
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return StreamingResponse(
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stream,
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media_type="application/vnd.openxmlformats-officedocument.wordprocessingml.document",
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headers={"Content-Disposition": f"attachment; filename*=UTF-8''{encoded_filename}"})
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"Unexpected error: {str(e)}")
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raise HTTPException(status_code=500, detail="Internal server error")
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finally:
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# 清理临时文件
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try:
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if temp_html and os.path.exists(temp_html):
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os.remove(temp_html)
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if output_file and os.path.exists(output_file):
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os.remove(output_file)
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except Exception as e:
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logger.warning(f"Failed to clean up temp files: {str(e)}")
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@app.post("/api/rag")
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async def rag_stream(request: Request):
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try:
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data = await request.json()
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query = data.get('query', '')
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query_tags = data.get('tags', [])
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# 获取EsSearchUtil实例
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es_search_util = EsSearchUtil(ES_CONFIG)
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# 执行混合搜索
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es_conn = es_search_util.es_pool.get_connection()
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try:
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# 向量搜索
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logger.info(f"\n=== 开始执行查询 ===")
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logger.info(f"原始查询文本: {query}")
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logger.info(f"查询标签: {query_tags}")
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logger.info("\n=== 向量搜索阶段 ===")
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logger.info("1. 文本分词和向量化处理中...")
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query_embedding = es_search_util.text_to_embedding(query)
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logger.info(f"2. 生成的查询向量维度: {len(query_embedding)}")
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logger.info(f"3. 前3维向量值: {query_embedding[:3]}")
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logger.info("4. 正在执行Elasticsearch向量搜索...")
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vector_results = es_conn.search(
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index=ES_CONFIG['index_name'],
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body={
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"query": {
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"script_score": {
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"query": {
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"bool": {
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"should": [
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{
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"terms": {
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"tags.tags": query_tags
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}
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}
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],
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"minimum_should_match": 1
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}
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},
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"script": {
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"source": "double score = cosineSimilarity(params.query_vector, 'embedding'); return score >= 0 ? score : 0",
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"params": {"query_vector": query_embedding}
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}
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}
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},
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"size": 3
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}
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)
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logger.info(f"5. 向量搜索结果数量: {len(vector_results['hits']['hits'])}")
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# 文本精确搜索
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logger.info("\n=== 文本精确搜索阶段 ===")
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logger.info("1. 正在执行Elasticsearch文本精确搜索...")
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text_results = es_conn.search(
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index=ES_CONFIG['index_name'],
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body={
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"query": {
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"bool": {
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"must": [
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{
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"match": {
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"user_input": query
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}
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},
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{
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"terms": {
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"tags.tags": query_tags
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}
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}
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]
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}
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},
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"size": 3
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}
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)
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logger.info(f"2. 文本搜索结果数量: {len(text_results['hits']['hits'])}")
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# 合并结果
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logger.info("\n=== 最终搜索结果 ===")
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logger.info(f"向量搜索结果: {len(vector_results['hits']['hits'])}条")
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for i, hit in enumerate(vector_results['hits']['hits'], 1):
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logger.info(f" {i}. 文档ID: {hit['_id']}, 相似度分数: {hit['_score']:.2f}")
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logger.info(f" 内容: {hit['_source']['user_input']}")
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logger.info("文本精确搜索结果:")
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for i, hit in enumerate(text_results['hits']['hits']):
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logger.info(f" {i + 1}. 文档ID: {hit['_id']}, 匹配分数: {hit['_score']:.2f}")
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logger.info(f" 内容: {hit['_source']['user_input']}")
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search_results = {
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"vector_results": [hit['_source'] for hit in vector_results['hits']['hits']],
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"text_results": [hit['_source'] for hit in text_results['hits']['hits']]
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}
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# 调用阿里云大模型整合结果
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aliyun_util = request.app.state.aliyun_util
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# 构建提示词
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context = "\n".join([
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f"结果{i + 1}: {res['tags']['full_content']}"
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for i, res in enumerate(search_results['vector_results'] + search_results['text_results'])
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])
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prompt = f"""
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信息检索与回答助手
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根据以下关于'{query}'的相关信息：
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基本信息
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- 语言: 中文
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- 描述: 根据提供的材料检索信息并回答问题
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- 特点: 快速准确提取关键信息，清晰简洁地回答
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相关信息
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{context}
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回答要求
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1. 依托给定的资料，快速准确地回答问题，可以添加一些额外的信息，但请勿重复内容。
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2. 如果未提供相关信息，请不要回答。
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3. 如果发现相关信息与原来的问题契合度低，也不要回答
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4. 使用HTML格式返回，包含适当的段落、列表和标题标签
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5. 确保内容结构清晰，便于前端展示
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"""
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# 调用阿里云大模型
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if len(context) > 0:
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# 调用大模型生成回答
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logger.info("正在调用阿里云大模型生成回答...")
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html_content = aliyun_util.chat(prompt)
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logger.info(f"调用阿里云大模型生成回答成功完成!")
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return {"data": html_content}
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else:
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logger.warning(f"未找到查询'{query}'的相关数据，tags: {query_tags}")
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return {"data": "没有在知识库中找到相关的信息，无法回答此问题。",
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"debug": {"query": query, "tags": query_tags}}
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except Exception as e:
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return {"data": f"生成报告时出错: {str(e)}"}
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finally:
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es_search_util.es_pool.release_connection(es_conn)
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except Exception as e:
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logger.error(f"RAG search error: {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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if __name__ == "__main__":
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uvicorn.run(app, host="0.0.0.0", port=8000)
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