diff --git a/pilot/commands/built_in/show_chart_gen.py b/pilot/commands/built_in/show_chart_gen.py new file mode 100644 index 000000000..160f16db8 --- /dev/null +++ b/pilot/commands/built_in/show_chart_gen.py @@ -0,0 +1,56 @@ + + +from pilot.commands.command_mange import command +from pilot.configs.config import Config +import pandas as pd +import base64 +import io +import matplotlib +import seaborn as sns +matplotlib.use("Agg") +import matplotlib.pyplot as plt + +from pilot.configs.model_config import LOGDIR +from pilot.utils import build_logger + +CFG = Config() +logger = build_logger("show_chart_gen", LOGDIR + "show_chart_gen.log") + + +@command("response_line_chart", "Use line chart to display SQL data", '"speak": "", "sql":"","db_name":""') +def response_line_chart(speak: str, sql: str, db_name: str) -> str: + logger.info(f"response_line_chart:{speak},{sql},{db_name}") + df = pd.read_sql(sql, CFG.LOCAL_DB_MANAGE.get_connect(db_name)) + columns = df.columns.tolist() + + if df.size <= 0: + raise ValueError("No Data！" + sql) + plt.rcParams["font.family"] = ["sans-serif"] + rc = {"font.sans-serif": "SimHei", "axes.unicode_minus": False} + sns.set(context="notebook", style="ticks", color_codes=True, rc=rc) + plt.subplots(figsize=(8, 5), dpi=100) + sns.barplot(df, x=columns[0], y=columns[1]) + plt.title("") + + buf = io.BytesIO() + plt.savefig(buf, format="png", dpi=100) + buf.seek(0) + data = base64.b64encode(buf.getvalue()).decode("ascii") + + html_img = f"""
{speak}
""" + return html_img + + + +@command("response_bar_chart", "Use bar chart to display SQL data", '"speak": "", "sql":"","db_name":""') +def response_bar_chart(speak: str, sql: str, db_name: str) -> str: + """ + """ + pass + + +@command("response_pie_chart", "Use pie chart to display SQL data", '"speak": "", "sql":"","db_name":""') +def response_pie_chart(speak: str, sql: str, db_name: str) -> str: + """ + """ + pass \ No newline at end of file diff --git a/pilot/commands/built_in/show_table_gen.py b/pilot/commands/built_in/show_table_gen.py new file mode 100644 index 000000000..3ac0f3728 --- /dev/null +++ b/pilot/commands/built_in/show_table_gen.py @@ -0,0 +1,21 @@ +import pandas as pd + +from pilot.commands.command_mange import command +from pilot.configs.config import Config + +from pilot.configs.model_config import LOGDIR +from pilot.utils import build_logger + +CFG = Config() +logger = build_logger("show_table_gen", LOGDIR + "show_table_gen.log") + + +@command("response_table", "Use table to display SQL data", '"speak": "", "sql":"","db_name":""') +def response_table(speak: str, sql: str, db_name: str) -> str: + logger.info(f"response_table:{speak},{sql},{db_name}") + df = pd.read_sql(sql, CFG.LOCAL_DB_MANAGE.get_connect(db_name)) + html_table = df.to_html(index=False, escape=False, sparsify=False) + table_str = "".join(html_table.split()) + html = f"""
{table_str}
""" + view_text = f"##### {str(speak)}" + "\n" + html.replace("\n", " ") + return view_text diff --git a/pilot/commands/built_in/show_text_gen.py b/pilot/commands/built_in/show_text_gen.py new file mode 100644 index 000000000..fed860b67 --- /dev/null +++ b/pilot/commands/built_in/show_text_gen.py @@ -0,0 +1,33 @@ +import pandas as pd + +from pilot.commands.command_mange import command +from pilot.configs.config import Config +from pilot.configs.model_config import LOGDIR +from pilot.utils import build_logger + +CFG = Config() +logger = build_logger("show_table_gen", LOGDIR + "show_table_gen.log") + + +@command("response_data_text", "Use text to display SQL data", + '"speak": "", "sql":"","db_name":""') +def response_data_text(speak: str, sql: str, db_name: str) -> str: + logger.info(f"response_data_text:{speak},{sql},{db_name}") + df = pd.read_sql(sql, CFG.LOCAL_DB_MANAGE.get_connect(db_name)) + data = df.values + + row_size = data.shape[0] + value_str, text_info = "" + if row_size > 1: + html_table = df.to_html(index=False, escape=False, sparsify=False) + table_str = "".join(html_table.split()) + html = f"""
{table_str}
""" + text_info = f"##### {str(speak)}" + "\n" + html.replace("\n", " ") + elif row_size == 1: + row = data[0] + for value in row: + value_str = value_str + f", ** {value} **" + text_info = f"{speak}: {value_str}" + else: + text_info = f"##### {speak}: _没有找到可用的数据_" + return text_info diff --git a/pilot/openapi/api_v1/editor/api_editor_v1.py b/pilot/openapi/api_v1/editor/api_editor_v1.py index d54f1cef6..33f04e99c 100644 --- a/pilot/openapi/api_v1/editor/api_editor_v1.py +++ b/pilot/openapi/api_v1/editor/api_editor_v1.py @@ -39,7 +39,7 @@ logger = build_logger("api_editor_v1", LOGDIR + "api_editor_v1.log") @router.get("/v1/editor/db/tables", response_model=Result[DbTable]) async def get_editor_tables(db_name: str, page_index: int, page_size: int, search_str: str = ""): - logger.info("get_editor_tables:{},{},{},{}", db_name, page_index, page_size, search_str) + logger.info(f"get_editor_tables:{db_name},{page_index},{page_size},{search_str}") db_conn = CFG.LOCAL_DB_MANAGE.get_connect(db_name) tables = db_conn.get_table_names() db_node: DataNode = DataNode(title=db_name, key=db_name, type="db") @@ -57,7 +57,7 @@ async def get_editor_tables(db_name: str, page_index: int, page_size: int, searc @router.get("/v1/editor/sql/rounds", response_model=Result[ChatDbRounds]) async def get_editor_sql_rounds(con_uid: str): - logger.info("get_editor_sql_rounds:{}", con_uid) + logger.info("get_editor_sql_rounds:{con_uid}") history_mem = DuckdbHistoryMemory(con_uid) history_messages: List[OnceConversation] = history_mem.get_messages() if history_messages: @@ -76,7 +76,7 @@ async def get_editor_sql_rounds(con_uid: str): @router.get("/v1/editor/sql", response_model=Result[dict]) async def get_editor_sql(con_uid: str, round: int): - logger.info("get_editor_sql:{},{}", con_uid, round) + logger.info(f"get_editor_sql:{con_uid},{round}") history_mem = DuckdbHistoryMemory(con_uid) history_messages: List[OnceConversation] = history_mem.get_messages() if history_messages: @@ -90,26 +90,33 @@ async def get_editor_sql(con_uid: str, round: int): @router.post("/v1/editor/sql/run", response_model=Result[SqlRunData]) async def editor_sql_run(run_param: dict = Body()): - logger.info("editor_sql_run:{}", run_param) + logger.info(f"editor_sql_run:{run_param}") db_name = run_param['db_name'] sql = run_param['sql'] if not db_name and not sql: return Result.faild("SQL run param error！") conn = CFG.LOCAL_DB_MANAGE.get_connect(db_name) - start_time = time.time() * 1000 - colunms, sql_result = conn.query_ex(sql) - # 计算执行耗时 - end_time = time.time() * 1000 - sql_run_data: SqlRunData = SqlRunData(result_info="", - run_cost=(end_time - start_time) / 1000, - colunms=colunms, - values=sql_result - ) - return Result.succ(sql_run_data) + try: + start_time = time.time() * 1000 + colunms, sql_result = conn.query_ex(sql) + # 计算执行耗时 + end_time = time.time() * 1000 + sql_run_data: SqlRunData = SqlRunData(result_info="", + run_cost=(end_time - start_time) / 1000, + colunms=colunms, + values=sql_result + ) + return Result.succ(sql_run_data) + except Exception as e: + return Result.succ(SqlRunData(result_info=str(e), + run_cost=0, + colunms=[], + values=[] + )) -@router.post("/v1/sql/editor/submit", response_model=Result) +@router.post("/v1/sql/editor/submit") async def sql_editor_submit(sql_edit_context: ChatSqlEditContext = Body()): logger.info(f"sql_editor_submit:{sql_edit_context.__dict__}") history_mem = DuckdbHistoryMemory(sql_edit_context.conv_uid) @@ -136,7 +143,7 @@ async def sql_editor_submit(sql_edit_context: ChatSqlEditContext = Body()): @router.get("/v1/editor/chart/list", response_model=Result[ChartList]) async def get_editor_chart_list(con_uid: str): - logger.info("get_editor_sql_rounds:{}", con_uid) + logger.info(f"get_editor_sql_rounds:{con_uid}", ) history_mem = DuckdbHistoryMemory(con_uid) history_messages: List[OnceConversation] = history_mem.get_messages() if history_messages: @@ -152,8 +159,7 @@ async def get_editor_chart_list(con_uid: str): @router.get("/v1/editor/chart/info", response_model=Result[ChartDetail]) async def get_editor_chart_info(con_uid: str, chart_title: str): - logger.info(f"get_editor_sql_rounds:{con_uid}") - logger.info("get_editor_sql_rounds:{}", con_uid) + logger.info(f"get_editor_chart_info:{con_uid},{chart_title}") history_mem = DuckdbHistoryMemory(con_uid) history_messages: List[OnceConversation] = history_mem.get_messages() if history_messages: @@ -184,24 +190,34 @@ async def get_editor_chart_info(con_uid: str, chart_title: str): @router.post("/v1/editor/chart/run", response_model=Result[ChartRunData]) -async def editor_chart_run(db_name: str, sql: str): - logger.info(f"editor_chart_run:{db_name},{sql}") +async def editor_chart_run(run_param: dict = Body()): + logger.info(f"editor_chart_run:{run_param}") + db_name = run_param['db_name'] + sql = run_param['sql'] + if not db_name and not sql: + return Result.faild("SQL run param error！") dashboard_data_loader: DashboardDataLoader = DashboardDataLoader() db_conn = CFG.LOCAL_DB_MANAGE.get_connect(db_name) field_names, chart_values = dashboard_data_loader.get_chart_values_by_conn(db_conn, sql) - start_time = time.time() * 1000 - colunms, sql_result = db_conn.query_ex(sql) - # 计算执行耗时 - end_time = time.time() * 1000 - sql_run_data: SqlRunData = SqlRunData(result_info="", - run_cost=(end_time - start_time) / 1000, - colunms=colunms, - values=sql_result - ) - return Result.succ(ChartRunData(sql_data=sql_run_data, chart_values=chart_values)) - + try: + start_time = time.time() * 1000 + colunms, sql_result = db_conn.query_ex(sql) + # 计算执行耗时 + end_time = time.time() * 1000 + sql_run_data: SqlRunData = SqlRunData(result_info="", + run_cost=(end_time - start_time) / 1000, + colunms=colunms, + values=sql_result + ) + return Result.succ(ChartRunData(sql_data=sql_run_data, chart_values=chart_values)) + except Exception as e: + return Result.succ(SqlRunData(result_info=str(e), + run_cost=0, + colunms=[], + values=[] + )) @router.post("/v1/chart/editor/submit", response_model=Result[bool]) async def chart_editor_submit(chart_edit_context: ChatChartEditContext = Body()): diff --git a/pilot/openapi/editor_view_model.py b/pilot/openapi/editor_view_model.py index 64d0c3eb5..e3024a424 100644 --- a/pilot/openapi/editor_view_model.py +++ b/pilot/openapi/editor_view_model.py @@ -57,8 +57,8 @@ class ChatSqlEditContext(BaseModel): old_sql: str old_speak: str - gmt_create: int + gmt_create: int = 0 new_sql: str - new_speak: str - new_view_info: str + new_speak: str = "" + new_view_info: str = "" diff --git a/pilot/out_parser/base.py b/pilot/out_parser/base.py index 013f15b1e..72e8c2536 100644 --- a/pilot/out_parser/base.py +++ b/pilot/out_parser/base.py @@ -226,13 +226,13 @@ class BaseOutputParser(ABC): """Instructions on how the LLM output should be formatted.""" raise NotImplementedError - @property - def _type(self) -> str: - """Return the type key.""" - raise NotImplementedError( - f"_type property is not implemented in class {self.__class__.__name__}." - " This is required for serialization." - ) + # @property + # def _type(self) -> str: + # """Return the type key.""" + # raise NotImplementedError( + # f"_type property is not implemented in class {self.__class__.__name__}." + # " This is required for serialization." + # ) def dict(self, **kwargs: Any) -> Dict: """Return dictionary representation of output parser.""" diff --git a/pilot/scene/base.py b/pilot/scene/base.py index d1a52845c..d81a328df 100644 --- a/pilot/scene/base.py +++ b/pilot/scene/base.py @@ -27,6 +27,21 @@ class ChatScene(Enum): "Dialogue with your private data through natural language.", ["DB Select"], ) + ExcelLearning = Scene( + "excel_learning", + "Excel Learning", + "Analyze and summarize your excel files.", + [], + True, + True + ) + ChatExcel = Scene( + "chat_excel", + "Chat Excel", + "Dialogue with your excel, use natural language.", + ["File Select"], + ) + ChatWithDbQA = Scene( "chat_with_db_qa", "Chat DB", diff --git a/pilot/scene/chat_data/chat_excel/excel_analyze/prompt.py b/pilot/scene/chat_data/chat_excel/excel_analyze/prompt.py new file mode 100644 index 000000000..e51785600 --- /dev/null +++ b/pilot/scene/chat_data/chat_excel/excel_analyze/prompt.py @@ -0,0 +1,51 @@ +import json +from pilot.prompts.prompt_new import PromptTemplate +from pilot.configs.config import Config +from pilot.scene.base import ChatScene +from pilot.scene.chat_db.auto_execute.out_parser import DbChatOutputParser, SqlAction +from pilot.common.schema import SeparatorStyle + +CFG = Config() + +PROMPT_SCENE_DEFINE = "You are a data analysis expert. " + +_DEFAULT_TEMPLATE = """ +This is an example data，please learn to understand the structure and content of this data: + {data_example} +Explain the meaning and function of each column, and give a simple and clear explanation of the technical terms. +Provide some analysis options,please think step by step. + +Please return your answer in JSON format, the return format is as follows: + {response} +""" + +RESPONSE_FORMAT_SIMPLE = { + "Data Analysis": "数据内容分析总结", + "Colunm Analysis": [{"colunm name": "字段介绍，专业术语解释(请尽量简单明了)"}], + "Analysis Program": ["1.分析方案1，图表展示方式1", "2.分析方案2，图表展示方式2"], +} + +PROMPT_SEP = SeparatorStyle.SINGLE.value + +PROMPT_NEED_NEED_STREAM_OUT = False + +# Temperature is a configuration hyperparameter that controls the randomness of language model output. +# A high temperature produces more unpredictable and creative results, while a low temperature produces more common and conservative output. +# For example, if you adjust the temperature to 0.5, the model will usually generate text that is more predictable and less creative than if you set the temperature to 1.0. +PROMPT_TEMPERATURE = 0.5 + +prompt = PromptTemplate( + template_scene=ChatScene.ChatExcel.value(), + input_variables=["data_example"], + response_format=json.dumps(RESPONSE_FORMAT_SIMPLE, ensure_ascii=False, indent=4), + template_define=PROMPT_SCENE_DEFINE, + template=_DEFAULT_TEMPLATE, + stream_out=PROMPT_NEED_NEED_STREAM_OUT, + output_parser=DbChatOutputParser( + sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT + ), + # example_selector=sql_data_example, + temperature=PROMPT_TEMPERATURE, +) +CFG.prompt_template_registry.register(prompt, is_default=True) + diff --git a/pilot/scene/chat_data/chat_excel/excel_learning/chat.py b/pilot/scene/chat_data/chat_excel/excel_learning/chat.py new file mode 100644 index 000000000..0c1d16423 --- /dev/null +++ b/pilot/scene/chat_data/chat_excel/excel_learning/chat.py @@ -0,0 +1,39 @@ +import json + +from pilot.scene.base_message import ( + HumanMessage, + ViewMessage, +) +from pilot.scene.base_chat import BaseChat +from pilot.scene.base import ChatScene +from pilot.common.sql_database import Database +from pilot.configs.config import Config +from pilot.common.markdown_text import ( + generate_htm_table, +) +from pilot.scene.chat_data.chat_excel.excel_learning.prompt import prompt + +CFG = Config() + + +class ExcelLearning(BaseChat): + chat_scene: str = ChatScene.ExcelLearning.value() + + def __init__(self, chat_session_id, file_path): + chat_mode = ChatScene.ChatWithDbExecute + """ """ + super().__init__( + chat_mode=chat_mode, + chat_session_id=chat_session_id, + select_param=file_path, + ) + + + def generate_input_values(self): + + input_values = { + "data_example": "", + } + return input_values + + diff --git a/pilot/scene/chat_data/chat_excel/excel_learning/out_parser.py b/pilot/scene/chat_data/chat_excel/excel_learning/out_parser.py new file mode 100644 index 000000000..90d4b6d7f --- /dev/null +++ b/pilot/scene/chat_data/chat_excel/excel_learning/out_parser.py @@ -0,0 +1,65 @@ +import json +import re +from abc import ABC, abstractmethod +from typing import Dict, NamedTuple, List +import pandas as pd +from pilot.utils import build_logger +from pilot.out_parser.base import BaseOutputParser, T +from pilot.configs.model_config import LOGDIR +from pilot.configs.config import Config + +CFG = Config() + + +class ExcelResponse(NamedTuple): + desciption: str + clounms: List + plans: List + + +logger = build_logger("chat_excel", LOGDIR + "ChatExcel.log") + + +class ChatExcelOutputParser(BaseOutputParser): + def __init__(self, sep: str, is_stream_out: bool): + super().__init__(sep=sep, is_stream_out=is_stream_out) + + def parse_prompt_response(self, model_out_text): + clean_str = super().parse_prompt_response(model_out_text) + print("clean prompt response:", clean_str) + response = json.loads(clean_str) + for key in sorted(response): + if key.strip() == "Data Analysis": + desciption = response[key] + if key.strip() == "Column Analysis": + clounms = response[key] + if key.strip() == "Analysis Program": + plans = response[key] + return ExcelResponse(desciption=desciption, clounms=clounms,plans=plans) + + def parse_view_response(self, speak, data) -> str: + ### tool out data to table view + + + + html_title= data["desciption"] + html_colunms= f"
数据结构
    " + for item in data["clounms"]: + html_colunms = html_colunms + "
  • " + keys = item.keys() + for key in keys: + html_colunms = html_colunms + f"{key}:{item[key]}" + html_colunms = html_colunms + "
    • " + html_colunms= html_colunms + "
" + + html_plans="
    " + for item in data["plans"]: + html_plans = html_plans + f"
  1. {item}
    2. " + html = f""" +
    +

    {html_title}

    +
    {html_colunms}
    +
    {html_plans}
    +
    + """ + return html \ No newline at end of file diff --git a/pilot/scene/chat_data/chat_excel/excel_learning/prompt.py b/pilot/scene/chat_data/chat_excel/excel_learning/prompt.py new file mode 100644 index 000000000..b4f0e10c0 --- /dev/null +++ b/pilot/scene/chat_data/chat_excel/excel_learning/prompt.py @@ -0,0 +1,51 @@ +import json +from pilot.prompts.prompt_new import PromptTemplate +from pilot.configs.config import Config +from pilot.scene.base import ChatScene +from pilot.scene.chat_db.auto_execute.out_parser import DbChatOutputParser, SqlAction +from pilot.common.schema import SeparatorStyle + +CFG = Config() + +PROMPT_SCENE_DEFINE = "You are a data analysis expert. " + +_DEFAULT_TEMPLATE = """ +This is an example data，please learn to understand the structure and content of this data: + {data_example} +Explain the meaning and function of each column, and give a simple and clear explanation of the technical terms. +Provide some analysis options,please think step by step. + +Please return your answer in JSON format, the return format is as follows: + {response} +""" + +RESPONSE_FORMAT_SIMPLE = { + "Data Analysis": "数据内容分析总结", + "Colunm Analysis": [{"colunm name": "字段介绍，专业术语解释(请尽量简单明了)"}], + "Analysis Program": ["1.分析方案1，图表展示方式1", "2.分析方案2，图表展示方式2"], +} + +PROMPT_SEP = SeparatorStyle.SINGLE.value + +PROMPT_NEED_NEED_STREAM_OUT = False + +# Temperature is a configuration hyperparameter that controls the randomness of language model output. +# A high temperature produces more unpredictable and creative results, while a low temperature produces more common and conservative output. +# For example, if you adjust the temperature to 0.5, the model will usually generate text that is more predictable and less creative than if you set the temperature to 1.0. +PROMPT_TEMPERATURE = 0.5 + +prompt = PromptTemplate( + template_scene=ChatScene.ExcelLearning.value(), + input_variables=["data_example"], + response_format=json.dumps(RESPONSE_FORMAT_SIMPLE, ensure_ascii=False, indent=4), + template_define=PROMPT_SCENE_DEFINE, + template=_DEFAULT_TEMPLATE, + stream_out=PROMPT_NEED_NEED_STREAM_OUT, + output_parser=DbChatOutputParser( + sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT + ), + # example_selector=sql_data_example, + temperature=PROMPT_TEMPERATURE, +) +CFG.prompt_template_registry.register(prompt, is_default=True) + diff --git a/pilot/scene/chat_data/chat_excel/excel_learning/test.py b/pilot/scene/chat_data/chat_excel/excel_learning/test.py new file mode 100644 index 000000000..45e01aa27 --- /dev/null +++ b/pilot/scene/chat_data/chat_excel/excel_learning/test.py @@ -0,0 +1,84 @@ +import os +import duckdb +import pandas as pd + +import time +from fsspec import filesystem +import spatial + + +if __name__ == "__main__": + # connect = duckdb.connect("/Users/tuyang.yhj/Downloads/example.xlsx") + # + + def csv_colunm_foramt(val): + if str(val).find("$") >= 0: + return float(val.replace('$', '').replace(',', '')) + if str(val).find("¥") >= 0: + return float(val.replace('¥', '').replace(',', '')) + return val + + # 获取当前时间戳，作为代码开始的时间 + start_time = int(time.time() * 1000) + + df = pd.read_excel('/Users/tuyang.yhj/Downloads/example.xlsx') + # 读取 Excel 文件为 Pandas DataFrame + df = pd.read_excel('/Users/tuyang.yhj/Downloads/example.xlsx', converters={i: csv_colunm_foramt for i in range(df.shape[1])}) + + d = df.values + print(d.shape[0]) + for row in d: + print(row[0]) + print(len(row)) + r = df.iterrows() + + # 获取当前时间戳，作为代码结束的时间 + end_time = int(time.time() * 1000) + + print(f"耗时:{(end_time-start_time)/1000}秒") + + # 连接 DuckDB 数据库 + con = duckdb.connect(database=':memory:', read_only=False) + + # 将 DataFrame 写入 DuckDB 数据库中的一个表 + con.register('example', df) + + # 查询 DuckDB 数据库中的表 + conn = con.cursor() + results = con.execute('SELECT * FROM example limit 5 ') + colunms = [] + for descrip in results.description: + colunms.append(descrip[0]) + print(colunms) + for row in results.fetchall(): + print(row) + + + # 连接 DuckDB 数据库 + # con = duckdb.connect(':memory:') + + # # 加载 spatial 扩展 + # con.execute('install spatial;') + # con.execute('load spatial;') + # + # # 查询 duckdb_internal 系统表，获取扩展列表 + # result = con.execute("SELECT * FROM duckdb_internal.functions WHERE schema='list_extensions';") + # + # # 遍历查询结果，输出扩展名称和版本号 + # for row in result: + # print(row['name'], row['return_type']) + # duckdb.read_csv('/Users/tuyang.yhj/Downloads/example_csc.csv') + # result = duckdb.sql('SELECT * FROM "/Users/tuyang.yhj/Downloads/yhj-zx.csv" ') + # result = duckdb.sql('SELECT * FROM "/Users/tuyang.yhj/Downloads/example_csc.csv" limit 20') + # for row in result.fetchall(): + # print(row) + + + # result = con.execute("SELECT * FROM st_read('/Users/tuyang.yhj/Downloads/example.xlsx', layer='Sheet1')") + # # 遍历查询结果 + # for row in result.fetchall(): + # print(row) + print("xx") + + + diff --git a/pilot/scene/chat_db/auto_execute/out_parser.py b/pilot/scene/chat_db/auto_execute/out_parser.py index d3d0e0621..ce20c6987 100644 --- a/pilot/scene/chat_db/auto_execute/out_parser.py +++ b/pilot/scene/chat_db/auto_execute/out_parser.py @@ -52,7 +52,3 @@ class DbChatOutputParser(BaseOutputParser): return data_loader.get_table_view_by_conn(data, speak) - - @property - def _type(self) -> str: - return "sql_chat"