import asyncio import io import traceback import orjson import pandas as pd from fastapi import APIRouter, HTTPException from fastapi.responses import StreamingResponse from sqlalchemy import and_, select 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, get_chat_with_records_with_data, get_chat_record_by_id, \ format_json_data from apps.chat.models.chat_model import CreateChat, ChatRecord, RenameChat, ChatQuestion, ExcelData from apps.chat.task.llm import LLMService from common.core.deps import CurrentAssistant, SessionDep, CurrentUser, Trans 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("/{chart_id}") async def get_chat(session: SessionDep, current_user: CurrentUser, chart_id: int, current_assistant: CurrentAssistant): def inner(): return get_chat_with_records(chart_id=chart_id, session=session, current_user=current_user, current_assistant=current_assistant) return await asyncio.to_thread(inner) @router.get("/{chart_id}/with_data") async def get_chat_with_data(session: SessionDep, current_user: CurrentUser, chart_id: int, current_assistant: CurrentAssistant): def inner(): return get_chat_with_records_with_data(chart_id=chart_id, session=session, current_user=current_user, current_assistant=current_assistant) return await asyncio.to_thread(inner) @router.get("/record/{chart_record_id}/data") async def chat_record_data(session: SessionDep, chart_record_id: int): def inner(): data = get_chat_chart_data(chart_record_id=chart_record_id, session=session) return format_json_data(data) return await asyncio.to_thread(inner) @router.get("/record/{chart_record_id}/predict_data") async def chat_predict_data(session: SessionDep, chart_record_id: int): def inner(): data = get_chat_predict_data(chart_record_id=chart_record_id, session=session) return format_json_data(data) return await asyncio.to_thread(inner) @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.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(origin=2), 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): def _return_empty(): yield 'data:' + orjson.dumps({'content': '[]', 'type': 'recommended_question'}).decode() + '\n\n' try: record = get_chat_record_by_id(session, chat_record_id) if not record: return StreamingResponse(_return_empty(), media_type="text/event-stream") request_question = ChatQuestion(chat_id=record.chat_id, question=record.question if record.question else '') llm_service = await LLMService.create(session, current_user, request_question, current_assistant, True) llm_service.set_record(record) llm_service.run_recommend_questions_task_async() except Exception as e: traceback.print_exc() def _err(_e: Exception): yield 'data:' + orjson.dumps({'content': str(_e), 'type': 'error'}).decode() + '\n\n' return StreamingResponse(_err(e), media_type="text/event-stream") return StreamingResponse(llm_service.await_result(), 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 = await LLMService.create(session, current_user, request_question, current_assistant, embedding=True) llm_service.init_record(session=session) llm_service.run_task_async() except Exception as e: traceback.print_exc() def _err(_e: Exception): yield 'data:' + orjson.dumps({'content': str(_e), 'type': 'error'}).decode() + '\n\n' return StreamingResponse(_err(e), media_type="text/event-stream") return StreamingResponse(llm_service.await_result(), 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): try: if action_type != 'analysis' and action_type != 'predict': raise Exception(f"Type {action_type} Not Found") record: ChatRecord | None = None stmt = select(ChatRecord.id, ChatRecord.question, ChatRecord.chat_id, ChatRecord.datasource, ChatRecord.engine_type, ChatRecord.ai_modal_id, ChatRecord.create_by, ChatRecord.chart, ChatRecord.data).where( and_(ChatRecord.id == chat_record_id)) result = session.execute(stmt) for r in result: record = ChatRecord(id=r.id, question=r.question, chat_id=r.chat_id, datasource=r.datasource, engine_type=r.engine_type, ai_modal_id=r.ai_modal_id, create_by=r.create_by, chart=r.chart, data=r.data) if not record: raise Exception(f"Chat record with id {chat_record_id} not found") if not record.chart: raise Exception( 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=record.question) llm_service = await LLMService.create(session, current_user, request_question, current_assistant) llm_service.run_analysis_or_predict_task_async(session, action_type, record) except Exception as e: traceback.print_exc() def _err(_e: Exception): yield 'data:' + orjson.dumps({'content': str(_e), 'type': 'error'}).decode() + '\n\n' return StreamingResponse(_err(e), media_type="text/event-stream") return StreamingResponse(llm_service.await_result(), media_type="text/event-stream") @router.post("/excel/export") async def export_excel(excel_data: ExcelData, trans: Trans): def inner(): _fields_list = [] data = [] if not excel_data.data: raise HTTPException( status_code=500, detail=trans("i18n_excel_export.data_is_empty") ) # 预处理数据并记录每列的格式类型 col_formats = {} # 格式类型：'text'（文本）、'number'（数字）、'default'（默认） for field_idx, field in enumerate(excel_data.axis): _fields_list.append(field.name) col_formats[field_idx] = 'default' # 默认不特殊处理 for _data in excel_data.data: _row = [] for field_idx, field in enumerate(excel_data.axis): value = _data.get(field.value) if value is not None: # 检查是否为数字且需要特殊处理 if isinstance(value, (int, float)): # 整数且超过15位 → 转字符串并标记为文本列 if isinstance(value, int) and len(str(abs(value))) > 15: value = str(value) col_formats[field_idx] = 'text' # 小数且超过15位有效数字 → 转字符串并标记为文本列 elif isinstance(value, float): decimal_str = format(value, '.16f').rstrip('0').rstrip('.') if len(decimal_str) > 15: value = str(value) col_formats[field_idx] = 'text' # 其他数字列标记为数字格式（避免科学记数法） elif col_formats[field_idx] != 'text': col_formats[field_idx] = 'number' _row.append(value) data.append(_row) df = pd.DataFrame(data, columns=_fields_list) buffer = io.BytesIO() with pd.ExcelWriter(buffer, engine='xlsxwriter', engine_kwargs={'options': {'strings_to_numbers': False}}) as writer: df.to_excel(writer, sheet_name='Sheet1', index=False) # 获取 xlsxwriter 的工作簿和工作表对象 workbook = writer.book worksheet = writer.sheets['Sheet1'] for col_idx, fmt_type in col_formats.items(): if fmt_type == 'text': worksheet.set_column(col_idx, col_idx, None, workbook.add_format({'num_format': '@'})) elif fmt_type == 'number': worksheet.set_column(col_idx, col_idx, None, workbook.add_format({'num_format': '0'})) buffer.seek(0) return io.BytesIO(buffer.getvalue()) result = await asyncio.to_thread(inner) return StreamingResponse(result, media_type="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet")