RAG-Agnostic-Guide

A comprehensive guide and collection of examples for building production-ready Retrieval-Augmented Generation (RAG) systems using various open-source tools. This repository demonstrates different approaches to implementing RAG pipelines, from local LLM deployment to vector stores and evaluation frameworks.

Key Features

• Multiple local LLM deployment options
• Vector store implementations and examples
• RAG evaluation frameworks and metrics
• Production-ready examples
• Comprehensive documentation for each component

Repository Structure

Local LLM Solutions

• Ollama - Easy-to-use tool for running LLMs locally
• LocalAI - OpenAI-compatible API for local model deployment
• LMStudio - Desktop application with user-friendly interface
• vLLM - High-performance inference engine with PagedAttention

Vector Stores & Search

• Milvus Demo - E-commerce semantic search implementation
• OpenLit - Fast inference engine with CUDA optimization

RAG Components

• Basic RAG - Simple RAG implementation example
• Resume Screener - Practical RAG application for resume analysis

Evaluation & Testing

• RAG Evaluator - Tools and metrics for RAG evaluation
• DeepEval Demo - Comprehensive RAG evaluation using DeepEval

Getting Started

Each component has its own setup instructions in its respective directory. Generally, you'll need:

Prerequisites

• Python 3.8+
• Conda (recommended) or pip
• GPU (optional, but recommended for better performance)

General Setup

1. Clone the repository:

git clone https://github.com/yourusername/RAG-Agnostic-Guide.git
cd RAG-Agnostic-Guide

2. Choose a component and follow its specific setup instructions in the respective README.

Documentation

Each component includes detailed documentation covering:

• Setup instructions
• Usage examples
• API references
• Performance considerations
• Best practices

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

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

Acknowledgments

Special thanks to all the open-source projects and their maintainers that make this guide possible:

• Ollama team
• LocalAI community
• LMStudio developers
• vLLM contributors
• Milvus community
• And many others!

For detailed information about specific components, please refer to their respective directories.