Legal_Expert_Contract_Advisor_Using_Precision_RAG

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
• Project Structure
• Installation
• Usage
• Development
• Evaluation
• Optimization Techniques
• Contributing
• License
• Acknowledgments
• 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

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

2. Navigate to project directory

cd Legal_Expert_Contract_Advisor_Using_Precision_RAG

3. Create a virtual environment

python -m venv venv

4. Activate the environment

source venv/bin/activate  # On Windows, use venvScriptsactivate

3. Install the required dependencies:

pip install -r requirements.txt

Usage

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

2. Preprocess the data

python src/data/preprocess.py

3. Run the RAG system

python main.py

4. Evaluate the system performance:

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 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.