WEB API independent

pilot/connections/rdbms/py_study/pd_study.py

@@ -1,94 +0,0 @@
-from pilot.configs.config import Config
-import pandas as pd
-from sqlalchemy import create_engine, pool
-import matplotlib.pyplot as plt
-import numpy as np
-from matplotlib.font_manager import FontProperties
-from pyecharts.charts import Bar
-from pyecharts import options as opts
-from test_cls_1 import TestBase, Test1
-from test_cls_2 import Test2
-
-CFG = Config()
-
-#
-# if __name__ == "__main__":
-#    # 创建连接池
-#    engine  = create_engine('mysql+pymysql://root:aa123456@localhost:3306/gpt-user')
-#
-#    # 从连接池中获取连接
-#
-#
-#    # 归还连接到连接池中
-#
-#    # 执行SQL语句并将结果转化为DataFrame
-#    query = "SELECT * FROM users"
-#    df = pd.read_sql(query, engine.connect())
-#    df.style.set_properties(subset=['name'], **{'font-weight': 'bold'})
-#    # 导出为HTML文件
-#    with open('report.html', 'w') as f:
-#       f.write(df.style.render())
-#
-#    # # 设置中文字体
-#    # font = FontProperties(fname='SimHei.ttf', size=14)
-#    #
-#    # colors = np.random.rand(df.shape[0])
-#    # df.plot.scatter(x='city', y='user_name', c=colors)
-#    # plt.show()
-#
-#    # 查看DataFrame
-#    print(df.head())
-#
-#
-#    # 创建数据
-#    x_data = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
-#    y_data = [820, 932, 901, 934, 1290, 1330, 1320]
-#
-#    # 生成图表
-#    bar = (
-#       Bar()
-#          .add_xaxis(x_data)
-#          .add_yaxis("销售额", y_data)
-#          .set_global_opts(title_opts=opts.TitleOpts(title="销售额统计"))
-#    )
-#
-#    # 生成HTML文件
-#    bar.render('report.html')
-#
-#
-
-
-# if __name__ == "__main__":
-
-# def __extract_json(s):
-#     i = s.index("{")
-#     count = 1  # 当前所在嵌套深度，即还没闭合的'{'个数
-#     for j, c in enumerate(s[i + 1 :], start=i + 1):
-#         if c == "}":
-#             count -= 1
-#         elif c == "{":
-#             count += 1
-#         if count == 0:
-#             break
-#     assert count == 0  # 检查是否找到最后一个'}'
-#     return s[i : j + 1]
-#
-# ss = """here's a sql statement that can be used to generate a histogram to analyze the distribution of user orders in different cities:select u.city, count(*) as order_countfrom tran_order oleft join user u on o.user_id = u.idgroup by u.city;this will return the number of orders for each city that has at least one order. we can use this data to generate a histogram that shows the distribution of orders across different cities.here's the response in the required format:{ "thoughts": "here's a sql statement that can be used to generate a histogram to analyze the distribution of user orders in different cities:\n\nselect u.city, count(*) as order_count\nfrom tran_order o\nleft join user u on o.user_id = u.id\ngroup by u.city;", "speak": "here's a sql statement that can be used to generate a histogram to analyze the distribution of user orders in different cities.", "command": { "name": "histogram-executor", "args": { "title": "distribution of user orders in different cities", "sql": "select u.city, count(*) as order_count\nfrom tran_order o\nleft join user u on o.user_id = u.id\ngroup by u.city;" } }}"""
-# print(__extract_json(ss))
-
-if __name__ == "__main__":
-    # test1 = Test1()
-    # test2 = Test2()
-    # test1.write()
-    # test1.test()
-    # test2.write()
-    # test1.test()
-    # test2.test()
-
-    # 定义包含元组的列表
-    data = [('key1', 'value1'), ('key2', 'value2'), ('key3', 'value3')]
-
-    # 使用字典解析将列表转换为字典
-    result = {k: v for k, v in data}
-
-    print(result)

pilot/connections/rdbms/py_study/study_data.py

@@ -1,20 +0,0 @@
-import json
-from pilot.common.sql_database import Database
-from pilot.configs.config import Config
-
-CFG = Config()
-
-if __name__ == "__main__":
-    # connect = CFG.local_db.get_session("gpt-user")
-    # datas = CFG.local_db.run(connect, "SELECT * FROM users; ")
-
-    # print(datas)
-
-    # str = """{ "thoughts": "thought text", "sql": "SELECT COUNT(DISTINCT user_id) FROM transactions_order WHERE user_id IN (SELECT DISTINCT user_id FROM users WHERE country='China') AND create_time BETWEEN 20230101 AND 20230131" ,}"""
-    #
-    # print(str.find("["))
-
-    test =["t1", "t2", "t3", "tx"]
-    print(str(test[1:]))
-
-

pilot/connections/rdbms/py_study/study_duckdb.py

@@ -1,16 +0,0 @@
-import json
-import os
-import duckdb
-
-default_db_path = os.path.join(os.getcwd(), "message")
-duckdb_path = os.getenv("DB_DUCKDB_PATH", default_db_path + "/chat_history.db")
-
-if __name__ == "__main__":
-    if os.path.isfile("../../../message/chat_history.db"):
-        cursor = duckdb.connect("../../../message/chat_history.db").cursor()
-        # cursor.execute("SELECT * FROM chat_history limit 20")
-        cursor.execute(
-            "SELECT * FROM chat_history  where conv_uid ='b54ae5fe-1624-11ee-a271-b26789cc3e58'"
-        )
-        data = cursor.fetchall()
-        print(data)

pilot/connections/rdbms/py_study/study_enum.py

@@ -1,44 +0,0 @@
-from enum import Enum
-from typing import List
-
-class Test(Enum):
-    XXX = ("x", "1", True)
-    YYY =("Y", "2", False)
-    ZZZ = ("Z", "3")
-    def __init__(self, code, v, flag= False):
-        self.code = code
-        self.v = v
-        self.flag = flag
-
-class Scene:
-    def __init__(self, code, name, describe, param_types:List=[], is_inner: bool = False):
-        self.code = code
-        self.name = name
-        self.describe = describe
-        self.param_types = param_types
-        self.is_inner = is_inner
-
-
-class ChatScene(Enum):
-
-    ChatWithDbExecute = Scene("chat_with_db_execute", "Chat Data", "Dialogue with your private data through natural language.", ["DB Select"])
-    ChatWithDbQA = Scene("chat_with_db_qa", "Chat Meta Data", "Have a Professional Conversation with Metadata.", ["DB Select"])
-    ChatExecution = Scene("chat_execution", "Chat Plugin", "Use tools through dialogue to accomplish your goals.", ["Plugin Select"])
-    ChatDefaultKnowledge = Scene("chat_default_knowledge", "Chat Default Knowledge", "Dialogue through natural language and private documents and knowledge bases.")
-    ChatNewKnowledge = Scene("chat_new_knowledge", "Chat New Knowledge", "Dialogue through natural language and private documents and knowledge bases.", ["Knowledge Select"])
-    ChatUrlKnowledge = Scene("chat_url_knowledge", "Chat URL", "Dialogue through natural language and private documents and knowledge bases.", ["Url Input"])
-    InnerChatDBSummary = Scene("inner_chat_db_summary", "DB Summary", "Db Summary.", True)
-
-    ChatNormal = Scene("chat_normal", "Chat Normal", "Native LLM large model AI dialogue.")
-    ChatDashboard = Scene("chat_dashboard", "Chat Dashboard", "Provide you with professional analysis reports through natural language.", ["DB Select"])
-    ChatKnowledge = Scene("chat_knowledge", "Chat Knowledge", "Dialogue through natural language and private documents and knowledge bases.", ["Knowledge Space Select"])
-
-    def scene_value(self):
-        return self.value.code;
-
-    def scene_name(self):
-       return self._value_.name;
-
-if __name__ == "__main__":
-    print(ChatScene.ChatWithDbExecute.scene_value())
-    # print(ChatScene.ChatWithDbExecute.value.describe)

pilot/connections/rdbms/py_study/test_cls_1.py

@@ -1,12 +0,0 @@
-from abc import ABC, abstractmethod
-from pydantic import BaseModel
-from test_cls_base import TestBase
-
-
-class Test1(TestBase):
-    mode: str = "456"
-
-    def write(self):
-        self.test_values.append("x")
-        self.test_values.append("y")
-        self.test_values.append("g")

pilot/connections/rdbms/py_study/test_cls_2.py

@@ -1,17 +0,0 @@
-from abc import ABC, abstractmethod
-from pydantic import BaseModel
-from test_cls_base import TestBase
-from typing import Any, Callable, Dict, List, Mapping, Optional, Set, Union
-
-
-class Test2(TestBase):
-    test_2_values: List = []
-    mode: str = "789"
-
-    def write(self):
-        self.test_values.append(1)
-        self.test_values.append(2)
-        self.test_values.append(3)
-        self.test_2_values.append("x")
-        self.test_2_values.append("y")
-        self.test_2_values.append("z")

pilot/connections/rdbms/py_study/test_cls_base.py

@@ -1,13 +0,0 @@
-from abc import ABC, abstractmethod
-from pydantic import BaseModel
-from typing import Any, Callable, Dict, List, Mapping, Optional, Set, Union
-
-
-class TestBase(BaseModel, ABC):
-    test_values: List = []
-    mode: str = "123"
-
-    def test(self):
-        print(self.__class__.__name__ + ":")
-        print(self.test_values)
-        print(self.mode)