Metadata-Version: 2.1
Name: groq-qa-generator
Version: 1.1.0
Summary: Groq QA is a CLI tool and Python library that generates question-answer pairs from text to aid in fine-tuning large language models (LLMs).
License: MIT
Author: Jordan Cassady
Author-email: jordan.cassady@gmail.com
Requires-Python: >=3.10,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: groq (>=0.11.0,<0.12.0)
Requires-Dist: python-dotenv (>=1.0.1,<2.0.0)
Requires-Dist: tiktoken (>=0.7.0,<0.8.0)
Description-Content-Type: text/markdown

# 🐱 Groq QA Generator
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<p align="center">
  <img src="./assets/images/logo.png" alt="Groqqy" /><br>
  <strong>Q</strong>: <em>“Would you tell me, please, which way I ought to go from here?”</em><br>
  <strong>A</strong>: <em>“That depends a good deal on where you want to get to,” said the Cat.</em><br>
  <em>— <strong>Alice's Adventures in Wonderland</strong></em>
</p>


|  <p align="left">**Groq QA** is a Python library that automates the creation of question-answer pairs from text, designed to aid in fine-tuning large language models (LLMs). Built with **[Groq](https://groq.com/)**, it leverages powerful models like **[LLaMA 3](https://www.llama.com/)** with 70 billion parameters and 128K tokens, ideal for generating high-quality QA pairs. This tool streamlines the process of preparing custom datasets, helping improve LLM performance on specialized tasks with minimal manual effort. It’s particularly useful for fine-tuning models in research, education, and domain-specific applications.</p> |
|---------------------------------------------------------------------------------------------------------------|
| <p align="center">**Note**: ***This project is not affiliated with or endorsed by Groq, Inc.***</p> |




## ✨ Features
|   | ✨ Feature                         | 📄 Description                                                             |
|----|-------------------------------------|-----------------------------------------------------------------------------|
| ✅ | **CLI**                             | Use the CLI tool with the `groq-qa` command.                                                 |
| ✅ | **Python Library**          | Import directly to your own Python code.                                             |
| ✅ | **Advanced Models**                   | Supports large models like **LLaMA 3.1 70B** via the Groq API.               |
| ✅ | **Automated QA Generation**         | Generate question-answer pairs from input text.                             |
| ✅ | **Prompt Templates**                | Flexible question generation through prompt templates.                      |
| ✅ | **Customizable Configurations**     | Configure via CLI, `config.json`, or directly in Python code.                              |

## 👨‍💻 About the Developer
Hey there! I’m **[Jordan](https://jordan.cassady.me/)**, a Canadian network engineer with over a decade of experience, especially from my time in the fast-paced world of California startups. My focus has been on automating test systems aligned with company KPIs, making it easier for teams to make data-driven decisions.

Whether it’s tackling tough challenges, improving codebases, or working on innovative ideas, I’m always up for the task. Let’s connect and make things happen! ✌️

[![ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/F2F814ELQM)

## 📄 Table of Contents
- [🐱 Groq QA Generator](#-groq-qa-generator)
- [✨ Features](#-features)
- [👨‍💻 About the Developer](#-about-the-developer)
- [🚀 Quick Start](#-quick-start)
- [📦 Upgrading](#-upgrading)
- [📖 Documentation](#-documentation)
  - [Documentation Generation](#documentation-generation)
- [⚙️ Using groq-qa](#-using-groq-qa)
  - [Setup the API Key](#setup-the-api-key)
  - [Setting the Environment Variable](#setting-the-environment-variable)
  - [Command-Line Interface (CLI)](#command-line-interface-cli)
    - [CLI Options](#cli-options)
- [🛠 Configuration](#-configuration)
  - [Directory Structure](#directory-structure)
  - [Default config.json](#default-configjson)
- [🔧 Customizing the Configuration](#-customizing-the-configuration)
- [🐇 Input Data](#-input-data)
  - [Sample Input Data](#sample-input-data)
- [🤖 Models](#-models)
- [🐍 Python Library](#-python-library)
  - [Example](#example)
- [🧠 Technical Overview](#-technical-overview)
- [🧪 Running Tests](#-running-tests)
- [🤝 How to Contribute](#-how-to-contribute)
- [❓ FAQ](#-faq)
- [⚖️ License](#-license)



## 🚀 Quick Start

1. Install the package via `pip`:
    ```bash
    pip install groq-qa-generator
    ```

2. Set up the API key:
    ```bash
    export GROQ_API_KEY=your_api_key_here
    ```

3. Run the `groq-qa` command with default settings (`~/.groq_qa/config.json`):
    ```bash
    groq-qa
    ```
4. View the results in `~/.groq_qa/qa_output.txt`.

## 📦 Upgrading

To ensure that you have the latest features, bug fixes, and improvements, it is recommended to periodically upgrade the `groq_qa_generator` package.

You can update `groq_qa_generator` to the latest version by running:

```bash
pip install --upgrade groq-qa-generator
```

## 📖 Documentation
You can access the full HTML documentation here:

👉 [**Groq QA Generator Documentation**](https://jcassady.github.io/groq-qa-generator/) 👈

### Documentation Generation
The documentation is automatically generated using **Sphinx**, a documentation generation tool for Python projects. Every change made to the documentation directory (`docs/`) triggers a GitHub Actions workflow that builds the HTML files and deploys them to **GitHub Pages**. This ensures that the documentation stays up-to-date with the latest project changes.


## ⚙️ Using groq-qa

### Setup the API Key

First, you need to acquire an API key. Sign up at the [Groq](https://groq.com) website and following their instructions for generating a key.

#### Setting the Environment Variable
Once you have the key, export it as an environment variable using the following commands based on your operating system:

**MacOS and Linux**

```bash
export GROQ_API_KEY=gsk_example_1234567890abcdef
```

**Windows:**

```powershell
set GROQ_API_KEY=gsk_example_1234567890abcdef
```

### Command-Line Interface (CLI)

Once installed, the command `groq-qa` becomes available. By default, this command reads from default configuration located at `~/.groq_qa/config.json`. 


Here are a few examples of how to use the `groq-qa` command:

```bash
# Run with default config.json:
groq-qa 

# Output results in JSON format:
groq-qa --json 

 # Run with model, temperature, questions, and json overrides:
groq-qa --model llama3-70b-8192 --temperature 0.9 --questions 1 --json

```

#### CLI Options:
* `--model`: The default model to be used for generating QA pairs is defined in `config.json`. The default is set to `llama3-70b-8192`.
* `--temperature`: Controls the randomness of the model's output. Lower values will result in more deterministic and focused outputs, while higher values will make the output more random. The default is set to `0.1`.
* `--questions`: Allows you to specify the exact number of question-answer pairs to generate per chunk of text. For example, using `1` will force the system to generate 1 QA pair for each chunk, regardless of chunk size or token limits.
* `--json`: If this flag is included, the output will be saved in a JSON format. By default, the output is stored as a plain text file. The default is set to `False`.

## 🛠 Configuration
When you run the `groq-qa` command for the first time, a user-specific configuration directory (`~/.groq_qa/`) is automatically created. This directory contains all the necessary configuration files and templates for customizing input, prompts, and output.

### Directory Structure
```bash
~/.groq_qa/
├── config.json
├── data
│   ├── alices_adventures_in_wonderland.txt
│   ├── sample_input_data.txt
│   └── prompts
│       ├── sample_question.txt
│       └── system_prompt.txt
├── qa_output.json
└── qa_output.txt
```

### Default config.json
```json
{
    "system_prompt": "system_prompt.txt",
    "sample_question": "sample_question.txt",
    "input_data": "sample_input_data.txt",
    "output_file": "qa_output.txt",
    "model": "llama3-70b-8192",
    "chunk_size": 512,
    "tokens_per_question": 60,
    "temperature": 0.1,
    "max_tokens": 1024
}
```

### 🔧 Customizing the Configuration

The `~/.groq_qa` directory contains essential files that can be customized to suit your specific needs. This directory includes the following components:

- 📄 **config.json**: This is the main configuration file where you can set various parameters for the QA generation process. You can customize settings such as:
  - 📝 **system_prompt**: Specify the path to your custom system prompt file that defines how the model should behave.
  - ❓ **sample_question**: Provide the path to a custom sample question file that helps guide the generation of questions.
  - 📖 **input_data**: Set the path to your own text file from which you want to generate question-answer pairs.
  - 💾 **output_file**: Define the path where the generated QA pairs will be saved.

Other configurable options include:
- 🤖 **model**: Select the model to be used for generating QA pairs (e.g., `llama3-70b-8192`).
- 📏 **chunk_size**: Set the number of tokens for each text chunk (e.g., `512`).
- 🪙 **tokens_per_question**: Specify the number of tokens allocated for each question (e.g., `60`).
- 🔥 **temperature**: Control the randomness of the model's output (e.g., `0.1`).
- 🪙 **max_tokens**: Define the maximum number of tokens the model can generate in the response (e.g., `1024`).

By adjusting these files and settings, you can create a personalized environment for generating question-answer pairs that align with your specific use case.


## 🐇 Input Data 

This project uses text data from *Alice's Adventures in Wonderland* by Lewis Carroll, sourced from [Project Gutenberg](https://www.gutenberg.org/). The full text is available in the included `data/alices_adventures_in_wonderland.txt` file.

### Sample Input Data

For demonstration purposes, a smaller sample of the full text is included in `data/sample_input_data.txt`. This file contains a portion of the main text, used to quickly test and generate question-answer pairs without processing the entire book.


## 🤖 Models
The `groq_qa_generator` currently supports the following models via the Groq API:


| Model Name           | Model ID                     | Developer | Context Window       | Description                                                                                       | Documentation Link                                                                 |
|----------------------|------------------------------|-----------|----------------------|---------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|
| **LLaMA 70B**        | llama3-70b-8192             | Meta      | 8,192 tokens         | A large language model with 70 billion parameters, suitable for high-quality QA pair generation. | [Model Card](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct)               |
| **LLaMA 3.1 70B**    | llama-3.1-70b-versatile     | Meta      | 128k tokens          | A versatile large language model suitable for diverse applications.                               | [Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md) |

**Note:** For optimal QA pair generation, it is recommended to use a larger model such as 70B, as its capacity helps ensure higher quality output. See Groq's [supported models](https://console.groq.com/docs/models) documentation for all options.

### 🐍 Python Library

In addition to CLI usage, the `groq_qa_generator` can be used directly in your Python project. Below is an example of how to configure and execute the question-answer generation process using a custom configuration:

#### Example

```python
# main.py

from groq_qa_generator import groq_qa

def main():
  # Define a custom configuration
    custom_config = {
        "system_prompt": "custom_system_prompt.txt",
        "sample_question": "custom_sample_question.txt",
        "input_data": "custom_input_data.txt",
        "output_file": "qa_output.txt",
        "model": "llama3-70b-8192",
        "chunk_size": 512,
        "tokens_per_question": 60,
        "temperature": 0.1,
        "max_tokens": 1024
    }

    qa_pairs = groq_qa.generate(custom_config)
    print(qa_pairs)

if __name__ == "__main__":
    main()
```
This allows you to integrate the functionality within any Python application easily.


## 🧠 Technical Overview
1. 🔑 **API Interaction**:
    - **API Key Management**: The API key is securely retrieved from environment variables to ensure safe access to the Groq API.
    - **Client Initialization**: A Groq API client is initialized to enable communication with the service, providing access to powerful models like **LLaMA 70B**.

2. 📄 **Text Processing**:
    - **Loading Prompts and Questions**: The library includes methods to load sample questions and system prompts from specified file paths. These prompts are essential for guiding **LLaMA 70B**'s response.
    - **Generating Full Prompts**: The system prompt and sample question are combined into a complete prompt for the Groq API.

3. 🤖 **QA Pair Generation**:
    - The core process involves taking a list of text chunks and generating question-answer pairs using the Groq API to prompt **LLaMA 70B** `Groq-qa-generator`:
        - Loads the system prompt and sample question.
        - Iterates through each text chunk, creating a full prompt for **LLaMA 70B**.
        - Retrieves the completion from the Groq API, and in turn the model.
        - Streams the completion response and converts it into question-answer pairs.
        - Writes the generated QA pairs to the output file.

## 🧪 Running Tests

To run the project's tests, you can use Poetry and pytest. Follow these steps:

1. 📦 **Install Poetry**: If you haven't already, install Poetry using pip.
   ```bash
   pip install poetry
   ```
2. 🔧 **Install Dependencies**: Navigate to the project directory and install the dependencies.
   ```bash
   cd groq-qa-generator
   poetry install
   ```
3. 🏃 **Run Tests**: Use pytest to run the tests.
   ```bash
   poetry run pytest
   ```

The tests cover various components, including:

- API interaction
- Configuration loading
- Tokenization
- QA generation


## 🤝 How to Contribute

1. 🍴 **Fork the Repository**: Click the "Fork" button at the top-right of the repository page to create your copy.

2. 📥 **Clone Your Fork**: Clone the forked repository to your local machine.
   ```bash
   git clone https://github.com/your-username/groq-qa-generator.git
   ```
3. 🌿 **Create a Branch**: Use a descriptive name for your branch to clearly indicate the purpose of your changes. This helps maintain organization and clarity in the project.
   ```bash
   git checkout -b feature/your-feature-name
   ```
4. 🔧 **Set Up the Environment**: Use Poetry to install the project dependencies.
   ```bash
   cd groq-qa-generator
   poetry install
   ```
5. ⚙️ **Confirm the Environment**: Verify that the virtual environment has been correctly set up and activated.
   ```bash
   poetry shell
   ```
6. 📦 **List Installed Packages**: Ensure that the dependencies have been installed correctly by listing the installed packages.
   ```bash
   poetry show
   ```

## ❓ FAQ
### 📁 Where can I find the generated QA pairs?
The generated QA pairs are saved to the `output_file` defined in your `config.json` file. By default, it saves the output in `qa_output.txt`, located in your home directory’s `.groq_qa` folder (`~/.groq_qa/qa_output.txt`).

To change the output file name, edit the output_file field in your `config.json` file:

```bash
{
    "output_file": "qa_custom_output.txt"
}
```


### 🛠 Can I modify the sample question or system prompt templates?
Yes, both the system prompt and the sample question can be modified. These templates are located in the prompts directory inside the `~/.groq_qa/` folder:

* `system_prompt.txt`: Defines how the model should behave and guide the generation process.
* `sample_question.txt`: Defines how sample questions should be structured.
Feel free to edit these templates to suit your needs.

### 🔄 How do I override default configuration settings?
You can override the default configuration settings in two ways:
1. Edit the `config.json` file located in the `~/.groq_qa/` directory.
2. Pass command-line arguments to override specific settings, for example:

```bash
groq-qa --model llama3-70b-8192 --temperature 0.9
```

### 🎛 How can I increase the randomness of the output?
Increase the `temperature` value in the configuration or pass it as a command-line argument (e.g., `--temperature 0.9`).


### 🐍 Can I use this tool in a larger Python project?
Yes, `groq_qa_generator` can be used as a Python library within your project. Simply import the module and configure it to generate QA pairs programmatically.

## ⚖️ License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

