Files
SQLBot/backend/apps/chat/api/chat.py
T

179 lines
5.8 KiB
Python

import traceback
from fastapi import APIRouter, HTTPException
from fastapi.responses import StreamingResponse
from apps.chat.curd.chat import list_chats, get_chat_with_records, create_chat, rename_chat, \
delete_chat, get_chat_chart_data, get_chat_predict_data
from apps.chat.models.chat_model import CreateChat, ChatRecord, RenameChat, ChatQuestion
from apps.chat.task.llm import LLMService, run_task, run_analysis_or_predict_task, run_recommend_questions_task
from common.core.deps import CurrentAssistant, SessionDep, CurrentUser
router = APIRouter(tags=["Data Q&A"], prefix="/chat")
@router.get("/list")
async def chats(session: SessionDep, current_user: CurrentUser):
return list_chats(session, current_user)
@router.get("/get/{chart_id}")
async def get_chat(session: SessionDep, current_user: CurrentUser, chart_id: int):
try:
return get_chat_with_records(chart_id=chart_id, session=session, current_user=current_user)
except Exception as e:
raise HTTPException(
status_code=500,
detail=str(e)
)
@router.get("/record/get/{chart_record_id}/data")
async def chat_record_data(session: SessionDep, chart_record_id: int):
try:
return get_chat_chart_data(chart_record_id=chart_record_id, session=session)
except Exception as e:
raise HTTPException(
status_code=500,
detail=str(e)
)
@router.get("/record/get/{chart_record_id}/predict_data")
async def chat_predict_data(session: SessionDep, chart_record_id: int):
try:
return get_chat_predict_data(chart_record_id=chart_record_id, session=session)
except Exception as e:
raise HTTPException(
status_code=500,
detail=str(e)
)
@router.post("/rename")
async def rename(session: SessionDep, chat: RenameChat):
try:
return rename_chat(session=session, rename_object=chat)
except Exception as e:
raise HTTPException(
status_code=500,
detail=str(e)
)
@router.get("/delete/{chart_id}")
async def delete(session: SessionDep, chart_id: int):
try:
return delete_chat(session=session, chart_id=chart_id)
except Exception as e:
raise HTTPException(
status_code=500,
detail=str(e)
)
@router.post("/start")
async def start_chat(session: SessionDep, current_user: CurrentUser, create_chat_obj: CreateChat):
try:
return create_chat(session, current_user, create_chat_obj)
except Exception as e:
raise HTTPException(
status_code=500,
detail=str(e)
)
@router.post("/assistant/start")
async def start_chat(session: SessionDep, current_user: CurrentUser):
try:
return create_chat(session, current_user, CreateChat(), False)
except Exception as e:
raise HTTPException(
status_code=500,
detail=str(e)
)
@router.post("/recommend_questions/{chat_record_id}")
async def recommend_questions(session: SessionDep, current_user: CurrentUser, chat_record_id: int, current_assistant: CurrentAssistant):
try:
record = session.get(ChatRecord, chat_record_id)
if not record:
raise HTTPException(
status_code=400,
detail=f"Chat record with id {chat_record_id} not found"
)
request_question = ChatQuestion(chat_id=record.chat_id, question=record.question if record.question else '')
llm_service = LLMService(session, current_user, request_question, current_assistant)
llm_service.set_record(record)
except Exception as e:
traceback.print_exc()
raise HTTPException(
status_code=500,
detail=str(e)
)
return StreamingResponse(run_recommend_questions_task(llm_service), media_type="text/event-stream")
@router.post("/question")
async def stream_sql(session: SessionDep, current_user: CurrentUser, request_question: ChatQuestion, current_assistant: CurrentAssistant):
"""Stream SQL analysis results
Args:
session: Database session
current_user: CurrentUser
request_question: User question model
Returns:
Streaming response with analysis results
"""
try:
llm_service = LLMService(session, current_user, request_question, current_assistant)
llm_service.init_record()
except Exception as e:
traceback.print_exc()
raise HTTPException(
status_code=500,
detail=str(e)
)
return StreamingResponse(run_task(llm_service), media_type="text/event-stream")
@router.post("/record/{chat_record_id}/{action_type}")
async def analysis_or_predict(session: SessionDep, current_user: CurrentUser, chat_record_id: int, action_type: str, current_assistant: CurrentAssistant):
if action_type != 'analysis' and action_type != 'predict':
raise HTTPException(
status_code=404,
detail="Not Found"
)
record = session.query(ChatRecord).get(chat_record_id)
if not record:
raise HTTPException(
status_code=400,
detail=f"Chat record with id {chat_record_id} not found"
)
if not record.chart:
raise HTTPException(
status_code=500,
detail=f"Chat record with id {chat_record_id} has not generated chart, do not support to analyze it"
)
request_question = ChatQuestion(chat_id=record.chat_id, question='')
try:
llm_service = LLMService(session, current_user, request_question, current_assistant)
except Exception as e:
traceback.print_exc()
raise HTTPException(
status_code=500,
detail=str(e)
)
return StreamingResponse(run_analysis_or_predict_task(llm_service, action_type, record),
media_type="text/event-stream")