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import json
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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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import warnings
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from io import BytesIO
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import fastapi
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import uvicorn
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from fastapi import FastAPI, HTTPException
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from openai import AsyncOpenAI
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from sse_starlette import EventSourceResponse
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from starlette.responses import StreamingResponse
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from starlette.staticfiles import StaticFiles
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from Config import Config
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from Util.EsSearchUtil import *
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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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# 配置日志处理器
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log_file = os.path.join(os.path.dirname(__file__), 'Logs', 'app.log')
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os.makedirs(os.path.dirname(log_file), exist_ok=True)
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# 文件处理器
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file_handler = RotatingFileHandler(
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log_file, maxBytes=1024 * 1024, backupCount=5, encoding='utf-8')
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file_handler.setFormatter(logging.Formatter(
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'%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
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# 控制台处理器
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console_handler = logging.StreamHandler()
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console_handler.setFormatter(logging.Formatter(
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'%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
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logger.addHandler(file_handler)
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logger.addHandler(console_handler)
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# 初始化异步 OpenAI 客户端
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client = AsyncOpenAI(
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api_key=Config.MODEL_API_KEY,
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base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
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)
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async def lifespan(app: FastAPI):
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# 抑制HTTPS相关警告
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warnings.filterwarnings('ignore', message='Connecting to .* using TLS with verify_certs=False is insecure')
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warnings.filterwarnings('ignore', message='Unverified HTTPS request is being made to host')
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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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@app.post("/api/save-word")
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async def save_to_word(request: fastapi.Request):
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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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markdown_content = data.get('markdown_content', '')
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if not markdown_content:
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raise ValueError("Empty MarkDown 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Markdown文件
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temp_md = os.path.join(tempfile.gettempdir(), uuid.uuid4().hex + ".md")
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with open(temp_md, "w", encoding="utf-8") as f:
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f.write(markdown_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_md, '-o', output_file, '--resource-path=static'], 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_md and os.path.exists(temp_md):
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os.remove(temp_md)
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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", response_model=None)
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async def rag(request: fastapi.Request):
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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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# 调用es进行混合搜索
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search_results = EsSearchUtil.queryByEs(query, query_tags, logger)
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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['text_results'])
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])
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# 添加图片识别提示
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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. 请仔细甄别原问题与提供材料的关联性，不相关的材料必须忽略，绝对不要包含无关信息！
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2. 如果发现相关信息与原来的问题契合度低，请直接回答"未找到相关信息"
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3. 严格保持原文中图片与上下文的顺序关系，确保语义相关性
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4. 使用Markdown格式返回，包含适当的标题、列表和代码块
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5. 直接返回Markdown内容，不要包含额外解释或说明
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6. 依托给定的资料，快速准确地回答问题
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7. 如果未提供相关信息，请直接回答"未找到相关信息"
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8. 确保内容结构清晰，便于前端展示
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"""
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async def generate_response_stream():
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try:
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# 流式调用大模型
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stream = await client.chat.completions.create(
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model=Config.MODEL_NAME,
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messages=[
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{'role': 'user', 'content': prompt}
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],
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max_tokens=8000,
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stream=True # 启用流式模式
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)
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# 流式返回模型生成的回复
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async for chunk in stream:
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if chunk.choices[0].delta.content:
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yield f"data: {json.dumps({'reply': chunk.choices[0].delta.content}, ensure_ascii=False)}\n\n"
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except Exception as e:
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yield f"data: {json.dumps({'error': str(e)})}\n\n"
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return EventSourceResponse(generate_response_stream())
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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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