Intro_to_Rag

Retrieval-Augmented Generation (RAG) from Scratch

This project demonstrates a simple implementation of a Retrieval-Augmented Generation (RAG) system using Python. It includes cosine similarity calculations to find the most relevant document from a corpus based on a user query and integrates with a local LLM (e.g., LLAMA2) for generating responses.

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Table of Contents

• Overview
• Features
• Installation
• Usage
• Code Structure
• API Integration
• Contributing
• License

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Overview

This project implements a basic RAG pipeline:

1. A corpus of documents is stored.
2. A user query is compared with the corpus using cosine similarity.
3. The most relevant document is retrieved.
4. The retrieved document and user query are passed to a local LLM for generating a response.

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Features

• Cosine Similarity: Calculates the similarity between the user query and documents in the corpus.
• Document Retrieval: Identifies the most relevant document from the corpus.
• LLM Integration: Uses a local LLM (e.g., LLAMA2) to generate responses based on the retrieved document.
• Customizable Corpus: Easily update the corpus with new documents.

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Installation

1. Clone the repository:

   git clone https://github.com/your-username/rag-from-scratch.git
   cd rag-from-scratch