semvec/pypi-packages huggingface dataset:
geoquest 0.0.1
Search, geocode, and reverse-geocode locations with ease using GeoQuest. A simple and intuitive API for location-based services.
Requires Python: >=3.8

# GeoQuest

[![PyPI - Version](https://img.shields.io/pypi/v/geoquest.svg)](https://pypi.org/project/geoquest)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/geoquest.svg)](https://pypi.org/project/geoquest)

-----

## Search, Geocode, and Reverse-Geocode Locations

GeoQuest is a lightweight Python package that provides an easy-to-use interface for searching, geocoding, and reverse-geocoding locations. With support for various APIs (including
Nominatim), it's perfect for location-based services, address validation, and route planning.


## Table of Contents

- [Features](#features)
- [Installation](#installation)
- [API Documentation](#api-documentation)
- [Contributing](#contributing)
- [License](#license)

## Features

* Search for locations by address or other criteria
* Geocode addresses into latitude/longitude pairs
* Reverse-geocode coordinates back into human-readable addresses
* Caching mechanism to improve performance and reduce API requests


## Installation

You can install GeoQuest using pip:
```console
pip install geoquest
```

## API Documentation

For a full list of available functions and their usage, please refer to the [API documentation](https://mohitshrestha.github.io/geoquest/).

## Contributing

If you'd like to contribute to GeoQuest or report any issues, feel free to open a pull request or issue on this repository.

## License

`GeoQuest` is distributed under the terms of the [MIT](https://github.com/mohitshrestha/geoquest/blob/main/LICENSE) license.

MIT License

Copyright (c) 2024-present Mohit Shrestha <contact@mohitshrestha.com.np>

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
mercadolibre-sdk 0.0.16
Description of your package

# Mercado Libre - SDK (unofficial)

## Configuration for using in  backend services

1. Install [ngrok](https://ngrok.com/) for localhost authorization

```shell
 curl -s https://ngrok-agent.s3.amazonaws.com/ngrok.asc | sudo tee /etc/apt/trusted.gpg.d/ngrok.asc >/dev/null && echo "deb https://ngrok-agent.s3.amazonaws.com buster main" | sudo tee /etc/apt/sources.list.d/ngrok.list && sudo apt update && sudo apt install ngrok
```

2. Follow the instructions in [ngrok page](https://dashboard.ngrok.com/get-started/your-authtoken)

3. Create a free domain in [ngrok domains page](https://dashboard.ngrok.com/cloud-edge/domains)

4. Set the environment variables

```shell
MERCADOLIBRE_CLIENT_ID
MERCADOLIBRE_CLIENT_SECRET
MERCADOLIBRE_REDIRECT_URI={this should be your ngrok domain url}
```

5. If you want to save your tokens in a Redis database set

```shell
MERCADOLIBRE_REDIS_HOST
MERCADOLIBRE_REDIS_PORT
```

6. Run in your terminal

```shell
mercadolibre.authorize
```

All envs variables can be defined in a .env file in your root folder

## Configuration for using with frontend services

Set the environment variables

```shell
MERCADOLIBRE_CLIENT_ID
MERCADOLIBRE_CLIENT_SECRET
MERCADOLIBRE_REDIRECT_URI={your backend url that handles login}
```

All envs variables can be defined in a .env file in your root folder

## Building from source
```shell
python setup.py sdist bdist_wheel
twine upload dist/*
```


## Licence

MIT License

Copyright (c) 2023 Mercado Radar

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

---

Mercado Libre and its trademarks are the property of MercadoLibre, Inc.
Mercado Radar is not affiliated, endorsed, or sponsored by MercadoLibre, Inc.
This client is an independent, unofficial software developed by Mercado Radar.
All copyrights and trademarks mentioned here are the property of their respective owners.
photoshoot 0.2.1
A pytest plugin for snapshot testing polars dataframes.
Requires Python: >=3.8

# photoshoot

Photoshoot is a pytest plugin libary for making snapshot testing with Polars DataFrames easy.

## Installation

```bash
pip install photoshoot
```

## Usage

On the first run of this test, it will fail. Because no previous snapshot has been created.
```python
import pytest
import polars as pl

def test_dataframe(local_snapshot):
    df = pl.DataFrame({
        "a": [1, 2, 3],
        "b": [4, 5, 6],
    })
    local_snapshot(df)
```

Running the test like this, will create a snapshot of the dataframe. All future runs will compare the dataframe with the snapshot.
```bash
pytest --new-snapshot
```

Rerunning the test now will pass, as the snapshot is already created.

## Features
Currenty this library only supports Polars DataFrames, and only supports local snapshots or gcs snapshots.
jaraco.packaging 10.2.3
tools to supplement packaging Python releases
Requires Python: >=3.8

.. image:: https://img.shields.io/pypi/v/jaraco.packaging.svg
   :target: https://pypi.org/project/jaraco.packaging

.. image:: https://img.shields.io/pypi/pyversions/jaraco.packaging.svg

.. image:: https://github.com/jaraco/jaraco.packaging/actions/workflows/main.yml/badge.svg
   :target: https://github.com/jaraco/jaraco.packaging/actions?query=workflow%3A%22tests%22
   :alt: tests

.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json
    :target: https://github.com/astral-sh/ruff
    :alt: Ruff

.. image:: https://readthedocs.org/projects/jaracopackaging/badge/?version=latest
   :target: https://jaracopackaging.readthedocs.io/en/latest/?badge=latest

.. image:: https://img.shields.io/badge/skeleton-2024-informational
   :target: https://blog.jaraco.com/skeleton

Tools for packaging.


sphinx
======

This package provides a Sphinx extension that will inject into the config
the following values from the project's package metadata (as presented by
distutils):

 - project (from name)
 - author
 - copyright (same as author)
 - version
 - release (same as version)
 - package_url (from url)

To enable, include 'jaraco.packaging' in the requirements and add
'jaraco.packaging.sphinx' to the list of extensions in a Sphinx config
file::

    extensions=['jaraco.packaging.sphinx']

The extension by default builds the project in an isolated environment in
order to extract the metadata. For offline builds, set
``BUILD_ENVIRONMENT=current`` and ensure the build dependencies are
met in the current environment.

Deprecated: To build the documentation offline,
provide an already built wheel by setting the environment variable
``JARACO_PACKAGING_SPHINX_WHEEL`` to the path of an existing wheel.


make-tree
=========

A utility for taking output from ``pipdeptree --json`` and producing a tree
rooted at a given package.

Usage::

    pipdeptree --json | python -m jaraco.packaging.make-tree mypkg


metadata
=========

A wrapper around ``build.util.project_wheel_metadata`` to enable dowstream
packagers to indicate that they need an isolated build. Set the environment
variable ``BUILD_ENVIRONMENT=current`` to bypass build isolation and use the
current isolation for loading metadata from a project.
git-monitor 0.1.6
Automatic print the git status of the package your are developing.
Requires Python: <4.0,>=3.11
Keywords: git, log, datascience

# Use Case

This project is intended to improve datascience workflows. You may want to do these simultaneously:
1. Developing a package (the *package project*), possibly a machine learning model or a data pipeline.
2. Perform some experiments in another project (the *experiment project*) by such *package project* by running a jupyter notebook or a script.
3. Log the git status of the *package project* for reproducibiliy.

`git-monitor` help you to conviniently log the git status.

If we don't want the notebooks to polute the *package project*, we have to separate the *experiment project* from the *package project*, then we cannot track everying directly in a single git repo. This problem is what `git-monitor` built for.

# How to

## Basic usage

1. Install `git-monitor` into the environment of your *package project*.
2. Make a `.git_monitor` file in the *experiment project*:
```
<pkg-nm-1>=<path-to-package-1-project-root>
<pkg-nm-2>=<path-to-package-2-project-root>
```
3. In the root `__init__.py` of the *package project*, add the lines:
```python
import git_monitor
git_monitor.Monitor.by_env("<pkg-nm-1>")
```

Then everytime you `import` or `reload` the *package project* under the *experiment project*, `git-monitor` will print the git status, including current branch, commit hash, untracked files and modified files.

## logger
```python
from git_monitor import logger
```
the messages of `git-monitor` are directed to this `logger`, which is a python native `logging.Logger` and an `logging.StreamHandler` has already been added to. The logging level is `INFO`, and the `StreamHandler` uses `sys.stdout`.
jaraco.imaging 2.1.1
Imaging support by jaraco
Requires Python: >=3.8

.. image:: https://img.shields.io/pypi/v/jaraco.imaging.svg
   :target: https://pypi.org/project/jaraco.imaging

.. image:: https://img.shields.io/pypi/pyversions/jaraco.imaging.svg

.. image:: https://github.com/jaraco/jaraco.imaging/actions/workflows/main.yml/badge.svg
   :target: https://github.com/jaraco/jaraco.imaging/actions?query=workflow%3A%22tests%22
   :alt: tests

.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json
    :target: https://github.com/astral-sh/ruff
    :alt: Ruff

.. .. image:: https://readthedocs.org/projects/PROJECT_RTD/badge/?version=latest
..    :target: https://PROJECT_RTD.readthedocs.io/en/latest/?badge=latest

.. image:: https://img.shields.io/badge/skeleton-2024-informational
   :target: https://blog.jaraco.com/skeleton
openfinance 4.0.0
an open financial agent framework powered by llm

# 🌴OpenFinance🌴
OpenFinance is based on the idea of Financial Egalitarianism with the help of llm. Poor or Rich, Invest for all
People can easily understand the knowledge behind the complex financial market World and the secret force behind the changement in the financial market or the consequence of different events.

## Quick Install
If you are a developper, you can try:
`pip install openfinance`

## 🤔 What is OpenFinance ?
  We have built a Digital Financial World called OpenFiannce Playground, it is based on the realtime worldwide event and the magnificent abilities of llm models like ChatGPTs.

Key Funcitons of the world is based on the following abilities.
- A Temperal Financial Graph which connects the Entity Graph to the Financial Indicator Graph
- A agentflow framework for designing AI Agent to solve financial tasks
- A Web frontend available to anyone who want to contribute to the digital financial world

** 💬 Web Front**

Our Website will be an interface for those who are willing to contribute theirs thoughts in the Digital Finance World. People can easily set up new tools or new factors and make their own Agents using our agentflow and all kinds of tools

[Click Here](http://zhubin.xyz)

** 🤖 Agents**

We have already developped several agents helpfully, but they are far from enough and perfert. We encourage all of you join us to build the Digital Financial World
A short list of Agents:
- Search Agent: An Agent for Search financial data
- Percept Agent: An Agent for observing the real time event to extract usefull information and store them into the Graph
- Analysis Agent: An Agent for analysis the reasons behind complex event

## 📖 Documentation

We will catch up as soon as possible, before that you can try
[Click Here](http://zhubin.xyz)

## 💁 Contributing


## 💬 Contact
ragdaemon 0.8.3
Generate and render a call graph for a Python project.
Requires Python: >=3.10

# Ragdaemon

Ragdaemon is a Retrieval-Augmented Generation (RAG) system for code. It runs a daemon (background process) to watch your active code, put it in a knowledge graph, and query the knowledge graph to (among other things) generate context for LLM completions.

Three ways to use Ragdaemon:
## 1. **Help me write code** 

Ragdaemon powers the 'auto-context' feature in [Mentat](http://github.com/AbanteAI/mentat), a command-line coding assistant. You can install Mentat using `pip install mentat`. Run with the `--auto-context-tokens <amount>` or `-a` (default=5000) flag, and ragdaemon-selected context will be added to all of your prompts. 

## 2. **Explore the knowledge graph**

Install locally to visualize and query the knowledge graph directly. 
Install using `pip install ragdaemon`, and run in your codebase's directory, e.g. `ragdaemon`. This will start a Daemon on your codebase, and an interface at `localhost:5001`. Options:
- `--chunk-extensions <ext>[..<ext>]`: Which file extensions to chunk. If not specified, defaults to the top 20 most common code file extensions.
- `--chunk-model`: OpenAI's `gpt-4-0215-preview` by default.
- `--embeddings-model`: OpenAI's `text-embedding-3-large` by default.
- `--diff`: A git diff to include in the knowledge graph. By default, the active diff (if any) is included with each code feature.

## 3. **Use ragdaemon Python API** 

Ragdaemon is released open-source as a standalone RAG system. It includes a library of python classes to generate and query the knowledge graph. The graph itself is a NetworkX MultiDiGraph which saves/loads to a `.json` file.

```python
import asyncio
from pathlib import Path
from ragdaemon.daemon import Daemon

async def main():
    cwd = Path.cwd()
    daemon = Daemon(cwd)
    await daemon.update()

    results = daemon.search("javascript")
    for result in results:
        print(f"{result['distance']} | {result['id']}")

    query = "How do I run the tests?"
    context_builder = daemon.get_context(
        query, 
        auto_tokens=5000
    )
    context = context_builder.render()
    messages = [
        {"role": "user", "content": query},
        {"role": "user", "content": f"CODE CONTEXT\n{context}"}
    ]
    print(messages)

asyncio.run(main())
```
hd_active 0.1.1
Prevent external HDs from becoming inactive (sleeping).

# HD Active

[![pypi](https://img.shields.io/pypi/v/hd_active.svg)](https://pypi.python.org/pypi/hd_active)
[![Project License - MIT](https://img.shields.io/pypi/l/hd_active.svg)](https://github.com/joaonc/show_dialog/blob/main/LICENSE.txt)

Prevent external HD's from becoming inactive (sleeping).

**[https://joaonc.github.io/hd_active](https://joaonc.github.io/hd_active)**

## Quick start
```
python -m src.hd_active.hd_active --conf src/hd_active/hd_active.ini
```

## Development
Note: Best to work on a virtual environment.
This page doesn't go into how to do that.

Install the development packages:
```
python -m pip install -r requirements-dev.txt
```

This project uses [pyinvoke](https://www.pyinvoke.org/) to facilitate common tasks.
For a list of tasks:
```
inv --list
```

## Licensing
Even though I'm providing this code under the MIT license, [Qt](https://www.qt.io) is used for the
UI component, meaning you'll be bound to its GPL/LGPL license (more info
[here](https://www.qt.io/licensing/open-source-lgpl-obligations)).

I'm not an expert on these things, so be advised.
ylz-translate 0.2.7
渐进式文档翻译系统-v2
Requires Python: <3.13,>=3.10

# setup
## step1 执行init_config,系统自动将config.yaml文件拷贝到～/.ylz_translate目录下
## step2 根据情况有关环境变量的设置:
### step2.1 LLM
#### export TOGETHER_API_KEYS=<用逗号分隔的together api token>
#### export TOGETHER_LLM_MODEL=<together llm model>  默认为 Qwen/Qwen1.5-72B-Chat

#### export SILICONFLOW_API_KEYS=<用逗号分隔的siliconflow api token>
#### export SILICONFLOW_LLM_MODEL=<siliconflow llm model> 默认为 alibaba/Qwen1.5-110B-Chat

#### export GROQ_API_KEYS=<用逗号分隔的groq api token>
#### export GROQ_LLM_MODEL=<groq llm model> 默认为 llama3-70b-8192

#### export DEEPSEEK_API_KEYS=<用逗号分隔的deepseek api token>
#### export DEEPSEEK_LLM_MODEL=<deepseek llm model>  默认为 deepseek-chat

#### export QIANFAN_API_KEYS=<用逗号分隔的qianfan api token>
#### export QIANFAN_SEC_KEYS=<用逗号分隔的qianfan sec token> 要与QIANFAN_API_KEYS对应
#### export QIANFAN_LLM_MODEL=<qianfan llm model>  默认为 Yi-34B-Chat

### step2.2 EMBEDDING
#### export TOGETHER_EMBEDDING_MODEL=<together embedding model>  默认为 BAAI/bge-large-en-v1.5

### step2.3 SEARCH_TOOL
#### export TAVILY_API_KEYS=<逗号分隔的tavily search api token>  

# run
## ylz_translate -h
HMautogroup 0.4.1
auto-cleaning-grouping-process
Requires Python: >=3.10

# Clean & Group Titles for HM

Coded for Colab

# Installation

1. Open Colab and load Startup_Dependency.ipynb Jupyter Notebook
   This is the set up to use this library
2. Install the library
   {pip install HMautogroup}
3. Use main function


This project is licensed under the terms of the MIT license.