semvec/pypi-packages huggingface dataset:
apsbot 0.3.1
A simple CLI tool to interact with the Autodesk Forge API.
Requires Python: >=3.9

# APSBot: Autodesk Platform Services CLI

Welcome to the CLI tool `apsbot`, designed to interact seamlessly with the Autodesk Platform Services (formerly Autodesk Forge) API. This tool provides a command-line interface to access and manage data across various Autodesk services.

![](docs/apsbot.png)

## Features

- Interactive with data by Command Line Interface(CLI)
- Snooping Data Around Autodesk Services (Hubs, Projects, Items, Versions, Folders, Buckets, etc.)
- Analyzing Revit Data
- Chat With AI Bot (OpenAI) to do data analysis and have fun.

## Installation

- Install [python](https://www.python.org/downloads/) version 3.9 or higher
- Install python package from [pypi](https://pypi.org/project/apsbot/) 

```bash
pip install apsbot --upgrade
```

## Requirements 

Setup environment variables

```bash
APS_CLIENT_ID = "your_client_id"
APS_CLIENT_SECRET = "your_client_secret"
OPENAI_API_KEY = "your_openai_api_key"
```

## Usage

- Command `apsbot` in terminal or command prompt to start :

![apsbot](docs/apsbot-macos.gif)

```bash
apsbot [OPTIONS] COMMAND [ARGS]...
```

- Without setting path configuration
```bash
python -m apsbot [OPTIONS] COMMAND [ARGS]...
```

Notes : `python -m` use when your computer can't find the `apsbot` command.

Let's start with `hubs` and `projects` command to list all hubs in your account.

![](docs/hubs-projects.gif)

Chat With AI Bot (OpenAI) to do data analysis and have fun.

![](docs/chat.gif)

Revit Data Analysis Example :

![](docs/revit-rooms.png)

... More, please explore by yourself.

## Contributing

Please read [dev.md](./docs/dev.md) for details on our code of conduct, and the process for submitting pull requests to us. I'm happy to receive your contributions.

## Disclaimer

This is project just play for fun with free time weekend. Please use it at your own risk. I am not responsible for any damage caused by this project.


## Many thanks 

I have a lot of inspiration from this topic :

- https://www.travisluong.com/how-to-build-a-command-line-interface-tool-with-python-pandas-typer-and-tabulate-for-data-analysis/
fastapi-whowhywhen 0.1.1
WhoWhyWhen middleware for FastAPI
Requires Python: >=3.8

# fastapi-whowhywhen

WhoWhyWhen middleware for FastAPI. Create a WhoWhyWhen account at https://whowhywhen.com/ and get your API key.

## Installation

```bash
pip install fastapi-whowhywhen
```

## Usage
```python
from fastapi import FastAPI
from fastapi_whowhywhen import WhoWhyWhenMiddleware
from starlette.background import BackgroundTasks

app = FastAPI()

app.add_middleware(
    WhoWhyWhenMiddleware,
    api_key="YOUR_API_KEY"
)

@app.get("/")
async def root():
    return {"message": "Hello, World!"}

# Add the following middleware to your FastAPI app
@app.middleware("http")
async def add_background_tasks(request: Request, call_next):
    request.state.background_tasks = BackgroundTasks()
    response = await call_next(request)
    response.background = request.state.background_tasks
    return response

```
thiliapr-tools 1.0.2
A library by thiliapr.
Requires Python: >=3.6

![GitHub top language](https://img.shields.io/github/languages/top/thiliapr/python3-tprtools)
![GitHub License](https://img.shields.io/badge/license-GPL--3.0--or--later-blue)
![GitHub repo size](https://img.shields.io/github/repo-size/thiliapr/python3-tprtools)
![GitHub forks](https://img.shields.io/github/forks/thiliapr/python3-tprtools)
![GitHub Repo stars](https://img.shields.io/github/stars/thiliapr/python3-tprtools)

# Thiliapr Tools
A Python3 library written by thiliapr containing various tools.
- Version: 1.0.2
- License: Distributed under GPL-3.0 or later.

## Languages
- [简体中文](README.zh-CN.md)
- [English](README.md)

## Tools
### JSONPath
This is a simple JSONPath parser.
See [JSONPath Documentation](docs/jsonpath.md).
youtube-suggestions 0.2.3
A package to get YouTube search suggestions with proxy support
Requires Python: >=3.6

# YouTube Suggestions

A simple Python package to get YouTube search suggestions with optional proxy support.

## Installation

You can install the YouTube Suggestions package using pip:

```
pip install youtube-suggestions
```

## Usage

Here's a quick example of how to use the package:

```python
from youtube_suggestions import get_suggestions

# Without proxy
suggestions = get_suggestions("python programming")
print(suggestions)

# With proxy
proxy = "gate.dc.smartproxy.com:1111:proxyname:password"
suggestions_with_proxy = get_suggestions("python programming", proxy=proxy)
print(suggestions_with_proxy)
```

This will print a list of search suggestions for "python programming", first without a proxy and then using the specified proxy.

## Features

- Get YouTube search suggestions
- Optional proxy support
- Fallback method if the primary method fails
- Easy to use

## Proxy Format

The proxy should be specified in the following format:

```
host:port:username:password
```

For example:

```
gate.dc.smartproxy.com:1111:proxyname:password
```

## Requirements

- Python 3.6+
- requests library

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License.
enova-instrumentation-llmo 0.0.4
enova-instrumentation-llmo
Requires Python: >=3.10

## 使用方式
安装whl包
```bash
pip install enova_instrumentation_llmo-0.0.4-py3-none-any.whl 
```
在vllm程序代码中进行ot配置和开启注入
```python

# 开启instrument
from enova.llmo import start
# 指定ot collector地址和service name
start(otlp_exporter_endpoint="localhost:4317", service_name="service_name")

#######接原代码内容#######
```

## Metrics 指标说明
- `avg_prompt_throughput` prompt 输入速率，单位 tokens/s
- `avg_generation_throughput` 生成速率，单位 tokens/s
- `running_requests` 当前 running 的 requests 数
- `swapped_requests` 当前 swapped 的 requests 数
- `pending_requests` 当前 pending 的 requests 数
- `gpu_kv_cache_usage` gpu kv cache 使用率
- `cpu_kv_cache_usage` cpu kv cache 使用率
- `generated_tokens` 生成的 tokens 数
- `llm_engine_init_config` engine启动参数，attributes如下
  - `model`
  - `tokenizer`
  - `tokenizer_mode`
  - `revision`
  - `tokenizer_revision`
  - `trust_remote_code`
  - `dtype`
  - `max_seq_len`
  - `download_dir`
  - `load_format`
  - `tensor_parallel_size`
  - `disable_custom_all_reduce`
  - `quantization`
  - `enforce_eager`
  - `kv_cache_dtype`
  - `seed`
  - `max_num_batched_tokens`
  - `max_num_seqs`
  - `max_paddings`
  - `pipeline_parallel_size`
  - `worker_use_ray`
  - `max_parallel_loading_workers`
- `http.server.active_requests` FastAPI 正在处理中的 HTTP 请求的数量
- `http.server.duration` FastAPI 服务端请求处理时间。
- `http.server.response.size` FastAPI HTTP 响应消息的大小
- `http.server.request.size` FastAPI HTTP 请求的大小


## trace span 说明
- `POST /generate` /generate请求
- `POST /generate prompt` 带有 `prompt` attribute
- `ModelRunner.execute_model` 模型execute，对应一次 token 生成
- `CUDAGraphRunner.forward` CUDA Graph的 forward 计算，在 `ModelRunner.execute_model` 中被调用
- `ChatGLMForCausalLM.forward` chatglm 模型 forward
- `LlamaForCausalLM.forward` llama 模型 forward
python-cex 0.0.6
The Cleaning and EXploration kit for Data Scientist.
Keywords: python, data science, cleaning, exploring, clean, explore


<img src="https://drive.google.com/uc?export=view&id=1XwHm3fgt0oQU1vcx-dlEOgTZA2q1HMli" alt="Thumbnail">

# CEX: Cleaning and EXploration kit for data scientist

| | |
| --- | --- |
| Package | [![PyPI Latest Release](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fpypi.org%2Fpypi%2Fpython-cex%2Fjson&query=%24.info.version&label=version)](https://pypi.org/project/python-cex/) [![License](https://img.shields.io/badge/License-GNU_GPL_v3.0-green)](https://github.com/johnw1llliam/CEX/blob/main/LICENSE)

## What is it?
The ultimate kit for Data Scientists to do data Cleaning and EXploration with 30+ built-in functions that can help data scientist to do data wrangling.
This package aims to make data wrangling easier by calling a function to do specific task without having to do raw coding.

## Features
<ul>
  <li>30+ built-in functions for data cleaning and exploration</li>
  <li>Compatible with popular data science libraries such as pandas and numpy</li>
  <li>Comprehensive error handling and validation</li>
</ul>

## Usage
Quick example:
```python
import cex
import pandas as pd

df = pd.read_csv("data.csv")
cex.col_category_graph(df)
```

Result:

<img src="https://drive.google.com/uc?export=view&id=1zLtv2IgZelCBqPFcsA4HFwhmFjpxo6kW" alt="Sample">

## Source Code
The source code is located <a href="https://github.com/johnw1llliam/CEX">here</a>
CHAa 0.2.0
Conversational Health Agents (CHAs) are interactive systems designed to enhance personalhealthcare services by engaging in empathetic conversations and processing multimodal data.
Requires Python: <3.12,>=3.9

Quick Start
===========
* [Documentation page](https://docs.opencha.com)
* [User Guide](https://docs.opencha.com/user_guide/index.html)
* [How to Contribute](https://docs.opencha.com/user_guide/contribute.html)
* [API Docs](https://docs.opencha.com/api/index.html)
* [Examples](https://docs.opencha.com/examples/index.html)

To use CHA in a safe and stable way, ensure you have Python 3.10 or higher installed. First, create a virtual environment:

```python
# Create the virtual environment
python -m venv /path/to/new/virtual/environment

# Activate the virtual environment
source /path/to/new/virtual/environment/bin/activate
```

Now, install the CHA package. Unfortunately, currently due to PyPi registration problem, we could not upload our package to be
installed via pip directly. For now you can simply clone our code and do the installation as follows:

```bash
git clone https://github.com/Institute4FutureHealth/CHA.git
cd CHA
pip install '.[all]'
```

To simplify installation with minimum requirements and be ready to go, you can use the following command. This installs OpenAI, React Planner, as well as SerpAPI (search) and Playwright (browser) tasks:

```bash
pip install '.[minimum]'
```

If you want to install all requirements for all tasks and other components, use the following command:

```bash
pip install '.[all]'
```

After installing the package, based on what tasks you want to use, you may need to acquire some api_keys. For example, to get started using openAI GPT3.5 model as LLM in CHA, you need to signup
in their website and get the api_key. Then you should add openAI api_key as environment vairable in your terminal:

```bash
export OPENAI_API_KEY="your api_key"
```

The same goes for using tasks like SerpAPI:

```bash
export SERPAPI_API_KEY="your api_key"
```

Finally, you can start running our framework with the following simple code:

```python
from src.CHA import CHA

cha = CHA()
cha.run_with_interface()
```

This code will run the default interface, and you can access it at the following URL:

**http://127.0.0.1:7860**

For more examples, visit the [Examples page](https://docs.opencha.com/examples/index.html).

![Alt Text](./docs/figs/Interface.png)