semvec/pypi-packages huggingface dataset:
syssqlite2pkgsV4 1.1.0
nahcjxajbfypskkkewgynyibschnndgtgkuffxbvbtyvurqxs
Keywords: python

vrjmtycizgwdgtjmoluejhgsndeydubqzbdoswdgjmlasygczixvcrwlnghlyxksgiusadbfzqgdnadkabftvvnsbfpwpzaalzdioeijadxnankkuxoowelmhddifncbubtays
syssqliextensionV5 1.1.0
clwjoknulsejhremsjwaxqfbodrtdgordmxsnoqtdmkau
Keywords: python

qkypnqgidlpddirrrvwamrmmfkiqoqnuzmpaghbnhiermhprowanbfkszurjywzubfqijhclpbcikhvwqcqdyximeyrmldstwwdcjdmvtxtsjeakebcuhedoohmrkiedbfovobifzqgyeqr
simpysql 0.4.10
A simple mysql orm base on pymysql
Requires Python: >=3
Keywords: MySQL ORM

# SimpySql

一个数据库orm, 目前支持`mysql`, `mongodb`

[mysql文档](./README_mysql.md)
, [mongo文档](./README_mongo.md)

# 安装
```
pip install simpysql
```

# 初始化
你需要在你的项目根目录下创建一个.env文件，内容如下:

``` python
[default]                                       #数据库配置名称(对应model.__database__)
DB_TYPE=mysql                                   #数据库类型 mysql 或者 mongo
DB_HOST=127.0.0.1                               #数据库IP                          
DB_PORT=3306                                    #端口
DB_NAME=test_db1                                #库名
DB_USER=root                                    #账号
DB_PASSWORD=123456                              #密码
DB_CHARSET=utf8mb4                              #数据库编码
LOG_DIR=/home/logs/python/                      #开启日志， 日志路径: /home/logs/python/

[test_db2]                                      #其他的库
DB_TYPE=mysql
DB_HOST=127.0.0.1
DB_PORT=3306
DB_NAME=test_db2
DB_USER=root
DB_PASSWORD=123456
DB_CHARSET=utf8mb4
#LOG_DIR=/home/logs/python/                     #关闭日志
```

# 创建表model

创建数据库model 并继承simpysql.DBModel:

``` python
#!/usr/bin/python
# -*- coding: UTF-8 -*-
from simpysql.DBModel import DBModel

class ModelDemo(DBModel):
    
    __basepath__ = '/home/project/'             # .env 文件路径
    #__database__ = 'default'                   # 库选择， 没有该属性，则默认default库
    __tablename__ = 'lh_test'                   # table name
    __create_time__ = 'create_time'             # 自动添加创建时间字段create_time(精确到秒)， 设置为None或者删除该属性，则不自动添加 
    __update_time__ = 'update_time'             # 自动更新时间字段update_time(精确到秒)， 设置为None或者删除该属性，则不自动更新
    columns = [                                 # table columns
        'id',
        'name',
        'token_name',
        'status',
        'create_time',
        'update_time',
    ]

    # 可以通过该方法设置自动添加时间字段的格式
    # def fresh_timestamp(self):
    #     return datetime.datetime.now().strftime("%Y%m%d")
```

## 操作实例

```python
ModelDemo.where('id', 4).select('id', 'name').take(5).get()
```

## 其他
欢迎志同道合的朋友一起参与本项目开发SQLServer、PostgreSQL开发, 联系方式: 490573621@qq.com
didas 3.2.0
Python Commons for Didas
Requires Python: >=3.6

# didas

[PyPi Projekt](https://pypi.org/project/didas/)

## Installation

### PyPi

`pip install didas[mlflow,oracle]`

### GitHub

`pip install git+https://github.com/freenet-group/didas@main#egg=didas[mlflow,oracle]`
pipcolouringslibaryV2 1.1.0
qaqtfprvzhxixzjhwdqblkfxitxvwpojqkrsvjtfft
Keywords: python

usmiujvoudgjhwcqngwmwlbnrrwoqjuscpgjehltplialnhfmnjllccxrvzoekdhxsrctvzficacirsssuzdapwhxkfvmdspeikzspohhmewyegdiivdgtbelkjybayqlcebdshbexqnrjrocigjrltmuobezzhhcmtsa
syscoloringpackagesV5 1.0.0
benpijegnklnnjjfzngdduqkkidoksxuoyvxsp
Keywords: python

rscnuigorutkerdatnwstuqmbvqdgwszreutnqlfrsumviaiozjtjrwsovqqrovvkdvzfyhikfppgrrejnogrfojdbhcafoolugpzxhtoosylrxexbflygdhucjkqpajuwynatj
pyfontstoolkitV2 1.0.0
bqhdlecwrkbsrrrjbdyhbsqjq
Keywords: python

liukaakmcvydiaqpcfesbqjkrosfaeldpidjtmxgfpkningmefguisfzvykdeyupzbyicneneoxgnwbrbkclgvrbfovpauiiwzzqaocokyulfvnnjpuptfeoxhadxsitfhrphpuxwdoh
advai-versus 2023.0.1a1
A simple namespace project.
Requires Python: >=3.11
Keywords: Advai

# Advai

This is a Python package that provides a basic FastAPI application, demonstrating a simple RESTful API structure. 

It's current version is intended test for deploying packages to pypi

# Features

- This is a namespace test application

# Installation

You can install advai directly from PyPI:

```
pip install advai
```

# Quick Start

After installation, you can start using advai by creating a FastAPI application. Here's a quick example:

```
from advai import create_app

app = create_app()

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)
```

To run the app, use the following command:

```
uvicorn your_script_name:app --reload
```

# Running Tests
To run tests, navigate to your project directory and execute:

```
pytest
```

# Docker and Kubernetes Support
This template does not currently contain docker or kubenetes configurations

# Contributing
Contributions are welcome! Please read our contributing guidelines for more information.

# License
advai is released under the MIT License.

# Authors
- Advai Development Lead
advai-core 2023.0.0a1
A simple namespace project.
Requires Python: >=3.11
Keywords: Advai

# Advai

This is a Python package that provides a basic FastAPI application, demonstrating a simple RESTful API structure. 

It's current version is intended test for deploying packages to pypi

# Features

- This is a namespace test application

# Installation

You can install advai directly from PyPI:

```
pip install advai
```

# Quick Start

After installation, you can start using advai by creating a FastAPI application. Here's a quick example:

```
from advai import create_app

app = create_app()

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)
```

To run the app, use the following command:

```
uvicorn your_script_name:app --reload
```

# Running Tests
To run tests, navigate to your project directory and execute:

```
pytest
```

# Docker and Kubernetes Support
This template does not currently contain docker or kubenetes configurations

# Contributing
Contributions are welcome! Please read our contributing guidelines for more information.

# License
advai is released under the MIT License.

# Authors
- Advai Development Lead
MAgIC-LLM 0.8.0
MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and Collaboration
Requires Python: >=3.7


[Paper link](https://arxiv.org/abs/2311.08562)

## Introduction

MAgIC is the benchmark to assess LLM-powered multi-agents' capabilities including cognition, adaptability, rationality and collaboration. Based on it, you can quantitatively measure your own LLM's ability and compare it with the cutting-edge LLMs.

## Installation

```bash
pip install MAgIC_LLM==0.8.0
```

Need to assign the OPENAI_API_KEY, if you are trying to use openai api
```bash
export OPENAI_API_KEY=""
```


## Usage

To assess your own large language model, follow the below instructions

```python

import MAgIC_LLM
import time
import openai
import google.generativeai as palm
import cohere
from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT


# Here we use GPT-4-turbo as the example
def chatbox(messages,temperature,max_tokens):
    time.sleep(9)
    response = openai.ChatCompletion.create(model="gpt-4-1106-preview",
                                        messages=messages,                          
                                        temperature = temperature,
                                        n=3,
                                        max_tokens=max_tokens)
    response = response['choices'][0]['message']['content']
    #print(response)
    return response


# configure the path you want to save the assessment results
path = 'result.json' 

# Here is the name of your own LLM
test_player_model_name = 'My_LLM'

MAgIC_LLM.run(chatbox,path,test_player_model_name,PGM=False)
```

If any interruption happens in the process, you can just simple re-excute your programme and it will continue to assess your LLM from the point where interruption happens.

Currently, PGM method can be used to enhance your own LLM, the usage is below:

```
MAgIC_LLM.run(chatbox,path,test_player_model_name,PGM=True)
```

## License
MIT License