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284 lines
11 KiB
Python
284 lines
11 KiB
Python
import asyncio
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import io
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import traceback
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from typing import Optional
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import orjson
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import pandas as pd
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from fastapi import APIRouter, HTTPException
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from fastapi.responses import StreamingResponse
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from sqlalchemy import and_, select
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from apps.chat.curd.chat import list_chats, get_chat_with_records, create_chat, rename_chat, \
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delete_chat, get_chat_chart_data, get_chat_predict_data, get_chat_with_records_with_data, get_chat_record_by_id, \
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format_json_data, format_json_list_data, get_chart_config, list_recent_questions
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from apps.chat.models.chat_model import CreateChat, ChatRecord, RenameChat, ChatQuestion, AxisObj
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from apps.chat.task.llm import LLMService
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from common.core.deps import CurrentAssistant, SessionDep, CurrentUser, Trans
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from common.utils.data_format import DataFormat
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router = APIRouter(tags=["Data Q&A"], prefix="/chat")
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@router.get("/list")
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async def chats(session: SessionDep, current_user: CurrentUser):
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return list_chats(session, current_user)
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@router.get("/{chart_id}")
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async def get_chat(session: SessionDep, current_user: CurrentUser, chart_id: int, current_assistant: CurrentAssistant):
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def inner():
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return get_chat_with_records(chart_id=chart_id, session=session, current_user=current_user,
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current_assistant=current_assistant)
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return await asyncio.to_thread(inner)
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@router.get("/{chart_id}/with_data")
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async def get_chat_with_data(session: SessionDep, current_user: CurrentUser, chart_id: int,
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current_assistant: CurrentAssistant):
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def inner():
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return get_chat_with_records_with_data(chart_id=chart_id, session=session, current_user=current_user,
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current_assistant=current_assistant)
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return await asyncio.to_thread(inner)
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@router.get("/record/{chat_record_id}/data")
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async def chat_record_data(session: SessionDep, chat_record_id: int):
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def inner():
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data = get_chat_chart_data(chat_record_id=chat_record_id, session=session)
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return format_json_data(data)
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return await asyncio.to_thread(inner)
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@router.get("/record/{chat_record_id}/predict_data")
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async def chat_predict_data(session: SessionDep, chat_record_id: int):
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def inner():
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data = get_chat_predict_data(chat_record_id=chat_record_id, session=session)
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return format_json_list_data(data)
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return await asyncio.to_thread(inner)
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@router.post("/rename")
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async def rename(session: SessionDep, chat: RenameChat):
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try:
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return rename_chat(session=session, rename_object=chat)
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except Exception as e:
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raise HTTPException(
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status_code=500,
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detail=str(e)
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)
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@router.delete("/{chart_id}")
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async def delete(session: SessionDep, chart_id: int):
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try:
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return delete_chat(session=session, chart_id=chart_id)
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except Exception as e:
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raise HTTPException(
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status_code=500,
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detail=str(e)
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)
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@router.post("/start")
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async def start_chat(session: SessionDep, current_user: CurrentUser, create_chat_obj: CreateChat):
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try:
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return create_chat(session, current_user, create_chat_obj)
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except Exception as e:
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raise HTTPException(
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status_code=500,
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detail=str(e)
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)
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@router.post("/assistant/start")
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async def start_chat(session: SessionDep, current_user: CurrentUser):
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try:
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return create_chat(session, current_user, CreateChat(origin=2), False)
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except Exception as e:
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raise HTTPException(
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status_code=500,
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detail=str(e)
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)
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@router.post("/recommend_questions/{chat_record_id}")
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async def recommend_questions(session: SessionDep, current_user: CurrentUser, chat_record_id: int,
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current_assistant: CurrentAssistant, articles_number: Optional[int] = 4):
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def _return_empty():
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yield 'data:' + orjson.dumps({'content': '[]', 'type': 'recommended_question'}).decode() + '\n\n'
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try:
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record = get_chat_record_by_id(session, chat_record_id)
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if not record:
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return StreamingResponse(_return_empty(), media_type="text/event-stream")
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request_question = ChatQuestion(chat_id=record.chat_id, question=record.question if record.question else '')
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llm_service = await LLMService.create(session, current_user, request_question, current_assistant, True)
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llm_service.set_record(record)
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llm_service.set_articles_number(articles_number)
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llm_service.run_recommend_questions_task_async()
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except Exception as e:
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traceback.print_exc()
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def _err(_e: Exception):
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yield 'data:' + orjson.dumps({'content': str(_e), 'type': 'error'}).decode() + '\n\n'
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return StreamingResponse(_err(e), media_type="text/event-stream")
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return StreamingResponse(llm_service.await_result(), media_type="text/event-stream")
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@router.get("/recent_questions/{datasource_id}")
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async def recommend_questions(session: SessionDep, current_user: CurrentUser, datasource_id: int):
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return list_recent_questions(session=session, current_user=current_user, datasource_id=datasource_id)
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@router.post("/question")
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async def stream_sql(session: SessionDep, current_user: CurrentUser, request_question: ChatQuestion,
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current_assistant: CurrentAssistant):
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"""Stream SQL analysis results
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Args:
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session: Database session
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current_user: CurrentUser
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request_question: User question model
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Returns:
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Streaming response with analysis results
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"""
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try:
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llm_service = await LLMService.create(session, current_user, request_question, current_assistant,
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embedding=True)
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llm_service.init_record(session=session)
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llm_service.run_task_async()
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except Exception as e:
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traceback.print_exc()
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def _err(_e: Exception):
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yield 'data:' + orjson.dumps({'content': str(_e), 'type': 'error'}).decode() + '\n\n'
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return StreamingResponse(_err(e), media_type="text/event-stream")
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return StreamingResponse(llm_service.await_result(), media_type="text/event-stream")
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@router.post("/record/{chat_record_id}/{action_type}")
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async def analysis_or_predict(session: SessionDep, current_user: CurrentUser, chat_record_id: int, action_type: str,
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current_assistant: CurrentAssistant):
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try:
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if action_type != 'analysis' and action_type != 'predict':
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raise Exception(f"Type {action_type} Not Found")
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record: ChatRecord | None = None
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stmt = select(ChatRecord.id, ChatRecord.question, ChatRecord.chat_id, ChatRecord.datasource,
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ChatRecord.engine_type,
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ChatRecord.ai_modal_id, ChatRecord.create_by, ChatRecord.chart, ChatRecord.data).where(
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and_(ChatRecord.id == chat_record_id))
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result = session.execute(stmt)
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for r in result:
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record = ChatRecord(id=r.id, question=r.question, chat_id=r.chat_id, datasource=r.datasource,
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engine_type=r.engine_type, ai_modal_id=r.ai_modal_id, create_by=r.create_by,
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chart=r.chart,
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data=r.data)
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if not record:
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raise Exception(f"Chat record with id {chat_record_id} not found")
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if not record.chart:
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raise Exception(
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f"Chat record with id {chat_record_id} has not generated chart, do not support to analyze it")
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request_question = ChatQuestion(chat_id=record.chat_id, question=record.question)
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llm_service = await LLMService.create(session, current_user, request_question, current_assistant)
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llm_service.run_analysis_or_predict_task_async(session, action_type, record)
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except Exception as e:
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traceback.print_exc()
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def _err(_e: Exception):
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yield 'data:' + orjson.dumps({'content': str(_e), 'type': 'error'}).decode() + '\n\n'
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return StreamingResponse(_err(e), media_type="text/event-stream")
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return StreamingResponse(llm_service.await_result(), media_type="text/event-stream")
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@router.get("/record/{chat_record_id}/excel/export")
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async def export_excel(session: SessionDep, chat_record_id: int, trans: Trans):
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chat_record = session.get(ChatRecord, chat_record_id)
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if not chat_record:
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raise HTTPException(
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status_code=500,
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detail=f"ChatRecord with id {chat_record_id} not found"
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)
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is_predict_data = chat_record.predict_record_id is not None
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_origin_data = format_json_data(get_chat_chart_data(chat_record_id=chat_record_id, session=session))
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_base_field = _origin_data.get('fields')
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_data = _origin_data.get('data')
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if not _data:
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raise HTTPException(
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status_code=500,
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detail=trans("i18n_excel_export.data_is_empty")
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)
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chart_info = get_chart_config(session, chat_record_id)
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_title = chart_info.get('title') if chart_info.get('title') else 'Excel'
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fields = []
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if chart_info.get('columns') and len(chart_info.get('columns')) > 0:
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for column in chart_info.get('columns'):
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fields.append(AxisObj(name=column.get('name'), value=column.get('value')))
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if chart_info.get('axis'):
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for _type in ['x', 'y', 'series']:
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if chart_info.get('axis').get(_type):
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column = chart_info.get('axis').get(_type)
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fields.append(AxisObj(name=column.get('name'), value=column.get('value')))
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_predict_data = []
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if is_predict_data:
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_predict_data = format_json_list_data(get_chat_predict_data(chat_record_id=chat_record_id, session=session))
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def inner():
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data_list = DataFormat.convert_large_numbers_in_object_array(_data + _predict_data)
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md_data, _fields_list = DataFormat.convert_object_array_for_pandas(fields, data_list)
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# data, _fields_list, col_formats = LLMService.format_pd_data(fields, _data + _predict_data)
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df = pd.DataFrame(md_data, columns=_fields_list)
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buffer = io.BytesIO()
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with pd.ExcelWriter(buffer, engine='xlsxwriter',
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engine_kwargs={'options': {'strings_to_numbers': False}}) as writer:
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df.to_excel(writer, sheet_name='Sheet1', index=False)
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# 获取 xlsxwriter 的工作簿和工作表对象
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# workbook = writer.book
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# worksheet = writer.sheets['Sheet1']
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#
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# for col_idx, fmt_type in col_formats.items():
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# if fmt_type == 'text':
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# worksheet.set_column(col_idx, col_idx, None, workbook.add_format({'num_format': '@'}))
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# elif fmt_type == 'number':
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# worksheet.set_column(col_idx, col_idx, None, workbook.add_format({'num_format': '0'}))
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buffer.seek(0)
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return io.BytesIO(buffer.getvalue())
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result = await asyncio.to_thread(inner)
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return StreamingResponse(result, media_type="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet")
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