import concurrent import json import traceback import warnings from concurrent.futures import ThreadPoolExecutor, Future from datetime import datetime from typing import Any, List, Optional, Union, Dict import numpy as np import orjson import pandas as pd import requests import sqlparse from langchain.chat_models.base import BaseChatModel from langchain_community.utilities import SQLDatabase from langchain_core.messages import BaseMessage, SystemMessage, HumanMessage, AIMessage, BaseMessageChunk from sqlalchemy import select from sqlalchemy.orm import sessionmaker from sqlmodel import create_engine, Session from apps.ai_model.model_factory import LLMConfig, LLMFactory, get_default_config from apps.chat.curd.chat import save_question, save_sql_answer, save_sql, \ save_error_message, save_sql_exec_data, save_chart_answer, save_chart, \ finish_record, save_analysis_answer, save_predict_answer, save_predict_data, \ save_select_datasource_answer, save_recommend_question_answer, \ get_old_questions, save_analysis_predict_record, rename_chat, get_chart_config, \ get_chat_chart_data, list_generate_sql_logs, list_generate_chart_logs, start_log, end_log from apps.chat.models.chat_model import ChatQuestion, ChatRecord, Chat, RenameChat, ChatLog, OperationEnum from apps.datasource.crud.datasource import get_table_schema from apps.datasource.crud.permission import get_row_permission_filters, is_normal_user from apps.datasource.models.datasource import CoreDatasource from apps.db.db import exec_sql, get_version, check_connection from apps.system.crud.assistant import AssistantOutDs, AssistantOutDsFactory, get_assistant_ds from apps.system.schemas.system_schema import AssistantOutDsSchema from apps.terminology.curd.terminology import get_terminology_template from common.core.config import settings from common.core.deps import CurrentAssistant, CurrentUser from common.error import SingleMessageError, SQLBotDBError, ParseSQLResultError, SQLBotDBConnectionError from common.utils.utils import SQLBotLogUtil, extract_nested_json, prepare_for_orjson warnings.filterwarnings("ignore") base_message_count_limit = 6 executor = ThreadPoolExecutor(max_workers=200) dynamic_ds_types = [1, 3] class LLMService: ds: CoreDatasource chat_question: ChatQuestion record: ChatRecord config: LLMConfig llm: BaseChatModel sql_message: List[Union[BaseMessage, dict[str, Any]]] = [] chart_message: List[Union[BaseMessage, dict[str, Any]]] = [] session: Session current_user: CurrentUser current_assistant: Optional[CurrentAssistant] = None out_ds_instance: Optional[AssistantOutDs] = None change_title: bool = False generate_sql_logs: List[ChatLog] = [] generate_chart_logs: List[ChatLog] = [] current_logs: dict[OperationEnum, ChatLog] = {} chunk_list: List[str] = [] future: Future def __init__(self, current_user: CurrentUser, chat_question: ChatQuestion, current_assistant: Optional[CurrentAssistant] = None, no_reasoning: bool = False, config: LLMConfig = None): self.chunk_list = [] engine = create_engine(str(settings.SQLALCHEMY_DATABASE_URI)) session_maker = sessionmaker(bind=engine) self.session = session_maker() self.session.exec = self.session.exec if hasattr(self.session, "exec") else self.session.execute self.current_user = current_user self.current_assistant = current_assistant # chat = self.session.query(Chat).filter(Chat.id == chat_question.chat_id).first() chat_id = chat_question.chat_id chat: Chat | None = self.session.get(Chat, chat_id) if not chat: raise SingleMessageError(f"Chat with id {chat_id} not found") ds: CoreDatasource | AssistantOutDsSchema | None = None if chat.datasource: # Get available datasource # ds = self.session.query(CoreDatasource).filter(CoreDatasource.id == chat.datasource).first() if current_assistant and current_assistant.type in dynamic_ds_types: self.out_ds_instance = AssistantOutDsFactory.get_instance(current_assistant) ds = self.out_ds_instance.get_ds(chat.datasource) if not ds: raise SingleMessageError("No available datasource configuration found") chat_question.engine = ds.type + get_version(ds) chat_question.db_schema = self.out_ds_instance.get_db_schema(ds.id) else: ds = self.session.get(CoreDatasource, chat.datasource) if not ds: raise SingleMessageError("No available datasource configuration found") chat_question.engine = (ds.type_name if ds.type != 'excel' else 'PostgreSQL') + get_version(ds) chat_question.db_schema = get_table_schema(session=self.session, current_user=current_user, ds=ds) self.generate_sql_logs = list_generate_sql_logs(session=self.session, chart_id=chat_id) self.generate_chart_logs = list_generate_chart_logs(session=self.session, chart_id=chat_id) self.change_title = len(self.generate_sql_logs) == 0 chat_question.lang = get_lang_name(current_user.language) self.ds = (ds if isinstance(ds, AssistantOutDsSchema) else CoreDatasource(**ds.model_dump())) if ds else None self.chat_question = chat_question self.config = config if no_reasoning: # only work while using qwen if self.config.additional_params: if self.config.additional_params.get('extra_body'): if self.config.additional_params.get('extra_body').get('enable_thinking'): del self.config.additional_params['extra_body']['enable_thinking'] self.chat_question.ai_modal_id = self.config.model_id self.chat_question.ai_modal_name = self.config.model_name # Create LLM instance through factory llm_instance = LLMFactory.create_llm(self.config) self.llm = llm_instance.llm self.init_messages() @classmethod async def create(cls, *args, **kwargs): config: LLMConfig = await get_default_config() instance = cls(*args, **kwargs, config=config) return instance def is_running(self, timeout=0.5): try: r = concurrent.futures.wait([self.future], timeout) if len(r.not_done) > 0: return True else: return False except Exception as e: return True def init_messages(self): last_sql_messages: List[dict[str, Any]] = self.generate_sql_logs[-1].messages if len( self.generate_sql_logs) > 0 else [] # todo maybe can configure count_limit = 0 - base_message_count_limit self.sql_message = [] # add sys prompt self.sql_message.append(SystemMessage(content=self.chat_question.sql_sys_question())) if last_sql_messages is not None and len(last_sql_messages) > 0: # limit count for last_sql_message in last_sql_messages[count_limit:]: _msg: BaseMessage if last_sql_message['type'] == 'human': _msg = HumanMessage(content=last_sql_message['content']) self.sql_message.append(_msg) elif last_sql_message['type'] == 'ai': _msg = AIMessage(content=last_sql_message['content']) self.sql_message.append(_msg) last_chart_messages: List[dict[str, Any]] = self.generate_chart_logs[-1].messages if len( self.generate_chart_logs) > 0 else [] self.chart_message = [] # add sys prompt self.chart_message.append(SystemMessage(content=self.chat_question.chart_sys_question())) if last_chart_messages is not None and len(last_chart_messages) > 0: # limit count for last_chart_message in last_chart_messages: _msg: BaseMessage if last_chart_message.get('type') == 'human': _msg = HumanMessage(content=last_chart_message.get('content')) self.chart_message.append(_msg) elif last_chart_message.get('type') == 'ai': _msg = AIMessage(content=last_chart_message.get('content')) self.chart_message.append(_msg) def init_record(self) -> ChatRecord: self.record = save_question(session=self.session, current_user=self.current_user, question=self.chat_question) return self.record def get_record(self): return self.record def set_record(self, record: ChatRecord): self.record = record def get_fields_from_chart(self): chart_info = get_chart_config(self.session, self.record.id) fields = [] if chart_info.get('columns') and len(chart_info.get('columns')) > 0: for column in chart_info.get('columns'): column_str = column.get('value') if column.get('value') != column.get('name'): column_str = column_str + '(' + column.get('name') + ')' fields.append(column_str) if chart_info.get('axis'): for _type in ['x', 'y', 'series']: if chart_info.get('axis').get(_type): column = chart_info.get('axis').get(_type) column_str = column.get('value') if column.get('value') != column.get('name'): column_str = column_str + '(' + column.get('name') + ')' fields.append(column_str) return fields def generate_analysis(self): fields = self.get_fields_from_chart() self.chat_question.fields = orjson.dumps(fields).decode() data = get_chat_chart_data(self.session, self.record.id) self.chat_question.data = orjson.dumps(data.get('data')).decode() analysis_msg: List[Union[BaseMessage, dict[str, Any]]] = [] self.chat_question.terminologies = get_terminology_template(self.session, self.chat_question.question, self.current_user.oid) analysis_msg.append(SystemMessage(content=self.chat_question.analysis_sys_question())) analysis_msg.append(HumanMessage(content=self.chat_question.analysis_user_question())) self.current_logs[OperationEnum.ANALYSIS] = start_log(session=self.session, ai_modal_id=self.chat_question.ai_modal_id, ai_modal_name=self.chat_question.ai_modal_name, operate=OperationEnum.ANALYSIS, record_id=self.record.id, full_message=[ {'type': msg.type, 'content': msg.content} for msg in analysis_msg]) full_thinking_text = '' full_analysis_text = '' res = self.llm.stream(analysis_msg) token_usage = {} for chunk in res: SQLBotLogUtil.info(chunk) reasoning_content_chunk = '' if 'reasoning_content' in chunk.additional_kwargs: reasoning_content_chunk = chunk.additional_kwargs.get('reasoning_content', '') # else: # reasoning_content_chunk = chunk.get('reasoning_content') if reasoning_content_chunk is None: reasoning_content_chunk = '' full_thinking_text += reasoning_content_chunk full_analysis_text += chunk.content yield {'content': chunk.content, 'reasoning_content': reasoning_content_chunk} get_token_usage(chunk, token_usage) analysis_msg.append(AIMessage(full_analysis_text)) self.current_logs[OperationEnum.ANALYSIS] = end_log(session=self.session, log=self.current_logs[ OperationEnum.ANALYSIS], full_message=[ {'type': msg.type, 'content': msg.content} for msg in analysis_msg], reasoning_content=full_thinking_text, token_usage=token_usage) self.record = save_analysis_answer(session=self.session, record_id=self.record.id, answer=orjson.dumps({'content': full_analysis_text}).decode()) def generate_predict(self): fields = self.get_fields_from_chart() self.chat_question.fields = orjson.dumps(fields).decode() data = get_chat_chart_data(self.session, self.record.id) self.chat_question.data = orjson.dumps(data.get('data')).decode() predict_msg: List[Union[BaseMessage, dict[str, Any]]] = [] predict_msg.append(SystemMessage(content=self.chat_question.predict_sys_question())) predict_msg.append(HumanMessage(content=self.chat_question.predict_user_question())) self.current_logs[OperationEnum.PREDICT_DATA] = start_log(session=self.session, ai_modal_id=self.chat_question.ai_modal_id, ai_modal_name=self.chat_question.ai_modal_name, operate=OperationEnum.PREDICT_DATA, record_id=self.record.id, full_message=[ {'type': msg.type, 'content': msg.content} for msg in predict_msg]) full_thinking_text = '' full_predict_text = '' res = self.llm.stream(predict_msg) token_usage = {} for chunk in res: SQLBotLogUtil.info(chunk) reasoning_content_chunk = '' if 'reasoning_content' in chunk.additional_kwargs: reasoning_content_chunk = chunk.additional_kwargs.get('reasoning_content', '') # else: # reasoning_content_chunk = chunk.get('reasoning_content') if reasoning_content_chunk is None: reasoning_content_chunk = '' full_thinking_text += reasoning_content_chunk full_predict_text += chunk.content yield {'content': chunk.content, 'reasoning_content': reasoning_content_chunk} get_token_usage(chunk, token_usage) predict_msg.append(AIMessage(full_predict_text)) self.record = save_predict_answer(session=self.session, record_id=self.record.id, answer=orjson.dumps({'content': full_predict_text}).decode()) self.current_logs[OperationEnum.PREDICT_DATA] = end_log(session=self.session, log=self.current_logs[ OperationEnum.PREDICT_DATA], full_message=[ {'type': msg.type, 'content': msg.content} for msg in predict_msg], reasoning_content=full_thinking_text, token_usage=token_usage) def generate_recommend_questions_task(self): # get schema if self.ds and not self.chat_question.db_schema: self.chat_question.db_schema = self.out_ds_instance.get_db_schema( self.ds.id) if self.out_ds_instance else get_table_schema(session=self.session, current_user=self.current_user, ds=self.ds) guess_msg: List[Union[BaseMessage, dict[str, Any]]] = [] guess_msg.append(SystemMessage(content=self.chat_question.guess_sys_question())) old_questions = list(map(lambda q: q.strip(), get_old_questions(self.session, self.record.datasource))) guess_msg.append( HumanMessage(content=self.chat_question.guess_user_question(orjson.dumps(old_questions).decode()))) self.current_logs[OperationEnum.GENERATE_RECOMMENDED_QUESTIONS] = start_log(session=self.session, ai_modal_id=self.chat_question.ai_modal_id, ai_modal_name=self.chat_question.ai_modal_name, operate=OperationEnum.GENERATE_RECOMMENDED_QUESTIONS, record_id=self.record.id, full_message=[ {'type': msg.type, 'content': msg.content} for msg in guess_msg]) full_thinking_text = '' full_guess_text = '' token_usage = {} res = self.llm.stream(guess_msg) for chunk in res: SQLBotLogUtil.info(chunk) reasoning_content_chunk = '' if 'reasoning_content' in chunk.additional_kwargs: reasoning_content_chunk = chunk.additional_kwargs.get('reasoning_content', '') # else: # reasoning_content_chunk = chunk.get('reasoning_content') if reasoning_content_chunk is None: reasoning_content_chunk = '' full_thinking_text += reasoning_content_chunk full_guess_text += chunk.content yield {'content': chunk.content, 'reasoning_content': reasoning_content_chunk} get_token_usage(chunk, token_usage) guess_msg.append(AIMessage(full_guess_text)) self.current_logs[OperationEnum.GENERATE_RECOMMENDED_QUESTIONS] = end_log(session=self.session, log=self.current_logs[ OperationEnum.GENERATE_RECOMMENDED_QUESTIONS], full_message=[ {'type': msg.type, 'content': msg.content} for msg in guess_msg], reasoning_content=full_thinking_text, token_usage=token_usage) self.record = save_recommend_question_answer(session=self.session, record_id=self.record.id, answer={'content': full_guess_text}) yield {'recommended_question': self.record.recommended_question} def select_datasource(self): datasource_msg: List[Union[BaseMessage, dict[str, Any]]] = [] datasource_msg.append(SystemMessage(self.chat_question.datasource_sys_question())) if self.current_assistant and self.current_assistant.type != 4: _ds_list = get_assistant_ds(session=self.session, llm_service=self) else: oid: str = self.current_user.oid stmt = select(CoreDatasource.id, CoreDatasource.name, CoreDatasource.description).where( CoreDatasource.oid == oid) _ds_list = [ { "id": ds.id, "name": ds.name, "description": ds.description } for ds in self.session.exec(stmt) ] """ _ds_list = self.session.exec(select(CoreDatasource).options( load_only(CoreDatasource.id, CoreDatasource.name, CoreDatasource.description))).all() """ ignore_auto_select = _ds_list and len(_ds_list) == 1 # ignore auto select ds full_thinking_text = '' full_text = '' if not ignore_auto_select: _ds_list_dict = [] for _ds in _ds_list: _ds_list_dict.append(_ds) datasource_msg.append( HumanMessage(self.chat_question.datasource_user_question(orjson.dumps(_ds_list_dict).decode()))) self.current_logs[OperationEnum.CHOOSE_DATASOURCE] = start_log(session=self.session, ai_modal_id=self.chat_question.ai_modal_id, ai_modal_name=self.chat_question.ai_modal_name, operate=OperationEnum.CHOOSE_DATASOURCE, record_id=self.record.id, full_message=[{'type': msg.type, 'content': msg.content} for msg in datasource_msg]) token_usage = {} res = self.llm.stream(datasource_msg) for chunk in res: SQLBotLogUtil.info(chunk) reasoning_content_chunk = '' if 'reasoning_content' in chunk.additional_kwargs: reasoning_content_chunk = chunk.additional_kwargs.get('reasoning_content', '') # else: # reasoning_content_chunk = chunk.get('reasoning_content') if reasoning_content_chunk is None: reasoning_content_chunk = '' full_thinking_text += reasoning_content_chunk full_text += chunk.content yield {'content': chunk.content, 'reasoning_content': reasoning_content_chunk} get_token_usage(chunk, token_usage) datasource_msg.append(AIMessage(full_text)) self.current_logs[OperationEnum.CHOOSE_DATASOURCE] = end_log(session=self.session, log=self.current_logs[ OperationEnum.CHOOSE_DATASOURCE], full_message=[ {'type': msg.type, 'content': msg.content} for msg in datasource_msg], reasoning_content=full_thinking_text, token_usage=token_usage) json_str = extract_nested_json(full_text) _error: Exception | None = None _datasource: int | None = None _engine_type: str | None = None try: data: dict = _ds_list[0] if ignore_auto_select else orjson.loads(json_str) if data.get('id') and data.get('id') != 0: _datasource = data['id'] _chat = self.session.get(Chat, self.record.chat_id) _chat.datasource = _datasource if self.current_assistant and self.current_assistant.type in dynamic_ds_types: _ds = self.out_ds_instance.get_ds(data['id']) self.ds = _ds self.chat_question.engine = _ds.type + get_version(self.ds) self.chat_question.db_schema = self.out_ds_instance.get_db_schema(self.ds.id) _engine_type = self.chat_question.engine _chat.engine_type = _ds.type else: _ds = self.session.get(CoreDatasource, _datasource) if not _ds: _datasource = None raise SingleMessageError(f"Datasource configuration with id {_datasource} not found") self.ds = CoreDatasource(**_ds.model_dump()) self.chat_question.engine = (_ds.type_name if _ds.type != 'excel' else 'PostgreSQL') + get_version( self.ds) self.chat_question.db_schema = get_table_schema(session=self.session, current_user=self.current_user, ds=self.ds) _engine_type = self.chat_question.engine _chat.engine_type = _ds.type_name # save chat self.session.add(_chat) self.session.flush() self.session.refresh(_chat) self.session.commit() elif data['fail']: raise SingleMessageError(data['fail']) else: raise SingleMessageError('No available datasource configuration found') except Exception as e: _error = e if not ignore_auto_select: self.record = save_select_datasource_answer(session=self.session, record_id=self.record.id, answer=orjson.dumps({'content': full_text}).decode(), datasource=_datasource, engine_type=_engine_type) self.chat_question.terminologies = get_terminology_template(self.session, self.chat_question.question, self.ds.oid if isinstance(self.ds, CoreDatasource) else 1) self.init_messages() if _error: raise _error def generate_sql(self): # append current question self.sql_message.append(HumanMessage( self.chat_question.sql_user_question(current_time=datetime.now().strftime('%Y-%m-%d %H:%M:%S')))) self.current_logs[OperationEnum.GENERATE_SQL] = start_log(session=self.session, ai_modal_id=self.chat_question.ai_modal_id, ai_modal_name=self.chat_question.ai_modal_name, operate=OperationEnum.GENERATE_SQL, record_id=self.record.id, full_message=[ {'type': msg.type, 'content': msg.content} for msg in self.sql_message]) full_thinking_text = '' full_sql_text = '' token_usage = {} res = self.llm.stream(self.sql_message) for chunk in res: SQLBotLogUtil.info(chunk) reasoning_content_chunk = '' if 'reasoning_content' in chunk.additional_kwargs: reasoning_content_chunk = chunk.additional_kwargs.get('reasoning_content', '') # else: # reasoning_content_chunk = chunk.get('reasoning_content') if reasoning_content_chunk is None: reasoning_content_chunk = '' full_thinking_text += reasoning_content_chunk full_sql_text += chunk.content yield {'content': chunk.content, 'reasoning_content': reasoning_content_chunk} get_token_usage(chunk, token_usage) self.sql_message.append(AIMessage(full_sql_text)) self.current_logs[OperationEnum.GENERATE_SQL] = end_log(session=self.session, log=self.current_logs[OperationEnum.GENERATE_SQL], full_message=[{'type': msg.type, 'content': msg.content} for msg in self.sql_message], reasoning_content=full_thinking_text, token_usage=token_usage) self.record = save_sql_answer(session=self.session, record_id=self.record.id, answer=orjson.dumps({'content': full_sql_text}).decode()) def generate_with_sub_sql(self, sql, sub_mappings: list): sub_query = json.dumps(sub_mappings, ensure_ascii=False) self.chat_question.sql = sql self.chat_question.sub_query = sub_query dynamic_sql_msg: List[Union[BaseMessage, dict[str, Any]]] = [] dynamic_sql_msg.append(SystemMessage(content=self.chat_question.dynamic_sys_question())) dynamic_sql_msg.append(HumanMessage(content=self.chat_question.dynamic_user_question())) self.current_logs[OperationEnum.GENERATE_DYNAMIC_SQL] = start_log(session=self.session, ai_modal_id=self.chat_question.ai_modal_id, ai_modal_name=self.chat_question.ai_modal_name, operate=OperationEnum.GENERATE_DYNAMIC_SQL, record_id=self.record.id, full_message=[{'type': msg.type, 'content': msg.content} for msg in dynamic_sql_msg]) full_thinking_text = '' full_dynamic_text = '' res = self.llm.stream(dynamic_sql_msg) token_usage = {} for chunk in res: SQLBotLogUtil.info(chunk) reasoning_content_chunk = '' if 'reasoning_content' in chunk.additional_kwargs: reasoning_content_chunk = chunk.additional_kwargs.get('reasoning_content', '') if reasoning_content_chunk is None: reasoning_content_chunk = '' full_thinking_text += reasoning_content_chunk full_dynamic_text += chunk.content get_token_usage(chunk, token_usage) dynamic_sql_msg.append(AIMessage(full_dynamic_text)) self.current_logs[OperationEnum.GENERATE_DYNAMIC_SQL] = end_log(session=self.session, log=self.current_logs[ OperationEnum.GENERATE_DYNAMIC_SQL], full_message=[ {'type': msg.type, 'content': msg.content} for msg in dynamic_sql_msg], reasoning_content=full_thinking_text, token_usage=token_usage) SQLBotLogUtil.info(full_dynamic_text) return full_dynamic_text def generate_assistant_dynamic_sql(self, sql, tables: List): ds: AssistantOutDsSchema = self.ds sub_query = [] for table in ds.tables: if table.name in tables and table.sql: sub_query.append({"table": table.name, "query": table.sql}) if not sub_query: return None return self.generate_with_sub_sql(sql=sql, sub_mappings=sub_query) def build_table_filter(self, sql: str, filters: list): filter = json.dumps(filters, ensure_ascii=False) self.chat_question.sql = sql self.chat_question.filter = filter permission_sql_msg: List[Union[BaseMessage, dict[str, Any]]] = [] permission_sql_msg.append(SystemMessage(content=self.chat_question.filter_sys_question())) permission_sql_msg.append(HumanMessage(content=self.chat_question.filter_user_question())) self.current_logs[OperationEnum.GENERATE_SQL_WITH_PERMISSIONS] = start_log(session=self.session, ai_modal_id=self.chat_question.ai_modal_id, ai_modal_name=self.chat_question.ai_modal_name, operate=OperationEnum.GENERATE_SQL_WITH_PERMISSIONS, record_id=self.record.id, full_message=[ {'type': msg.type, 'content': msg.content} for msg in permission_sql_msg]) full_thinking_text = '' full_filter_text = '' res = self.llm.stream(permission_sql_msg) token_usage = {} for chunk in res: SQLBotLogUtil.info(chunk) reasoning_content_chunk = '' if 'reasoning_content' in chunk.additional_kwargs: reasoning_content_chunk = chunk.additional_kwargs.get('reasoning_content', '') # else: # reasoning_content_chunk = chunk.get('reasoning_content') if reasoning_content_chunk is None: reasoning_content_chunk = '' full_thinking_text += reasoning_content_chunk full_filter_text += chunk.content # yield {'content': chunk.content, 'reasoning_content': reasoning_content_chunk} get_token_usage(chunk, token_usage) permission_sql_msg.append(AIMessage(full_filter_text)) self.current_logs[OperationEnum.GENERATE_SQL_WITH_PERMISSIONS] = end_log(session=self.session, log=self.current_logs[ OperationEnum.GENERATE_SQL_WITH_PERMISSIONS], full_message=[ {'type': msg.type, 'content': msg.content} for msg in permission_sql_msg], reasoning_content=full_thinking_text, token_usage=token_usage) SQLBotLogUtil.info(full_filter_text) return full_filter_text def generate_filter(self, sql: str, tables: List): filters = get_row_permission_filters(session=self.session, current_user=self.current_user, ds=self.ds, tables=tables) if not filters: return None return self.build_table_filter(sql=sql, filters=filters) def generate_assistant_filter(self, sql, tables: List): ds: AssistantOutDsSchema = self.ds filters = [] for table in ds.tables: if table.name in tables and table.rule: filters.append({"table": table.name, "filter": table.rule}) if not filters: return None return self.build_table_filter(sql=sql, filters=filters) def generate_chart(self, chart_type: Optional[str] = ''): # append current question self.chart_message.append(HumanMessage(self.chat_question.chart_user_question(chart_type))) self.current_logs[OperationEnum.GENERATE_CHART] = start_log(session=self.session, ai_modal_id=self.chat_question.ai_modal_id, ai_modal_name=self.chat_question.ai_modal_name, operate=OperationEnum.GENERATE_CHART, record_id=self.record.id, full_message=[ {'type': msg.type, 'content': msg.content} for msg in self.chart_message]) full_thinking_text = '' full_chart_text = '' token_usage = {} res = self.llm.stream(self.chart_message) for chunk in res: SQLBotLogUtil.info(chunk) reasoning_content_chunk = '' if 'reasoning_content' in chunk.additional_kwargs: reasoning_content_chunk = chunk.additional_kwargs.get('reasoning_content', '') # else: # reasoning_content_chunk = chunk.get('reasoning_content') if reasoning_content_chunk is None: reasoning_content_chunk = '' full_thinking_text += reasoning_content_chunk full_chart_text += chunk.content yield {'content': chunk.content, 'reasoning_content': reasoning_content_chunk} get_token_usage(chunk, token_usage) self.chart_message.append(AIMessage(full_chart_text)) self.record = save_chart_answer(session=self.session, record_id=self.record.id, answer=orjson.dumps({'content': full_chart_text}).decode()) self.current_logs[OperationEnum.GENERATE_CHART] = end_log(session=self.session, log=self.current_logs[OperationEnum.GENERATE_CHART], full_message=[ {'type': msg.type, 'content': msg.content} for msg in self.chart_message], reasoning_content=full_thinking_text, token_usage=token_usage) @staticmethod def check_sql(res: str) -> tuple[str, Optional[list]]: json_str = extract_nested_json(res) if json_str is None: raise SingleMessageError(orjson.dumps({'message': 'Cannot parse sql from answer', 'traceback': "Cannot parse sql from answer:\n" + res}).decode()) sql: str data: dict try: data = orjson.loads(json_str) if data['success']: sql = data['sql'] else: message = data['message'] raise SingleMessageError(message) except SingleMessageError as e: raise e except Exception: raise SingleMessageError(orjson.dumps({'message': 'Cannot parse sql from answer', 'traceback': "Cannot parse sql from answer:\n" + res}).decode()) if sql.strip() == '': raise SingleMessageError("SQL query is empty") return sql, data.get('tables') @staticmethod def get_chart_type_from_sql_answer(res: str) -> Optional[str]: json_str = extract_nested_json(res) if json_str is None: return None chart_type: Optional[str] data: dict try: data = orjson.loads(json_str) if data['success']: chart_type = data['chart-type'] else: return None except Exception: return None return chart_type def check_save_sql(self, res: str) -> str: sql, *_ = self.check_sql(res=res) save_sql(session=self.session, sql=sql, record_id=self.record.id) self.chat_question.sql = sql return sql def check_save_chart(self, res: str) -> Dict[str, Any]: json_str = extract_nested_json(res) if json_str is None: raise SingleMessageError(orjson.dumps({'message': 'Cannot parse chart config from answer', 'traceback': "Cannot parse chart config from answer:\n" + res}).decode()) data: dict chart: Dict[str, Any] = {} message = '' error = False try: data = orjson.loads(json_str) if data['type'] and data['type'] != 'error': # todo type check chart = data if chart.get('columns'): for v in chart.get('columns'): v['value'] = v.get('value').lower() if chart.get('axis'): if chart.get('axis').get('x'): chart.get('axis').get('x')['value'] = chart.get('axis').get('x').get('value').lower() if chart.get('axis').get('y'): chart.get('axis').get('y')['value'] = chart.get('axis').get('y').get('value').lower() if chart.get('axis').get('series'): chart.get('axis').get('series')['value'] = chart.get('axis').get('series').get('value').lower() elif data['type'] == 'error': message = data['reason'] error = True else: raise Exception('Chart is empty') except Exception: error = True message = orjson.dumps({'message': 'Cannot parse chart config from answer', 'traceback': "Cannot parse chart config from answer:\n" + res}).decode() if error: raise SingleMessageError(message) save_chart(session=self.session, chart=orjson.dumps(chart).decode(), record_id=self.record.id) return chart def check_save_predict_data(self, res: str) -> bool: json_str = extract_nested_json(res) if not json_str: json_str = '' save_predict_data(session=self.session, record_id=self.record.id, data=json_str) if json_str == '': return False return True def save_error(self, message: str): return save_error_message(session=self.session, record_id=self.record.id, message=message) def save_sql_data(self, data_obj: Dict[str, Any]): try: data_result = data_obj.get('data') limit = 1000 if data_result: data_result = prepare_for_orjson(data_result) if data_result and len(data_result) > limit: data_obj['data'] = data_result[:limit] data_obj['limit'] = limit else: data_obj['data'] = data_result return save_sql_exec_data(session=self.session, record_id=self.record.id, data=orjson.dumps(data_obj).decode()) except Exception as e: raise e def finish(self): return finish_record(session=self.session, record_id=self.record.id) def execute_sql(self, sql: str): """Execute SQL query Args: ds: Data source instance sql: SQL query statement Returns: Query results """ SQLBotLogUtil.info(f"Executing SQL on ds_id {self.ds.id}: {sql}") try: return exec_sql(self.ds, sql) except Exception as e: if isinstance(e, ParseSQLResultError): raise e else: err = traceback.format_exc(limit=1, chain=True) raise SQLBotDBError(err) def pop_chunk(self): try: chunk = self.chunk_list.pop(0) return chunk except IndexError as e: return None def await_result(self): while self.is_running(): while True: chunk = self.pop_chunk() if chunk is not None: yield chunk else: break while True: chunk = self.pop_chunk() if chunk is None: break yield chunk def run_task_async(self, in_chat: bool = True): self.future = executor.submit(self.run_task_cache, in_chat) def run_task_cache(self, in_chat: bool = True): for chunk in self.run_task(in_chat): self.chunk_list.append(chunk) def run_task(self, in_chat: bool = True): try: if self.ds: self.chat_question.terminologies = get_terminology_template(self.session, self.chat_question.question, self.ds.oid if isinstance(self.ds, CoreDatasource) else 1) self.init_messages() # return id if in_chat: yield 'data:' + orjson.dumps({'type': 'id', 'id': self.get_record().id}).decode() + '\n\n' # return title if self.change_title: if self.chat_question.question or self.chat_question.question.strip() != '': brief = rename_chat(session=self.session, rename_object=RenameChat(id=self.get_record().chat_id, brief=self.chat_question.question.strip()[:20])) if in_chat: yield 'data:' + orjson.dumps({'type': 'brief', 'brief': brief}).decode() + '\n\n' # select datasource if datasource is none if not self.ds: ds_res = self.select_datasource() for chunk in ds_res: SQLBotLogUtil.info(chunk) if in_chat: yield 'data:' + orjson.dumps( {'content': chunk.get('content'), 'reasoning_content': chunk.get('reasoning_content'), 'type': 'datasource-result'}).decode() + '\n\n' if in_chat: yield 'data:' + orjson.dumps({'id': self.ds.id, 'datasource_name': self.ds.name, 'engine_type': self.ds.type_name or self.ds.type, 'type': 'datasource'}).decode() + '\n\n' self.chat_question.db_schema = self.out_ds_instance.get_db_schema( self.ds.id) if self.out_ds_instance else get_table_schema(session=self.session, current_user=self.current_user, ds=self.ds) else: self.validate_history_ds() # check connection connected = check_connection(ds=self.ds, trans=None) if not connected: raise SQLBotDBConnectionError('Connect DB failed') # generate sql sql_res = self.generate_sql() full_sql_text = '' for chunk in sql_res: full_sql_text += chunk.get('content') if in_chat: yield 'data:' + orjson.dumps( {'content': chunk.get('content'), 'reasoning_content': chunk.get('reasoning_content'), 'type': 'sql-result'}).decode() + '\n\n' if in_chat: yield 'data:' + orjson.dumps({'type': 'info', 'msg': 'sql generated'}).decode() + '\n\n' # filter sql SQLBotLogUtil.info(full_sql_text) chart_type = self.get_chart_type_from_sql_answer(full_sql_text) use_dynamic_ds: bool = self.current_assistant and self.current_assistant.type in dynamic_ds_types # todo row permission if (not self.current_assistant and is_normal_user(self.current_user)) or use_dynamic_ds: sql, tables = self.check_sql(res=full_sql_text) sql_result = None dynamic_sql_result = None if self.current_assistant: dynamic_sql_result = self.generate_assistant_dynamic_sql(sql, tables) if dynamic_sql_result: SQLBotLogUtil.info(dynamic_sql_result) sql, *_ = self.check_sql(res=dynamic_sql_result) sql_result = self.generate_assistant_filter(sql, tables) else: sql_result = self.generate_filter(sql, tables) # maybe no sql and tables if sql_result: SQLBotLogUtil.info(sql_result) sql = self.check_save_sql(res=sql_result) elif dynamic_sql_result: sql = self.check_save_sql(res=dynamic_sql_result) else: sql = self.check_save_sql(res=full_sql_text) else: sql = self.check_save_sql(res=full_sql_text) SQLBotLogUtil.info(sql) format_sql = sqlparse.format(sql, reindent=True) if in_chat: yield 'data:' + orjson.dumps({'content': format_sql, 'type': 'sql'}).decode() + '\n\n' else: yield f'```sql\n{format_sql}\n```\n\n' # execute sql result = self.execute_sql(sql=sql) self.save_sql_data(data_obj=result) if in_chat: yield 'data:' + orjson.dumps({'content': 'execute-success', 'type': 'sql-data'}).decode() + '\n\n' # generate chart chart_res = self.generate_chart(chart_type) full_chart_text = '' for chunk in chart_res: full_chart_text += chunk.get('content') if in_chat: yield 'data:' + orjson.dumps( {'content': chunk.get('content'), 'reasoning_content': chunk.get('reasoning_content'), 'type': 'chart-result'}).decode() + '\n\n' if in_chat: yield 'data:' + orjson.dumps({'type': 'info', 'msg': 'chart generated'}).decode() + '\n\n' # filter chart SQLBotLogUtil.info(full_chart_text) chart = self.check_save_chart(res=full_chart_text) SQLBotLogUtil.info(chart) if in_chat: yield 'data:' + orjson.dumps( {'content': orjson.dumps(chart).decode(), 'type': 'chart'}).decode() + '\n\n' else: data = [] _fields = {} if chart.get('columns'): for _column in chart.get('columns'): if _column: _fields[_column.get('value')] = _column.get('name') if chart.get('axis'): if chart.get('axis').get('x'): _fields[chart.get('axis').get('x').get('value')] = chart.get('axis').get('x').get('name') if chart.get('axis').get('y'): _fields[chart.get('axis').get('y').get('value')] = chart.get('axis').get('y').get('name') if chart.get('axis').get('series'): _fields[chart.get('axis').get('series').get('value')] = chart.get('axis').get('series').get( 'name') _fields_list = [] _fields_skip = False for _data in result.get('data'): _row = [] for field in result.get('fields'): _row.append(_data.get(field)) if not _fields_skip: _fields_list.append(field if not _fields.get(field) else _fields.get(field)) data.append(_row) _fields_skip = True df = pd.DataFrame(np.array(data), columns=_fields_list) markdown_table = df.to_markdown(index=False) yield markdown_table + '\n\n' record = self.finish() if in_chat: yield 'data:' + orjson.dumps({'type': 'finish'}).decode() + '\n\n' else: # todo generate picture if chart['type'] != 'table': yield '### generated chart picture\n\n' image_url = request_picture(self.record.chat_id, self.record.id, chart, result) SQLBotLogUtil.info(image_url) yield f'![{chart["type"]}]({image_url})' except Exception as e: error_msg: str if isinstance(e, SingleMessageError): error_msg = str(e) elif isinstance(e, SQLBotDBConnectionError): error_msg = orjson.dumps( {'message': str(e), 'type': 'db-connection-err'}).decode() elif isinstance(e, SQLBotDBError): error_msg = orjson.dumps( {'message': 'Execute SQL Failed', 'traceback': str(e), 'type': 'exec-sql-err'}).decode() else: error_msg = orjson.dumps({'message': str(e), 'traceback': traceback.format_exc(limit=1)}).decode() self.save_error(message=error_msg) if in_chat: yield 'data:' + orjson.dumps({'content': error_msg, 'type': 'error'}).decode() + '\n\n' else: yield f'> ❌ **ERROR**\n\n> \n\n> {error_msg}。' def run_recommend_questions_task_async(self): self.future = executor.submit(self.run_recommend_questions_task_cache) def run_recommend_questions_task_cache(self): for chunk in self.run_recommend_questions_task(): self.chunk_list.append(chunk) def run_recommend_questions_task(self): res = self.generate_recommend_questions_task() for chunk in res: if chunk.get('recommended_question'): yield 'data:' + orjson.dumps( {'content': chunk.get('recommended_question'), 'type': 'recommended_question'}).decode() + '\n\n' else: yield 'data:' + orjson.dumps( {'content': chunk.get('content'), 'reasoning_content': chunk.get('reasoning_content'), 'type': 'recommended_question_result'}).decode() + '\n\n' def run_analysis_or_predict_task_async(self, action_type: str, base_record: ChatRecord): self.set_record(save_analysis_predict_record(self.session, base_record, action_type)) self.future = executor.submit(self.run_analysis_or_predict_task_cache, action_type) def run_analysis_or_predict_task_cache(self, action_type: str): for chunk in self.run_analysis_or_predict_task(action_type): self.chunk_list.append(chunk) def run_analysis_or_predict_task(self, action_type: str): try: yield 'data:' + orjson.dumps({'type': 'id', 'id': self.get_record().id}).decode() + '\n\n' if action_type == 'analysis': # generate analysis analysis_res = self.generate_analysis() for chunk in analysis_res: yield 'data:' + orjson.dumps( {'content': chunk.get('content'), 'reasoning_content': chunk.get('reasoning_content'), 'type': 'analysis-result'}).decode() + '\n\n' yield 'data:' + orjson.dumps({'type': 'info', 'msg': 'analysis generated'}).decode() + '\n\n' yield 'data:' + orjson.dumps({'type': 'analysis_finish'}).decode() + '\n\n' elif action_type == 'predict': # generate predict analysis_res = self.generate_predict() full_text = '' for chunk in analysis_res: yield 'data:' + orjson.dumps( {'content': chunk.get('content'), 'reasoning_content': chunk.get('reasoning_content'), 'type': 'predict-result'}).decode() + '\n\n' full_text += chunk.get('content') yield 'data:' + orjson.dumps({'type': 'info', 'msg': 'predict generated'}).decode() + '\n\n' _data = self.check_save_predict_data(res=full_text) if _data: yield 'data:' + orjson.dumps({'type': 'predict-success'}).decode() + '\n\n' else: yield 'data:' + orjson.dumps({'type': 'predict-failed'}).decode() + '\n\n' yield 'data:' + orjson.dumps({'type': 'predict_finish'}).decode() + '\n\n' self.finish() except Exception as e: error_msg: str if isinstance(e, SingleMessageError): error_msg = str(e) else: error_msg = orjson.dumps({'message': str(e), 'traceback': traceback.format_exc(limit=1)}).decode() self.save_error(message=error_msg) yield 'data:' + orjson.dumps({'content': error_msg, 'type': 'error'}).decode() + '\n\n' finally: # end pass def validate_history_ds(self): _ds = self.ds if not self.current_assistant or self.current_assistant.type == 4: try: current_ds = self.session.get(CoreDatasource, _ds.id) if not current_ds: raise SingleMessageError('chat.ds_is_invalid') except Exception as e: raise SingleMessageError("chat.ds_is_invalid") else: try: _ds_list: list[dict] = get_assistant_ds(session=self.session, llm_service=self) match_ds = any(item.get("id") == _ds.id for item in _ds_list) if not match_ds: type = self.current_assistant.type msg = f"ds is invalid [please check ds list and public ds list]" if type == 0 else f"ds is invalid [please check ds api]" raise SingleMessageError(msg) except Exception as e: raise SingleMessageError(f"ds is invalid [{str(e)}]") def execute_sql_with_db(db: SQLDatabase, sql: str) -> str: """Execute SQL query using SQLDatabase Args: db: SQLDatabase instance sql: SQL query statement Returns: str: Query results formatted as string """ try: # Execute query result = db.run(sql) if not result: return "Query executed successfully but returned no results." # Format results return str(result) except Exception as e: error_msg = f"SQL execution failed: {str(e)}" SQLBotLogUtil.exception(error_msg) raise RuntimeError(error_msg) def request_picture(chat_id: int, record_id: int, chart: dict, data: dict): file_name = f'c_{chat_id}_r_{record_id}' columns = chart.get('columns') if chart.get('columns') else [] x = None y = None series = None if chart.get('axis'): x = chart.get('axis').get('x') y = chart.get('axis').get('y') series = chart.get('axis').get('series') axis = [] for v in columns: axis.append({'name': v.get('name'), 'value': v.get('value')}) if x: axis.append({'name': x.get('name'), 'value': x.get('value'), 'type': 'x'}) if y: axis.append({'name': y.get('name'), 'value': y.get('value'), 'type': 'y'}) if series: axis.append({'name': series.get('name'), 'value': series.get('value'), 'type': 'series'}) request_obj = { "path": (settings.MCP_IMAGE_PATH if settings.MCP_IMAGE_PATH[-1] == '/' else ( settings.MCP_IMAGE_PATH + '/')) + file_name, "type": chart['type'], "data": orjson.dumps(data.get('data') if data.get('data') else []).decode(), "axis": orjson.dumps(axis).decode(), } requests.post(url=settings.MCP_IMAGE_HOST, json=request_obj) return f'{(settings.SERVER_IMAGE_HOST if settings.SERVER_IMAGE_HOST[-1] == "/" else (settings.SERVER_IMAGE_HOST + "/"))}{file_name}.png' def get_token_usage(chunk: BaseMessageChunk, token_usage: dict = {}): try: if chunk.usage_metadata: token_usage['input_tokens'] = chunk.usage_metadata.get('input_tokens') token_usage['output_tokens'] = chunk.usage_metadata.get('output_tokens') token_usage['total_tokens'] = chunk.usage_metadata.get('total_tokens') except Exception: pass def get_lang_name(lang: str): if lang and lang == 'en': return '英文' return '简体中文'