semvec/pypi-packages huggingface dataset:
vlsi 0.0.3
variable length spatial index - encode arbitary length bytes in nifti + data sidecar file
Requires Python: >=3.10

# vlsi

Variable-Length-Spatial-Index (or velvet-silk) is an implementation of variable length datatype for 3-dimensional array. It is designed with write-once-read-many paradigm, and is thus optimized for fast and memory efficient read operations. 

## Background

see [background.md](./background.md)

## Specification

see [specifications.md](./specifications.md)

## Usages

A trivial example is included below. For more realistic usages, please refer to e2e tests.

```python
import numpy as np
from vlsi import WritableSpatialIndex, ReadableSpatialIndex, SpatialIndexWriteExc

def main():

    windex = WritableSpatialIndex("testfile")
    windex.write(
        [
            [0, 0, 0],
            [42, 42, 42],
        ],
        [b"origin of life", b"meaning of life"],
    )
    windex.write([[0, 0, 0]], [b"another write"])
    windex.save(np.eye(4), shape=(100, 200, 100))

    try:
        windex.write([[0, 0, 0]], [b"should fail"])
    except SpatialIndexWriteExc:
        ...

    try:
        windex.save(np.eye(4), shape=(100, 200, 100))
    except SpatialIndexWriteExc:
        ...

        
    rindex = ReadableSpatialIndex("testfile")
    assert rindex.read([[0, 0, 0]]) == [b"origin of lifeanother write"]
    assert rindex.read([[42, 42, 42]]) == [b"meaning of life"]
    assert rindex.read([[1, 1, 1]]) == []

if __name__ == "__main__":
    main()

```

## Similar projects

[sparseindex in siibra-python](https://github.com/FZJ-INM1-BDA/siibra-python/blob/66c13cd/siibra/volumes/sparsemap.py#L30) (original inspiration of this project)

[v2 sparsedex in siibra-python](https://github.com/FZJ-INM1-BDA/siibra-python/blob/2997206/siibra/atlases/sparsemap.py) (first implementation of `vlsi`)

[vlen codec in numcodecs](https://github.com/zarr-developers/numcodecs/blob/e0ddee6b6d01bfd35a91085ec0a20dae6bf50e13/numcodecs/vlen.pyx#L48)

[numpy.save](https://numpy.org/doc/stable/reference/generated/numpy.lib.format.html#module-numpy.lib.format)

## LICENSE

apache 2.0
yinshield 0.0.1
A zero-config, local-first privacy layer for AI APIs with semantic-preserving de-identification.
Requires Python: >=3.7
Keywords: privacy, llm, openai, deepseek, pii, security, ai-safety

# 🛡️ YinShield (隐)

**Make your data invisible to AI, like a shadow.**  
**让你的数据对 AI 隐形，如影随形。**

[![PyPI version](https://img.shields.io/pypi/v/yinshield.svg)](https://pypi.org/project/yinshield/)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](LICENSE)

---

## ☯️ What is YinShield? | 什么是隐盾？

**Yin (隐)** in Chinese means *Hidden* or *Invisible*. 

**YinShield** is a lightweight, zero-config Python library designed to protect your privacy when using AI APIs (like OpenAI, DeepSeek, Claude). It intercepts sensitive information locally, replaces it with semantically-consistent tokens, and restores it automatically after the AI responds.

**隐盾 (YinShield)** 是一款轻量级、零配置的 Python 库，专为保护 AI API 使用过程中的隐私而生。它在本地拦截敏感信息，将其替换为保持语义一致的占位符，并在 AI 回复后自动还原。

### 🌟 Key Features | 核心特性

- **🚀 Zero Config**: `pip install` and you are ready. No complex setups or local servers required.
- **🧠 Semantic Preservation**: Unlike brute-force masking, YinShield uses "Semantic Aliases" (e.g., replacing "Zhang San" with "Li Si") to keep the AI's reasoning capabilities intact.
- **🔒 Local-First**: All detection and masking happen on your machine. No sensitive data ever leaves your device.
- **🇨🇳 Optimized for China**: Built-in support for Chinese-specific PII (WeChat ID, ID cards, Chinese addresses, etc.).
- **🔌 Drop-in Replacement**: Seamlessly integrates with your existing AI SDK calls.

---

## 🛠️ Quick Start | 快速上手

### Installation | 安装
```bash
pip install yinshield
```

### Basic Usage | 基本用法 (Coming Soon)
```python
from yinshield import Shield

# Initialize the shield
shield = Shield()

raw_text = "我叫张三，我的手机号是13812345678，我住在北京市朝阳区。"

# 1. Locally mask sensitive data
masked_text, mapping = shield.mask(raw_text)
# masked_text becomes: "我叫<PERSON_1>，我的手机号是<PHONE_1>，我住在<LOC_1>。"

# 2. Call your AI API with masked_text...
# (AI processes the data without knowing who you are)

# 3. Restore the response locally
final_response = shield.unmask(ai_response, mapping)
```

---

## 🗺️ Roadmap | 开发路线图

- [ ] v0.1.0: Core Regex-based Chinese PII masking.
- [ ] v0.2.0: Lightweight ONNX model integration for high-accuracy NER.
- [ ] v0.3.0: Semantic Alias engine (Consistent persona mapping).
- [ ] v0.4.0: Image/OCR privacy protection.

---

## 🤝 Contributing | 贡献

YinShield is an open-source project. We welcome contributions to help make AI safer for everyone!

## 📄 License | 许可证

[Apache-2.0 License](LICENSE)
robotframework-aiagent-examples 0.3.0
robotframework-aiagent examples
Requires Python: >=3.10

# Examples for robotframework-aiagent

This directory contains example Robot Framework tests and small helper modules that demonstrate how to use the `robotframework-aiagent` package and the included example tools.

Contents
 - `src/AIAgentExamples/WeatherTools.py` - A small example tool demonstrating how a weather tool can be implemented and used by the example tests.
 - `tests/` - Robot Framework test files that exercise the example tools and show common usage patterns.

-Prerequisites
 - Python 3.10+ (the project uses a virtual environment in the repository root).
 - Robot Framework installed in your environment. Preferably install dependencies from the repository's top-level `pyproject.toml` into a virtual environment.

Notes
 - The examples are intentionally small and meant for learning and experimentation. Adapt the tests and example tools to fit your own agent implementations.
 - If you see import errors, ensure the project package is installed into the virtual environment or update `PYTHONPATH` to include the repository root.

License
 - See the repository `LICENSE` or top-level README for license details.

```
interweave 0.2.5
Voice I/O MCP server for Claude Code — speak and listen through your mic and speakers
Requires Python: >=3.11

# Interweave

Voice I/O MCP server for Claude Code. Speak and listen through your mic and speakers.

Uses Kokoro MLX (TTS), Parakeet MLX (STT), and Silero VAD. Runs locally on Apple Silicon.

## Install

```bash
curl -fsSL https://raw.githubusercontent.com/EndlessHoper/interweave/main/install.sh | bash
```

This will:
1. Install Python dependencies (~4.5GB total, includes torch and ML models)
2. Download ML models (Kokoro TTS, Parakeet STT, Silero VAD)
3. Register interweave with Claude Code

After install, start a new Claude Code session and talk.

### Manual install

If you prefer to install step by step:

```bash
uv tool install --python ">=3.11" interweave
claude mcp add interweave -- interweave
```

### Codex

```bash
uv tool install --python ">=3.11" interweave
codex mcp add interweave -- interweave
```

### OpenCode

```bash
uv tool install --python ">=3.11" interweave
```

Then add to `opencode.json`:

```json
{
  "mcp": {
    "interweave": {
      "type": "local",
      "command": ["interweave"],
      "enabled": true
    }
  }
}
```

## Uninstall

```bash
uv tool uninstall interweave
claude mcp remove interweave

# Optional: remove cached ML models (~2GB)
rm -rf ~/.cache/huggingface/hub/models--mlx-community--Kokoro-82M-bf16
rm -rf ~/.cache/huggingface/hub/models--animaslabs--parakeet-tdt-0.6b-v3-mlx
rm -rf ~/.cache/torch/hub/snakers4_silero-vad_master
```

## Requirements

- macOS with Apple Silicon
- Python 3.11+
- Microphone and speakers
- [uv](https://docs.astral.sh/uv/) (`curl -LsSf https://astral.sh/uv/install.sh | sh`)
slmcontrol 0.3.0
Add your description here
Requires Python: >=3.11

# slmcontrol

This is a package that allows one to control a Spatial Light Modulator (SLM) with a simple an intuitive syntax. We support Python >= 3.10 and all major platforms (Windows, MacOS and Linux).

If this package is useful to you, please give it a star on GitHub.

## Links

- [**PyPI**](https://pypi.org/project/slmcontrol/)
- [**Documentation**](https://marcsgil.github.io/slmcontrol/)
- [**Source code**](https://github.com/marcsgil/slmcontrol/tree/main)

## Instalation

This package is registered on PyPi, so, o install it, simply run

```
pip install slmcontrol
```

on a terminal.