import json

import fastapi
import uvicorn
from fastapi import FastAPI
from lightrag import QueryParam
from sse_starlette import EventSourceResponse
from starlette.staticfiles import StaticFiles

from utils.LightRagUtil import *

# 在程序开始时添加以下配置
logging.basicConfig(
    level=logging.INFO,  # 设置日志级别为INFO
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)

# 或者如果你想更详细地控制日志输出
logger = logging.getLogger('lightrag')
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
logger.addHandler(handler)


async def lifespan(app: FastAPI):
    yield


async def print_stream(stream):
    async for chunk in stream:
        if chunk:
            print(chunk, end="", flush=True)


app = FastAPI(lifespan=lifespan)

# 挂载静态文件目录
app.mount("/static", StaticFiles(directory="Static"), name="static")


@app.post("/api/rag")
async def rag(request: fastapi.Request):
    data = await request.json()
    topic = data.get("topic")  # Chinese, Math
    mode = data.get("mode", "hybrid")  # 默认为hybrid模式
    # 拼接路径
    WORKING_PATH = "./Topic/" + topic
    # 查询的问题
    query = data.get("query")
    # 关闭参考资料
    user_prompt = "\n 1、不要输出参考资料 或者 References ！"
    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 ！"
    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！"
    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！"
    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！"
    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！"

    async def generate_response_stream(query: str):
        try:
            rag = LightRAG(
                working_dir=WORKING_PATH,
                llm_model_func=create_llm_model_func(),
                embedding_func=create_embedding_func()
            )

            await rag.initialize_storages()
            await initialize_pipeline_status()
            resp = await rag.aquery(
                query=query,
                param=QueryParam(mode=mode, stream=True, user_prompt=user_prompt))
            # hybrid naive

            async for chunk in resp:
                if not chunk:
                    continue
                yield f"data: {json.dumps({'reply': chunk})}\n\n"
                print(chunk, end='', flush=True)
        except Exception as e:
            yield f"data: {json.dumps({'error': str(e)})}\n\n"
        finally:
            # 清理资源
            await rag.finalize_storages()

    return EventSourceResponse(generate_response_stream(query=query))


if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)