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114 lines
3.4 KiB
114 lines
3.4 KiB
import json
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import logging
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import os
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from logging.handlers import RotatingFileHandler
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import fastapi
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import uvicorn
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from fastapi import FastAPI
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from lightrag import LightRAG
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from lightrag.kg.shared_storage import initialize_pipeline_status
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from raganything import RAGAnything
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from sse_starlette import EventSourceResponse
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from starlette.staticfiles import StaticFiles
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from Util.RagUtil import initialize_rag, create_llm_model_func, create_vision_model_func, create_embedding_func
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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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async def lifespan(app: FastAPI):
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# 初始化RAG
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app.state.rag = await initialize_rag(working_dir="./Topic/Math")
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yield
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# 清理资源
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await app.state.rag.finalize_storages()
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async def print_stream(stream):
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async for chunk in stream:
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if chunk:
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print(chunk, end="", flush=True)
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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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# 数学索引文件目录
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#WORKING_PATH = "./Topic/Math"
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# 苏轼索引文件目录
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WORKING_PATH = "./Topic/Chinese"
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@app.post("/api/rag")
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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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async def generate_response_stream(query: str):
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try:
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llm_model_func = create_llm_model_func()
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vision_model_func = create_vision_model_func(llm_model_func)
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embedding_func = create_embedding_func()
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lightrag_instance = LightRAG(
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working_dir=WORKING_PATH,
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llm_model_func=llm_model_func,
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embedding_func=embedding_func
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)
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await lightrag_instance.initialize_storages()
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await initialize_pipeline_status()
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rag = RAGAnything(
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lightrag=lightrag_instance,
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vision_model_func=vision_model_func,
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)
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# 使用stream=True参数确保流式输出
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resp = await rag.aquery(
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query=query,
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mode="hybrid", # 直接传入mode参数
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stream=True # 直接传入stream参数
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)
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# 直接处理流式响应
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async for chunk in resp:
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if not chunk:
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continue
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yield f"data: {json.dumps({'reply': chunk})}\n\n"
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print(chunk, end='', flush=True)
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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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logger.error(f"处理查询时出错: {query}. 错误: {str(e)}")
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return EventSourceResponse(generate_response_stream(query=query))
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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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