semvec/pypi-packages huggingface dataset:
jingke 1.1.6
A Python Developer, Used to generate, debug and upload the code in GITHUB.
Requires Python: >=3.10

# JINGKE 2.0

JINGKE 2.0 is a command-line tool designed to streamline project management, environment setup, and GitHub integration.

## Features
- **Project Setup**: Easily initialize and configure your development environment.
- **Project Management**: View, load, and remove projects effortlessly.
- **VS Code Integration**: Open projects directly in Visual Studio Code.
- **GitHub Support**: Push your project to GitHub seamlessly.
- **User Initialization**: Set up your user profile for personalized usage.

## Installation
```sh
pip install jingke
```

## Usage
Run the following command to access the tool:
```sh
jingke --help
```

### Available Commands
- `--init, -i` : Setup Jingke for first-time use.
- `--setup, -s` : Initialize a project in the environment.
- `--view, -v` : View all available projects.
- `--load, -l` : Load a selected project.
- `--remove, -rm` : Remove a project from the environment.
- `--vscode, -vs` : Open the project in VS Code.
- `--git, -git` : Push the project to GitHub.

## Example Usage
```sh
jingke -s  # Setup a new project
jingke -v  # View existing projects
jingke -l  # Load a project
jingke -vs # Open project in VS Code
jingke -git # Push project to GitHub
```

## API Authentication
JINGKE requires an API key for authentication. Ensure you have a valid API key configured before running the tool.

## License
This project is licensed under the MIT License.

## Contributing
Feel free to submit pull requests or report issues. We welcome contributions!

## Contact
For any inquiries, contact the developer or open an issue on GitHub.
sadrec 0.2.0
Light-weight electrophysiology audio recorder with live plotting and spike detection
Requires Python: >=3.9

 Soundcard ADC Recoder (SADRec)

The  **SADRec** is a lightweight Python app for viewing and recording any signals connected to your soundcard microphone input. 
It is based on the following Python Packages:

 - PyAudio (https://people.csail.mit.edu/hubert/pyaudio/)
 - PyQt6 (https://pypi.org/project/PyQt6/)
 - PyQtGraph (https://www.pyqtgraph.org/)
 - Numpy
 - SciPy


## Installation

If you are using Anaconda, it is recommended to first create a new environment.

    conda create --name sadrec
    conda activate sadrec

Then install sadrec via pip:

    pip install sadrec

Open your terminal and run:

    sadrec

## Features
- Live View - Audio Monitor  
- Store recording in a wav file  
- Low and High Pass Filter  
- Navigate the Viewer with shortcuts  
- Simple Sine Wave Stimulator (via Speaker)  
- Open and view wav files (e.g. your recordings)
- Live Spike Detector

## Navigation
There are several handy keyboard shortcuts for navigation:

"M" to mute and unmute audio monitor  
"R" to start and stop recording  
"B" to reset the view  
"C" to center the view (y-axis)  
"T" and "Shift+T" to zoom the x-axis  
"X" and "Shift+X" to zoom the y-axis  
"Left" and "Right" to move along the x-axis  
"Up" and "Down" to move along the y-axis  


![Alt text](./images/sadrec_gui.png "GUI at start up")


----------
Nils Brehm - 2025
hasnainkk-07 1.0.0
An enhanced Pyrogram-like filter and handler system using Kurigram.
Requires Python: >=3.8

# hasnainkk

An enhanced Pyrogram-like filter and handler system built on Kurigram.

## Installation

```bash
pip install hasnainkk

```
### How to use it

```python
from hasnainkk.client import Client
from hasnainkk.handler import handler
from hasnainkk.filters import command, group

client = Client("mybot", api_id=12345, api_hash="xyz", bot_token="TOKEN")

@handler("start")
async def start(client, message):
    await message.reply_text("Hello!")

client.run()
create-rag-app 0.1.2
A command-line tool to generate modern RAG applications with various components.
Requires Python: >=3.12

# 🚀 create-rag-app

A CLI tool that lets you generate production-ready RAG (Retrieval-Augmented Generation) applications in seconds.

⚙️ Choose your stack. 📦 Get a complete Dockerized project. 🧠 Power your app with your data + LLMs.

## 🌟 Why `create-rag-app`?

Building RAG apps is **complex**—you need to juggle LLMs, embeddings, chunking, retrieval, APIs, and more. With `create-rag-app`, all of this becomes **plug-and-play**:

✅ Standardized architecture  
✅ Developer-first CLI  
✅ Open-source & extensible

Whether you're building an AI chatbot over your docs or a powerful knowledge assistant, this CLI sets you up with a solid, scalable foundation.

## 🔧 What It Will Do

You run:

```bash
npx create-rag-app
```

And answer a few questions:

- **LLM Provider?** (OpenAI, Claude, Local)
- **Vector DB?** (Qdrant)
- **Frontend?** (None, Streamlit, Next.js)
- **Document Loader?** (Local, Web, Notion, YouTube)
- **Chunking Strategy?** (Fixed, Recursive, Metadata-aware)
- **Embedding Model?** (OpenAI, HuggingFace, Local)
- **RAG Framework?** (LangChain, LlamaIndex, None)
- **Prompt Type?** (Basic, Conversational, Agentic)
- **Auth?** (None, JWT, Basic)
- **Extras?** (Monitoring, Eval Setup, Dataset preload)

💥 **Boom.** Your RAG app scaffold is ready inside a Docker container.

## 📁 Example Output Structure

```
my-rag-app/
├── backend/
│   ├── api/
│   ├── loaders/
│   ├── retriever/
│   ├── llm/
│   └── main.py
├── frontend/ (optional)
│   └── pages/
├── data/
├── .env.template
├── docker-compose.yml
└── README.md
```

## 🚧 Features To Be Implemented

Here's what's on the roadmap for the first MVP:

### ✅ Core CLI
- Interactive CLI with `inquirer`
- Dynamic template scaffolding
- Backend stack choices (FastAPI, Express)

### 🧠 RAG Configuration
- LLM selection (OpenAI, Anthropic, local models)
- Embedder selection (OpenAI, BGE, Cohere, etc.)
- Vector DB support (Qdrant)
- Data loader types (PDF, YouTube, Notion, Web)

### 🧱 Frontend (optional)
- Streamlit or Next.js integration
- Basic chat UI with source highlighting

### 🧪 Extras
- Prompt customization options
- Eval flow scaffold (Precision@K, feedback loop)
- Monitoring/logging (basic + OpenTelemetry)
- Auth layer (JWT / basic auth)

### 🐳 DevOps
- Dockerized full-stack output
- Git auto-init and install
- `.env` templating and secrets handling

## 🤝 Contributing

Got an idea? Want to add a new integration (e.g., Qdrant or Supabase)? We'd love to have you onboard.

```bash
git clone https://github.com/yourname/create-rag-app
cd create-rag-app
npm install
npm link
create-rag-app
```

## 🛠 Tech Stack (Planned)

- **CLI:** Node.js + TypeScript
- **CLI UI:** Inquirer.js, Chalk
- **Templates:** EJS-based folders
- **Backend:** FastAPI (Python) or Express (Node)
- **Frontend:** Next.js or Streamlit
- **DevOps:** Docker, Docker Compose
mlgos 0.1.2
A C++ linear regression extension using pybind11
Requires Python: >=3.8

# mlgos

A C++ Linear Regression library with Python bindings using pybind11.

## Overview

`mlgos` is a lightweight, high-performance linear regression implementation in C++.  
It exposes a Python interface via pybind11, enabling seamless integration with Python workflows.  
This library serves as a foundation to build and add more machine learning models in C++ with Python bindings.

## Features

- Fit linear regression models to data
- Get slope and intercept coefficients
- Predict values for new inputs
- Easily extendable for additional models

## Installation

Build from source using CMake and Visual Studio (Windows):

```bash
git clone https://github.com/yourusername/mlgos.git
cd mlgos
mkdir build
cd build
cmake .. -G "Visual Studio 17 2022" -A x64 -DCMAKE_BUILD_TYPE=Release
cmake --build . --config Release
```
The compiled Python extension will be available as a .pyd file inside the build/Release directory.

## Usage

```python
import mlgos

model = mlgos.LinearRegression()
X = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

model.fit(X, y)
print("Slope:", model.get_slope())
print("Intercept:", model.get_intercept())

predictions = model.predict([6, 7])
print("Predictions:", predictions)
```
## Testing

To test the Linear Regression model, run the test script:

```bash
python test_linear_regression.py
```

If you want, I can help you add sample test script content or anything else!
aiossechat 0.0.5
A simple sse implementation with aiohttp, which is specifically designed for chatting with LLM.


build cmd：
 ```python setup.py sdist bdist_wheel```

upload cmd:
 ```twine upload dist/*```

If fail：
 - 不要用powershell，可能複製token錯誤，用cmd
 - 大概率是"file exist", 因為忘了改版本號
python-automation-toolkit 2.0.0
A powerful cross-platform Python Automation Toolkit CLI
Requires Python: >=3.6

# Python Automation Toolkit

A CLI tool for:
✅ File Automation (create, move, delete)  
✅ Linux Commands Automation  
✅ GitHub API Automation  
✅ REST API Automation  
✅ Job Scheduling (like cron)

## Installation