semvec/pypi-packages huggingface dataset:
plada 0.0.13
PlaDa: Platform for Data market

# plada
**PlaDa** (**Pla**tform for **Da**ta market)

PlaDa is a simulation platform designed to model and analyze data marketplaces. It provides tools for setting up market models, configuring simulation parameters, running simulations, and analyzing results.

PlaDaは、データ市場のモデル化や分析を行うためのシミュレーションプラットフォームです。PlaDaを使うことで、市場のモデル化、シミュレーションのパラメータ設定、シミュレーションの実行、結果の分析が行えます。

## Install
PlaDa is available on PyPI and can be installed using pip.

PlaDaはPyPIから利用でき、pipを使ってインストールできます。

#### Using pip
```
$ pip install plada
$ python
>> import plada
```
#### Using pip in a Jupyter Notebook
When installing within a Jupyter Notebook, use the !pip command:
```
!pip install plada
import plada
```

#### Using Google Colab
Google Colabで使用する場合は、以下のノートブックを参考にしてください：
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/junsashihara/plada/blob/master/examples/test.ipynb)

For more detailed examples, refer to the [test.ipynb](https://github.com/junsashihara/plada/blob/master/examples/test.ipynb) notebook.

詳細については、[test.ipynb](https://github.com/junsashihara/plada/blob/master/examples/test.ipynb)をご覧ください。

## Usage
#### Step1：Set up the Market Model.
Note: The model must be a Graph object and must contain "variables".
```
model = nx.read_graphml("test.graphml")
```
#### Step2：Configure Settings
Define the configuration for the simulation, market, and agents.
```
config = {
    "Simulation":{
        "num_iterations": 10,
        "num_steps": 10,
        "isPrice": True,
    },
    "Market":{
        "model": model,
    },
    "Agent": {
        "num_buyers": 10,
        "strategy_weights": {
            "random": 0.0,
            "related": 0.0,
            "ranking": 1.0,
        },
        "new_buyer_probability": 0.8,
    }
}
```
#### Step3：Run the Simulation
Initialize the `Saver` and `Runner` classes with the configuration settings and logger. Then, execute the main simulation process.
```
saver = Saver()
runner = Runner(settings=config, logger=saver)

runner.main()
```

## Explanation
buyer.py
- **Configuration of Buyer Agent**
- Manages the state and strategies of buyers, updates budget, and saves purchased data.

market.py
- **Market Class**: Manages the overall market.
- **Data Class**: Manages the data.
- **Variable Class**: Manages the variables.
- Primarily responsible for updating prices and related market dynamics.

simulator.py
- **Setup of the Purchase Simulation Workflow**
- Organizes the information to be logged (requires improvement)

runner.py
- **Execution of the Simulation**

logger.py
- **Saving the Results of the Simulation**

analyzer.py
- **Basic Analysis of the Results**
- Visualizes the distribution of purchase counts.
canonicalwebteam.directory-parser 1.2.10
Flask extension to parse websites and extract structured data to build sitemaps.

# canonicalwebteam.directory-parser
Flask extension to parse websites and extract structured data to build sitemaps.

## Install
Install the project with pip: `pip install canonicalwebteam.directory-parser`


## Using the directory parser

### Sitemap templates
Include sitemap templates in your Flask app. Copy the following codeblock to where your application is instantiated e.g `app.py`. The template loader should be placed right after the app is instantiated.

```
from jinja2 import ChoiceLoader, FileSystemLoader
from pathlib import Path
import canonicalwebteam.directory_parser as directory_parser


# Set up Flask application
app = FlaskBase(...)


# Include directory parser templates
directory_parser_templates = (
    Path(directory_parser.__file__).parent / "templates"
)

loader = ChoiceLoader(
    [
        FileSystemLoader(str(directory_parser_templates)),
    ]
)

app.jinja_loader = loader
```

### Generate sitemaps
The `generate_sitemap` function will generate a sitemap given directory path and base url using the sitemap templates.

```
# Dynamic sitemaps that do not need to be included in the sitemap tree.
# Differ from project to project, can be checked on /sitemap.xml
DYNAMIC_SITEMAPS = [
    "tutorials",
    "engage",
    "ceph/docs",
    "blog",
    "security/notices",
    "security/cves",
    "security/livepatch/docs",
    "robotics/docs",
]

directory_path = os.getcwd() + "/templates"
base_url = "https://ubuntu.com"

xml_sitemap = directory_parser.generate_sitemap(
                directory_path, 
                base_url, 
                exclude_paths=DYNAMIC_SITEMAPS
              )

if xml_sitemap:
    with open(sitemap_path, "w") as f:
        f.write(xml_sitemap)

# Serve the existing sitemap
with open(sitemap_path, "r") as f:
    xml_sitemap = f.read()

response = flask.make_response(xml_sitemap)
response.headers["Content-Type"] = "application/xml"
return response
```

### Parse project directory tree
If you'd like to get the parsed tree of a given directory, you can use the `scan_directory` function. 

```
directory_path = os.getcwd() + "/templates"
tree = directory_parser.scan_directory(
            directory_path, exclude_paths=DYNAMIC_SITEMAPS
        )
```
`tree` will return a tree of all the templates given in the `directory_path`


## Local development
### Running the project
This guide assumes that you are using [dotrun](https://github.com/canonical/dotrun/) to run your Flask app.

#### Include a relative path to the project
This example assumes both project exist in the same directory

In `requirements.txt`:
```
# Comment out package import
# canonicalwebteam.directory-parser==1.2.6

-e ../directory-parser
```

#### Run project with a mounted additor
`dotrun -m /path/to/canonicalwebteam.directory-parser:../directory-parser`


### Linting and formatting

To follow the standard linting rules of this project, we are using [Tox](https://tox.wiki/en/latest/)
```
pip3 install tox  # Install tox
tox -e lint       # Check the format of Python code
tox -e format     # Reformat the Python code
```
create-agentic-rag 0.1.2
A boilerplate for creating agentic RAG applications
Requires Python: >=3.8

# Agentic RAG Boilerplate

A powerful boilerplate for creating Retrieval-Augmented Generation (RAG) applications with agentic capabilities. This template provides a solid foundation for building AI-powered applications that can retrieve and process information intelligently.

## Features

- 🚀 FastAPI-based REST API
- 🤖 LangChain integration for RAG capabilities
- 🔍 Efficient document processing and retrieval
- 🔐 Environment-based configuration
- 📦 Well-structured project layout
- 🛠️ Easy to extend and customize

## Quick Start

### Installation

```bash
pip install create-agentic-rag
```

### Create a New Project

```bash
create-agentic-rag my-project-name
```

This will create a new directory with your project name and set up the basic structure.

### Setup Your Project

1. Navigate to your project directory:

```bash
cd my-project-name
```

2. Create and activate a virtual environment:

```bash
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
```

3. Install dependencies:

```bash
pip install -r requirements.txt
```

4. Configure your environment:

```bash
cp .env.example .env
```

Edit the `.env` file with your configuration settings.

### Running the Application

Start the FastAPI server:

```bash
uvicorn app.main:app --reload
```

The API will be available at `http://localhost:8000`

## Project Structure

```
.
├── app/
│   ├── api/           # API routes and endpoints
│   ├── core/          # Core application logic
│   ├── models/        # Data models
│   ├── services/      # Business logic and services
│   └── main.py        # Application entry point
├── .env.example       # Example environment variables
├── requirements.txt   # Project dependencies
└── README.md         # This file
```

## API Documentation

Once the server is running, you can access the interactive API documentation at:

- Swagger UI: `http://localhost:8000/docs`
- ReDoc: `http://localhost:8000/redoc`

## Environment Variables

The following environment variables are required:

- `OPENAI_API_KEY`: Your OpenAI API key
- `LANGCHAIN_API_KEY`: Your LangChain API key
- `LANGCHAIN_PROJECT`: Your LangChain project name
- `LANGCHAIN_TRACING_V2`: Enable/disable LangChain tracing
- `LANGCHAIN_ENDPOINT`: LangChain API endpoint

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Support

If you encounter any issues or have questions, please open an issue in the repository.
django-sysinfo 2.7.0
Simple django app to expose system infos: libraries version, databae server infos...
Requires Python: >=3.10

# django-sysinfo

[![Pypi](https://badge.fury.io/py/django-sysinfo.svg)](https://badge.fury.io/py/django-sysinfo)
[![coverage](https://codecov.io/github/saxix/django-sysinfo/coverage.svg?branch=develop)](https://codecov.io/github/saxix/django-sysinfo?branch=develop)
[![Test](https://github.com/saxix/django-sysinfo/actions/workflows/test.yml/badge.svg)](https://github.com/saxix/django-sysinfo/actions/workflows/test.yml)
[![Django](https://img.shields.io/pypi/frameworkversions/django/django-sysinfo)](https://pypi.org/project/django-sysinfo/)


Simple django app to expose system infos like libraries version, database server.

Easy to extend to add custom checks.

## Features


    - dump system informations
    - admin integration
    - API to add custom checks
    - simple echo
    - retrieve library version


## Quickstart

Install django-sysinfo::

    pip install django-sysinfo

put it in your `INSTALLED_APPS`::

    INSTALLED_APPS=[...
     'django_sysinfo'
    ]

add relevant entries in your url.conf::

    urlpatterns = (
        ....
        url(r'', include(django_sysinfo.urls)),
    )

or customize them::

    from django_sysinfo.views import http_basic_login, sysinfo

    urlpatterns = (
        url('sys/info/$', http_basic_login(sysinfo), name='sys-info'),
        url('sys/version/(?P<name>.*)/$', version, name='sys-version')
    )


Known issues and limitations
----------------------------

There are some limitations in the metrics returned by sysinfo, anyway this package is
not intended to be used as host/resources monitoring tool.

    - Disk space returns device info, any soft limits are ignored
    - Memory can be wrong in some virtual environments
cursor-init 0.1.9
프로젝트 규칙 파일을 간편하게 세팅하는 CLI
Requires Python: >=3.10

# cursor-init

프로젝트 규칙/문서 템플릿을 손쉽게 세팅하는 CLI 도구 (PyPI 배포용)

---

## ✨ 주요 기능

- `.cursorrules`, rules, docs 등 실무 규칙/문서 템플릿 일괄 복사 및 초기화
- 실무 규칙/자동화/상호 참조 구조를 표준화
- 신규 프로젝트, 팀 온보딩, 문서 일관성 유지에 최적화

---

## 🗂️ 템플릿/문서 구조

```
cursor_init/templates/profile/
├── default/
│   ├── .cursorrules
│   ├── .cursor/
│   │   ├── mcp.json
│   │   └── rules/
│   └── .cursor-init/
│       └── docs/
└── ... (프로필별 디렉토리)

.cursor-init/
├── docs/           # 공식 기술문서/가이드/프로세스 관리 경로
└── profiles/       # 프로필별 문서/규칙/스냅샷 관리
```

- `.cursorrules`: 프로젝트 전체 규칙 정의
- `rules/`: 각종 실무/자동화 규칙(mdc)
- `docs/`: 공식 기술문서/가이드/프로세스
- `profiles/`: 프로필별 문서/규칙/스냅샷 관리

---

## 🚀 사용법

### 템플릿 초기화

```bash
cursor-init docs sync
```

### 도움말

```bash
cursor-init --help
```

---

## 💡 실무 적용 예시

1. 템플릿/문서 구조 동기화

   ```bash
   cursor-init docs sync
   ```

2. 요구사항 정의 및 agent 활용
   ```bash
   # 예시: 신규 회원가입 기능 설계 요구사항을 agent에게 입력
   agent "신규 회원가입 기능 설계: 이메일 인증, 비밀번호 정책, API 명세를 포함. 요구사항에 맞게 문서를 업데이트 해주세요."
   ```

- agent를 활용하면 요구사항 분석, 시스템 설계, 문서화, 체크리스트 관리 등 실무 전 과정을 일관되게 자동화할 수 있습니다.
- 팀/고객/상황별로 프로필을 분리해 다양한 규칙/문서 세트를 손쉽게 적용하세요.

---

## 🛠️ 개발/기여

1. 이 저장소를 fork/clone
2. 의존성 설치:
   ```bash
   pip install -e .
   ```
3. 템플릿/코어 로직 수정 후 PR 제출

---

## 📝 참고

- pyproject.toml의 `[project.scripts]`로 CLI 명령어 등록
- 템플릿 경로는 `cursor_init/templates/profile/` 기준
- 공식 문서/구조/규칙은 `.cursor-init/docs/`, `.cursor-init/profiles/` 참고
- 자세한 구조/실무 규칙은 `.cursor-init/docs/` 내 문서 참조

---

## License

MIT
ai_command_master 1.1.0
ai command master example
Requires Python: >=3.8

# AI Command Master

一个基于AI的命令行工具，帮助用户更高效地完成命令行操作。

## 功能特点

- 支持多种AI模型配置（OpenAI、Deepseek、智谱AI等）
- 智能命令解析和执行（自然语言转命令行）
- 多配置文件管理（支持环境隔离）
- 跨平台支持（Windows/macOS/Linux）

## 典型场景

- 快速查询复杂命令语法
- 自动化重复性运维操作
- 安全执行危险命令前确认
- 开发环境快速配置

## 安装

### 环境要求

- Python >= 3.8（建议3.13.0）
- pip 23.0+（Python包管理器）
- Git 2.30+（源码安装需要）

### 安装步骤

#### 方法一：通过pip安装（推荐）

使用清华PyPI镜像加速安装：
```bash
pip install ai-command-master -i https://pypi.tuna.tsinghua.edu.cn/simple
```

#### 方法二：从源码安装

1. 克隆项目代码：
```bash
git clone https://gitee.com/yourusername/ai-command-master.git
cd ai-command-master
```

2. 安装依赖（使用镜像源）：
```bash
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple .
```

## 快速开始

### 1. 创建配置文件

```bash
# Windows系统需要以管理员模式运行CMD/PowerShell
# myconfig为文件名，建议使用model名命名
ai config create myconfig
```

### 2. 配置文件示例
```bash
# 请以官方提供的API文档为准
model_provider = "Moonshot"
model_name = "moonshot-v1-8k"
base_url = "https://api.moonshot.cn/v1"
api_key = "your_api_key"
max_token: 8192
temperature: 0.3
```

### 3. 配置文件管理

```bash
# 列出所有配置文件
ai config list

# 显示当前配置
ai config show

# 切换配置文件
ai config switch myconfig
```

## 使用指南

### 基础使用
```bash
# 获取帮助
ai --help

# 执行命令（Windows路径示例）
ai ask 如何批量重命名当前目录下所有.txt文件为.md文件？
```

## 开发说明

### 环境配置（Windows）

1. 创建虚拟环境：
```powershell
python -m venv venv
venv\Scripts\activate
```

2. 安装包：
```bash
# 使用.virtualenv文件安装所有依赖
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple e .

```

## 许可证

本项目采用 MIT 许可证 - 查看 [LICENSE](LICENSE) 文件了解详情
file-monitor-system 0.1.0
Cross-platform real-time file monitoring and integrity verification
Requires Python: >=3.7

# File Monitor Library

A cross-platform file monitoring and integrity verification tool with real-time alerts.

## Features

- Real-time file change detection
- SHA-256 file integrity check
- Email alert support
- Windows and Linux/macOS support
- Audit log stubs for advanced access tracking

## Usage

```python
from file_monitor import FileMonitor

monitor = FileMonitor(path="/etc", alert_callback=print)
monitor.start()
```