RAG-LLM: Retrieval-Augmented Generation System from Scratch

This project implements a Retrieval-Augmented Generation (RAG) system built completely from scratch.

Overview

RAG-LLM combines the power of:

• Custom-built retrieval systems to fetch relevant context from a knowledge base
• Large Language Models for context-aware text generation

Features

RAG Implementation

• Document Processing: Pipeline for ingesting, cleaning, and chunking documents
• Vector Embedding: Custom embedding system for semantic understanding of content
• Efficient Retrieval: Advanced retrieval mechanisms to find the most relevant information
• Context Integration: Seamless merging of retrieved context with user queries
• Response Generation: High-quality responses based on retrieved information

Architecture

RAG-LLM/
├── data/               # Document storage and vector databases
├── models/             # Model implementations and configs
├── src/
│   ├── embeddings/     # Vector embedding components
│   ├── retrieval/      # Retrieval system implementation
│   ├── generation/     # Text generation components
│   ├── stable_diff/    # Stable Diffusion API integration
│   └── utils/          # Helper functions and utilities
└── api/                # API endpoints for using the system