diff --git a/backend/apps/chat/api/chat.py b/backend/apps/chat/api/chat.py
index 674494c4..32f338a7 100644
--- a/backend/apps/chat/api/chat.py
+++ b/backend/apps/chat/api/chat.py
@@ -1,20 +1,14 @@
import traceback
-from typing import List
import orjson
from fastapi import APIRouter, HTTPException
from fastapi.responses import StreamingResponse
-from sqlmodel import select
from apps.chat.curd.chat import list_chats, get_chat_with_records, create_chat, rename_chat, \
- delete_chat, list_records
-from apps.chat.models.chat_model import CreateChat, ChatRecord, RenameChat, Chat, ChatQuestion, ChatMcp
-from apps.chat.task.llm import LLMService
-from apps.datasource.crud.datasource import get_table_schema
-from apps.datasource.models.datasource import CoreDatasource
-from apps.system.crud.user import get_user_info
-from apps.system.models.system_model import AiModelDetail
-from common.core.deps import SessionDep, CurrentUser, get_current_user
+ delete_chat
+from apps.chat.models.chat_model import CreateChat, ChatRecord, RenameChat, ChatQuestion
+from apps.chat.task.llm import LLMService, run_task
+from common.core.deps import SessionDep, CurrentUser
router = APIRouter(tags=["Data Q&A"], prefix="/chat")
@@ -81,107 +75,17 @@ async def stream_sql(session: SessionDep, current_user: CurrentUser, request_que
Streaming response with analysis results
"""
- chat = session.query(Chat).filter(Chat.id == request_question.chat_id).first()
- if not chat:
- raise HTTPException(
- status_code=400,
- detail=f"Chat with id {request_question.chat_id} not found"
- )
- ds: CoreDatasource | None = None
- if chat.datasource:
- # Get available datasource
- ds = session.query(CoreDatasource).filter(CoreDatasource.id == chat.datasource).first()
- if not ds:
- raise HTTPException(
- status_code=500,
- detail="No available datasource configuration found"
- )
-
- request_question.engine = ds.type_name if ds.type != 'excel' else 'PostgreSQL'
-
- # Get available AI model
- aimodel = session.exec(select(AiModelDetail).where(
- AiModelDetail.status == True,
- AiModelDetail.api_key.is_not(None)
- )).first()
- if not aimodel:
+ try:
+ llm_service = LLMService(session, current_user, request_question)
+ llm_service.init_record()
+ except Exception as e:
+ traceback.print_exc()
raise HTTPException(
status_code=500,
- detail="No available AI model configuration found"
+ detail=str(e)
)
- history_records: List[ChatRecord] = list(filter(lambda r: True if r.first_chat != True else False,
- list_records(session=session, current_user=current_user,
- chart_id=request_question.chat_id)))
- # get schema
- if ds:
- request_question.db_schema = get_table_schema(session=session, ds=ds)
-
- db_user = get_user_info(session=session, user_id=current_user.id)
- request_question.lang = db_user.language
-
- llm_service = LLMService(request_question, aimodel, history_records,
- CoreDatasource(**ds.model_dump()) if ds else None)
-
- llm_service.init_record(session=session, current_user=current_user)
-
- def run_task():
- try:
- # return id
- yield orjson.dumps({'type': 'id', 'id': llm_service.get_record().id}).decode() + '\n\n'
-
- # select datasource if datasource is none
- if not ds:
- ds_res = llm_service.select_datasource(session=session)
- for chunk in ds_res:
- yield orjson.dumps({'content': chunk, 'type': 'datasource-result'}).decode() + '\n\n'
- yield orjson.dumps({'id': llm_service.ds.id, 'datasource_name': llm_service.ds.name,
- 'engine_type': llm_service.ds.type_name, 'type': 'datasource'}).decode() + '\n\n'
-
- llm_service.chat_question.db_schema = get_table_schema(session=session, ds=llm_service.ds)
-
- # generate sql
- sql_res = llm_service.generate_sql(session=session)
- full_sql_text = ''
- for chunk in sql_res:
- full_sql_text += chunk
- yield orjson.dumps({'content': chunk, 'type': 'sql-result'}).decode() + '\n\n'
- yield orjson.dumps({'type': 'info', 'msg': 'sql generated'}).decode() + '\n\n'
-
- # filter sql
- print(full_sql_text)
- sql = llm_service.check_save_sql(session=session, res=full_sql_text)
- print(sql)
- yield orjson.dumps({'content': sql, 'type': 'sql'}).decode() + '\n\n'
-
- # execute sql
- result = llm_service.execute_sql(sql=sql)
- llm_service.save_sql_data(session=session, data_obj=result)
- yield orjson.dumps({'content': orjson.dumps(result).decode(), 'type': 'sql-data'}).decode() + '\n\n'
-
- # generate chart
- chart_res = llm_service.generate_chart(session=session)
- full_chart_text = ''
- for chunk in chart_res:
- full_chart_text += chunk
- yield orjson.dumps({'content': chunk, 'type': 'chart-result'}).decode() + '\n\n'
- yield orjson.dumps({'type': 'info', 'msg': 'chart generated'}).decode() + '\n\n'
-
- # filter chart
- print(full_chart_text)
- chart = llm_service.check_save_chart(session=session, res=full_chart_text)
- print(chart)
- yield orjson.dumps({'content': orjson.dumps(chart).decode(), 'type': 'chart'}).decode() + '\n\n'
-
- llm_service.finish(session=session)
- yield orjson.dumps({'type': 'finish'}).decode() + '\n\n'
-
- except Exception as e:
- traceback.print_exc()
- llm_service.save_error(session=session, message=str(e))
- yield orjson.dumps({'content': str(e), 'type': 'error'}).decode() + '\n\n'
-
- return StreamingResponse(run_task(), media_type="text/event-stream")
+ return StreamingResponse(run_task(llm_service, session), media_type="text/event-stream")
@router.post("/record/{chart_record_id}/{action_type}")
@@ -205,35 +109,9 @@ async def analysis_or_predict(session: SessionDep, current_user: CurrentUser, ch
detail=f"Chat record with id {chart_record_id} has not generated chart, do not support to analyze it"
)
- chat = session.query(Chat).filter(Chat.id == record.chat_id).first()
- if not chat:
- raise HTTPException(
- status_code=400,
- detail=f"Chat with id {record.chart_id} not found"
- )
+ request_question = ChatQuestion(chat_id=record.chat_id, question='')
- if chat.create_by != current_user.id:
- raise HTTPException(
- status_code=401,
- detail=f"You cannot use the chat with id {record.chart_id}"
- )
-
- # Get available AI model
- aimodel = session.exec(select(AiModelDetail).where(
- AiModelDetail.status == True,
- AiModelDetail.api_key.is_not(None)
- )).first()
- if not aimodel:
- raise HTTPException(
- status_code=500,
- detail="No available AI model configuration found"
- )
-
- request_question = ChatQuestion(chat_id=chat.id, question='')
- db_user = get_user_info(session=session, user_id=current_user.id)
- request_question.lang = db_user.language
-
- llm_service = LLMService(request_question, aimodel)
+ llm_service = LLMService(session, current_user, request_question)
llm_service.set_record(record)
def run_task():
@@ -249,14 +127,14 @@ async def analysis_or_predict(session: SessionDep, current_user: CurrentUser, ch
elif action_type == 'predict':
# generate predict
- analysis_res = llm_service.generate_predict(session=session)
+ analysis_res = llm_service.generate_predict()
full_text = ''
for chunk in analysis_res:
yield orjson.dumps({'content': chunk, 'type': 'predict-result'}).decode() + '\n\n'
full_text += chunk
yield orjson.dumps({'type': 'info', 'msg': 'predict generated'}).decode() + '\n\n'
- _data = llm_service.check_save_predict_data(session=session, res=full_text)
+ _data = llm_service.check_save_predict_data(res=full_text)
yield orjson.dumps({'type': 'predict', 'content': _data}).decode() + '\n\n'
yield orjson.dumps({'type': 'predict_finish'}).decode() + '\n\n'
diff --git a/backend/apps/chat/task/llm.py b/backend/apps/chat/task/llm.py
index 8cc9d80c..18e7b1ff 100644
--- a/backend/apps/chat/task/llm.py
+++ b/backend/apps/chat/task/llm.py
@@ -3,6 +3,7 @@ import warnings
from typing import Any, List, Union, Dict
import orjson
+import pandas as pd
from langchain_community.utilities import SQLDatabase
from langchain_core.language_models import BaseLLM
from langchain_core.messages import BaseMessage, SystemMessage, HumanMessage, AIMessage, AIMessageChunk
@@ -13,10 +14,12 @@ from apps.ai_model.model_factory import LLMConfig, LLMFactory, get_llm_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_select_datasource_message_and_answer, list_records
from apps.chat.models.chat_model import ChatQuestion, ChatRecord, Chat
+from apps.datasource.crud.datasource import get_table_schema
from apps.datasource.models.datasource import CoreDatasource
from apps.db.db import exec_sql
+from apps.system.crud.user import get_user_info
from apps.system.models.system_model import AiModelDetail
from common.core.deps import SessionDep, CurrentUser
@@ -34,13 +37,48 @@ class LLMService:
sql_message: List[Union[BaseMessage, dict[str, Any]]] = []
chart_message: List[Union[BaseMessage, dict[str, Any]]] = []
history_records: List[ChatRecord] = []
+ session: SessionDep
+ _current_user: CurrentUser
- def __init__(self, chat_question: ChatQuestion, aimodel: AiModelDetail, history_records: List[ChatRecord] = [],
- ds: CoreDatasource = None):
- self.ds = ds
+ def __init__(self, session: SessionDep, current_user: CurrentUser, chat_question: ChatQuestion):
+
+ self.session = session
+ self.current_user = current_user
+
+ chat = self.session.query(Chat).filter(Chat.id == chat_question.chat_id).first()
+ if not chat:
+ raise Exception(f"Chat with id {chat_question.chat_id} not found")
+ ds: CoreDatasource | None = None
+ if chat.datasource:
+ # Get available datasource
+ ds = self.session.query(CoreDatasource).filter(CoreDatasource.id == chat.datasource).first()
+ if not ds:
+ raise Exception("No available datasource configuration found")
+
+ chat_question.engine = ds.type_name if ds.type != 'excel' else 'PostgreSQL'
+
+ # Get available AI model
+ aimodel = self.session.exec(select(AiModelDetail).where(
+ AiModelDetail.status == True,
+ AiModelDetail.api_key.is_not(None)
+ )).first()
+ if not aimodel and aimodel[0]:
+ raise Exception("No available AI model configuration found")
+
+ history_records: List[ChatRecord] = list(filter(lambda r: True if r.first_chat != True else False,
+ list_records(session=self.session, current_user=current_user,
+ chart_id=chat_question.chat_id)))
+ # get schema
+ if ds:
+ chat_question.db_schema = get_table_schema(session=self.session, ds=ds)
+
+ db_user = get_user_info(session=self.session, user_id=current_user.id)
+ chat_question.lang = db_user.language
+
+ self.ds = CoreDatasource(**ds.model_dump()) if ds else None
self.chat_question = chat_question
- self.chat_question.ai_modal_id = aimodel.id
- self.config = get_llm_config(aimodel)
+ self.chat_question.ai_modal_id = aimodel[0].id
+ self.config = get_llm_config(aimodel[0])
# Create LLM instance through factory
llm_instance = LLMFactory.create_llm(self.config)
@@ -112,8 +150,8 @@ class LLMService:
_msg = AIMessage(content=last_chart_message['content'])
self.chart_message.append(_msg)
- def init_record(self, session: SessionDep, current_user: CurrentUser) -> ChatRecord:
- self.record = save_question(session=session, current_user=current_user, question=self.chat_question)
+ 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):
@@ -183,7 +221,7 @@ class LLMService:
in
analysis_msg]).decode())
- def generate_predict(self, session: SessionDep):
+ def generate_predict(self):
fields = self.get_fields_from_chart()
self.chat_question.fields = orjson.dumps(fields).decode()
@@ -196,7 +234,7 @@ class LLMService:
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=session, record_id=self.record.id, answer='',
+ 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,
@@ -218,7 +256,7 @@ class LLMService:
continue
predict_msg.append(AIMessage(full_predict_text))
- self.record = save_full_predict_message_and_answer(session=session, record_id=self.record.id,
+ self.record = save_full_predict_message_and_answer(session=self.session, record_id=self.record.id,
answer=full_predict_text, data='',
full_message=orjson.dumps(history_msg +
[{'type': msg.type,
@@ -226,10 +264,10 @@ class LLMService:
in
predict_msg]).decode())
- def select_datasource(self, session: SessionDep):
+ def select_datasource(self):
datasource_msg: List[Union[BaseMessage, dict[str, Any]]] = []
datasource_msg.append(SystemMessage(self.chat_question.datasource_sys_question()))
- _ds_list = session.exec(select(CoreDatasource).options(
+ _ds_list = self.session.exec(select(CoreDatasource).options(
load_only(CoreDatasource.id, CoreDatasource.name, CoreDatasource.description))).all()
_ds_list_dict = []
for _ds in _ds_list:
@@ -241,7 +279,7 @@ class LLMService:
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=session, record_id=self.record.id,
+ 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,
@@ -273,7 +311,7 @@ class LLMService:
if data['id'] and data['id'] != 0:
_datasource = data['id']
- _ds = session.query(CoreDatasource).filter(CoreDatasource.id == _datasource).first()
+ _ds = self.session.query(CoreDatasource).filter(CoreDatasource.id == _datasource).first()
if not _ds:
_datasource = None
raise Exception(f"Datasource configuration with id {_datasource} not found")
@@ -281,14 +319,14 @@ class LLMService:
self.chat_question.engine = _ds.type_name if _ds.type != 'excel' else 'PostgreSQL'
_engine_type = self.chat_question.engine
# save chat
- _chat = session.query(Chat).filter(Chat.id == self.record.chat_id).first()
+ _chat = self.session.query(Chat).filter(Chat.id == self.record.chat_id).first()
_chat.datasource = _datasource
_chat.engine_type = _ds.type_name
- session.add(_chat)
- session.flush()
- session.refresh(_chat)
- session.commit()
+ self.session.add(_chat)
+ self.session.flush()
+ self.session.refresh(_chat)
+ self.session.commit()
elif data['fail']:
raise Exception(data['fail'])
@@ -298,7 +336,7 @@ class LLMService:
except Exception as e:
_error = e
- self.record = save_full_select_datasource_message_and_answer(session=session, record_id=self.record.id,
+ self.record = save_full_select_datasource_message_and_answer(session=self.session, record_id=self.record.id,
answer=full_text, datasource=_datasource,
engine_type=_engine_type,
full_message=orjson.dumps(history_msg +
@@ -312,10 +350,10 @@ class LLMService:
if _error:
raise _error
- def generate_sql(self, session: SessionDep):
+ 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=session, record_id=self.record.id,
+ 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())
@@ -333,16 +371,16 @@ class LLMService:
continue
self.sql_message.append(AIMessage(full_sql_text))
- self.record = save_full_sql_message_and_answer(session=session, record_id=self.record.id,
+ self.record = save_full_sql_message_and_answer(session=self.session, record_id=self.record.id,
answer=full_sql_text,
full_message=orjson.dumps(
[{'type': msg.type, 'content': msg.content} for msg in
self.sql_message]).decode())
- def generate_chart(self, session: SessionDep):
+ 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=session, record_id=self.record.id,
+ 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())
@@ -359,13 +397,13 @@ class LLMService:
continue
self.chart_message.append(AIMessage(full_chart_text))
- self.record = save_full_chart_message_and_answer(session=session, record_id=self.record.id,
+ self.record = save_full_chart_message_and_answer(session=self.session, record_id=self.record.id,
answer=full_chart_text,
full_message=orjson.dumps(
[{'type': msg.type, 'content': msg.content} for msg in
self.chart_message]).decode())
- def check_save_sql(self, session: SessionDep, res: str) -> str:
+ def check_save_sql(self, res: str) -> str:
json_str = extract_nested_json(res)
data = orjson.loads(json_str)
@@ -385,13 +423,13 @@ class LLMService:
if sql.strip() == '':
raise Exception("SQL query is empty")
- save_sql(session=session, sql=sql, record_id=self.record.id)
+ save_sql(session=self.session, sql=sql, record_id=self.record.id)
self.chat_question.sql = sql
return sql
- def check_save_chart(self, session: SessionDep, res: str) -> Dict[str, Any]:
+ def check_save_chart(self, res: str) -> Dict[str, Any]:
json_str = extract_nested_json(res)
data = orjson.loads(json_str)
@@ -413,30 +451,30 @@ class LLMService:
if error:
raise Exception(message)
- save_chart(session=session, chart=orjson.dumps(chart).decode(), record_id=self.record.id)
+ save_chart(session=self.session, chart=orjson.dumps(chart).decode(), record_id=self.record.id)
return chart
- def check_save_predict_data(self, session: SessionDep, res: str) -> Dict[str, Any]:
+ def check_save_predict_data(self, res: str) -> Dict[str, Any]:
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)
+ save_predict_data(session=self.session, record_id=self.record.id, data=json_str)
return json_str
- def save_error(self, session: SessionDep, message: str):
- return save_error_message(session=session, record_id=self.record.id, message=message)
+ 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, session: SessionDep, data_obj: Dict[str, Any]):
- return save_sql_exec_data(session=session, record_id=self.record.id,
+ def save_sql_data(self, data_obj: Dict[str, Any]):
+ return save_sql_exec_data(session=self.session, record_id=self.record.id,
data=orjson.dumps(data_obj).decode())
- def finish(self, session: SessionDep):
- return finish_record(session=session, record_id=self.record.id)
+ def finish(self):
+ return finish_record(session=self.session, record_id=self.record.id)
def execute_sql(self, sql: str):
"""Execute SQL query
@@ -503,3 +541,87 @@ def execute_sql_with_db(db: SQLDatabase, sql: str) -> str:
error_msg = f"SQL execution failed: {str(e)}"
logging.error(error_msg)
raise RuntimeError(error_msg)
+
+
+def run_task(llm_service: LLMService, session: SessionDep, stream: bool = True):
+ try:
+ # return id
+ if stream:
+ yield orjson.dumps({'type': 'id', 'id': llm_service.get_record().id}).decode() + '\n\n'
+
+ # select datasource if datasource is none
+ if not llm_service.ds:
+ ds_res = llm_service.select_datasource()
+ if stream:
+ for chunk in ds_res:
+ yield orjson.dumps({'content': chunk, 'type': 'datasource-result'}).decode() + '\n\n'
+ yield orjson.dumps({'id': llm_service.ds.id, 'datasource_name': llm_service.ds.name,
+ 'engine_type': llm_service.ds.type_name, 'type': 'datasource'}).decode() + '\n\n'
+
+ llm_service.chat_question.db_schema = get_table_schema(session=session, ds=llm_service.ds)
+
+ # generate sql
+ sql_res = llm_service.generate_sql()
+ full_sql_text = ''
+ for chunk in sql_res:
+ full_sql_text += chunk
+ if stream:
+ yield orjson.dumps({'content': chunk, 'type': 'sql-result'}).decode() + '\n\n'
+ if stream:
+ yield orjson.dumps({'type': 'info', 'msg': 'sql generated'}).decode() + '\n\n'
+
+ # filter sql
+ print(full_sql_text)
+ sql = llm_service.check_save_sql(res=full_sql_text)
+ print(sql)
+ if stream:
+ yield orjson.dumps({'content': sql, 'type': 'sql'}).decode() + '\n\n'
+
+ # execute sql
+ result = llm_service.execute_sql(sql=sql)
+ llm_service.save_sql_data(data_obj=result)
+ if stream:
+ yield orjson.dumps({'content': orjson.dumps(result).decode(), 'type': 'sql-data'}).decode() + '\n\n'
+
+ # generate chart
+ chart_res = llm_service.generate_chart()
+ full_chart_text = ''
+ for chunk in chart_res:
+ full_chart_text += chunk
+ if stream:
+ yield orjson.dumps({'content': chunk, 'type': 'chart-result'}).decode() + '\n\n'
+ if stream:
+ yield orjson.dumps({'type': 'info', 'msg': 'chart generated'}).decode() + '\n\n'
+
+ # filter chart
+ print(full_chart_text)
+ chart = llm_service.check_save_chart(res=full_chart_text)
+ print(chart)
+ if stream:
+ yield orjson.dumps({'content': orjson.dumps(chart).decode(), 'type': 'chart'}).decode() + '\n\n'
+
+ record = llm_service.finish()
+ if stream:
+ yield orjson.dumps({'type': 'finish'}).decode() + '\n\n'
+ else:
+ md_str = f'```sql\n{sql}\n```\n\n'
+ # todo generate picture
+ if chart['type'] == 'table':
+ data = {}
+ for _data in result['data']:
+ for field in result['fields']:
+ if not data[field]:
+ data[field] = []
+ data[field].append(_data[field])
+ df = pd.DataFrame(data, columns=result['fields'])
+ markdown_table = df.to_markdown(index=False)
+ md_str += markdown_table
+ else:
+ md_str += ''
+
+ except Exception as e:
+ llm_service.save_error(message=str(e))
+ if stream:
+ yield orjson.dumps({'content': str(e), 'type': 'error'}).decode() + '\n\n'
+ else:
+ raise e
diff --git a/backend/apps/mcp/mcp.py b/backend/apps/mcp/mcp.py
index 5f33a0b0..91347485 100644
--- a/backend/apps/mcp/mcp.py
+++ b/backend/apps/mcp/mcp.py
@@ -1,19 +1,22 @@
# Author: Junjun
# Date: 2025/7/1
+from datetime import timedelta
from typing import Annotated
+
from fastapi import APIRouter, Depends, HTTPException
from fastapi.security import OAuth2PasswordRequestForm
-from common.core.deps import SessionDep, get_current_user
-from apps.system.crud.user import authenticate
-from common.core.security import create_access_token
-from datetime import timedelta
-from common.core.config import settings
-from common.core.schemas import Token
-from apps.datasource.crud.datasource import get_datasource_list
-from apps.system.models.system_model import AiModelDetail
+
+from apps.chat.api.chat import create_chat
from apps.chat.models.chat_model import ChatMcp, CreateChat
-from apps.chat.api.chat import create_chat, stream_sql
+from apps.chat.task.llm import LLMService, run_task
+from apps.datasource.crud.datasource import get_datasource_list
+from apps.system.crud.user import authenticate
+from apps.system.models.system_model import AiModelDetail
+from common.core.config import settings
+from common.core.deps import SessionDep, get_current_user
+from common.core.schemas import Token
+from common.core.security import create_access_token
router = APIRouter(tags=["mcp"], prefix="/mcp")
@@ -52,6 +55,12 @@ async def mcp_start(session: SessionDep, chat: ChatMcp):
@router.post("/mcp_question", operation_id="mcp_question")
async def mcp_question(session: SessionDep, chat: ChatMcp):
user = await get_current_user(session, chat.token)
+
+ llm_service = LLMService(session, user, chat)
+ llm_service.init_record()
+
+ run_task(llm_service, session)
+
# return await stream_sql(session, user, chat)
return {"content": """这是一段写死的测试内容：
@@ -68,4 +77,4 @@ async def mcp_question(session: SessionDep, chat: ChatMcp):
```json
{"success":true,"sql":"SELECT COUNT(*) AS \"TotalOrders\" FROM \"public\".\"Sheet1_c27345b66e\" WHERE \"城市\" = '上海';"}
```
- 
"""}
\ No newline at end of file
+ """}