semvec/pypi-packages huggingface dataset:
multimodel-ai 0.1.6
A Python module for efficient multi-model AI inference with memory management
Requires Python: >=3.8

# MultiModel-AI

A Python module for efficient multi-model AI inference with memory management.

## Features

- Efficient memory management for multiple AI models
- Automatic model loading and unloading
- Support for various AI model types
- Easy-to-use API

## Installation

```bash
pip install multimodel-ai
```

## Usage

```python
from multimodel_ai import ModelManager

# Initialize the model manager
manager = ModelManager()

# Load a model
model = manager.load_model("model_name")

# Use the model
result = model.predict(input_data)

# The model will be automatically unloaded when not in use
```

## Examples

### Basic Usage

```python
from multimodel_ai import ModelManager

# Initialize the manager
manager = ModelManager()

# Load a model
model = manager.load_model("gpt2")

# Generate text
text = model.generate("Hello, world!")

# The model will be automatically unloaded
```

### Multiple Models

```python
from multimodel_ai import ModelManager

# Initialize the manager
manager = ModelManager()

# Load multiple models
model1 = manager.load_model("gpt2")
model2 = manager.load_model("bert")

# Use the models
text1 = model1.generate("Hello")
text2 = model2.classify("World")

# Models will be automatically unloaded when not in use
```

### Custom Model Configuration

```python
from multimodel_ai import ModelManager

# Initialize the manager with custom settings
manager = ModelManager(
    max_memory_usage=0.8,  # Use up to 80% of available memory
    model_cache_dir="./models"  # Custom cache directory
)

# Load a model with specific configuration
model = manager.load_model(
    "gpt2",
    device="cuda",
    precision="fp16"
)

# Use the model
result = model.generate("Hello, world!")
```

### Error Handling

```python
from multimodel_ai import ModelManager, ModelError

try:
    manager = ModelManager()
    model = manager.load_model("non_existent_model")
except ModelError as e:
    print(f"Error loading model: {e}")
```

## 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.
mcp-cryptokit 0.1.2
A MCP server project to handle cryptographic operations.
Requires Python: >=3.11

# MCP协议密码套件 (mcp-cryptokit)

[![GitHub](https://img.shields.io/github/license/sansec-ai/mcp-cryptokit)](https://github.com/sansec-ai/mcp-cryptokit)

## 简介

mcp-cryptokit 是一个基于MCP协议的密码套件，旨在为AI应用提供高效的密码学支持。它支持国密标准算法和通用密码算法，并提供密钥管理功能。

**mcp-cryptokit**：提供以下功能：
  - 对称加解密
  - 非对称加解密
  - 签名验签
  - Hash计算
  - 密钥管理

## 功能特点

- **标准MCP协议**：方便AI应用集成
- **支持国密标准算法**：包括SM2/SM3/SM4等
- **支持通用密码算法**：如AES、RSA、SHA等
- **支持密钥管理**：提供安全的密钥生成、存储和管理功能

## 安装与使用

### 安装

```bash
# 下载项目到本地目录, 如：/work/mcp-cryptokit
git clone https://github.com/sansec-ai/mcp-cryptokit.git  /work/mcp-cryptokit
cd mcp-cryptokit
uv venv
source .venv/bin/activate
# 安装依赖
uv pip install .
```
### 使用示例
可以在任何支持mcp协议的客户端使用本密码套件。
#### mcp 配置
以roo code编程为例，其配置步骤如下：
- 在vscode中安装roo code插件(>=v3.11)，并配置大模型提供商，可参考[Roo-Code](https://github.com/RooVetGit/Roo-Code/blob/main/locales/zh-CN/README.md):。
- 在roo code的MCP服务器配置文件中增加配置如下：

```json
{
  "mcpServers": {
    "mcp-cryptokit": {
      "command": "uv",
      "args": [
        "--directory",
        "/work/mcp-cryptokit",
        "run",
        "server.py"
      ],
      "timeout": 30,
      "alwaysAllow": [],
      "disabled": false,
      "env": {
        "CRYPTOKIT_KEY_STORE_PATH": "/data/keystore.json"
      }
    }
  }
}

```
配置成功，可在Roo Code的`MCP 服务器`查看到`mcp-cryptokit`服务。

![mcp-cryptokit服务](doc/roocode.png)

#### 使用
在`Roo Code`中配置`mcp-cryptokit`服务成功，可在对话框输入以下请求：
```bash
使用sm4密钥加密数据：我是明文
```
`Roo Code`会自动调用`mcp-cryptokit`服务，创建sm4密钥，并使用sm4密钥加密明文。示例如下：
##### 1.第一步，尝试查询sm4类型密钥
![](doc/chat1.png)
##### 2.第二步，如果密钥不存在，则创建sm4密钥，并返回密钥ID。
![](doc/chat2.png)
##### 3.第三步，使用密钥ID和明文，调用sm4加密服务，并返回密文。
![](doc/chat3.png)

也可以直接使用指定密钥加密数据，请求如下：
```bash
使用1号sm4密钥加密数据：我是明文
```

## 贡献指南
欢迎贡献代码或提出改进建议！请参考贡献指南了解如何参与项目。
## 许可证
mcp-cryptokit 遵循 Apache License 2.0 协议，允许自由使用、修改和分发。
## 联系我们
如需进一步了解或技术支持，请访问 GitHub项目页面 或联系项目维护者。
pyiotdevice 1.0.20
A Python library for IoT device security and communication
Requires Python: >=3.9

# pyiotdevice

`pyiotdevice` is a standalone Python library for IoT applications developed by iota labs. It provides secure communication, AES encryption, CRC checksum calculation, and device communication.

## Features
- **AES Encryption & Decryption**: Securely encrypt and decrypt data using AES in CFB mode.
- **CRC Calculation**: Compute CRC checksums for data integrity verification.
- **IoT Device Communication**: Fetch device status and send control commands.

## Installation
```sh
pip install pyiotdevice