import concurrent import json import os import traceback import urllib.parse import warnings from concurrent.futures import ThreadPoolExecutor, Future from datetime import datetime from typing import Any, List, Optional, Union, Dict, Iterator 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 and_, select from sqlalchemy.orm import sessionmaker, scoped_session from sqlbot_xpack.custom_prompt.curd.custom_prompt import find_custom_prompts from sqlbot_xpack.custom_prompt.models.custom_prompt_model import CustomPromptTypeEnum from sqlbot_xpack.license.license_manage import SQLBotLicenseUtil from sqlmodel import 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, \ get_last_execute_sql_error from apps.chat.models.chat_model import ChatQuestion, ChatRecord, Chat, RenameChat, ChatLog, OperationEnum, \ ChatFinishStep from apps.data_training.curd.data_training import get_training_template 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.embedding.ds_embedding import get_ds_embedding 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.db import engine 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] dynamic_subsql_prefix = 'select * from sqlbot_dynamic_temp_table_' session_maker = scoped_session(sessionmaker(bind=engine, class_=Session)) 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 = db_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 last_execute_sql_error: str = None def __init__(self, session: Session, current_user: CurrentUser, chat_question: ChatQuestion, current_assistant: Optional[CurrentAssistant] = None, no_reasoning: bool = False, embedding: bool = False, config: LLMConfig = None): self.chunk_list = [] self.current_user = current_user self.current_assistant = current_assistant chat_id = chat_question.chat_id chat: Chat | None = 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 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, chat_question.question) else: ds = 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=session, current_user=current_user, ds=ds, question=chat_question.question, embedding=embedding) self.generate_sql_logs = list_generate_sql_logs(session=session, chart_id=chat_id) self.generate_chart_logs = list_generate_chart_logs(session=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 # get last_execute_sql_error last_execute_sql_error = get_last_execute_sql_error(session, self.chat_question.chat_id) if last_execute_sql_error: self.chat_question.error_msg = f''' {last_execute_sql_error} ''' else: self.chat_question.error_msg = '' @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, session: Session) -> ChatRecord: self.record = save_question(session=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, _session: Session): chart_info = get_chart_config(_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, _session: Session): fields = self.get_fields_from_chart(_session) self.chat_question.fields = orjson.dumps(fields).decode() data = get_chat_chart_data(_session, self.record.id) self.chat_question.data = orjson.dumps(data.get('data')).decode() analysis_msg: List[Union[BaseMessage, dict[str, Any]]] = [] ds_id = self.ds.id if isinstance(self.ds, CoreDatasource) else None self.chat_question.terminologies = get_terminology_template(_session, self.chat_question.question, self.current_user.oid, ds_id) if SQLBotLicenseUtil.valid(): self.chat_question.custom_prompt = find_custom_prompts(_session, CustomPromptTypeEnum.ANALYSIS, self.current_user.oid, ds_id) 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=_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 = '' token_usage = {} res = process_stream(self.llm.stream(analysis_msg), token_usage) for chunk in res: if chunk.get('content'): full_analysis_text += chunk.get('content') if chunk.get('reasoning_content'): full_thinking_text += chunk.get('reasoning_content') yield chunk analysis_msg.append(AIMessage(full_analysis_text)) self.current_logs[OperationEnum.ANALYSIS] = end_log(session=_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=_session, record_id=self.record.id, answer=orjson.dumps({'content': full_analysis_text}).decode()) def generate_predict(self, _session: Session): fields = self.get_fields_from_chart(_session) self.chat_question.fields = orjson.dumps(fields).decode() data = get_chat_chart_data(_session, self.record.id) self.chat_question.data = orjson.dumps(data.get('data')).decode() if SQLBotLicenseUtil.valid(): ds_id = self.ds.id if isinstance(self.ds, CoreDatasource) else None self.chat_question.custom_prompt = find_custom_prompts(_session, CustomPromptTypeEnum.PREDICT_DATA, self.current_user.oid, ds_id) 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=_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 = '' token_usage = {} res = process_stream(self.llm.stream(predict_msg), token_usage) for chunk in res: if chunk.get('content'): full_predict_text += chunk.get('content') if chunk.get('reasoning_content'): full_thinking_text += chunk.get('reasoning_content') yield chunk predict_msg.append(AIMessage(full_predict_text)) self.record = save_predict_answer(session=_session, record_id=self.record.id, answer=orjson.dumps({'content': full_predict_text}).decode()) self.current_logs[OperationEnum.PREDICT_DATA] = end_log(session=_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, _session: Session): # 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, self.chat_question.question) if self.out_ds_instance else get_table_schema( session=_session, current_user=self.current_user, ds=self.ds, question=self.chat_question.question, embedding=False) 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(_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=_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 = process_stream(self.llm.stream(guess_msg), token_usage) for chunk in res: if chunk.get('content'): full_guess_text += chunk.get('content') if chunk.get('reasoning_content'): full_thinking_text += chunk.get('reasoning_content') yield chunk guess_msg.append(AIMessage(full_guess_text)) self.current_logs[OperationEnum.GENERATE_RECOMMENDED_QUESTIONS] = end_log(session=_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=_session, record_id=self.record.id, answer={'content': full_guess_text}) yield {'recommended_question': self.record.recommended_question} def select_datasource(self, _session: Session): 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=_session, llm_service=self) else: stmt = select(CoreDatasource.id, CoreDatasource.name, CoreDatasource.description).where( and_(CoreDatasource.oid == self.current_user.oid)) _ds_list = [ { "id": ds.id, "name": ds.name, "description": ds.description } for ds in _session.exec(stmt) ] if not _ds_list: raise SingleMessageError('No available datasource configuration found') ignore_auto_select = _ds_list and len(_ds_list) == 1 # ignore auto select ds full_thinking_text = '' full_text = '' if not ignore_auto_select: if settings.TABLE_EMBEDDING_ENABLED and ( not self.current_assistant or (self.current_assistant and self.current_assistant.type != 1)): ds = get_ds_embedding(_session, self.current_user, _ds_list, self.out_ds_instance, self.chat_question.question, self.current_assistant) yield {'content': '{"id":' + str(ds.get('id')) + '}'} else: _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=_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 = process_stream(self.llm.stream(datasource_msg), token_usage) for chunk in res: if chunk.get('content'): full_text += chunk.get('content') if chunk.get('reasoning_content'): full_thinking_text += chunk.get('reasoning_content') yield chunk datasource_msg.append(AIMessage(full_text)) self.current_logs[OperationEnum.CHOOSE_DATASOURCE] = end_log(session=_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) if json_str is None: raise SingleMessageError(f'Cannot parse datasource from answer: {full_text}') ds = orjson.loads(json_str) _error: Exception | None = None _datasource: int | None = None _engine_type: str | None = None try: data: dict = _ds_list[0] if ignore_auto_select else ds if data.get('id') and data.get('id') != 0: _datasource = data['id'] _chat = _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, self.chat_question.question) _engine_type = self.chat_question.engine _chat.engine_type = _ds.type else: _ds = _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=_session, current_user=self.current_user, ds=self.ds, question=self.chat_question.question) _engine_type = self.chat_question.engine _chat.engine_type = _ds.type_name # save chat with _session.begin_nested(): # 为了能继续记日志，先单独处理下事务 try: _session.add(_chat) _session.flush() _session.refresh(_chat) _session.commit() except Exception as e: _session.rollback() raise e 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 and not settings.TABLE_EMBEDDING_ENABLED: self.record = save_select_datasource_answer(session=_session, record_id=self.record.id, answer=orjson.dumps({'content': full_text}).decode(), datasource=_datasource, engine_type=_engine_type) if self.ds: oid = self.ds.oid if isinstance(self.ds, CoreDatasource) else 1 ds_id = self.ds.id if isinstance(self.ds, CoreDatasource) else None self.chat_question.terminologies = get_terminology_template(_session, self.chat_question.question, oid, ds_id) self.chat_question.data_training = get_training_template(_session, self.chat_question.question, ds_id, oid) if SQLBotLicenseUtil.valid(): self.chat_question.custom_prompt = find_custom_prompts(_session, CustomPromptTypeEnum.GENERATE_SQL, oid, ds_id) self.init_messages() if _error: raise _error def generate_sql(self, _session: Session): # 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=_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 = process_stream(self.llm.stream(self.sql_message), token_usage) for chunk in res: if chunk.get('content'): full_sql_text += chunk.get('content') if chunk.get('reasoning_content'): full_thinking_text += chunk.get('reasoning_content') yield chunk self.sql_message.append(AIMessage(full_sql_text)) self.current_logs[OperationEnum.GENERATE_SQL] = end_log(session=_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=_session, record_id=self.record.id, answer=orjson.dumps({'content': full_sql_text}).decode()) def generate_with_sub_sql(self, _session: Session, 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=_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 = '' token_usage = {} res = process_stream(self.llm.stream(dynamic_sql_msg), token_usage) for chunk in res: if chunk.get('content'): full_dynamic_text += chunk.get('content') if chunk.get('reasoning_content'): full_thinking_text += chunk.get('reasoning_content') dynamic_sql_msg.append(AIMessage(full_dynamic_text)) self.current_logs[OperationEnum.GENERATE_DYNAMIC_SQL] = end_log(session=_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, _session: Session, sql, tables: List): ds: AssistantOutDsSchema = self.ds sub_query = [] result_dict = {} for table in ds.tables: if table.name in tables and table.sql: # sub_query.append({"table": table.name, "query": table.sql}) result_dict[table.name] = table.sql sub_query.append({"table": table.name, "query": f'{dynamic_subsql_prefix}{table.name}'}) if not sub_query: return None temp_sql_text = self.generate_with_sub_sql(session=_session, sql=sql, sub_mappings=sub_query) result_dict['sqlbot_temp_sql_text'] = temp_sql_text return result_dict def build_table_filter(self, session: Session, 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=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 = '' token_usage = {} res = process_stream(self.llm.stream(permission_sql_msg), token_usage) for chunk in res: if chunk.get('content'): full_filter_text += chunk.get('content') if chunk.get('reasoning_content'): full_thinking_text += chunk.get('reasoning_content') permission_sql_msg.append(AIMessage(full_filter_text)) self.current_logs[OperationEnum.GENERATE_SQL_WITH_PERMISSIONS] = end_log(session=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, _session: Session, sql: str, tables: List): filters = get_row_permission_filters(session=_session, current_user=self.current_user, ds=self.ds, tables=tables) if not filters: return None return self.build_table_filter(session=_session, sql=sql, filters=filters) def generate_assistant_filter(self, _session: Session, 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(session=_session, sql=sql, filters=filters) def generate_chart(self, _session: Session, 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=_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 = process_stream(self.llm.stream(self.chart_message), token_usage) for chunk in res: if chunk.get('content'): full_chart_text += chunk.get('content') if chunk.get('reasoning_content'): full_thinking_text += chunk.get('reasoning_content') yield chunk self.chart_message.append(AIMessage(full_chart_text)) self.record = save_chart_answer(session=_session, record_id=self.record.id, answer=orjson.dumps({'content': full_chart_text}).decode()) self.current_logs[OperationEnum.GENERATE_CHART] = end_log(session=_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, session: Session, res: str) -> str: sql, *_ = self.check_sql(res=res) save_sql(session=session, sql=sql, record_id=self.record.id) self.chat_question.sql = sql return sql def check_save_chart(self, session: Session, 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=session, chart=orjson.dumps(chart).decode(), record_id=self.record.id) return chart def check_save_predict_data(self, session: Session, res: str) -> bool: json_str = extract_nested_json(res) if not json_str: json_str = '' save_predict_data(session=session, record_id=self.record.id, data=json_str) if json_str == '': return False return True def save_error(self, session: Session, message: str): return save_error_message(session=session, record_id=self.record.id, message=message) def save_sql_data(self, session: Session, 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=session, record_id=self.record.id, data=orjson.dumps(data_obj).decode()) except Exception as e: raise e def finish(self, session: Session): return finish_record(session=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(ds=self.ds, sql=sql, origin_column=False) 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, stream: bool = True, finish_step: ChatFinishStep = ChatFinishStep.GENERATE_CHART): if in_chat: stream = True self.future = executor.submit(self.run_task_cache, in_chat, stream, finish_step) def run_task_cache(self, in_chat: bool = True, stream: bool = True, finish_step: ChatFinishStep = ChatFinishStep.GENERATE_CHART): for chunk in self.run_task(in_chat, stream, finish_step): self.chunk_list.append(chunk) def run_task(self, in_chat: bool = True, stream: bool = True, finish_step: ChatFinishStep = ChatFinishStep.GENERATE_CHART): json_result: Dict[str, Any] = {'success': True} _session = None try: _session = session_maker() if self.ds: oid = self.ds.oid if isinstance(self.ds, CoreDatasource) else 1 ds_id = self.ds.id if isinstance(self.ds, CoreDatasource) else None self.chat_question.terminologies = get_terminology_template(_session, self.chat_question.question, oid, ds_id) self.chat_question.data_training = get_training_template(_session, self.chat_question.question, ds_id, oid) if SQLBotLicenseUtil.valid(): self.chat_question.custom_prompt = find_custom_prompts(_session, CustomPromptTypeEnum.GENERATE_SQL, oid, ds_id) self.init_messages() # return id if in_chat: yield 'data:' + orjson.dumps({'type': 'id', 'id': self.get_record().id}).decode() + '\n\n' if not stream: json_result['record_id'] = self.get_record().id # return title if self.change_title: if self.chat_question.question or self.chat_question.question.strip() != '': brief = rename_chat(session=_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' if not stream: json_result['title'] = brief # select datasource if datasource is none if not self.ds: ds_res = self.select_datasource(_session) 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, self.chat_question.question) if self.out_ds_instance else get_table_schema( session=_session, current_user=self.current_user, ds=self.ds, question=self.chat_question.question) else: self.validate_history_ds(_session) # 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(_session) 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 is_page_embedded: bool = self.current_assistant and self.current_assistant.type == 4 dynamic_sql_result = None sqlbot_temp_sql_text = None assistant_dynamic_sql = None # row permission if ((not self.current_assistant or is_page_embedded) and is_normal_user( self.current_user)) or use_dynamic_ds: sql, tables = self.check_sql(res=full_sql_text) sql_result = None if use_dynamic_ds: dynamic_sql_result = self.generate_assistant_dynamic_sql(_session, sql, tables) sqlbot_temp_sql_text = dynamic_sql_result.get( 'sqlbot_temp_sql_text') if dynamic_sql_result else None # sql_result = self.generate_assistant_filter(sql, tables) else: sql_result = self.generate_filter(_session, sql, tables) # maybe no sql and tables if sql_result: SQLBotLogUtil.info(sql_result) sql = self.check_save_sql(session=_session, res=sql_result) elif dynamic_sql_result and sqlbot_temp_sql_text: assistant_dynamic_sql = self.check_save_sql(session=_session, res=sqlbot_temp_sql_text) else: sql = self.check_save_sql(session=_session, res=full_sql_text) else: sql = self.check_save_sql(session=_session, res=full_sql_text) SQLBotLogUtil.info('sql: ' + sql) if not stream: json_result['sql'] = sql format_sql = sqlparse.format(sql, reindent=True) if in_chat: yield 'data:' + orjson.dumps({'content': format_sql, 'type': 'sql'}).decode() + '\n\n' else: if stream: yield f'```sql\n{format_sql}\n```\n\n' # execute sql real_execute_sql = sql if sqlbot_temp_sql_text and assistant_dynamic_sql: dynamic_sql_result.pop('sqlbot_temp_sql_text') for origin_table, subsql in dynamic_sql_result.items(): assistant_dynamic_sql = assistant_dynamic_sql.replace(f'{dynamic_subsql_prefix}{origin_table}', subsql) real_execute_sql = assistant_dynamic_sql if finish_step.value <= ChatFinishStep.GENERATE_SQL.value: if in_chat: yield 'data:' + orjson.dumps({'type': 'finish'}).decode() + '\n\n' if not stream: yield json_result return result = self.execute_sql(sql=real_execute_sql) self.save_sql_data(session=_session, data_obj=result) if in_chat: yield 'data:' + orjson.dumps({'content': 'execute-success', 'type': 'sql-data'}).decode() + '\n\n' if not stream: json_result['data'] = result.get('data') if finish_step.value <= ChatFinishStep.QUERY_DATA.value: if stream: if in_chat: yield 'data:' + orjson.dumps({'type': 'finish'}).decode() + '\n\n' else: data = [] _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) data.append(_row) _fields_skip = True if not data or not _fields_list: yield 'The SQL execution result is empty.\n\n' else: df = pd.DataFrame(data, columns=_fields_list) markdown_table = df.to_markdown(index=False) yield markdown_table + '\n\n' else: yield json_result return # generate chart chart_res = self.generate_chart(_session, 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(session=_session, res=full_chart_text) SQLBotLogUtil.info(chart) if not stream: json_result['chart'] = chart if in_chat: yield 'data:' + orjson.dumps( {'content': orjson.dumps(chart).decode(), 'type': 'chart'}).decode() + '\n\n' else: if stream: 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 if not data or not _fields_list: yield 'The SQL execution result is empty.\n\n' else: df = pd.DataFrame(data, columns=_fields_list) markdown_table = df.to_markdown(index=False) yield markdown_table + '\n\n' 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) if stream: yield f'![{chart["type"]}]({image_url})' else: json_result['image_url'] = image_url if not stream: yield json_result except Exception as e: traceback.print_exc() 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() if _session: self.save_error(session=_session, message=error_msg) if in_chat: yield 'data:' + orjson.dumps({'content': error_msg, 'type': 'error'}).decode() + '\n\n' else: if stream: yield f'> ❌ **ERROR**\n\n> \n\n> {error_msg}。' else: json_result['success'] = False json_result['message'] = error_msg yield json_result finally: self.finish(_session) session_maker.remove() 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): try: _session = session_maker() res = self.generate_recommend_questions_task(_session) 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' except Exception: traceback.print_exc() finally: session_maker.remove() def run_analysis_or_predict_task_async(self, session: Session, action_type: str, base_record: ChatRecord): self.set_record(save_analysis_predict_record(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): _session = None try: _session = session_maker() 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(_session) 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(_session) 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(session=_session, 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(_session) 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() if _session: self.save_error(session=_session, message=error_msg) yield 'data:' + orjson.dumps({'content': error_msg, 'type': 'error'}).decode() + '\n\n' finally: # end session_maker.remove() def validate_history_ds(self, session: Session): _ds = self.ds if not self.current_assistant or self.current_assistant.type == 4: try: current_ds = 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=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"[please check ds list and public ds list]" if type == 0 else f"[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": os.path.join(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) request_path = urllib.parse.urljoin(settings.SERVER_IMAGE_HOST, f"{file_name}.png") return request_path def get_token_usage(chunk: BaseMessageChunk, token_usage: dict = None): try: if chunk.usage_metadata: if token_usage is None: token_usage = {} 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 process_stream(res: Iterator[BaseMessageChunk], token_usage: Dict[str, Any] = None, enable_tag_parsing: bool = settings.PARSE_REASONING_BLOCK_ENABLED, start_tag: str = settings.DEFAULT_REASONING_CONTENT_START, end_tag: str = settings.DEFAULT_REASONING_CONTENT_END ): if token_usage is None: token_usage = {} in_thinking_block = False # 标记是否在思考过程块中 current_thinking = '' # 当前收集的思考过程内容 pending_start_tag = '' # 用于缓存可能被截断的开始标签部分 for chunk in res: SQLBotLogUtil.info(chunk) reasoning_content_chunk = '' content = chunk.content output_content = '' # 实际要输出的内容 # 检查additional_kwargs中的reasoning_content if 'reasoning_content' in chunk.additional_kwargs: reasoning_content = chunk.additional_kwargs.get('reasoning_content', '') if reasoning_content is None: reasoning_content = '' # 累积additional_kwargs中的思考内容到current_thinking current_thinking += reasoning_content reasoning_content_chunk = reasoning_content # 只有当current_thinking不是空字符串时才跳过标签解析 if not in_thinking_block and current_thinking.strip() != '': output_content = content # 正常输出content yield { 'content': output_content, 'reasoning_content': reasoning_content_chunk } get_token_usage(chunk, token_usage) continue # 跳过后续的标签解析逻辑 # 如果没有有效的思考内容，并且启用了标签解析，才执行标签解析逻辑 # 如果有缓存的开始标签部分，先拼接当前内容 if pending_start_tag: content = pending_start_tag + content pending_start_tag = '' # 检查是否开始思考过程块（处理可能被截断的开始标签） if enable_tag_parsing and not in_thinking_block and start_tag: if start_tag in content: start_idx = content.index(start_tag) # 只有当开始标签前面没有其他文本时才认为是真正的思考块开始 if start_idx == 0 or content[:start_idx].strip() == '': # 完整标签存在且前面没有其他文本 output_content += content[:start_idx] # 输出开始标签之前的内容 content = content[start_idx + len(start_tag):] # 移除开始标签 in_thinking_block = True else: # 开始标签前面有其他文本，不认为是思考块开始 output_content += content content = '' else: # 检查是否可能有部分开始标签 for i in range(1, len(start_tag)): if content.endswith(start_tag[:i]): # 只有当当前内容全是空白时才缓存部分标签 if content[:-i].strip() == '': pending_start_tag = start_tag[:i] content = content[:-i] # 移除可能的部分标签 output_content += content content = '' break # 处理思考块内容 if enable_tag_parsing and in_thinking_block and end_tag: if end_tag in content: # 找到结束标签 end_idx = content.index(end_tag) current_thinking += content[:end_idx] # 收集思考内容 reasoning_content_chunk += current_thinking # 添加到当前块的思考内容 content = content[end_idx + len(end_tag):] # 移除结束标签后的内容 current_thinking = '' # 重置当前思考内容 in_thinking_block = False output_content += content # 输出结束标签之后的内容 else: # 在遇到结束标签前，持续收集思考内容 current_thinking += content reasoning_content_chunk += content content = '' else: # 不在思考块中或标签解析未启用，正常输出 output_content += content yield { 'content': output_content, 'reasoning_content': reasoning_content_chunk } get_token_usage(chunk, token_usage) def get_lang_name(lang: str): if not lang: return '简体中文' normalized = lang.lower() if normalized.startswith('en'): return '英文' if normalized.startswith('ko'): return '韩语' return '简体中文'