semvec/pypi-packages huggingface dataset:
earthquake-check 0.1.0
A package for checking earthquake information in Taiwan.
Requires Python: >=3.6

# Earthquake checker 
## 一個讓你可以用 Command line 快速確認剛剛有沒有地震的工具
近期因為地震太多，工作的時候總是有同事問剛剛是不是有地震。在這個時候可以輕鬆的用 Command line 去確認有沒有地震，不用打開網頁視窗，是一個雞肋的小工具。這個工具能幫你查詢中央氣象署的資料，並將處理後的資料輸出在 terminal 上面，免取上網查詢的動作

## Get started
earthquake-check requires Python 3.6+ along with the requests and pydantic.
1. 安裝套件
    ```
    pip install earthquake-check
    ```
2. 檢視最近 1 小時內是否有地震
    ```
    equake check
    ```
    ![image](https://github.com/ChickenBenny/equake-check/assets/96862600/8439637c-2d24-4a87-b835-a5156ef9d313)

    **預設是檢查一小時內，可以使用 `--hour=${希望的小時}`**
3. 顯示近期地震的資訊
    ```
    equake show
    ```
    ![image](https://github.com/ChickenBenny/equake-check/assets/96862600/e248fe55-43ee-4cc6-9349-789cc51d2d68)
    **預設是五筆，可以使用 `--hour=${希望的小時}`**

## 結語
總之這就是一個雞肋的小套件，大家可以載來玩玩看。阿如果中央氣象署的資料改了，可能也不會繼續維護。
![image](https://github.com/ChickenBenny/equake-check/assets/96862600/a4d3e791-b597-4377-8265-7ee9ef3ef558)
ALLMDEV 1.2.5
A simple and efficient python library for fast inference of GGUF Large Language Models.
Keywords: GGUF, GGUF Large Language Model, GGUF Large Language Models, GGUF Large Language Modeling, GGUF Large Language Modeling Library

# ALLM

ALLM is a Python library designed for fast inference of GGUF (Generic Global Unsupervised Features) Large Language Models (LLMs) on both CPU and GPU. It provides a convenient interface for loading pre-trained GGUF models and performing inference using them. This library is ideal for applications where quick response times are crucial, such as chatbots, text generation, and more.

## Features

- **Efficient Inference**: ALLM leverages the power of GGUF models to provide fast and accurate inference.
- **CPU and GPU Support**: The library is optimized for both CPU and GPU, allowing you to choose the best hardware for your application.
- **Simple Interface**: With a straightforward command line support, you can easily load models and perform inference with just a single command.
- **Flexible Configuration**: Customize inference settings such as temperature and model path to suit your needs.

## Installation

You can install ALLM using pip:

```bash
pip install allm
```

## Usage

You can start inference with a simple 'allm-run' command. The command takes name or path, temperature(optional), max new tokens(optional) and additional model kwargs(optional) as arguments.

```bash
allm-run --name model_name_or_path
```

## API

You can initiate the inference API by simply using the 'allm-serve' command. This command launches the API server on the default host, 127.0.0.1:5000. If you prefer to run the API server on a different port and host, you have the option to customize the apiconfig.txt file within your model directory.

```bash
allm-serve
```

==========================================================================================================================================


## ALLM AGENTS 

## Local Agent Inference

To create local agent, begin by loading your knowledge documents into the database using the allm-newagent command and specifying the agent name:

```bash
allm-newagent --doc "document_path" --agent agent_name
```

or

```bash
allm-newagent --dir "directory containing files to be ingested" --agent agent_name
```

After agent is created successfully with your knowledge document, you can start the local agent chat with the allm-agentchat command:


```bash
allm-agentchat --agent agent name
```

After your agents are created you can also initiate agent-specific API server using the allm-agentapi command:


```bash
allm-agentapi --agent agent name
```

## Supported Model names
Llama2, llama, llama2_chat, Llama_chat, Mistral, Mistral_instruct
func_adl_servicex_xaodr21 2.0.0.21.2.231a12
func-adl typed datasets for Servicex for xAOD R21 21.2.231
Requires Python: >=3.8

# func_adl_servicex_xaodr21

This package contains the types and metadata for xAOD R21 21.2.231.

It was generated by the [`func_adl_servicex_type_generator`](https://github.com/gordonwatts/func_adl_servicex_type_generator) package.

For examples on how to use this, please see the [jupyter-book](https://gordonwatts.github.io/xaod_usage).

## Installation

If you want to use `func_adl_servicex` and the older verison of the servicex frontend, make sure to install `func_adl_servicex` in addition.

If you want to use the new 3.0 `servicex` frontend, just install that.
func_adl_servicex_xaodr22 2.0.0.22.2.107a12
func-adl typed datasets for Servicex for xAOD R22 22.2.107
Requires Python: >=3.8

# func_adl_servicex_xaodr22

This package contains the types and metadata for xAOD R22 22.2.107.

It was generated by the [`func_adl_servicex_type_generator`](https://github.com/gordonwatts/func_adl_servicex_type_generator) package.

For examples on how to use this, please see the [jupyter-book](https://gordonwatts.github.io/xaod_usage).

## Installation

If you want to use `func_adl_servicex` and the older verison of the servicex frontend, make sure to install `func_adl_servicex` in addition.

If you want to use the new 3.0 `servicex` frontend, just install that.
arcade-accelerate 1.0.2
A companion library for Arcade providing accelerated Rust functions
Requires Python: >=3.7


# arcade-accelerate

An experimental library for accelerating [Arcade](https://github.com/pythonarcade/arcade) using Rust. The module can
be imported and monkey patch Arcade, replacing functions and types with rust versions.

```py
import arcade_accelerate
arcade_accelerate.bootstrap()

import arcade
```

It is important to run the arcade-accelerate bootstrapping process before importing Arcade, otherwise the monkey-patched versions will not be fully applied.

## Build / Setup

First create and activate a Python virtual environment, then install maturin:

```bash
pip install maturin
```

Install the crate as module in the current virtual environment using Maturin. Generally
when working on performance enhancements you will want to use the `--release` flag.

```sh
# Debug
maturin develop

# Release
maturin develop --release
```

Then you can install [Arcade](https://github.com/pythonarcade/arcade) into the same virtual environment
and run any of it's examples:

```sh
cd <directory of arcade project>
pip install -e .
```

Optimally testing should be done against the `development` branch of Arcade.
In order to enable `arcade-accelerate` add these two lines anytime before importing `arcade`. It is important that
the bootstrap is done prior to importing Arcade, otherwise the monkey-patched functions/classes will not fully apply.

```py
import arcade_accelerate
arcade_accelerate.bootstrap()

import arcade
```

If you would like to run Arcade's test suite with arcade-accelerate enabled, you can do so by setting the `ARCADE_PYTEST_USE_RUST` environment variable before running pytest on Arcade. You just need to ensure that both Arcade and arcade-accelerate are installed in the same environment.
pulsesec 0.0.5
Pulse Security - Python SDK
Requires Python: >=3.8

# Pulse Security - Python SDK

## Installation

```sh
$ pip install pulsesec
```

## Example

```py
from pulse import Pulse, TokenNotFoundError, TokenUsedError, TokenExpiredError
import os


async def main():
    client = Pulse(os.getenv("PULSE_SITE_KEY"), os.getenv("PULSE_SECRET_KEY"))

    async def classify(token: str) -> bool:
        try:
            is_bot = await client.classify(token)
            return is_bot
        except TokenNotFoundError:
            raise "Token not found"
        except TokenUsedError:
            raise "Token already used"
        except TokenExpiredError:
            raise "Token expired"
        except Exception as e:
            raise e
```
rocketmq-wise 2.0.1
RocketMQ Python Client wise lib
Keywords: rocketmq

# rocketmq-client-python

[![License](https://img.shields.io/badge/license-Apache%202-4EB1BA.svg)](https://www.apache.org/licenses/LICENSE-2.0.html)
[![Build Status](https://travis-ci.org/apache/rocketmq-client-python.svg?branch=master)](https://travis-ci.org/apache/rocketmq-client-python)
[![codecov](https://codecov.io/gh/apache/rocketmq-client-python/branch/ctypes/graph/badge.svg)](https://codecov.io/gh/apache/rocketmq-client-python/branch/ctypes)
[![PyPI](https://img.shields.io/pypi/v/rocketmq-client-python.svg)](https://pypi.org/project/rocketmq-client-python)

RocketMQ Python client, based on [rocketmq-client-cpp](https://github.com/apache/rocketmq-client-cpp), supports Linux and macOS

## Installation

```bash
pip install rocketmq-client-python
```

## Usage

### Producer

```python
from rocketmq.client import Producer, Message

producer = Producer('PID-XXX')
producer.set_name_server_address('127.0.0.1:9876')
producer.start()

msg = Message('YOUR-TOPIC')
msg.set_keys('XXX')
msg.set_tags('XXX')
msg.set_body('XXXX')
ret = producer.send_sync(msg)
print(ret.status, ret.msg_id, ret.offset)
producer.shutdown()
```

### PushConsumer

```python
import time

from rocketmq.client import PushConsumer


def callback(msg):
    print(msg.id, msg.body)


consumer = PushConsumer('CID_XXX')
consumer.set_name_server_address('127.0.0.1:9876')
consumer.subscribe('YOUR-TOPIC', callback)
consumer.start()

while True:
    time.sleep(3600)

consumer.shutdown()

```

## License
[Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0.html) Copyright (C) Apache Software Foundation