# Contract Q&A RAG System

## Overview

This project aims to develop a high-precision legal expert system for contract Q&A using Retrieval-Augmented Generation (RAG). The system leverages advanced natural language processing (NLP) techniques to provide accurate and context-aware answers to questions about legal contracts and integrates a powerful language model with a custom retrieval mechanism to provide accurate and contextually relevant answers to contract-related queries.

## Table of Contents

- [Overview](#overview)
- [Project Structure](#project-structure)
- [Installation](#installation)
- [Usage](#usage)
- [Development](#development)
- [Evaluation](#evaluation)
- [Optimization Techniques](#optimization-techniques)
- [Contributing](#contributing)
- [License](#license)
- [Acknowledgments](#acknowledgments)
- [Contact](#contact)

## Features

- Retrieval-Augmented Generation (RAG) pipeline for contract Q&A
- Customizable retriever and generator components
- Evaluation framework using RAGAS metrics
- Optimization techniques for improved performance

## Project Structure

    Legal_Expert_Contract_Advisor_Using_Precision_RAG/
    ├── data/
    │   ├── raw/
    │   ├── processed/
    │   └── evaluation/
    ├── notebooks/
    │   ├── 1_data_exploration.ipynb
    │   ├── 2_rag_implementation.ipynb
    │   └── 3_evaluation_and_optimization.ipynb
    ├── src/
    │   ├── data/
    │   │   ├── __init__.py
    │   │   ├── preprocess.py
    │   │   └── load_data.py
    │   ├── models/
    │   │   ├── __init__.py
    │   │   ├── retriever.py
    │   │   └── generator.py
    │   ├── evaluation/
    │   │   ├── __init__.py
    │   │   └── metrics.py
    │   └── utils/
    │       ├── __init__.py
    │       └── helpers.py
    ├── tests/
    │   ├── test_data.py
    │   ├── test_models.py
    │   └── test_evaluation.py
    ├── config.yaml
    ├── requirements.txt
    ├── setup.py
    ├── main.py
    ├── .gitignore
    └── README.md

- `data/`: Contains raw and processed data files
- `notebooks/`: Jupyter notebooks for exploration, implementation, and evaluation
- `src/`: Source code for the RAG system
  - `data/`: Data loading and preprocessing scripts
  - `models/`: Retriever and generator model implementations
  - `evaluation/`: Evaluation metrics and scripts
  - `utils/`: Helper functions and utilities
- `tests/`: Unit tests for various components
- `config.yaml`: Configuration file for project settings
- `requirements.txt`: List of project dependencies
- `setup.py`: Setup script for the project
- `main.py`: Main entry point for running the RAG system

## Installation

1. Clone the repository

```sh
git clone https://github.com/dev-abuke/Legal_Expert_Contract_Advisor_Using_Precision_RAG.git
```

2. Navigate to project directory

```sh
cd Legal_Expert_Contract_Advisor_Using_Precision_RAG
```

3. Create a virtual environment

```sh
python -m venv venv
```

4. Activate the environment

```sh
source venv/bin/activate  # On Windows, use venv\Scripts\activate
```

3. Install the required dependencies:

```sh
pip install -r requirements.txt
```

## Usage

1. Prepare your contract data and place it in the `data/raw/` directory.

2. Preprocess the data

```sh
python src/data/preprocess.py
```

3. Run the RAG system

```sh
python main.py
```

4. Evaluate the system performance:

```sh
python src/evaluation/evaluate.py
```

## Development

- Use the Jupyter notebooks in the `notebooks/` directory for exploration and prototyping.
- Implement core functionality in the `src/` directory.
- Add unit tests in the `tests/` directory.
- Use `config.yaml` to manage project settings.

## Evaluation

The system's performance is evaluated using the following metrics

- Retrieval precision and recall
- Answer relevance
- Factual accuracy
- Response coherence

Refer to the evaluation notebook for detailed results and analysis.

## Optimization Techniques

This project explores various optimization techniques, including

1. Advanced embedding models for retrieval
2. Hybrid search methods
3. Query expansion
4. Chunking strategies
5. Prompt engineering

## Contributing

Contributions to improve the system are welcome. Please follow these steps:

1. Fork the repository
2. Create a new branch (`git checkout -b feature/your-feature`)
3. Make your changes and commit them (`git commit -am 'Add new feature'`)
4. Push to the branch (`git push origin feature/your-feature`)
5. Create a new Pull Request

## License

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

## Acknowledgments

- 10 Academy for providing the challenge and learning opportunity
- LizzyAI for the project inspiration and guidance

## Contact

For any queries, please open an issue on this repository or contact [Abubeker Shamil](hello@abubekershamil.com).
