90 lines
3.2 KiB
Python
90 lines
3.2 KiB
Python
import json
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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 QueryParam
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from sse_starlette import EventSourceResponse
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from starlette.staticfiles import StaticFiles
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from utils.LightRagUtil import *
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# 在程序开始时添加以下配置
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logging.basicConfig(
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level=logging.INFO, # 设置日志级别为INFO
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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# 或者如果你想更详细地控制日志输出
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logger = logging.getLogger('lightrag')
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logger.setLevel(logging.INFO)
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handler = logging.StreamHandler()
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handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
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logger.addHandler(handler)
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async def lifespan(app: FastAPI):
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yield
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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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@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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topic = data.get("topic") # Chinese, Math
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mode = data.get("mode", "hybrid") # 默认为hybrid模式
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# 拼接路径
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WORKING_PATH = "./Topic/" + topic
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# 查询的问题
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query = data.get("query")
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# 关闭参考资料
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user_prompt = "\n 1、不要输出参考资料 或者 References ！"
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user_prompt = user_prompt + "\n 2、资料中提供化学反应方程式的，一定要严格按提供的Latex公式输出，绝对不允许对Latex公式进行修改 ！"
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user_prompt = user_prompt + "\n 3、如果资料中提供了图片的，一定要严格按照原文提供图片输出，不允许省略或不输出！"
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user_prompt = user_prompt + "\n 4、资料中提到的知识内容，需要判断是否与本次问题相关，不相关的绝对不要输出！"
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user_prompt = user_prompt + "\n 5、如果问题与提供的知识库内容不符，则明确告诉未在知识库范围内提到！"
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user_prompt = user_prompt + "\n 6、发现输出内容中包含Latex公式的，一定要检查是不是包含了$$或$的包含符号,不能让Latex无包含符号出现！"
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async def generate_response_stream(query: str):
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try:
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rag = LightRAG(
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working_dir=WORKING_PATH,
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llm_model_func=create_llm_model_func(),
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embedding_func=create_embedding_func()
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)
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await rag.initialize_storages()
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await initialize_pipeline_status()
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resp = await rag.aquery(
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query=query,
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param=QueryParam(mode=mode, stream=True, user_prompt=user_prompt))
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# hybrid naive
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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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finally:
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# 清理资源
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await rag.finalize_storages()
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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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