semvec/pypi-packages huggingface dataset:
wyra 0.0.6
Fine Tuning Data Maker
Requires Python: >=3.10

# Wyra

Wyra is a Python library designed to create and format data for fine-tuning OpenAI models, specifically tailored for Azure OpenAI services. It simplifies the process of converting text content into the JSON Lines (JSONL) format required for fine-tuning conversational AI models.

"Wyra" is a term used in indigenous languages to refer to a bird. Just like a "bird", this library will help you "fly" through the implementation of fine-tuning. Enjoy!

## Features

- **Easy usability**: Any text in multiple languages ​​can be used.
- **Easy Integration**: Seamlessly integrates with Azure OpenAI services.
- **Secure Data Handling**: Utilizes encryption for sensitive data.
- **Automated Formatting**: Converts text content into JSONL format effortlessly.
- **Customizable Prompts**: Allows for the creation of flexible fine-tuning datasets for various use cases.

## Installation

To install Wyra, use pip:

```sh
pip install wyra
```

## Usage

Here's a basic example of how to use Wyra:

```python
# Import necessary libraries
import json
from wyra import FineTuningDataMaker

# Initialize the Fine-Tuning data maker
creator = FineTuningDataMaker()

# Sample content to format
content = "Your text content here (You can use any text in any language you want)."

# Create and format data
formatted_data = creator.format_data(content)

# Save JSONL data to a file
with open('formatted_data.jsonl', 'w') as file:
    file.write(formatted_data)
```

## Multiple Texts

Here's an example of how to use Wyra with multiple texts:

```python
# Import necessary libraries
import json
from wyra import FineTuningDataMaker

# Initialize the Fine-Tuning data maker
creator = FineTuningDataMaker()

# Sample contents to format
contents = [
    "First text content here.",
    "Second text content here.",
    "Third text content here."
]

# Create and format data for multiple contents
formatted_data = [creator.format_data(content) for content in contents]

# Save JSONL data to a file
with open('formatted_data.jsonl', 'w') as file:
    for data in formatted_data:
        file.write(data + '\n')
```

## License

This project is licensed under the MIT License. See the [LICENSE](https://github.com/sauloleite/wyra/blob/main/LICENSE) file for details.

## Contact

For any questions or feedback, this is my Github [GitHub repositories](https://github.com/sauloleite).

## Other AI Projects

Check out my GitHub Pages: [GitHub IO](https://sauloleite.github.io/).
llm-context-packer 0.1.1
Turn your entire codebase into a single prompt-ready text file.
Requires Python: >=3.9

# LLM Context Packer

> "Stop manually opening 15 files and copy-pasting them one by one. LLM Context Packer packs your entire codebase into a single AI-ready prompt in <1 second."

**Turn your entire codebase into a single prompt-ready text file.**

## 🚀 Why?
When working with LLMs (ChatGPT, Claude, Gemini), context is everything. You often need to paste multiple files to get a good refactor or answer. Manually copying file content, adding filenames, and formatting is a pain.

**LLM Context Packer** does it for you:
1.  **Traverses** your project.
2.  **Respects** `.gitignore` (no `node_modules`, `.env`, or secrets).
3.  **Formats** everything into an LLM-friendly structure.
4.  **Copies** the result strictly to your clipboard.
5.  **Counts** the tokens so you know if you fit the context window.

## 📦 Installation

```
pip install llm-context-packer
```

Or manually:

```bash
# Clone the repository
git clone https://github.com/overcrash66/LLM_Context_Packer.git
cd LLM_Context_Packer

# Install locally
pip install .
```

## 🛠 Usage

You can use `llm-context-packer`.

### Basic Usage
Run in your project root. It automatically copies to clipboard.
```bash
llm-context-packer .
```

### Options

```bash
# Don't copy to clipboard, just print to stdout
llm-context-packer . --no-copy

# Write to a file instead of clipboard
llm-context-packer . --output context.txt

# Verbose mode (see what files are being packed)
llm-context-packer . --verbose
```

## 📝 Output Format
The tool generates a clean Markdown format that LLMs understand perfectly:

## 🛡 Security
- **Strictly follows `.gitignore`**: If you ignore it in git, it won't be packed.
- **Hidden files ignored**: Skips `.git` directory automatically.