import concurrent import json import traceback import warnings from concurrent.futures import ThreadPoolExecutor, Future 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_full_sql_message, save_full_sql_message_and_answer, save_sql, \ save_error_message, save_sql_exec_data, save_full_chart_message, save_full_chart_message_and_answer, save_chart, \ finish_record, save_full_analysis_message_and_answer, save_full_predict_message_and_answer, save_predict_data, \ save_full_select_datasource_message_and_answer, save_full_recommend_question_message_and_answer, \ get_old_questions, save_analysis_predict_record, list_base_records, rename_chat from apps.chat.models.chat_model import ChatQuestion, ChatRecord, Chat, RenameChat 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 from apps.system.crud.assistant import AssistantOutDs, AssistantOutDsFactory, get_assistant_ds from apps.system.schemas.system_schema import AssistantOutDsSchema from common.core.config import settings from common.core.deps import CurrentAssistant, CurrentUser from common.error import SingleMessageError from common.utils.utils import SQLBotLogUtil, extract_nested_json, prepare_for_orjson warnings.filterwarnings("ignore") base_message_count_limit = 5 executor = ThreadPoolExecutor(max_workers=200) 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]]] = [] history_records: List[ChatRecord] = [] session: Session current_user: CurrentUser current_assistant: Optional[CurrentAssistant] = None out_ds_instance: Optional[AssistantOutDs] = None change_title: bool = False chunk_list: List[str] = [] future: Future def __init__(self, current_user: CurrentUser, chat_question: ChatQuestion, current_assistant: Optional[CurrentAssistant] = 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 == 1: 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 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' chat_question.db_schema = get_table_schema(session=self.session, current_user=current_user, ds=ds) history_records: List[ChatRecord] = list( map(lambda x: ChatRecord(**x.model_dump()), filter(lambda r: True if r.first_chat != True else False, list_base_records(session=self.session, current_user=current_user, chart_id=chat_id)))) self.change_title = len(history_records) == 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 = get_default_config() self.chat_question.ai_modal_id = self.config.model_id # Create LLM instance through factory llm_instance = LLMFactory.create_llm(self.config) self.llm = llm_instance.llm self.history_records = history_records self.init_messages() 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): # self.agent_executor = create_react_agent(self.llm) last_sql_messages = list( filter(lambda r: True if r.full_sql_message is not None and r.full_sql_message.strip() != '' else False, self.history_records)) last_sql_message_str = "[]" if last_sql_messages is None or len(last_sql_messages) == 0 else last_sql_messages[ -1].full_sql_message last_chart_messages = list( filter( lambda r: True if r.full_chart_message is not None and r.full_chart_message.strip() != '' else False, self.history_records)) last_chart_message_str = "[]" if last_chart_messages is None or len(last_chart_messages) == 0 else \ last_chart_messages[-1].full_chart_message last_sql_messages: List[dict[str, Any]] = orjson.loads(last_sql_message_str) # 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]] = orjson.loads(last_chart_message_str) 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['type'] == 'human': _msg = HumanMessage(content=last_chart_message['content']) self.chart_message.append(_msg) elif last_chart_message['type'] == 'ai': _msg = AIMessage(content=last_chart_message['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 = orjson.loads(self.record.chart) 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() self.chat_question.data = orjson.dumps(orjson.loads(self.record.data).get('data')).decode() analysis_msg: List[Union[BaseMessage, dict[str, Any]]] = [] analysis_msg.append(SystemMessage(content=self.chat_question.analysis_sys_question())) analysis_msg.append(HumanMessage(content=self.chat_question.analysis_user_question())) history_msg = [] if self.record.full_analysis_message and self.record.full_analysis_message.strip() != '': history_msg = orjson.loads(self.record.full_analysis_message) self.record = save_full_analysis_message_and_answer(session=self.session, record_id=self.record.id, answer='', full_message=orjson.dumps(history_msg + [{'type': msg.type, 'content': msg.content} for msg in analysis_msg]).decode()) 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.record = save_full_analysis_message_and_answer(session=self.session, record_id=self.record.id, token_usage=token_usage, answer=orjson.dumps({'content': full_analysis_text, 'reasoning_content': full_thinking_text}).decode(), full_message=orjson.dumps(history_msg + [{'type': msg.type, 'content': msg.content} for msg in analysis_msg]).decode()) def generate_predict(self): fields = self.get_fields_from_chart() self.chat_question.fields = orjson.dumps(fields).decode() self.chat_question.data = orjson.dumps(orjson.loads(self.record.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())) history_msg = [] if self.record.full_predict_message and self.record.full_predict_message.strip() != '': history_msg = orjson.loads(self.record.full_predict_message) self.record = save_full_predict_message_and_answer(session=self.session, record_id=self.record.id, answer='', data='', full_message=orjson.dumps(history_msg + [{'type': msg.type, 'content': msg.content} for msg in predict_msg]).decode()) 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_full_predict_message_and_answer(session=self.session, record_id=self.record.id, token_usage=token_usage, answer=orjson.dumps({'content': full_predict_text, 'reasoning_content': full_thinking_text}).decode(), data='', full_message=orjson.dumps(history_msg + [{'type': msg.type, 'content': msg.content} for msg in predict_msg]).decode()) 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[0].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.record = save_full_recommend_question_message_and_answer(session=self.session, record_id=self.record.id, full_message=orjson.dumps([{'type': msg.type, 'content': msg.content} for msg in guess_msg]).decode()) 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.record = save_full_recommend_question_message_and_answer(session=self.session, record_id=self.record.id, token_usage=token_usage, answer={'content': full_guess_text, 'reasoning_content': full_thinking_text}, full_message=orjson.dumps([{'type': msg.type, 'content': msg.content} for msg in guess_msg]).decode()) 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: _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 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()))) history_msg = [] if self.record.full_select_datasource_message and self.record.full_select_datasource_message.strip() != '': history_msg = orjson.loads(self.record.full_select_datasource_message) self.record = save_full_select_datasource_message_and_answer(session=self.session, record_id=self.record.id, answer='', full_message=orjson.dumps(history_msg + [{'type': msg.type, 'content': msg.content} for msg in datasource_msg]).decode()) full_thinking_text = '' full_text = '' 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)) 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 == 1: _ds = self.out_ds_instance.get_ds(data['id']) self.ds = _ds self.chat_question.engine = _ds.type 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' 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_full_select_datasource_message_and_answer(session=self.session, record_id=self.record.id, answer=orjson.dumps({'content': full_text, 'reasoning_content': full_thinking_text}).decode(), datasource=_datasource, engine_type=_engine_type, full_message=orjson.dumps(history_msg + [{'type': msg.type, 'content': msg.content} for msg in datasource_msg]).decode()) 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())) self.record = save_full_sql_message(session=self.session, record_id=self.record.id, full_message=orjson.dumps( [{'type': msg.type, 'content': msg.content} for msg in self.sql_message]).decode()) 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.record = save_full_sql_message_and_answer(session=self.session, record_id=self.record.id, token_usage=token_usage, answer=orjson.dumps({'content': full_sql_text, 'reasoning_content': full_thinking_text}).decode(), full_message=orjson.dumps( [{'type': msg.type, 'content': msg.content} for msg in self.sql_message]).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 msg: List[Union[BaseMessage, dict[str, Any]]] = [] msg.append(SystemMessage(content=self.chat_question.dynamic_sys_question())) msg.append(HumanMessage(content=self.chat_question.dynamic_user_question())) full_thinking_text = '' full_dynamic_text = '' res = self.llm.stream(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) 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 msg: List[Union[BaseMessage, dict[str, Any]]] = [] msg.append(SystemMessage(content=self.chat_question.filter_sys_question())) msg.append(HumanMessage(content=self.chat_question.filter_user_question())) history_msg = [] # if self.record.full_analysis_message and self.record.full_analysis_message.strip() != '': # history_msg = orjson.loads(self.record.full_analysis_message) # self.record = save_full_analysis_message_and_answer(session=self.session, record_id=self.record.id, answer='', # full_message=orjson.dumps(history_msg + # [{'type': msg.type, # 'content': msg.content} for msg # in # msg]).decode()) full_thinking_text = '' full_filter_text = '' res = self.llm.stream(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) msg.append(AIMessage(full_filter_text)) # self.record = save_full_analysis_message_and_answer(session=self.session, record_id=self.record.id, # token_usage=token_usage, # answer=orjson.dumps({'content': full_analysis_text, # 'reasoning_content': full_thinking_text}).decode(), # full_message=orjson.dumps(history_msg + # [{'type': msg.type, # 'content': msg.content} for msg # in # analysis_msg]).decode()) 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): # append current question self.chart_message.append(HumanMessage(self.chat_question.chart_user_question())) self.record = save_full_chart_message(session=self.session, record_id=self.record.id, full_message=orjson.dumps( [{'type': msg.type, 'content': msg.content} for msg in self.chart_message]).decode()) 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_full_chart_message_and_answer(session=self.session, record_id=self.record.id, token_usage=token_usage, answer=orjson.dumps({'content': full_chart_text, 'reasoning_content': full_thinking_text}).decode(), full_message=orjson.dumps( [{'type': msg.type, 'content': msg.content} for msg in self.chart_message]).decode()) def check_sql(self, res: str) -> tuple[any]: 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()) data: dict = orjson.loads(json_str) sql = '' message = '' if data['success']: sql = data['sql'] else: message = data['message'] raise SingleMessageError(message) if sql.strip() == '': raise SingleMessageError("SQL query is empty") return sql, data.get('tables') 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 = orjson.loads(json_str) chart: Dict[str, Any] = {} message = '' error = False 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: message = 'Chart is empty' error = True 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]): data_result = data_obj.get('data') if data_result: data_result = prepare_for_orjson(data_result) data_obj['data'] = data_result return save_sql_exec_data(session=self.session, record_id=self.record.id, data=orjson.dumps(data_obj).decode()) 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}") return exec_sql(self.ds, sql) 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: # return id if in_chat: yield 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 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 orjson.dumps( {'content': chunk.get('content'), 'reasoning_content': chunk.get('reasoning_content'), 'type': 'datasource-result'}).decode() + '\n\n' if in_chat: yield 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() # 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 orjson.dumps( {'content': chunk.get('content'), 'reasoning_content': chunk.get('reasoning_content'), 'type': 'sql-result'}).decode() + '\n\n' if in_chat: yield orjson.dumps({'type': 'info', 'msg': 'sql generated'}).decode() + '\n\n' # filter sql SQLBotLogUtil.info(full_sql_text) use_dynamic_ds: bool = self.current_assistant and self.current_assistant.type == 1 # 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 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 orjson.dumps({'content': 'execute-success', 'type': 'sql-data'}).decode() + '\n\n' # generate chart chart_res = self.generate_chart() full_chart_text = '' for chunk in chart_res: full_chart_text += chunk.get('content') if in_chat: yield orjson.dumps( {'content': chunk.get('content'), 'reasoning_content': chunk.get('reasoning_content'), 'type': 'chart-result'}).decode() + '\n\n' if in_chat: yield 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 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 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) 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 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 orjson.dumps( {'content': chunk.get('recommended_question'), 'type': 'recommended_question'}).decode() + '\n\n' else: yield 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 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 orjson.dumps( {'content': chunk.get('content'), 'reasoning_content': chunk.get('reasoning_content'), 'type': 'analysis-result'}).decode() + '\n\n' yield orjson.dumps({'type': 'info', 'msg': 'analysis generated'}).decode() + '\n\n' yield 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 orjson.dumps( {'content': chunk.get('content'), 'reasoning_content': chunk.get('reasoning_content'), 'type': 'predict-result'}).decode() + '\n\n' full_text += chunk.get('content') yield orjson.dumps({'type': 'info', 'msg': 'predict generated'}).decode() + '\n\n' _data = self.check_save_predict_data(res=full_text) if _data: yield orjson.dumps({'type': 'predict-success'}).decode() + '\n\n' else: yield orjson.dumps({'type': 'predict-failed'}).decode() + '\n\n' yield 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 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: 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'