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test:delete unusual test file
This commit is contained in:
aries_ckt
parent
ec19a0e1df
commit
b68bb2b5d5
@ -268,6 +268,31 @@ class Database:
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result.insert(0, field_names)
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return result
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def query_ex(self, session, query, fetch: str = "all"):
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"""
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only for query
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Args:
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session:
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query:
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fetch:
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Returns:
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"""
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print(f"Query[{query}]")
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if not query:
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return []
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cursor = session.execute(text(query))
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if cursor.returns_rows:
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if fetch == "all":
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result = cursor.fetchall()
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elif fetch == "one":
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result = cursor.fetchone()[0] # type: ignore
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else:
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raise ValueError("Fetch parameter must be either 'one' or 'all'")
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field_names = list(i[0:] for i in cursor.keys())
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result = list(result)
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return field_names, result
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def run(self, session, command: str, fetch: str = "all") -> List:
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"""Execute a SQL command and return a string representing the results."""
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print("SQL:" + command)
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@ -1,94 +0,0 @@
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from pilot.configs.config import Config
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import pandas as pd
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from sqlalchemy import create_engine, pool
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import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib.font_manager import FontProperties
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from pyecharts.charts import Bar
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from pyecharts import options as opts
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from test_cls_1 import TestBase, Test1
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from test_cls_2 import Test2
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CFG = Config()
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#
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# if __name__ == "__main__":
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# # 创建连接池
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# engine = create_engine('mysql+pymysql://root:aa123456@localhost:3306/gpt-user')
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#
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# # 从连接池中获取连接
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#
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#
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# # 归还连接到连接池中
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#
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# # 执行SQL语句并将结果转化为DataFrame
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# query = "SELECT * FROM users"
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# df = pd.read_sql(query, engine.connect())
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# df.style.set_properties(subset=['name'], **{'font-weight': 'bold'})
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# # 导出为HTML文件
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# with open('report.html', 'w') as f:
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# f.write(df.style.render())
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#
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# # # 设置中文字体
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# # font = FontProperties(fname='SimHei.ttf', size=14)
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# #
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# # colors = np.random.rand(df.shape[0])
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# # df.plot.scatter(x='city', y='user_name', c=colors)
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# # plt.show()
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#
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# # 查看DataFrame
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# print(df.head())
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#
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#
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# # 创建数据
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# x_data = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
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# y_data = [820, 932, 901, 934, 1290, 1330, 1320]
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#
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# # 生成图表
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# bar = (
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# Bar()
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# .add_xaxis(x_data)
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# .add_yaxis("销售额", y_data)
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# .set_global_opts(title_opts=opts.TitleOpts(title="销售额统计"))
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# )
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#
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# # 生成HTML文件
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# bar.render('report.html')
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#
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#
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# if __name__ == "__main__":
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# def __extract_json(s):
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# i = s.index("{")
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# count = 1 # 当前所在嵌套深度，即还没闭合的'{'个数
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# for j, c in enumerate(s[i + 1 :], start=i + 1):
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# if c == "}":
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# count -= 1
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# elif c == "{":
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# count += 1
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# if count == 0:
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# break
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# assert count == 0 # 检查是否找到最后一个'}'
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# return s[i : j + 1]
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#
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# 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;" } }}"""
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# print(__extract_json(ss))
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if __name__ == "__main__":
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# test1 = Test1()
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# test2 = Test2()
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# test1.write()
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# test1.test()
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# test2.write()
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# test1.test()
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# test2.test()
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# 定义包含元组的列表
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data = [("key1", "value1"), ("key2", "value2"), ("key3", "value3")]
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# 使用字典解析将列表转换为字典
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result = {k: v for k, v in data}
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print(result)
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@ -1,15 +0,0 @@
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import json
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from pilot.common.sql_database import Database
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from pilot.configs.config import Config
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CFG = Config()
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if __name__ == "__main__":
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# connect = CFG.local_db.get_session("gpt-user")
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# datas = CFG.local_db.run(connect, "SELECT * FROM users; ")
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# print(datas)
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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" ,}"""
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print(str.find("["))
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@ -1,16 +0,0 @@
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import json
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import os
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import duckdb
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default_db_path = os.path.join(os.getcwd(), "message")
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duckdb_path = os.getenv("DB_DUCKDB_PATH", default_db_path + "/chat_history.db")
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if __name__ == "__main__":
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if os.path.isfile("../../../message/chat_history.db"):
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cursor = duckdb.connect("../../../message/chat_history.db").cursor()
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# cursor.execute("SELECT * FROM chat_history limit 20")
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cursor.execute(
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"SELECT * FROM chat_history where conv_uid ='b54ae5fe-1624-11ee-a271-b26789cc3e58'"
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)
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data = cursor.fetchall()
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print(data)
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@ -1,92 +0,0 @@
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from enum import Enum
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from typing import List
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class Test(Enum):
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XXX = ("x", "1", True)
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YYY = ("Y", "2", False)
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ZZZ = ("Z", "3")
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def __init__(self, code, v, flag=False):
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self.code = code
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self.v = v
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self.flag = flag
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class Scene:
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def __init__(
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self, code, name, describe, param_types: List = [], is_inner: bool = False
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):
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self.code = code
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self.name = name
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self.describe = describe
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self.param_types = param_types
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self.is_inner = is_inner
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class ChatScene(Enum):
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ChatWithDbExecute = Scene(
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"chat_with_db_execute",
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"Chat Data",
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"Dialogue with your private data through natural language.",
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["DB Select"],
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)
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ChatWithDbQA = Scene(
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"chat_with_db_qa",
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"Chat Meta Data",
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"Have a Professional Conversation with Metadata.",
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["DB Select"],
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)
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ChatExecution = Scene(
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"chat_execution",
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"Chat Plugin",
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"Use tools through dialogue to accomplish your goals.",
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["Plugin Select"],
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)
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ChatDefaultKnowledge = Scene(
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"chat_default_knowledge",
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"Chat Default Knowledge",
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"Dialogue through natural language and private documents and knowledge bases.",
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)
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ChatNewKnowledge = Scene(
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"chat_new_knowledge",
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"Chat New Knowledge",
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"Dialogue through natural language and private documents and knowledge bases.",
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["Knowledge Select"],
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)
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ChatUrlKnowledge = Scene(
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"chat_url_knowledge",
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"Chat URL",
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"Dialogue through natural language and private documents and knowledge bases.",
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["Url Input"],
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)
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InnerChatDBSummary = Scene(
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"inner_chat_db_summary", "DB Summary", "Db Summary.", True
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)
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ChatNormal = Scene(
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"chat_normal", "Chat Normal", "Native LLM large model AI dialogue."
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)
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ChatDashboard = Scene(
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"chat_dashboard",
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"Chat Dashboard",
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"Provide you with professional analysis reports through natural language.",
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["DB Select"],
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)
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ChatKnowledge = Scene(
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"chat_knowledge",
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"Chat Knowledge",
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"Dialogue through natural language and private documents and knowledge bases.",
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["Knowledge Space Select"],
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)
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def scene_value(self):
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return self.value.code
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def scene_name(self):
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return self._value_.name
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if __name__ == "__main__":
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print(ChatScene.ChatWithDbExecute.scene_value())
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# print(ChatScene.ChatWithDbExecute.value.describe)
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@ -1,12 +0,0 @@
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from abc import ABC, abstractmethod
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from pydantic import BaseModel
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from test_cls_base import TestBase
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class Test1(TestBase):
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mode: str = "456"
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def write(self):
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self.test_values.append("x")
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self.test_values.append("y")
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self.test_values.append("g")
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@ -1,17 +0,0 @@
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from abc import ABC, abstractmethod
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from pydantic import BaseModel
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from test_cls_base import TestBase
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from typing import Any, Callable, Dict, List, Mapping, Optional, Set, Union
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class Test2(TestBase):
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test_2_values: List = []
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mode: str = "789"
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def write(self):
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self.test_values.append(1)
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self.test_values.append(2)
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self.test_values.append(3)
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self.test_2_values.append("x")
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self.test_2_values.append("y")
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self.test_2_values.append("z")
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@ -1,13 +0,0 @@
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from abc import ABC, abstractmethod
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from pydantic import BaseModel
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from typing import Any, Callable, Dict, List, Mapping, Optional, Set, Union
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class TestBase(BaseModel, ABC):
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test_values: List = []
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mode: str = "123"
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def test(self):
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print(self.__class__.__name__ + ":")
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print(self.test_values)
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print(self.mode)
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@ -2,6 +2,8 @@ import json
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import os
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import uuid
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from typing import Dict, NamedTuple, List
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from decimal import Decimal
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from pilot.scene.base_message import (
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HumanMessage,
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ViewMessage,
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@ -17,6 +19,7 @@ from pilot.scene.chat_dashboard.prompt import prompt
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from pilot.scene.chat_dashboard.data_preparation.report_schma import (
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ChartData,
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ReportData,
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ValueItem,
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)
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CFG = Config()
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@ -74,7 +77,40 @@ class ChatDashboard(BaseChat):
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chart_datas: List[ChartData] = []
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for chart_item in prompt_response:
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try:
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datas = self.database.run(self.db_connect, chart_item.sql)
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field_names, datas = self.database.query_ex(
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self.db_connect, chart_item.sql
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)
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values: List[ValueItem] = []
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data_map = {}
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field_map = {}
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index = 0
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for field_name in field_names:
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data_map.update({f"{field_name}": [row[index] for row in datas]})
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index += 1
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if not data_map[field_name]:
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field_map.update({f"{field_name}": False})
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else:
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field_map.update(
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{
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f"{field_name}": all(
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isinstance(item, (int, float, Decimal))
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for item in data_map[field_name]
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)
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}
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)
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for field_name in field_names[1:]:
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if not field_map[field_name]:
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print("more than 2 non-numeric column")
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else:
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for data in datas:
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value_item = ValueItem(
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name=data[0],
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type=field_name,
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value=data[field_names.index(field_name)],
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)
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values.append(value_item)
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chart_datas.append(
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ChartData(
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chart_uid=str(uuid.uuid1()),
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@ -82,8 +118,8 @@ class ChatDashboard(BaseChat):
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chart_type=chart_item.showcase,
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chart_desc=chart_item.thoughts,
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chart_sql=chart_item.sql,
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column_name=datas[0],
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values=datas,
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column_name=field_names,
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values=values,
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)
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)
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except Exception as e:
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@ -4,6 +4,15 @@ from typing import TypeVar, Union, List, Generic, Any
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from dataclasses import dataclass, asdict
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class ValueItem(BaseModel):
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name: str
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type: str = None
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value: float
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def dict(self, *args, **kwargs):
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return {"name": self.name, "type": self.type, "value": self.value}
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class ChartData(BaseModel):
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chart_uid: str
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chart_name: str
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@ -11,7 +20,7 @@ class ChartData(BaseModel):
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chart_desc: str
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chart_sql: str
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column_name: List
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values: List
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values: List[ValueItem]
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style: Any = None
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def dict(self, *args, **kwargs):
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@ -22,7 +31,7 @@ class ChartData(BaseModel):
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"chart_desc": self.chart_desc,
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"chart_sql": self.chart_sql,
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"column_name": [str(item) for item in self.column_name],
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"values": [[str(item) for item in sublist] for sublist in self.values],
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"values": [value.dict() for value in self.values],
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"style": self.style,
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}
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@ -13,8 +13,14 @@ PROMPT_SCENE_DEFINE = """You are a {dialect} data analysis expert, please provid
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_DEFAULT_TEMPLATE = """
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According to the structure definition in the following tables:
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{table_info}
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Provide professional data analysis, use as few dimensions as possible, but no less than three, and no more than eight dimensions.
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Used to support goal: {input}
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Provide professional data analysis to support the goal:
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{input}
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Constraint:
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Provide multi-dimensional analysis as much as possible according to the target requirements, no less than three and no more than 8 dimensions.
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The data columns of the analysis output should not exceed 4.
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According to the characteristics of the analyzed data, choose the most suitable one from the charts provided below for display, chart type:
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{supported_chat_type}
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Pay attention to the length of the output content of the analysis result, do not exceed 4000tokens
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According to the characteristics of the analyzed data, choose the best one from the charts provided below to display, use different types of charts as much as possible，chart types:
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@ -22,7 +28,7 @@ According to the characteristics of the analyzed data, choose the best one from
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Give {dialect} data analysis SQL, analysis title, display method and analytical thinking,respond in the following json format:
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{response}
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Do not use unprovided fields and do not use unprovided data in the where condition of sql.
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Do not use unprovided fields and value in the where condition of sql.
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Ensure the response is correct json and can be parsed by Python json.loads
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"""
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