semvec/pypi-packages huggingface dataset:
malette-awe 0.1.14
AWE
Requires Python: >=3.7

# AWE (AI Workflow Engine)

AWE 是一个 AI 工作流引擎,用于对接 ComfyUI 并提供工作流调度能力。

## 功能特性

- 支持通过 RocketMQ 接收任务请求
- 支持调用 ComfyUI API 执行工作流
- 支持工作流模板和参数配置
- 支持进度、错误、完成事件通知
- 支持中英文提示词翻译
- 支持图片上传和结果处理

## 安装

```bash
pip install malette-awe
```

## 环境变量配置

需要配置以下环境变量:

```bash
# ComfyUI 配置
COMFYUI_HOST=localhost
COMFYUI_PORT=8188
# RocketMQ 配置
ROCKETMQ_ENDPOINT=xxx
ROCKETMQ_ACCESS_KEY=xxx
ROCKETMQ_ACCESS_SECRET=xxx
ROCKETMQ_INSTANCE_ID=xxx
ROCKETMQ_BASE_REQUEST_TOPIC=xxx
ROCKETMQ_BASE_RESPONSE_TOPIC=xxx
ROCKETMQ_BASE_GROUP_ID=xxx
ROCKETMQ_MESSAGE_TAG=xxx
ROCKETMQ_STATIC_TOPIC=false
ROCKETMQ_STATIC_GROUP=false
# 通义千问配置(用于中英文翻译)
DASHSCOPE_API_KEY=xxx
# 应用配置
ENV=dev
APPNAME=your_app_name
```

## 使用示例

```python
from awe import AWE

# 初始化 AWE
awe = AWE(
  comfyui_host="localhost",
  comfyui_port="8188"
)
# 启动服务
awe.start()
```

## 运行测试

安装测试依赖:

```bash
pip install -e ".[test]"
```

运行测试:

```bash
pytest tests/
```

运行测试覆盖率报告:

```bash
pytest --cov=src tests/
```
beancount-balexpr 0.5.0
Check balances against simple expressions in beancount
Requires Python: >=3.3

# beancount_balexpr

Check balances against simple expressions combining multiple accounts in beancount.

## Installation

```
pip install beancount_balexpr
```

## Examples

```
option "plugin_processing_mode" "raw"

plugin "beancount.ops.documents"
plugin "beancount.ops.pad"
plugin "beancount_balexpr.balexpr" ; <- If padding entries are used, please make sure this plugin loads after beancount.ops.pad
plugin "beancount.ops.balance"

1990-01-01 open Assets:A USD
1990-01-01 open Assets:B USD
1990-01-01 open Equity:OpenBalance USD
1991-01-01 pad Assets:A Equity:OpenBalance
1991-01-01 pad Assets:B Equity:OpenBalance
1991-01-02 balance Assets:A 213.00 USD
1991-01-02 balance Assets:B 264.00 USD

1991-01-03 custom "balexpr" "Assets:A+Assets:B"               477.00 USD
1991-01-03 custom "balexpr" "Assets:A+200.00"                 413.00 USD
1991-01-03 custom "balexpr" "Assets:B*(Assets:A+200.00)"   109032.00 USD
1991-01-03 custom "balexpr" "Assets:A-Assets:B"               -51.00 USD
1991-01-03 custom "balexpr" "Assets:A*Assets:B"             56232.00 USD
1991-01-03 custom "balexpr" "Assets:A/Assets:B"                 0.81 USD
1991-01-03 custom "balexpr" "Assets:A+Assets:A*Assets:B"    56445.00 USD
1991-01-03 custom "balexpr" "(Assets:A+Assets:A)*Assets:B" 112464.00 USD

1991-01-03 custom "balexpr" "
    Assets:A +
    Assets:B"               477.00 USD
```

## Limitations

- Does not support the account names with dashes because they are conflicting with the minus sign
edge-mdt-cl-dev 1.1.0.dev2
Edge MDT Custom Layers package

# Edge MDT Custom Layers (EdgeMDT CL) 

Edge MDT Custom Layers (EdgeMDT CL) is an open-source project implementing detection post process NN layers not supported by the TensorFlow Keras API or Torch's torch.nn for the easy integration of those layers into pretrained models.

## Table of Contents

- [Getting Started](#getting-started)
  - [Installation](#installation)
  - [Supported Versions](#supported-versions)
- [API](#api)
  - [TensorFlow API](#tensorflow-api)
  - [PyTorch API](#pytorch-api)
- [License](#license)


## Getting Started

This section provides an installation and a quick starting guide.

### Installation

To install the latest stable release of SCL, run the following command:
```
pip install edge-mdt-cl
```
By default, no framework dependencies are installed.
To install SCL including the latest tested dependencies (up to patch version) for TensorFlow:
```
pip install edge-mdt-cl[tf]
```
To install SCL including the latest tested dependencies (up to patch version) for PyTorch/ONNX/OnnxRuntime:
```
pip install edge-mdt-cl[torch]
```
### Supported Versions

#### TensorFlow

| **Tested FW versions** | **Tested Python version** | **Serialization** |
|------------------------|---------------------------|-------------------|
| 2.14                   | 3.9-3.11                  | .keras            |
| 2.15                   | 3.9-3.11                  | .keras            |

#### PyTorch

| **Tested FW versions**                                                                                                   | **Tested Python version** | **Serialization**              |
|--------------------------------------------------------------------------------------------------------------------------|---------------------------|--------------------------------|
| torch 2.3-2.6<br/>torchvision 0.18-0.21<br/>onnxruntime 1.15-1.21<br/>onnxruntime_extensions 0.8-0.13<br/>onnx 1.14-1.17 | 3.9-3.12                  | .onnx (via torch.onnx.export)  |

## API
For edge-mdt-cl API see https://sonysemiconductorsolutions.github.io/aitrios-edge-mdt-cl

### TensorFlow API
For TensorFlow layers see
[KerasAPI](https://sonysemiconductorsolutions.github.io/aitrios-edge-mdt-cl/edgemdt_cl/keras.html)

To load a model with custom layers in TensorFlow, see [custom_layers_scope](https://sonysemiconductorsolutions.github.io/aitrios-edge-mdt-cl/edgemdt_cl/keras.html#custom_layers_scope)

### PyTorch API
For PyTorch layers see
[PyTorchAPI](https://sonysemiconductorsolutions.github.io/aitrios-edge-mdt-cl/edgemdt_cl/pytorch.html)

No special handling is required for torch.onnx.export and onnx.load.

For OnnxRuntime support see [load_custom_ops](https://sonysemiconductorsolutions.github.io/aitrios-edge-mdt-cl/edgemdt_cl/pytorch.html#load_custom_ops) 

## License
[Apache License 2.0](LICENSE.md).
types-mock 5.2.0.20250924
Typing stubs for mock
Requires Python: >=3.9

## Typing stubs for mock

This is a [type stub package](https://typing.python.org/en/latest/tutorials/external_libraries.html)
for the [`mock`](https://github.com/testing-cabal/mock) package. It can be used by type checkers
to check code that uses `mock`. This version of
`types-mock` aims to provide accurate annotations for
`mock==5.2.*`.

This package is part of the [typeshed project](https://github.com/python/typeshed).
All fixes for types and metadata should be contributed there.
See [the README](https://github.com/python/typeshed/blob/main/README.md)
for more details. The source for this package can be found in the
[`stubs/mock`](https://github.com/python/typeshed/tree/main/stubs/mock)
directory.

This package was tested with the following type checkers:
* [mypy](https://github.com/python/mypy/) 1.18.1
* [pyright](https://github.com/microsoft/pyright) 1.1.405

It was generated from typeshed commit
[`2cc9c1667d94002f74a7e7757558a16ea6d4872e`](https://github.com/python/typeshed/commit/2cc9c1667d94002f74a7e7757558a16ea6d4872e).
hydron 0.0.6
Python interface for launching and post-processing SWMF magnetohydrodynamic simulations
Requires Python: >=3.11

# **hydron**
Python interface for launching and post-processing SWMF magnetohydrodynamic simulations

## Installation
All examples assume the use of `uv`.
### Express
For when you're using the package just for analysis.
```bash
uv pip install hydron
```

### Developer
For when you are developing a project of your own and are trying to add `hydron` as a dependency.
```bash
uv add hydron
```

## Contributor Guide
Contributors are expected to be using `uv`.
Please fork `hydron` and submit your changes in a pull request.
The following are some common workflows.

### Testing
```bash
    uv run --extra test pytest
```

### Previewing Documentation
```bash
    uv run --extra docs myst start
```
project-ai-mcp 0.1.2
A Model Context Protocol (MCP) server for Project AI
Requires Python: >=3.12
Keywords: mcp, project-ai

# Project AI MCP Server

一个用于 [Project AI](https://project-ai.hailiangedu.com) 平台集成的 [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) 服务器。

## 功能特性

- 🚀 基于 FastMCP 构建的高性能 MCP 服务器
- 📋 支持 Project AI 功能点状态管理
- 🔐 安全的环境变量配置管理
- 🛠 支持多种安装和运行方式

## 环境要求

- Python 3.12+
- 可访问 Project AI API 端点

## 安装

### 方法一：使用 uvx（推荐）

```bash
# 临时运行
uvx project-ai-mcp

# 或永久安装
uv tool install project-ai-mcp
```

### 方法二：使用 pip

```bash
pip install project-ai-mcp
```

### 方法三：开发安装

```bash
git clone <repository-url>
cd project-ai-mcp
uv sync
uv run python main.py
```

## 使用方法

### 作为独立服务器运行

```bash
# 使用 uvx
uvx project-ai-mcp

# 或使用已安装的命令
project-ai-mcp

# 或使用 Python 模块
python -m project_ai_mcp
```

### 在 Claude Code 中使用

**注意**: 如果不设置 `PROJECT_AI_BASE_URL`，将使用默认的 API 端点：`https://project-ai.hailiangedu.com`

```base
# 使用默认的 API 端点
claude mcp add project-ai -s user -- uvx project-ai-mcp


# 使用自定义的 API 端点
claude mcp add project-ai -s user -e "PROJECT_AI_BASE_URL=XXXXX" -- uvx project-ai-mcp
```


## 可用工具

### `update_feature_status`

修改 Project AI 平台上功能点的状态。

**参数：**
- `feature_id` (string): 功能点 ID
- `status` (int): 功能点状态，支持：
  - `1`: 未开始
  - `2`: 进行中
  - `3`: 已完成

**返回值：**
- `"success"`: 操作成功
- `"failed: [错误信息]"`: 操作失败及错误详情

**示例使用：**
```python
# 将功能点标记为进行中
update_feature_status(
    feature_id="feature_123",
    status=2
)

# 将功能点标记为已完成
update_feature_status(
    feature_id="feature_456",
    status=3
)
```

## 开发

### 环境设置

```bash
# 克隆仓库
git clone <repository-url>
cd project-ai-mcp

# 创建虚拟环境并安装依赖
uv venv
uv sync

# 运行开发服务器
uv run python main.py
```

### 代码质量检查

```bash
# 运行 linter
ruff check

# 格式化代码
ruff format
```

### 构建和发布

```bash
# 构建包
uv build

# 发布到 PyPI（需要配置 PyPI 凭据）
twine upload dist/*
```

## 许可证

MIT License - 详见 [LICENSE](LICENSE) 文件。

## 贡献

欢迎提交 Issue 和 Pull Request！

## 支持

如有问题，请在 [GitHub Issues](https://github.com/philoveritas/project-ai-mcp/issues) 中提出。