55 lines
2.0 KiB
Python
55 lines
2.0 KiB
Python
from fastapi import APIRouter, Request, HTTPException
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import json
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import logging
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from Util import OcrUtil, LlmUtil
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from sse_starlette.sse import EventSourceResponse
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# 创建路由路由器
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router = APIRouter(prefix="/api", tags=["大模型通用回答"])
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# 配置日志
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logger = logging.getLogger(__name__)
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@router.post("/llm")
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async def llm(request: Request):
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try:
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# 获取请求参数
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try:
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data = await request.json()
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question = data.get("question")
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except Exception:
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form = await request.form()
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question = form.get("question")
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# 验证参数
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if not question:
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raise HTTPException(status_code=400, detail="缺少必要参数: question")
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async def generate_llm_response():
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try:
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query_text = f'你是一名K12的专业教师，擅长回答学生的问题。下面是问题内容：{question}'
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full_response = []
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async for chunk in LlmUtil.get_llm_response_async(query_text):
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# 确保chunk不是None且非空字符串
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if chunk is not None and chunk.strip():
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full_response.append(chunk)
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yield {"data": json.dumps({'step': 'review', 'content': chunk}, ensure_ascii=False)}
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# 发送完成信号
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yield {"data": json.dumps({'step': 'review', 'content': '回答完成', 'done': True}, ensure_ascii=False)}
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yield f"DONE"
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except Exception as e:
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logger.error(f"异常: {str(e)}")
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yield {"data": json.dumps({'error': f'处理异常: {str(e)}'}, ensure_ascii=False)}
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yield f"DONE"
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# 返回SSE响应
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return EventSourceResponse(generate_llm_response())
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except HTTPException as e:
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raise e
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except Exception as e:
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logger.error(f"接口异常: {str(e)}")
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raise HTTPException(status_code=500, detail=f"服务器内部错误: {str(e)}")
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