semvec/pypi-packages huggingface dataset:
bospy 0.0.18
a package for accessing bos resources.
Requires Python: >=3.8

# bospy
python wrappers for accessing bos services.

Points are accessed via "pointers". You get a pointer by using a query function like `NameToPoint` and pass the output to a function like `Get` or `Set`.

Pointers are the uri strings that uniquely identify each point in the `sysmod`. 

## `Get`
`Get(points)` takes one or more point uris and returns values for each uri passed.

Getting a single point by name:
``` python
name = 'BLDG3.AHU2.RM1.TEMP'
pt = NameToPoint(name)
value = Get(pt)
print(name, value)
```
Output:
``` shell
$ python get_example.py
BLDG3.AHU2.RM1.TEMP 18.0
```
Getting multiple values by location:
```python
pts = LocationToPoint('ROOM_1')
resp = Get(pts)
for k, v in resp.items()
    name = GetPointName(k)
    print(name, v)
```
Output
``` bash
$ python get_multiple_example.py
BLDG3.AHU2.RM1.TEMP 22.5
BLDG3.AHU2.RM1.SETPOINT 21.0
BLDG3.AHU2.RM1.DAMPER_POS 85.0
```

## `Set`
`Set(points, values)` takes 1 or more point uris and an equal number of values. You may also pass a single value to be written to all points. 

Usage:
``` python
pt = GetPointByName('BLDG3.AHU2.RM1.SETPOINT')
ok = Set(pt, 23)
if ok:
    print('success')
else:
    print('failed to write', key)
```
Output:
``` bash
$ python set_example.py
success
```
hotpod-cli 1.2.0
A command line tool for GCS
Requires Python: >=3.6

# HotPod CLI

Hotpod命令行工具，用于管理GCS实例。

## 安装

```bash
pip install hotpod-cli
```

## 配置

在使用该工具之前，需要设置京东云API凭证。

1. 设置以下环境变量：

```bash
export JDCLOUD_AK=your_access_key
export JDCLOUD_SK=your_secret_key
```

或者在.env文件中配置JDCLOUD_AK和JDCLOUD_SK

## 使用方法

### 基本信息

显示关于HotPod CLI的信息：

```bash
hotpod info
```

### 实例管理

列出所有GCS实例：

```bash
hotpod instance list
```

创建新的GCS实例（默认：1个）：

```bash
hotpod instance create
```

使用指定的镜像ID和SKU创建实例：

```bash
hotpod instance create --imageid <镜像ID> --sku <SKU ID>
```

删除GCS实例：

```bash
hotpod instance delete --id <实例ID>
```

显示GCS实例的详细信息：

```bash
hotpod instance show --id <实例ID>
```

### 文件操作

上传文件到GCS实例：

```bash
hotpod file upload --instance <实例ID> --local <本地文件路径> --remote <远程目录路径>
```

列出GCS实例上的文件和目录：

```bash
hotpod file list --instance <实例ID> --path <远程目录路径>
```

### ComfyUI任务

使用指定实例运行ComfyUI任务：

```bash
hotpod comfyui runtask --instance <实例ID> --task <任务配置文件路径>
```

使用指定集群运行ComfyUI任务：

```bash
hotpod comfyui runtask --cluster <集群名称> --task <任务配置文件路径>
```

### LLM任务


向特定实例上的模型对话，model包括qwq和deepseek

```bash
hotpod llm runtask --instance <实例ID> --model <model> --prompt "你好，请介绍一下自己"
```

向指定集群上的模型对话

```bash
hotpod llm runtask --cluster <集群名称> --model <model> --prompt "你好，请介绍一下自己"
```

## 开发

1. 克隆仓库
2. 创建虚拟环境
3. 安装开发依赖：
   ```bash
   pip install -e .
   ```

## 许可证

MIT
openai-mcp-server 1.0.0
Dockerized MCP server for OpenAI models
Requires Python: >=3.13

# openai_mcp_server MCP server

Dockerized MCP server for OpenAI models

## Components

### Resources

The server implements a simple note storage system with:
- Custom note:// URI scheme for accessing individual notes
- Each note resource has a name, description and text/plain mimetype

### Prompts

The server provides a single prompt:
- summarize-notes: Creates summaries of all stored notes
  - Optional "style" argument to control detail level (brief/detailed)
  - Generates prompt combining all current notes with style preference

### Tools

The server implements one tool:
- add-note: Adds a new note to the server
  - Takes "name" and "content" as required string arguments
  - Updates server state and notifies clients of resource changes

## Configuration

[TODO: Add configuration details specific to your implementation]

## Quickstart

### Install

#### Claude Desktop

On MacOS: `~/Library/Application\ Support/Claude/claude_desktop_config.json`
On Windows: `%APPDATA%/Claude/claude_desktop_config.json`

<details>
  <summary>Development/Unpublished Servers Configuration</summary>
  ```
  "mcpServers": {
    "openai_mcp_server": {
      "command": "uv",
      "args": [
        "--directory",
        "Z:\FUCK",
        "run",
        "openai_mcp_server"
      ]
    }
  }
  ```
</details>

<details>
  <summary>Published Servers Configuration</summary>
  ```
  "mcpServers": {
    "openai_mcp_server": {
      "command": "uvx",
      "args": [
        "openai_mcp_server"
      ]
    }
  }
  ```
</details>

## Development

### Building and Publishing

To prepare the package for distribution:

1. Sync dependencies and update lockfile:
```bash
uv sync
```

2. Build package distributions:
```bash
uv build
```

This will create source and wheel distributions in the `dist/` directory.

3. Publish to PyPI:
```bash
uv publish
```

Note: You'll need to set PyPI credentials via environment variables or command flags:
- Token: `--token` or `UV_PUBLISH_TOKEN`
- Or username/password: `--username`/`UV_PUBLISH_USERNAME` and `--password`/`UV_PUBLISH_PASSWORD`

### Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging
experience, we strongly recommend using the [MCP Inspector](https://github.com/modelcontextprotocol/inspector).

You can launch the MCP Inspector via [`npm`](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) with this command:

```bash
npx @modelcontextprotocol/inspector uv --directory Z:\FUCK run openai-mcp-server
```

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

## Building the Docker Image
```bash
docker build -t openai-mcp-server .
```

## Running the Docker Container
```bash
docker run -d -p 8000:8000 -e OPENAI_API_KEY=your_api_key_here openai-mcp-server
```

Replace `your_api_key_here` with your actual OpenAI API key.
sbcli-ro 0.1.2
CLI for managing SimplyBlock cluster


# Simply Block
[![Docker Image Build](https://github.com/simplyblock-io/sbcli/actions/workflows/docker-image.yml/badge.svg)](https://github.com/simplyblock-io/sbcli/actions/workflows/docker-image.yml)

[![Python Unit Testing](https://github.com/simplyblock-io/sbcli/actions/workflows/python-testing.yml/badge.svg)](https://github.com/simplyblock-io/sbcli/actions/workflows/python-testing.yml)


## Install
Add the package repo from AWS CodeArtifact using [awscli](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html)

```bash
aws codeartifact login --tool pip --repository sbcli --domain simplyblock --domain-owner 565979732541 --region eu-west-1
```
Install package
```bash
pip install --extra-index-url https://pypi.org/simple sbcli-dev
```

# Components

## Simply Block Core
Contains core logic and controllers for the simplyblock cluster

## Simply Block CLI
Please see this document
[README.md](../main/simplyblock_cli/README.md)


## Simply Block Web API
Please see this document
[README.md](../main/simplyblock_web/README.md)



### local development

FoundationDB requires a client library (libfdb_c.dylib) for the Python bindings to interact with the database.
Depending on the OS architecture, please install the appropriate version from the official github repo

```
wget https://github.com/apple/foundationdb/releases/download/7.3.3/FoundationDB-7.3.3_arm64.pkg
```

setup the code on a management node and the webApp code can be developed by building the `docker-compose-dev.yml` file.


```
sudo docker compose -f docker-compose-dev.yml up --build -d
```
shelper 0.1.4
The shell helper
Requires Python: >=3.12

# Shelper

[![PyPI version](https://img.shields.io/pypi/v/shelper.svg)](https://pypi.org/project/shelper/)
[![Python versions](https://img.shields.io/pypi/pyversions/shelper.svg)](https://pypi.org/project/shelper/)
[![PyPI downloads](https://img.shields.io/pypi/dm/shelper.svg)](https://pypi.org/project/shelper/)
[![License](https://img.shields.io/pypi/l/shelper.svg)](https://github.com/dannystewart/shelper/blob/main/LICENSE)

The shell helper.
textparse 0.1.2
The missing text toolkit for Python
Requires Python: >=3.12

# TextParse

[![PyPI version](https://img.shields.io/pypi/v/textparse.svg)](https://pypi.org/project/textparse/)
[![Python versions](https://img.shields.io/pypi/pyversions/textparse.svg)](https://pypi.org/project/textparse/)
[![PyPI downloads](https://img.shields.io/pypi/dm/textparse.svg)](https://pypi.org/project/textparse/)

The missing text toolkit for Python. From splitting to truncating, parsing to pluralization, formatting to robust Markdown handling, it's simple elegant formatting plus a whole lot more.
mcp-askcpg 0.1.5
This is a MCP tools for connecting to Qmed Copilot askCPG feature, where it enable your LLM to get the medical knowledge from Malaysia Clinical Practice Guideline.
Requires Python: >=3.11

How to publish: learn from https://medium.com/@blackary/publishing-a-python-package-from-github-to-pypi-in-2024-a6fb8635d45d