semvec/pypi-packages huggingface dataset:
docume-llm 0.1.0
A Python package for document processing with LLMs
Requires Python: >=3.7


# Project Name

## Setup Instructions

### Step 1: Create a `.env` File

```python
from docume_llm.main import generate_documentation
import json, os, re

file_path = "kan72.json"

with open(file_path, 'r') as file:
    json_data = json.load(file)
    
app_type = 'jira'
    
args = {
    'app_type': app_type,
    'llm_type': 'openai',
    'api_key': 'your-api-key-here'
}
    
def give_all_files_from_directory(directory):
    return os.listdir(directory)
    
def give_next_filename(files):
    # Regex pattern to match files with the base name and extension
    pattern = re.compile(rf"zero_shot_(\d+)\.txt")
    # Find all matching files and extract their numbers
    numbers = []
    for file in files:
        match = pattern.match(file)
        if match:
            numbers.append(int(match.group(1)))
    
    # Determine the next number
    if numbers:
        next_number = max(numbers) + 1
    else:
        next_number = 1
    return f"zero_shot_{next_number}.txt"

queries, answers = generate_documentation(json_data, **args)

files = give_all_files_from_directory('.')
filename = give_next_filename(files)

with open(filename, "w") as file:
    for q,a in zip(queries, answers):
        file.write(q + "\n" + a + "\n\n")
        file.write("*" * 100)
izzy-devices 0.2.0
Client and Server classes for the IZZY Project.
Requires Python: >=3.13

# izzy-devices
The `izzy-devices` package provides the `Client` and `Server` classes that are part of the IZZY (Intelligent 
Scenery Simulation Project) as well as enumerations for statuses for both client and server devices.

## Installation
Use the package manager [pip](https://pip.pypa.io/en/stable/) to install `izzy-devices`.
```bash
pip install izzy-devices
```

## Background
"IZZY," or the Intelligent Scenery Simulation Platform, is an ongoing research project in applied robotics and 
control systems for moving scenery in live entertainment. Computerized control of scenery is not new; technologies 
for controlling both simple and complex movement of scenery in multiple axes have been around for decades. Many 
traditional approaches borrow heavily from industrial automation systems, employing programmable logic controllers 
(sometimes with more-user-friendly PC-style front-end interfaces), roller switches, high-powered motors, and cable 
winches. However, because of the very real safety concerns related to hauling scenic units weighing as much as a 
couple of tons across the stage at relatively high velocities, typical automation systems rely heavily on 
highly-choreographed movements and attentive operators to ensure safety. Additionally, traditional approaches often 
require the construction of expensive subfloors and decking systems to provide pathways for winched cables and 
tracks for scenic units to follow.

## IZZY and Mother
The IZZY (Intelligent Scenery Simulation Platform) project software is built around two software objects: a client
and a server. An implementation of IZZY is a ``client``; the offstage control and programming interface ``server`` has
been affectionately dubbed "Mother" (a not-so-subtle nod to *Alien*). A ``client`` stores information about an IZZY
device, including physical dimensions, network information installed sensor packages, and position and movement
information. A ``server`` stores network information and a collection of ``client`` instances--one instance for each
physical IZZY device on the network.

>[!NOTE]
>When instantiated in software on an IZZY device, a ``server`` instance should not be
>used to hold a list of ``client`` devices as remote devices should not communicate to
>each other. A ``server`` instance on an IZZY device should only be used to store network
>information about the Mother device.

The ``izzy-devices`` package provides the ``Client`` and ``Server`` classes as well as enumerations for statuses for
both ``client`` and ``server`` devices.
freedom-search 0.2.2
Empower your AI models with ethical, open-source web intelligence
Requires Python: >=3.8

# FreedomSearch

Empower your AI models with ethical, open-source web intelligence.

FreedomSearch is a Python library designed to enhance AI prompts and queries with real-time web information. It respects fair use principles and leverages multiple search engines to provide diverse, up-to-date context for your language models.

Key Features:
- 🌐 Multi-engine support (DuckDuckGo, Google, and expandable)
- 🔍 Efficient caching and rate limiting
- 🧠 Smart text preprocessing and formatting for LLMs
- 🛡️ Built with privacy and fair use in mind
- 🔧 Easy to integrate and extend

Whether you're building the next-gen chatbot or fine-tuning language models, FreedomSearch provides the tools to ethically augment your AI's knowledge base.

## Quick Start

```python
from freedom_search import InternetSearchEnhancer

enhancer = InternetSearchEnhancer('duckduckgo')
original_prompt = "Explain quantum computing"
enhanced_prompt = enhancer.enhance_llm_input(original_prompt, "recent quantum computing breakthroughs")

print(enhanced_prompt)
```

Contribute to FreedomSearch and help shape the future of ethical AI augmentation!
```

This description:

1. Starts with a catchy tagline that encapsulates the library's purpose and ethical stance.
2. Briefly explains what FreedomSearch does and its target use case.
3. Lists key features using emojis for visual appeal and quick scanning.
4. Includes a short pitch for potential users and contributors.
5. Provides a quick start code snippet to show how easy it is to use.
6. Ends with a call-to-action for contributions, emphasizing the open-source nature of the project.