Contract Advisor RAG: Building A High-Precision Legal Expert LLM APP

Introduction

This project focuses on building a high precision contract analysis tool using Retrieval Augmented Generation (RAG). This approach leverages the power of Large Language Models (LLMs) to retrieve relevant information from external knowledge sources, in this case, legal contracts. The goal is to create a system that can accurately and efficiently answer questions about contract content, acting as a virtual legal assistant.

Table of Contents

1. Key Features
2. Project Structure
3. Getting Started
4. Installation
5. Usage
6. Testing
7. Contributing
8. License
9. Contact

Key Features

• RAG Framework: Leverages the LangChain framework to seamlessly integrate LLMs with contract data.
• OpenAI GPT Models: Utilizes OpenAI's GPT models for natural language understanding and generation.
• AutoGen Integration: Integration with Microsoft AutoGen to create conversational AI agents for a more interactive user experience.
• Web Interface: Provides a user-friendly web interface for users to interact with the system and ask questions about contract content.
• FastAPI Backend: Uses FastAPI to create a high-performance backend server for handling user requests using using websockets and allow interaction with the RAG pipeline.
• Vector Database: Employs a vector database (e.g., Chroma) to store and retrieve contract information efficiently.
• Multi-Query Retrieval: Generates multiple query variations to improve the chances of finding relevant information.
• Re-ranking: Employs re-ranking mechanisms to prioritize the most contextually relevant information.
• Evaluation using RAGAS: Includes a robust evaluation pipeline using metrics like accuracy, relevance, and response time to assess and improve performance.

Project Structure

project_root/
├── .github/         # GitHub Actions workflows
├── frontend/        # React frontend code
├── backend/         # FastAPI backend code
├── src/             # Source code for RAG pipeline, utils, etc.
├── tests/           # Unit and integration tests
├── README.md        # This file
└── requirements.txt # Project dependencies

Getting Started

Prerequisites

• Python 3.9+
• pip
• Node.js v18+
• npm - for installing frontend dependencies

1. Clone this repo

git clone git@github.com:hillaryke/contract-qa-high-precision-rag.git
cd contract-qa-high-precision-rag

2. Configure the environment

Add your OPENAI_API_KEY to a .env file in the project root directory.

Installation

1. Install dependencies

pip install -r requirements.txt

Usage

1. Start the FastAPI server

Run the command to start the FastAPI server:

make run-server

The server will start at http://localhost:8000/

2. Start the React frontend

make run-frontend

On your browser access the frontend app from http://localhost:3000/

Testing

1. Run tests

To run the tests, use the following command:

make test

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For any questions or support, please contact me through email Hillary K..

Enjoy!