From 60e9b7a3b4ae726539ad306f4a69e533bcd1e601 Mon Sep 17 00:00:00 2001 From: ulleo Date: Thu, 26 Jun 2025 19:06:00 +0800 Subject: [PATCH] feat: auto select datasource --- backend/alembic/versions/016_modify_chat.py | 71 ++++++++++++ backend/apps/chat/api/chat.py | 43 +++++--- backend/apps/chat/curd/chat.py | 43 ++++++-- backend/apps/chat/models/chat_model.py | 34 +++++- backend/apps/chat/task/llm.py | 103 +++++++++++++++++- .../generate_guess_question/__init__.py | 0 .../generate_guess_question/generator.py | 6 + .../template/select_datasource/__init__.py | 0 .../template/select_datasource/generator.py | 6 + backend/template.yaml | 55 +++++++--- .../chat/component/DisplayChartBlock.vue | 3 +- frontend/src/views/chat/index.vue | 2 + 12 files changed, 316 insertions(+), 50 deletions(-) create mode 100644 backend/alembic/versions/016_modify_chat.py create mode 100644 backend/apps/template/generate_guess_question/__init__.py create mode 100644 backend/apps/template/generate_guess_question/generator.py create mode 100644 backend/apps/template/select_datasource/__init__.py create mode 100644 backend/apps/template/select_datasource/generator.py diff --git a/backend/alembic/versions/016_modify_chat.py b/backend/alembic/versions/016_modify_chat.py new file mode 100644 index 00000000..9e29bfe1 --- /dev/null +++ b/backend/alembic/versions/016_modify_chat.py @@ -0,0 +1,71 @@ +"""016_modify_chat + +Revision ID: 031148da1d81 +Revises: 02d84523a979 +Create Date: 2025-06-26 17:00:07.054531 + +""" +from alembic import op +import sqlalchemy as sa +import sqlmodel.sql.sqltypes +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = '031148da1d81' +down_revision = '02d84523a979' +branch_labels = None +depends_on = None + + +def upgrade(): + # ### commands auto generated by Alembic - please adjust! ### + op.alter_column('chat', 'datasource', + existing_type=sa.INTEGER(), + nullable=True) + op.add_column('chat_record', sa.Column('ai_modal_id', sa.Integer(), nullable=True)) + op.add_column('chat_record', sa.Column('first_chat', sa.Boolean(), nullable=True)) + op.add_column('chat_record', sa.Column('recommended_question_answer', sa.Text(), nullable=True)) + op.add_column('chat_record', sa.Column('recommended_question', sa.Text(), nullable=True)) + op.add_column('chat_record', sa.Column('datasource_select_answer', sa.Text(), nullable=True)) + op.add_column('chat_record', sa.Column('token_sql', sa.Integer(), nullable=True)) + op.add_column('chat_record', sa.Column('token_chart', sa.Integer(), nullable=True)) + op.add_column('chat_record', sa.Column('token_analysis', sa.Integer(), nullable=True)) + op.add_column('chat_record', sa.Column('token_predict', sa.Integer(), nullable=True)) + op.add_column('chat_record', sa.Column('full_recommended_question_message', sa.Text(), nullable=True)) + op.add_column('chat_record', sa.Column('token_recommended_question', sa.Integer(), nullable=True)) + op.add_column('chat_record', sa.Column('full_select_datasource_message', sa.Text(), nullable=True)) + op.add_column('chat_record', sa.Column('token_select_datasource_question', sa.Integer(), nullable=True)) + op.alter_column('chat_record', 'chat_id', + existing_type=sa.INTEGER(), + nullable=False) + op.alter_column('chat_record', 'datasource', + existing_type=sa.INTEGER(), + nullable=True) + # ### end Alembic commands ### + + +def downgrade(): + # ### commands auto generated by Alembic - please adjust! ### + op.alter_column('chat_record', 'datasource', + existing_type=sa.INTEGER(), + nullable=False) + op.alter_column('chat_record', 'chat_id', + existing_type=sa.INTEGER(), + nullable=True) + op.drop_column('chat_record', 'token_select_datasource_question') + op.drop_column('chat_record', 'full_select_datasource_message') + op.drop_column('chat_record', 'token_recommended_question') + op.drop_column('chat_record', 'full_recommended_question_message') + op.drop_column('chat_record', 'token_predict') + op.drop_column('chat_record', 'token_analysis') + op.drop_column('chat_record', 'token_chart') + op.drop_column('chat_record', 'token_sql') + op.drop_column('chat_record', 'datasource_select_answer') + op.drop_column('chat_record', 'recommended_question') + op.drop_column('chat_record', 'recommended_question_answer') + op.drop_column('chat_record', 'first_chat') + op.drop_column('chat_record', 'ai_modal_id') + op.alter_column('chat', 'datasource', + existing_type=sa.INTEGER(), + nullable=False) + # ### end Alembic commands ### diff --git a/backend/apps/chat/api/chat.py b/backend/apps/chat/api/chat.py index 3517eef8..d1971558 100644 --- a/backend/apps/chat/api/chat.py +++ b/backend/apps/chat/api/chat.py @@ -72,7 +72,7 @@ async def start_chat(session: SessionDep, current_user: CurrentUser, create_chat async def mcp_question(session: SessionDep, token: str, request_question: ChatQuestion): user = await get_current_user(session, token) # return await stream_sql(session, user, request_question) - return {"content":"""步骤1: 确定需要查询的字段。 + return {"content": """步骤1: 确定需要查询的字段。 我们需要统计上海的订单总数，因此需要从"城市"字段中筛选出值为"上海"的记录，并使用COUNT函数计算这些记录的数量。 步骤2: 确定筛选条件。 @@ -106,16 +106,17 @@ async def stream_sql(session: SessionDep, current_user: CurrentUser, request_que 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" + ) - # 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' + request_question.engine = ds.type_name if ds.type != 'excel' else 'PostgreSQL' # Get available AI model aimodel = session.exec(select(AiModelDetail).where( @@ -128,14 +129,18 @@ async def stream_sql(session: SessionDep, current_user: CurrentUser, request_que detail="No available AI model configuration found" ) - history_records: List[ChatRecord] = list_records(session=session, current_user=current_user, - chart_id=request_question.chat_id) + 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 - request_question.db_schema = get_table_schema(session=session, ds=ds) + 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())) + 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) @@ -144,6 +149,16 @@ async def stream_sql(session: SessionDep, current_user: CurrentUser, request_que # 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 = '' diff --git a/backend/apps/chat/curd/chat.py b/backend/apps/chat/curd/chat.py index ee10d7d6..e8750fa5 100644 --- a/backend/apps/chat/curd/chat.py +++ b/backend/apps/chat/curd/chat.py @@ -60,6 +60,8 @@ def get_chat_with_records(session: SessionDep, chart_id: int, current_user: Curr load_only(ChatRecord.id, ChatRecord.chat_id, ChatRecord.create_time, ChatRecord.finish_time, ChatRecord.question, ChatRecord.sql_answer, ChatRecord.sql, ChatRecord.data, ChatRecord.chart_answer, ChatRecord.chart, ChatRecord.analysis, ChatRecord.predict, + ChatRecord.datasource_select_answer, ChatRecord.recommended_question_answer, + ChatRecord.recommended_question, ChatRecord.predict_data, ChatRecord.finish, ChatRecord.error, ChatRecord.run_time)).filter( and_(Chat.create_by == current_user.id, ChatRecord.chat_id == chart_id)).order_by(ChatRecord.create_time).all() @@ -74,7 +76,8 @@ def list_records(session: SessionDep, chart_id: int, current_user: CurrentUser) return record_list -def create_chat(session: SessionDep, current_user: CurrentUser, create_chat_obj: CreateChat, require_datasource: bool = True) -> ChatInfo: +def create_chat(session: SessionDep, current_user: CurrentUser, create_chat_obj: CreateChat, + require_datasource: bool = True) -> ChatInfo: if not create_chat_obj.datasource and require_datasource: raise Exception("Datasource cannot be None") @@ -84,7 +87,7 @@ def create_chat(session: SessionDep, current_user: CurrentUser, create_chat_obj: chat = Chat(create_time=datetime.datetime.now(), create_by=current_user.id, brief=create_chat_obj.question.strip()[:20]) - ds: CoreDatasource = None + ds: CoreDatasource | None = None if create_chat_obj.datasource: chat.datasource = create_chat_obj.datasource ds = session.query(CoreDatasource).filter(CoreDatasource.id == create_chat_obj.datasource).first() @@ -102,15 +105,9 @@ def create_chat(session: SessionDep, current_user: CurrentUser, create_chat_obj: chat_info.id = chat.id session.commit() - if not create_chat_obj.datasource: - # use AI to get ds - - if not ds: - raise Exception(f"Datasource with id {create_chat_obj.datasource} not found") - - - chat_info.datasource_exists = True - chat_info.datasource_name = ds.name + if ds: + chat_info.datasource_exists = True + chat_info.datasource_name = ds.name return chat_info @@ -205,6 +202,30 @@ def save_full_predict_message_and_answer(session: SessionDep, record_id: int, an return result +def save_full_select_datasource_message_and_answer(session: SessionDep, record_id: int, answer: str, + full_message: str, datasource: int = None, + engine_type: str = None) -> ChatRecord: + if not record_id: + raise Exception("Record id cannot be None") + record = session.query(ChatRecord).filter(ChatRecord.id == record_id).first() + record.full_select_datasource_message = full_message + record.datasource_select_answer = answer + + if datasource: + record.datasource = datasource + record.engine_type = engine_type + + result = ChatRecord(**record.model_dump()) + + session.add(record) + session.flush() + session.refresh(record) + + session.commit() + + return result + + def save_sql(session: SessionDep, record_id: int, sql: str) -> ChatRecord: if not record_id: raise Exception("Record id cannot be None") diff --git a/backend/apps/chat/models/chat_model.py b/backend/apps/chat/models/chat_model.py index e5098ae7..3b0824d6 100644 --- a/backend/apps/chat/models/chat_model.py +++ b/backend/apps/chat/models/chat_model.py @@ -7,8 +7,10 @@ from sqlmodel import SQLModel, Field from apps.template.generate_analysis.generator import get_analysis_template from apps.template.generate_chart.generator import get_chart_template +from apps.template.generate_guess_question.generator import get_guess_question_template from apps.template.generate_predict.generator import get_predict_template from apps.template.generate_sql.generator import get_sql_template +from apps.template.select_datasource.generator import get_datasource_template class Chat(SQLModel, table=True): @@ -25,11 +27,13 @@ class Chat(SQLModel, table=True): class ChatRecord(SQLModel, table=True): __tablename__ = "chat_record" id: Optional[int] = Field(sa_column=Column(Integer, Identity(always=True), primary_key=True)) - chat_id: int = Field(sa_column=Column(Integer)) + chat_id: int = Field(sa_column=Column(Integer, nullable=False)) + ai_modal_id: Optional[int] = Field(sa_column=Column(Integer)) + first_chat: bool = Field(sa_column=Column(Boolean, nullable=True, default=False)) create_time: datetime = Field(sa_column=Column(DateTime(timezone=True), nullable=True)) finish_time: datetime = Field(sa_column=Column(DateTime(timezone=True), nullable=True)) create_by: int = Field(sa_column=Column(BigInteger, nullable=True)) - datasource: int = Field(sa_column=Column(Integer, nullable=False)) + datasource: int = Field(sa_column=Column(Integer, nullable=True)) engine_type: str = Field(max_length=64) question: str = Field(sa_column=Column(Text, nullable=True)) sql_answer: str = Field(sa_column=Column(Text, nullable=True)) @@ -41,10 +45,21 @@ class ChatRecord(SQLModel, table=True): analysis: str = Field(sa_column=Column(Text, nullable=True)) predict: str = Field(sa_column=Column(Text, nullable=True)) predict_data: str = Field(sa_column=Column(Text, nullable=True)) + recommended_question_answer: str = Field(sa_column=Column(Text, nullable=True)) + recommended_question: str = Field(sa_column=Column(Text, nullable=True)) + datasource_select_answer: str = Field(sa_column=Column(Text, nullable=True)) full_sql_message: str = Field(sa_column=Column(Text, nullable=True)) + token_sql: int = Field(default=0, nullable=True) full_chart_message: str = Field(sa_column=Column(Text, nullable=True)) + token_chart: int = Field(default=0, nullable=True) full_analysis_message: str = Field(sa_column=Column(Text, nullable=True)) + token_analysis: int = Field(default=0, nullable=True) full_predict_message: str = Field(sa_column=Column(Text, nullable=True)) + token_predict: int = Field(default=0, nullable=True) + full_recommended_question_message: str = Field(sa_column=Column(Text, nullable=True)) + token_recommended_question: int = Field(default=0, nullable=True) + full_select_datasource_message: str = Field(sa_column=Column(Text, nullable=True)) + token_select_datasource_question: int = Field(default=0, nullable=True) finish: bool = Field(sa_column=Column(Boolean, nullable=True, default=False)) error: str = Field(sa_column=Column(Text, nullable=True)) run_time: float = Field(default=0) @@ -67,7 +82,7 @@ class ChatInfo(BaseModel): create_by: int = None brief: str = '' chat_type: str = "chat" - datasource: int = None + datasource: Optional[int] = None engine_type: str = '' datasource_name: str = '' datasource_exists: bool = True @@ -108,6 +123,19 @@ class AiModelQuestion(BaseModel): def predict_user_question(self): return get_predict_template()['user'].format(fields=self.fields, data=self.data, lang=self.lang) + def datasource_sys_question(self): + return get_datasource_template()['system'] + + def datasource_user_question(self, datasource_list: str = "[]"): + return get_datasource_template()['user'].format(question=self.question, data=datasource_list, lang=self.lang) + + def datasource_guess_sys_question(self): + return get_guess_question_template()['system'] + + def datasource_guess_user_question(self, old_questions: str = "[]"): + return get_guess_question_template()['user'].format(question=self.question, schema=self.db_schema, + old_questions=old_questions, lang=self.lang) + class ChatQuestion(AiModelQuestion): question: str diff --git a/backend/apps/chat/task/llm.py b/backend/apps/chat/task/llm.py index 1e3f440c..5935f9f9 100644 --- a/backend/apps/chat/task/llm.py +++ b/backend/apps/chat/task/llm.py @@ -6,12 +6,15 @@ import orjson from langchain_community.utilities import SQLDatabase from langchain_core.language_models import BaseLLM from langchain_core.messages import BaseMessage, SystemMessage, HumanMessage, AIMessage, AIMessageChunk +from sqlalchemy import select +from sqlalchemy.orm import load_only 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 -from apps.chat.models.chat_model import ChatQuestion, ChatRecord + finish_record, save_full_analysis_message_and_answer, save_full_predict_message_and_answer, save_predict_data, \ + save_full_select_datasource_message_and_answer +from apps.chat.models.chat_model import ChatQuestion, ChatRecord, Chat from apps.datasource.models.datasource import CoreDatasource from apps.db.db import exec_sql from apps.system.models.system_model import AiModelDetail @@ -30,6 +33,7 @@ class LLMService: llm: BaseLLM sql_message: List[Union[BaseMessage, dict[str, Any]]] = [] chart_message: List[Union[BaseMessage, dict[str, Any]]] = [] + history_records: List[ChatRecord] = [] def __init__(self, chat_question: ChatQuestion, aimodel: AiModelDetail, history_records: List[ChatRecord] = [], ds: CoreDatasource = None): @@ -41,17 +45,22 @@ class LLMService: llm_instance = LLMFactory.create_llm(self.config) self.llm = llm_instance.llm + self.history_records = history_records + + self.init_messages() + + 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, - history_records)) + 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, - history_records)) + 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 @@ -216,6 +225,92 @@ class LLMService: in predict_msg]).decode()) + def select_datasource(self, session: SessionDep): + 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( + load_only(CoreDatasource.id, CoreDatasource.name, CoreDatasource.description))).all() + _ds_list_dict = [] + for _ds in _ds_list: + _ds_list_dict.append({'id': _ds[0].id, 'name': _ds[0].name, 'description': _ds[0].description}) + 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=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_text = '' + res = self.llm.stream(datasource_msg) + for chunk in res: + print(chunk) + if isinstance(chunk, dict): + full_text += chunk['content'] + yield chunk['content'] + continue + if isinstance(chunk, AIMessageChunk): + full_text += chunk.content + yield chunk.content + continue + 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 = orjson.loads(json_str) + + if data['id'] and data['id'] != 0: + _datasource = data['id'] + _ds = session.query(CoreDatasource).filter(CoreDatasource.id == _datasource).first() + if not _ds: + _datasource = None + raise Exception(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' + _engine_type = self.chat_question.engine + # save chat + _chat = 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() + + elif data['fail']: + raise Exception(data['fail']) + else: + raise Exception('No available datasource configuration found') + + except Exception as e: + _error = e + + self.record = save_full_select_datasource_message_and_answer(session=session, record_id=self.record.id, + answer=full_text, 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, session: SessionDep): # append current question self.sql_message.append(HumanMessage(self.chat_question.sql_user_question())) diff --git a/backend/apps/template/generate_guess_question/__init__.py b/backend/apps/template/generate_guess_question/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/backend/apps/template/generate_guess_question/generator.py b/backend/apps/template/generate_guess_question/generator.py new file mode 100644 index 00000000..79076a29 --- /dev/null +++ b/backend/apps/template/generate_guess_question/generator.py @@ -0,0 +1,6 @@ +from apps.template.template import get_base_template + + +def get_guess_question_template(): + template = get_base_template() + return template['template']['guess'] diff --git a/backend/apps/template/select_datasource/__init__.py b/backend/apps/template/select_datasource/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/backend/apps/template/select_datasource/generator.py b/backend/apps/template/select_datasource/generator.py new file mode 100644 index 00000000..11b96c46 --- /dev/null +++ b/backend/apps/template/select_datasource/generator.py @@ -0,0 +1,6 @@ +from apps.template.template import get_base_template + + +def get_datasource_template(): + template = get_base_template() + return template['template']['datasource'] diff --git a/backend/template.yaml b/backend/template.yaml index 5b1f2e55..f3d0b7ba 100644 --- a/backend/template.yaml +++ b/backend/template.yaml @@ -10,9 +10,9 @@ template: - SQL查询的字段名不要自动翻译，别名必须为英文。 - 计算占比，百分比类型字段，保留两位小数，以%结尾。 - 生成SQL时，必须避免关键字冲突: - - 如{engine}是 PostgreSQL、Oracle，则在schema、表名、字段名、别名外层加双引号； - - 如{engine}是 MySQL，则在表名、字段名、别名外层加反引号； - - 如{engine}是 Microsoft SQL Server，则在schema、表名、字段名、别名外层加方括号。 + - 如数据库引擎是 PostgreSQL、Oracle，则在schema、表名、字段名、别名外层加双引号； + - 如数据库引擎是 MySQL，则在表名、字段名、别名外层加反引号； + - 如数据库引擎是 Microsoft SQL Server，则在schema、表名、字段名、别名外层加方括号。 - 生成的SQL使用JSON格式返回： {{"success":true,"sql":"生成的SQL语句"}} - 问题与生成SQL无关时，直接回答： @@ -94,30 +94,31 @@ template: guess: system: | ### 说明： - 您的任务是根据给定的表结构和问题，推测用户接下来可能提问的1-4个问题。 + 您的任务是根据给定的表结构，用户问题以及以往用户提问，推测用户接下来可能提问的1-4个问题。 请遵循以下规则： - - 推测的问题不能与用户问题重复。 - - 推测的问题必须与给出的表结构相关。 + - 推测的问题不能与当前用户问题重复 + - 推测的问题必须与给出的表结构相关 + - 若有以往用户提问列表，则根据以往用户提问列表，推测用户最频繁提问的问题，加入到你生成的推测问题中 - 生成的推测问题使用JSON格式返回： - {["推测问题1", "推测问题2", "推测问题3", "推测问题4"]} + ["推测问题1", "推测问题2", "推测问题3", "推测问题4"] + - 最多返回4个你推测出的结果 + - 若无法推测,则返回空数据JSON: + [] ### 响应: - 根据您的指示，这是我生成的与 表结构 和 问题 相关的 JSON： ```json - - ### 请遵守以下输出要求: - 请逐步推理后给出答案 - 推理过程中不需要输出JSON，仅在最终结果内输出符合要求的JSON - 步骤1: [思考内容] - 步骤2: [思考内容] - …… - 最终答案: [结果] + user: | + ### 请使用 i18n: {lang} 对应的语言输出你的结果 + ### 表结构: {schema} - ### 问题: + ### 当前问题: {question} + + ### 以往提问: + {old_questions} analysis: system: | ### 说明： @@ -148,3 +149,23 @@ template: ### 数据: {data} + datasource: + system: | + ### 说明： + 你是一个数据分析师，你需要根据用户的提问，以及提供的数据源列表（格式为JSON数组:[{{"id": 数据源ID1,"name":"数据源名称1","description":"数据源描述1"}},{{"id": 数据源ID2,"name":"数据源名称2","description":"数据源描述2"}}]），根据名称和描述找出最符合用户提问的数据源，这个数据源后续将被用来进行数据的分析 + + ### 要求： + - 以JSON格式返回你找到的符合提问的数据源ID，格式为：{{"id": 符合要求的数据源ID}} + - 如果匹配到多个数据源，则只需要返回其中一个即可 + - 如果没有符合要求的数据源，则返回：{{"fail":"没有找到匹配的数据源"}} + - 不需要思考过程，请直接返回JSON结果 + + ```json + user: | + ### 请使用 i18n: {lang} 对应的语言输出你的结果 + + ### 数据源列表: + {data} + + ### 问题: + {question} \ No newline at end of file diff --git a/frontend/src/views/chat/component/DisplayChartBlock.vue b/frontend/src/views/chat/component/DisplayChartBlock.vue index e16c6e9a..dc39dda0 100644 --- a/frontend/src/views/chat/component/DisplayChartBlock.vue +++ b/frontend/src/views/chat/component/DisplayChartBlock.vue @@ -63,7 +63,7 @@ defineExpose({ diff --git a/frontend/src/views/chat/index.vue b/frontend/src/views/chat/index.vue index d2621287..71ba0070 100644 --- a/frontend/src/views/chat/index.vue +++ b/frontend/src/views/chat/index.vue @@ -409,6 +409,8 @@ const sendMessage = async () => { currentRecord.error = currentRecord.error + 'Error:' + error console.error('Error:', error) isTyping.value = false + } finally { + loading.value = false } }