Contract_advisor_RAG

Introduction

This project builds a question-and-answer (Q&A) pipeline for extracting information from legal contracts. It leverages the power of RAG (Retriever-Augmented Generator) with Langchain and Large Language Models (LLMs) to provide accurate and efficient access to contractual knowledge.

Project Goal

• Create a high-performing, accurate, and user-friendly Q&A system for contracts.
• Achieve target performance metrics in terms of accuracy, relevance, and response time.

Requirements

• Python 3.8+
• Langchain library
• A dataset of contracts and corresponding Q&A pairs

Installation

1. Clone this repository: git clone https://github.com/Misganaw-Berihun/Contract_advisor_RAG
2. Create a virtual environment: python -m venv venv
3. Activate the virtual environment: source venv/bin/activate (Windows: venv\Scripts\activate)
4. Install dependencies: pip install -r requirements.txt