feat(editor): ChatExcel

ChatExcel devlop part 2

pilot/commands/built_in/show_chart_gen.py

@@ -1,56 +0,0 @@
-
-
-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": "<speak>", "sql":"<sql>","db_name":"<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"""<h5>{speak}</h5><img style='max-width: 120%; max-height: 80%;'  src="data:image/png;base64,{data}" />"""
-    return html_img
-
-
-
-@command("response_bar_chart", "Use bar chart to display SQL data", '"speak": "<speak>", "sql":"<sql>","db_name":"<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": "<speak>", "sql":"<sql>","db_name":"<db_name>"')
-def response_pie_chart(speak: str, sql: str, db_name: str) -> str:
-    """
-    """
-    pass

pilot/commands/built_in/show_table_gen.py

@@ -1,21 +0,0 @@
-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": "<speak>", "sql":"<sql>","db_name":"<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"""<div class="w-full overflow-auto">{table_str}</div>"""
-    view_text = f"##### {str(speak)}" + "\n" + html.replace("\n", " ")
-    return view_text

pilot/commands/built_in/show_text_gen.py

@@ -1,33 +0,0 @@
-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": "<speak>", "sql":"<sql>","db_name":"<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"""<div class="w-full overflow-auto">{table_str}</div>"""
-        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

pilot/commands/disply_type/show_chart_gen.py

@@ -17,7 +17,7 @@ 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": "<speak>", "df":"<data frame>"')
+@command("response_line_chart", "Line chart display, used to display comparative trend analysis data", '"speak": "<speak>", "df":"<data frame>"')
 def response_line_chart(speak: str,  df: DataFrame) -> str:
     logger.info(f"response_line_chart:{speak},")
 
@@ -27,9 +27,10 @@ def response_line_chart(speak: str,  df: DataFrame) -> str:
         raise ValueError("No Data！")
     plt.rcParams["font.family"] = ["sans-serif"]
     rc = {"font.sans-serif": "SimHei", "axes.unicode_minus": False}
+    sns.set_style(rc={'font.sans-serif': "Microsoft Yahei"})
     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])
+    sns.lineplot(df, x=columns[0], y=columns[1])
     plt.title("")
 
     buf = io.BytesIO()
@@ -37,20 +38,54 @@ def response_line_chart(speak: str,  df: DataFrame) -> str:
     buf.seek(0)
     data = base64.b64encode(buf.getvalue()).decode("ascii")
 
-    html_img = f"""<h5>{speak}</h5><img style='max-width: 120%; max-height: 80%;'  src="data:image/png;base64,{data}" />"""
+    html_img = f"""<h5>{speak}</h5><img style='max-width: 100%; max-height: 70%;'  src="data:image/png;base64,{data}" />"""
     return html_img
 
 
 
-# @command("response_bar_chart", "Use bar chart to display SQL data", '"speak": "<speak>", "sql":"<sql>","db_name":"<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": "<speak>", "sql":"<sql>","db_name":"<db_name>"')
-# def response_pie_chart(speak: str, sql: str, db_name: str) -> str:
-#     """
-#     """
-#     pass
+@command("response_bar_chart", "Histogram, suitable for comparative analysis of multiple target values",  '"speak": "<speak>", "df":"<data frame>"')
+def response_bar_chart(speak: str,  df: DataFrame) -> str:
+    logger.info(f"response_bar_chart:{speak},")
+    columns = df.columns.tolist()
+    if df.size <= 0:
+        raise ValueError("No Data！")
+    plt.rcParams["font.family"] = ["sans-serif"]
+    rc = {"font.sans-serif": "SimHei", "axes.unicode_minus": False}
+    sns.set_style(rc={'font.sans-serif': "Microsoft Yahei"})
+    sns.set(context="notebook", style="ticks", color_codes=True, rc=rc)
+    plt.subplots(figsize=(8, 5), dpi=100)
+    sns.barplot(df, x=df[columns[0]], y=df[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"""<h5>{speak}</h5><img style='max-width: 100%; max-height: 70%;'  src="data:image/png;base64,{data}" />"""
+    return html_img
+
+
+
+@command("response_pie_chart", "Pie chart, suitable for scenarios such as proportion and distribution statistics",  '"speak": "<speak>", "df":"<data frame>"')
+def response_pie_chart(speak: str,  df: DataFrame) -> str:
+    logger.info(f"response_pie_chart:{speak},")
+    columns = df.columns.tolist()
+    if df.size <= 0:
+        raise ValueError("No Data！")
+    plt.rcParams["font.family"] = ["sans-serif"]
+    rc = {"font.sans-serif": "SimHei", "axes.unicode_minus": False}
+    sns.set_style(rc={'font.sans-serif': "Microsoft Yahei"})
+    sns.set(context="notebook", style="ticks", color_codes=True, rc=rc)
+    sns.set_palette("Set3")  # 设置颜色主题
+    plt.pie(columns[1], labels=columns[0], autopct='%1.1f%%', startangle=90)
+    plt.axis('equal')  # 使饼图为正圆形
+    plt.title(columns[0])
+
+    buf = io.BytesIO()
+    plt.savefig(buf, format="png", dpi=100)
+    buf.seek(0)
+    data = base64.b64encode(buf.getvalue()).decode("ascii")
+
+    html_img = f"""<h5>{speak}</h5><img style='max-width: 100%; max-height: 70%;'  src="data:image/png;base64,{data}" />"""
+    return html_img

pilot/commands/disply_type/show_table_gen.py

@@ -11,7 +11,7 @@ CFG = Config()
 logger = build_logger("show_table_gen", LOGDIR + "show_table_gen.log")
 
 
-@command("response_table", "Use table to display SQL data", '"speak": "<speak>", "df":"<data frame>"')
+@command("response_table", "Table display, suitable for display with many display columns or non-numeric columns", '"speak": "<speak>", "df":"<data frame>"')
 def response_table(speak: str,  df: DataFrame) -> str:
     logger.info(f"response_table:{speak}")
     html_table = df.to_html(index=False, escape=False, sparsify=False)

pilot/commands/disply_type/show_text_gen.py

@@ -10,7 +10,7 @@ CFG = Config()
 logger = build_logger("show_table_gen", LOGDIR + "show_table_gen.log")
 
 
-@command("response_data_text", "Use text to display data",
+@command("response_data_text", "Text display, the default display method, suitable for single-line or simple content display",
          '"speak": "<speak>", "df":"<data frame>"')
 def response_data_text(speak: str,  df: DataFrame) -> str:
     logger.info(f"response_data_text:{speak}")

pilot/connections/manages/connection_manager.py

@@ -126,7 +126,7 @@ class ConnectManager:
             db_user = db_config.get("db_user")
             db_pwd = db_config.get("db_pwd")
             return connect_instance.from_uri_db(
-                db_host, db_port, db_user, db_pwd, db_name
+                host=db_host, port=db_port, user=db_user, pwd=db_pwd, db_name=db_name
             )
 
     def get_db_list(self):

pilot/connections/rdbms/base.py

@@ -95,13 +95,13 @@ class RDBMSDatabase(BaseConnect):
         db_url: str = (
             cls.driver
             + "://"
-            + CFG.LOCAL_DB_USER
+            + user
             + ":"
-            + CFG.LOCAL_DB_PASSWORD
+            + pwd
             + "@"
-            + CFG.LOCAL_DB_HOST
+            + host
             + ":"
-            + str(CFG.LOCAL_DB_PORT)
+            + str(port)
             + "/"
             + db_name
         )

pilot/json_utils/utilities.py

@@ -2,6 +2,8 @@
 import json
 import os.path
 import re
+import json
+from datetime import datetime
 
 from jsonschema import Draft7Validator
 
@@ -79,3 +81,10 @@ def is_string_valid_json(json_string: str, schema_name: str) -> bool:
     """
 
     return validate_json_string(json_string, schema_name) is not None
+
+
+class DateTimeEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, datetime):
+            return obj.isoformat()
+        return super().default(obj)

pilot/openapi/api_v1/api_v1.py

@@ -139,6 +139,7 @@ async def dialogue_scenes():
     scene_vos: List[ChatSceneVo] = []
     new_modes: List[ChatScene] = [
         ChatScene.ChatWithDbExecute,
+        ChatScene.ChatExcel,
         ChatScene.ChatWithDbQA,
         ChatScene.ChatKnowledge,
         ChatScene.ChatDashboard,
@@ -229,9 +230,10 @@ async def chat_prepare(dialogue: ConversationVo = Body()):
     logger.info(f"chat_prepare:{dialogue}")
     ## check conv_uid
     chat: BaseChat = get_chat_instance(dialogue)
-    if not chat.history_message:
+    if len(chat.history_message) >0:
         return Result.succ(None)
-    return Result.succ(chat.prepare())
+    resp = chat.prepare()
+    return Result.succ(resp)
 
 
 @router.post("/v1/chat/completions")

pilot/scene/base.py

@@ -33,7 +33,7 @@ class ChatScene(Enum):
         code = "excel_learning",
         name = "Excel Learning",
         describe = "Analyze and summarize your excel files.",
-        param_types = [],
+        param_types=["File Select"],
         is_inner = True,
     )
     ChatExcel = Scene(

pilot/scene/base_chat.py

@@ -85,7 +85,7 @@ class BaseChat(ABC):
                 proxyllm_backend=CFG.PROXYLLM_BACKEND,
             )
         )
-        if not chat_mode.is_inner():
+
         ### can configurable storage methods
         self.memory = DuckdbHistoryMemory(chat_session_id)
 
@@ -181,7 +181,6 @@ class BaseChat(ABC):
                 return response
             else:
                 from pilot.server.llmserver import worker
-
                 return worker.generate_stream_gate(payload)
         except Exception as e:
             print(traceback.format_exc())

pilot/scene/chat_data/chat_excel/excel_analyze/chat.py

@@ -1,6 +1,8 @@
 import json
 import os
 
+
+from typing import List, Any, Dict
 from pilot.scene.base_message import (
     HumanMessage,
     ViewMessage,
@@ -12,21 +14,19 @@ 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
+from pilot.scene.chat_data.chat_excel.excel_analyze.prompt import prompt
 from pilot.scene.chat_data.chat_excel.excel_reader import ExcelReader
-
+from pilot.scene.chat_data.chat_excel.excel_learning.chat import ExcelLearning
 CFG = Config()
 
 
-class ExcelLearning(BaseChat):
+class ChatExcel(BaseChat):
     chat_scene: str = ChatScene.ChatExcel.value()
-
+    chat_retention_rounds = 2
     def __init__(self, chat_session_id, user_input, select_param: str = ""):
         chat_mode = ChatScene.ChatExcel
 
         self.excel_file_path = select_param
-        file_name, file_extension = os.path.splitext(select_param)
-        self.excel_file_name = file_name
         self.excel_reader = ExcelReader(select_param)
 
         super().__init__(
@@ -36,10 +36,40 @@ class ExcelLearning(BaseChat):
             select_param=select_param,
         )
 
+
+    def _generate_command_string(self, command: Dict[str, Any]) -> str:
+        """
+        Generate a formatted string representation of a command.
+
+        Args:
+            command (dict): A dictionary containing command information.
+
+        Returns:
+            str: The formatted command string.
+        """
+        args_string = ", ".join(
+            f'"{key}": "{value}"' for key, value in command["args"].items()
+        )
+        return f'{command["label"]}: "{command["name"]}", args: {args_string}'
+
+    def _generate_numbered_list(self) -> str:
+        command_strings = []
+        if CFG.command_disply:
+            command_strings += [
+                str(item)
+                for item in CFG.command_disply.commands.values()
+                if item.enabled
+            ]
+        return "\n".join(f"{i+1}. {item}" for i, item in enumerate(command_strings))
+
+
     def generate_input_values(self):
 
+
         input_values = {
-            "data_example": json.dumps(self.excel_reader.get_sample_data()),
+            "user_input":  self.current_user_input,
+            "table_name": self.excel_reader.table_name,
+            "disply_type": self._generate_numbered_list(),
         }
         return input_values
 
@@ -47,19 +77,21 @@ class ExcelLearning(BaseChat):
         logger.info(f"{self.chat_mode} prepare start!")
         chat_param = {
             "chat_session_id": self.chat_session_id,
-            "user_input": self.excel_file_name + " analysis！",
+            "user_input": self.excel_reader.excel_file_name + " analysis！",
             "select_param": self.excel_file_path
         }
-        chat: BaseChat = ExcelLearning(**chat_param)
-
-        return chat.call()
+        learn_chat = ExcelLearning(**chat_param)
+        result = learn_chat.nostream_call()
+        return result
 
 
     def do_action(self, prompt_response):
         print(f"do_action:{prompt_response}")
-        param= {
-            "speak": prompt_response["thoughts"],
-            "df": self.excel_reader.get_df_by_sql(prompt_response["sql"])
-        }
-        return CFG.command_disply.call(prompt_response['display'], **param)
+
+        # colunms, datas = self.excel_reader.run(prompt_response.sql)
+        param= {
+            "speak": prompt_response.thoughts,
+            "df": self.excel_reader.get_df_by_sql_ex(prompt_response.sql)
+        }
+        return CFG.command_disply.call(prompt_response.display, **param)
 

pilot/scene/chat_data/chat_excel/excel_analyze/out_parser.py

@@ -33,9 +33,9 @@ class ChatExcelOutputParser(BaseOutputParser):
                 sql = response[key]
             if key.strip() == "thoughts":
                 thoughts = response[key]
-            if key.strip() == "disply":
-                disply = response[key]
-        return ExcelAnalyzeResponse(sql, thoughts, disply)
+            if key.strip() == "display":
+                display = response[key]
+        return ExcelAnalyzeResponse(sql, thoughts, display)
 
     def parse_view_response(self, speak, data) -> str:
         ### tool out data to table view

pilot/scene/chat_data/chat_excel/excel_analyze/prompt.py

@@ -2,7 +2,7 @@ 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.scene.chat_data.chat_excel.excel_analyze.out_parser import ChatExcelOutputParser
 from pilot.common.schema import SeparatorStyle
 
 CFG = Config()
@@ -10,11 +10,14 @@ CFG = Config()
 PROMPT_SCENE_DEFINE = "You are a data analysis expert. "
 
 _DEFAULT_TEMPLATE = """
-Please give data analysis SQL based on the following user goals: {user_input}
+Please use the data structure information of the above historical dialogue, make sure not to use column names that are not in the data structure.
+According to the user goal: {user_input}，give the correct duckdb SQL for data analysis.
+Use the table name: {table_name}
+
+According to the analysis SQL obtained by the user's goal, select the best one from the following display forms, if it cannot be determined, use Text  as the display.
 Display type: 
     {disply_type}
     
-According to the analysis SQL obtained by the user's goal, select the best one from the following display forms, if it cannot be determined, use Text  as the display.
 Respond in the following json format:
     {response}
 Ensure the response is correct json and can be parsed by Python json.loads
@@ -39,14 +42,15 @@ PROMPT_TEMPERATURE = 0.8
 
 prompt = PromptTemplate(
     template_scene=ChatScene.ChatExcel.value(),
-    input_variables=["user_input", "disply_type"],
+    input_variables=["user_input", "table_name", "disply_type"],
     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(
+    output_parser=ChatExcelOutputParser(
         sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT
     ),
+    need_historical_messages = True,
     # example_selector=sql_data_example,
     temperature=PROMPT_TEMPERATURE,
 )

pilot/scene/chat_data/chat_excel/excel_learning/chat.py

@@ -1,4 +1,5 @@
 import json
+import os
 
 from pilot.scene.base_message import (
     HumanMessage,
@@ -13,6 +14,7 @@ from pilot.common.markdown_text import (
 )
 from pilot.scene.chat_data.chat_excel.excel_learning.prompt import prompt
 from pilot.scene.chat_data.chat_excel.excel_reader import ExcelReader
+from pilot.json_utils.utilities import DateTimeEncoder
 
 CFG = Config()
 
@@ -20,15 +22,16 @@ CFG = Config()
 class ExcelLearning(BaseChat):
     chat_scene: str = ChatScene.ExcelLearning.value()
 
-    def __init__(self, chat_session_id, user_input, file_path):
-        chat_mode = ChatScene.ChatWithDbExecute
+    def __init__(self, chat_session_id, user_input, select_param:str=None):
+        chat_mode = ChatScene.ExcelLearning
         """ """
-        self.excel_reader = ExcelReader(file_path)
+        self.excel_file_path = select_param
+        self.excel_reader = ExcelReader(select_param)
         super().__init__(
             chat_mode=chat_mode,
             chat_session_id=chat_session_id,
             current_user_input = user_input,
-            select_param=file_path,
+            select_param=select_param,
         )
 
 
@@ -38,7 +41,7 @@ class ExcelLearning(BaseChat):
         datas.insert(0, colunms)
 
         input_values = {
-            "data_example": datas,
+            "data_example": json.dumps(self.excel_reader.get_sample_data(), cls=DateTimeEncoder),
         }
         return input_values
 

pilot/scene/chat_data/chat_excel/excel_learning/out_parser.py

@@ -20,7 +20,7 @@ class ExcelResponse(NamedTuple):
 logger = build_logger("chat_excel", LOGDIR + "ChatExcel.log")
 
 
-class ChatExcelOutputParser(BaseOutputParser):
+class LearningExcelOutputParser(BaseOutputParser):
     def __init__(self, sep: str, is_stream_out: bool):
         super().__init__(sep=sep, is_stream_out=is_stream_out)
 
@@ -39,27 +39,15 @@ class ChatExcelOutputParser(BaseOutputParser):
 
     def parse_view_response(self, speak, data) -> str:
         ### tool out data to table view
-
-
-
-        html_title= data["desciption"]
-        html_colunms= f"<h5>数据结构</h5><ul>"
-        for item in data["clounms"]:
-            html_colunms = html_colunms + "<li>"
+        html_title = f"### **数据简介:**\n{data.desciption} \n"
+        html_colunms = f"### **数据结构:**\n"
+        for item in data.clounms:
             keys = item.keys()
             for key in keys:
-                html_colunms = html_colunms + f"{key}:{item[key]}"
-            html_colunms = html_colunms + "</li>"
-        html_colunms= html_colunms + "</ul>"
+                html_colunms = html_colunms + f"-   **{key}**:{item[key]} \n"
 
-        html_plans="<ol>"
-        for item in data["plans"]:
-            html_plans = html_plans + f"<li>{item}</li>"
-        html = f"""
-                <div>
-                   <h4>{html_title}</h4>  
-                   <div>{html_colunms}</div>
-                   <div>{html_plans}</div>
-               <div>
-                """
+        html_plans = f"\n ### **分析计划:** \n"
+        for item in data.plans:
+            html_plans = html_plans + f"-   {item} \n"
+        html = f"""{html_title}{html_colunms}{html_plans}"""
         return html

pilot/scene/chat_data/chat_excel/excel_learning/prompt.py

@@ -2,7 +2,7 @@ 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.scene.chat_data.chat_excel.excel_learning.out_parser import LearningExcelOutputParser
 from pilot.common.schema import SeparatorStyle
 
 CFG = Config()
@@ -21,7 +21,7 @@ Please return your answer in JSON format, the return format is as follows:
 
 RESPONSE_FORMAT_SIMPLE =     {
     "DataAnalysis": "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)"}],
+    "ColumnAnalysis": [{"column name1": "字段1介绍，专业术语解释(请尽量简单明了)"}],
     "AnalysisProgram": ["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"],
 }
 
@@ -43,7 +43,7 @@ prompt = PromptTemplate(
     template_define=PROMPT_SCENE_DEFINE,
     template=_DEFAULT_TEMPLATE,
     stream_out=PROMPT_NEED_NEED_STREAM_OUT,
-    output_parser=DbChatOutputParser(
+    output_parser=LearningExcelOutputParser(
         sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT
     ),
     # example_selector=sql_data_example,

pilot/scene/chat_data/chat_excel/excel_learning/test.py

@@ -6,79 +6,88 @@ import time
 from fsspec import filesystem
 import  spatial
 
+from pilot.scene.chat_data.chat_excel.excel_reader import ExcelReader
+
 
 if __name__ == "__main__":
     # connect = duckdb.connect("/Users/tuyang.yhj/Downloads/example.xlsx")
     #
+    excel_reader = ExcelReader("/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
+    # colunms, datas = excel_reader.run( "SELECT CONCAT(Year, '-', Quarter) AS QuarterYear, SUM(Sales) AS TotalSales FROM example GROUP BY QuarterYear ORDER BY QuarterYear")
+    colunms, datas = excel_reader.run( """ SELECT Month_Name, SUM(Sales) AS Total_Sales FROM example WHERE Year = '2019' GROUP BY Month_Name """)
 
-    # 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 Country, SUM(Profit) AS Total_Profit FROM example GROUP BY Country ORDER BY Total_Profit DESC LIMIT 1;')
-    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")
-
-
-
+    #
+    #
+    # 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 Country, SUM(Profit) AS Total_Profit FROM example GROUP BY Country ORDER BY Total_Profit DESC LIMIT 1;')
+    # 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")
+    #
+    #
+    #

pilot/scene/chat_data/chat_excel/excel_reader.py

@@ -1,19 +1,35 @@
 import duckdb
+import os
 import pandas as pd
 
 from pilot.common.pd_utils import csv_colunm_foramt
 
+def excel_colunm_format(old_name:str)->str:
+    new_column = old_name.strip()
+    new_column = new_column.replace(" ", "_")
+    return new_column
 
 class ExcelReader:
 
     def __init__(self, file_path):
         # read excel filt
         df_tmp = pd.read_excel(file_path)
+
+
         self.df = pd.read_excel(file_path, converters={i: csv_colunm_foramt for i in range(df_tmp.shape[1])})
+        self.columns_map = {}
+        for column_name in df_tmp.columns:
+            self.columns_map.update({column_name: excel_colunm_format(column_name)})
+
+        self.df = self.df.rename(columns=lambda x: x.strip().replace(' ', '_'))
+
         # connect DuckDB
         self.db = duckdb.connect(database=':memory:', read_only=False)
+        file_name = os.path.basename(file_path)
+        file_name_without_extension = os.path.splitext(file_name)[0]
 
-        self.table_name = f"excel"
+        self.excel_file_name = file_name_without_extension
+        self.table_name = file_name_without_extension
         # write data in duckdb
         self.db.register(self.table_name, self.df)
 

pilot/scene/chat_factory.py

@@ -9,6 +9,7 @@ from pilot.scene.chat_db.auto_execute.chat import ChatWithDbAutoExecute
 from pilot.scene.chat_dashboard.chat import ChatDashboard
 from pilot.scene.chat_knowledge.v1.chat import ChatKnowledge
 from pilot.scene.chat_knowledge.inner_db_summary.chat import InnerChatDBSummary
+from pilot.scene.chat_data.chat_excel.excel_analyze.chat import ChatExcel
 
 
 class ChatFactory(metaclass=Singleton):

pilot/server/dbgpt_server.py

@@ -75,7 +75,7 @@ app.include_router(knowledge_router, prefix="/api")
 app.include_router(api_editor_route_v1, prefix="/api")
 
 # app.include_router(api_v1)
-# app.include_router(knowledge_router)
+app.include_router(knowledge_router)
 # app.include_router(api_editor_route_v1)
 
 app.mount("/_next/static", StaticFiles(directory=static_file_path + "/_next/static"))

pilot/server/static/404.html

@@ -1 +1 @@
-<!DOCTYPE html><html><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><title>404: This page could not be found</title><meta name="next-head-count" content="3"/><noscript data-n-css=""></noscript><script defer="" nomodule="" src="/_next/static/chunks/polyfills-78c92fac7aa8fdd8.js"></script><script src="/_next/static/chunks/webpack-ea98197dd6536d07.js" defer=""></script><script src="/_next/static/chunks/framework-4498e84bb0ba1830.js" defer=""></script><script src="/_next/static/chunks/main-74fdb7a2c21bf459.js" defer=""></script><script src="/_next/static/chunks/pages/_app-8fa61e72220a040a.js" defer=""></script><script src="/_next/static/chunks/pages/_error-bcb3296e330590f7.js" defer=""></script><script src="/_next/static/8J6oF0PtATupjimCxWfXw/_buildManifest.js" defer=""></script><script src="/_next/static/8J6oF0PtATupjimCxWfXw/_ssgManifest.js" defer=""></script></head><body><div id="__next"><div style="font-family:system-ui,&quot;Segoe UI&quot;,Roboto,Helvetica,Arial,sans-serif,&quot;Apple Color Emoji&quot;,&quot;Segoe UI Emoji&quot;;height:100vh;text-align:center;display:flex;flex-direction:column;align-items:center;justify-content:center"><div style="line-height:48px"><style>body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}</style><h1 class="next-error-h1" style="display:inline-block;margin:0 20px 0 0;padding-right:23px;font-size:24px;font-weight:500;vertical-align:top">404</h1><div style="display:inline-block"><h2 style="font-size:14px;font-weight:400;line-height:28px">This page could not be found<!-- -->.</h2></div></div></div></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{"statusCode":404}},"page":"/_error","query":{},"buildId":"8J6oF0PtATupjimCxWfXw","nextExport":true,"isFallback":false,"gip":true,"scriptLoader":[]}</script></body></html>
+<!DOCTYPE html><html><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><title>404: This page could not be found</title><meta name="next-head-count" content="3"/><noscript data-n-css=""></noscript><script defer="" nomodule="" src="/_next/static/chunks/polyfills-78c92fac7aa8fdd8.js"></script><script src="/_next/static/chunks/webpack-e4cf89f137f492b8.js" defer=""></script><script src="/_next/static/chunks/framework-4498e84bb0ba1830.js" defer=""></script><script src="/_next/static/chunks/main-74fdb7a2c21bf459.js" defer=""></script><script src="/_next/static/chunks/pages/_app-8fa61e72220a040a.js" defer=""></script><script src="/_next/static/chunks/pages/_error-bcb3296e330590f7.js" defer=""></script><script src="/_next/static/ae8AMgI-SPy2zYTXfjFnE/_buildManifest.js" defer=""></script><script src="/_next/static/ae8AMgI-SPy2zYTXfjFnE/_ssgManifest.js" defer=""></script></head><body><div id="__next"><div style="font-family:system-ui,&quot;Segoe UI&quot;,Roboto,Helvetica,Arial,sans-serif,&quot;Apple Color Emoji&quot;,&quot;Segoe UI Emoji&quot;;height:100vh;text-align:center;display:flex;flex-direction:column;align-items:center;justify-content:center"><div style="line-height:48px"><style>body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}</style><h1 class="next-error-h1" style="display:inline-block;margin:0 20px 0 0;padding-right:23px;font-size:24px;font-weight:500;vertical-align:top">404</h1><div style="display:inline-block"><h2 style="font-size:14px;font-weight:400;line-height:28px">This page could not be found<!-- -->.</h2></div></div></div></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{"statusCode":404}},"page":"/_error","query":{},"buildId":"ae8AMgI-SPy2zYTXfjFnE","nextExport":true,"isFallback":false,"gip":true,"scriptLoader":[]}</script></body></html>

pilot/server/static/404/index.html

@@ -1 +1 @@
-<!DOCTYPE html><html><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><title>404: This page could not be found</title><meta name="next-head-count" content="3"/><noscript data-n-css=""></noscript><script defer="" nomodule="" src="/_next/static/chunks/polyfills-78c92fac7aa8fdd8.js"></script><script src="/_next/static/chunks/webpack-ea98197dd6536d07.js" defer=""></script><script src="/_next/static/chunks/framework-4498e84bb0ba1830.js" defer=""></script><script src="/_next/static/chunks/main-74fdb7a2c21bf459.js" defer=""></script><script src="/_next/static/chunks/pages/_app-8fa61e72220a040a.js" defer=""></script><script src="/_next/static/chunks/pages/_error-bcb3296e330590f7.js" defer=""></script><script src="/_next/static/8J6oF0PtATupjimCxWfXw/_buildManifest.js" defer=""></script><script src="/_next/static/8J6oF0PtATupjimCxWfXw/_ssgManifest.js" defer=""></script></head><body><div id="__next"><div style="font-family:system-ui,&quot;Segoe UI&quot;,Roboto,Helvetica,Arial,sans-serif,&quot;Apple Color Emoji&quot;,&quot;Segoe UI Emoji&quot;;height:100vh;text-align:center;display:flex;flex-direction:column;align-items:center;justify-content:center"><div style="line-height:48px"><style>body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}</style><h1 class="next-error-h1" style="display:inline-block;margin:0 20px 0 0;padding-right:23px;font-size:24px;font-weight:500;vertical-align:top">404</h1><div style="display:inline-block"><h2 style="font-size:14px;font-weight:400;line-height:28px">This page could not be found<!-- -->.</h2></div></div></div></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{"statusCode":404}},"page":"/_error","query":{},"buildId":"8J6oF0PtATupjimCxWfXw","nextExport":true,"isFallback":false,"gip":true,"scriptLoader":[]}</script></body></html>
+<!DOCTYPE html><html><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><title>404: This page could not be found</title><meta name="next-head-count" content="3"/><noscript data-n-css=""></noscript><script defer="" nomodule="" src="/_next/static/chunks/polyfills-78c92fac7aa8fdd8.js"></script><script src="/_next/static/chunks/webpack-e4cf89f137f492b8.js" defer=""></script><script src="/_next/static/chunks/framework-4498e84bb0ba1830.js" defer=""></script><script src="/_next/static/chunks/main-74fdb7a2c21bf459.js" defer=""></script><script src="/_next/static/chunks/pages/_app-8fa61e72220a040a.js" defer=""></script><script src="/_next/static/chunks/pages/_error-bcb3296e330590f7.js" defer=""></script><script src="/_next/static/ae8AMgI-SPy2zYTXfjFnE/_buildManifest.js" defer=""></script><script src="/_next/static/ae8AMgI-SPy2zYTXfjFnE/_ssgManifest.js" defer=""></script></head><body><div id="__next"><div style="font-family:system-ui,&quot;Segoe UI&quot;,Roboto,Helvetica,Arial,sans-serif,&quot;Apple Color Emoji&quot;,&quot;Segoe UI Emoji&quot;;height:100vh;text-align:center;display:flex;flex-direction:column;align-items:center;justify-content:center"><div style="line-height:48px"><style>body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}</style><h1 class="next-error-h1" style="display:inline-block;margin:0 20px 0 0;padding-right:23px;font-size:24px;font-weight:500;vertical-align:top">404</h1><div style="display:inline-block"><h2 style="font-size:14px;font-weight:400;line-height:28px">This page could not be found<!-- -->.</h2></div></div></div></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{"statusCode":404}},"page":"/_error","query":{},"buildId":"ae8AMgI-SPy2zYTXfjFnE","nextExport":true,"isFallback":false,"gip":true,"scriptLoader":[]}</script></body></html>

pilot/server/static/_next/static/9LH3ZUeLe8qGf5V4iFVgD/_buildManifest.js

@@ -1 +0,0 @@
-self.__BUILD_MANIFEST={__rewrites:{beforeFiles:[],afterFiles:[],fallback:[]},"/_error":["static/chunks/pages/_error-f5357f382422dd96.js"],sortedPages:["/_app","/_error"]},self.__BUILD_MANIFEST_CB&&self.__BUILD_MANIFEST_CB();

pilot/server/static/_next/static/9LH3ZUeLe8qGf5V4iFVgD/_ssgManifest.js

@@ -1 +0,0 @@
-self.__SSG_MANIFEST=new Set([]);self.__SSG_MANIFEST_CB&&self.__SSG_MANIFEST_CB()

pilot/server/static/_next/static/A5QlvQugyBg85syAKbPsn/_buildManifest.js

@@ -1 +0,0 @@
-self.__BUILD_MANIFEST={__rewrites:{beforeFiles:[],afterFiles:[],fallback:[]},"/_error":["static/chunks/pages/_error-bcb3296e330590f7.js"],sortedPages:["/_app","/_error"]},self.__BUILD_MANIFEST_CB&&self.__BUILD_MANIFEST_CB();

pilot/server/static/_next/static/A5QlvQugyBg85syAKbPsn/_ssgManifest.js

@@ -1 +0,0 @@
-self.__SSG_MANIFEST=new Set([]);self.__SSG_MANIFEST_CB&&self.__SSG_MANIFEST_CB()

pilot/server/static/_next/static/chunks/app/datastores/documents/chunklist/page-0f2d3429fd2ed723.js

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