semvec/pypi-packages huggingface dataset:
mcp-vision-gateway 0.15
连接MCP与视觉AI模型的网关服务，支持图像处理和纯文本对话
Requires Python: >=3.7

# MCP Vision Gateway (视觉AI网关)

一个连接MCP与视觉AI模型的网关服务，支持纯文本对话和图像分析（通过URL或Base64编码）。

## 安装

```bash
pip install mcp-vision-gateway
```

## 功能

### 工具 (Tools)

- `vision_query`: 向AI模型发送查询，支持纯文本对话和图像分析
  - 支持通过URL引用图像
  - 支持通过image_ref引用图像（推荐方式）
  - 支持通过base64编码传递图像
  - 可自定义模型、温度等参数

- `upload_image_from_url`: 从URL下载图像并获取引用ID
  - 返回一个图像引用ID而非base64字符串
  - 避免传输大量base64文本

- `upload_local_image`: 从本地文件读取图像并获取引用ID
  - 返回一个图像引用ID而非base64字符串
  - 避免传输大量base64文本

- `list_available_models`: 列出可用的AI模型

## 环境变量配置

设置以下环境变量来配置默认参数：

- `VISION_API_BASE`: API基础URL (默认: https://api.ssopen.top)
- `VISION_API_KEY`: API密钥 (必须设置)
- `VISION_DEFAULT_MODEL`: 默认模型 (默认: gpt-4o)
- `VISION_DEBUG`: 调试模式 (设置为"true"启用)

## 在阿里云百炼中使用

在阿里云百炼平台的MCP服务配置中，使用以下JSON：

```json
{
  "mcpServers": {
    "vision-gateway": {
      "command": "uvx",
      "args": ["mcp-vision-gateway"],
      "env": {
        "VISION_API_KEY": "你的API密钥",
        "VISION_DEFAULT_MODEL": "gpt-4o"
      }
    }
  }
}
```

## 本地运行

安装后，在设置好环境变量的情况下，可以通过以下方式运行：

```bash
# 使用uvx
uvx mcp-vision-gateway

# 使用包提供的命令
mcp-vision-gateway

# 使用Python -m
python -m mcp_vision_gateway
```

## 使用示例

### 1. 纯文本对话

```python
vision_query(
    prompt="你好，请问今天的天气如何？"
)
```

### 2. 使用URL传递图像

```python
vision_query(
    prompt="描述这张图片",
    image_url="https://example.com/image.jpg"
)
```

### 3. 使用图像引用ID（推荐方式）

```python
# 从本地文件获取图像引用ID
image_id = upload_local_image("C:/Users/YourName/Pictures/photo.jpg")

# 或从URL获取图像引用ID
# image_id = upload_image_from_url("https://example.com/image.jpg")

# 使用图像引用ID进行查询
vision_query(
    prompt="描述这张图片",
    image_ref=image_id
)
```

### 4. 指定自定义参数

```python
vision_query(
    prompt="描述这张图片",
    image_ref=image_id,
    model="gpt-4o-mini",
    temperature=0.5,
    max_tokens=500,
    api_key="自定义API密钥"
)
```

## 许可证

MIT License
euma 2.0.2
Euma, a color and design theme
Keywords: theme, colors, design-tokens

# Euma

Euma, a color and design theme

## Links 
- [Website](https://39zde.codeberg.page/euma/@main/.codeberg/webpage/dist/index.html)
- [Repo](https://codeberg.org/39zde/euma)

# License

- [MIT](https://codeberg.org/39zde/euma/raw/branch/main/LICENSE) © Leonhard Maier
- ℹ️ - The logo is a restricted asset for the exclusive use by Leonhard Maier.
ghswitch 0.1.1
A tool for managing multiple GitHub accounts on a single device
Requires Python: >=3.6
Keywords: github, git, account, management

# GitHub Account Manager (ghswitch)

A command-line tool for managing multiple GitHub accounts on a single device. Works on both macOS and Windows.

## Features

- Add multiple GitHub accounts with their configurations (username, email, SSH key, token)
- Switch between accounts globally or per repository
- Automatically handle SSH key switching and Git configuration updates
- List all configured accounts
- Set a primary account for new repositories
- Remove accounts when no longer needed

## Installation

```bash
pip install ghswitch
```

Or install from source:

```bash
git clone https://github.com/yourusername/ghswitch.git
cd ghswitch
pip install -e .
```

## Usage

### Adding a new account

```bash
ghswitch add --name work --username workuser --email work@example.com --ssh-key ~/.ssh/id_rsa_work
```

### Listing all accounts

```bash
ghswitch list
```

### Setting the primary account

```bash
ghswitch set-primary work
```

### Switching accounts for a specific repository

```bash
cd /path/to/your/repo
ghswitch use personal
```

### Switching the global account

```bash
ghswitch use work --global
```

### Removing an account

```bash
ghswitch remove personal
```

## Configuration

The configuration is stored in `~/.ghswitch/config.yaml` (macOS/Linux) or `%USERPROFILE%\.ghswitch\config.yaml` (Windows).

## License

MIT
boto3-pydantic-ds 1.0.2
Pydantic models for AWS Ds
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, ds

# boto3-pydantic-ds

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Ds.
boto3-pydantic-drs 1.0.2
Pydantic models for AWS Drs
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, drs

# boto3-pydantic-drs

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Drs.
boto3-pydantic-docdb-elastic 1.0.2
Pydantic models for AWS Docdb_elastic
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, docdb_elastic

# boto3-pydantic-docdb_elastic

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Docdb_elastic.
boto3-pydantic-docdb 1.0.2
Pydantic models for AWS Docdb
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, docdb

# boto3-pydantic-docdb

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Docdb.
boto3-pydantic-dms 1.0.2
Pydantic models for AWS Dms
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, dms

# boto3-pydantic-dms

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Dms.
boto3-pydantic-dlm 1.0.2
Pydantic models for AWS Dlm
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, dlm

# boto3-pydantic-dlm

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Dlm.
boto3-pydantic-discovery 1.0.2
Pydantic models for AWS Discovery
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, discovery

# boto3-pydantic-discovery

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Discovery.