This repository provides a comprehensive, community-driven collection of advanced Retrieval-Augmented Generation (RAG) techniques. It targets AI researchers and practitioners seeking to enhance RAG system accuracy, efficiency, and contextual relevance, offering practical implementations and detailed explanations for each method.

How It Works

The project systematically categorizes and details numerous RAG enhancement strategies, ranging from foundational implementations to sophisticated architectural patterns. It covers query enhancement, context enrichment, advanced retrieval methods, iterative techniques, evaluation frameworks, and explainability. The core approach involves leveraging techniques like hypothetical document embeddings (HyPE), semantic chunking, fusion retrieval, and knowledge graph integration to improve information retrieval and LLM response generation.

Quick Start & Requirements

• Install/Run: Clone the repository (git clone https://github.com/NirDiamant/RAG_Techniques.git) and navigate to specific technique directories for implementation guides.
• Prerequisites: Primarily relies on Python and libraries like LangChain and LlamaIndex. Specific techniques may require additional dependencies or API keys (e.g., OpenAI).
• Resources: Setup time and resource requirements vary per technique; some may benefit from GPU acceleration for embedding or LLM operations.
• Links: Contributing.md for contribution guidelines.

Highlighted Details

• Extensive coverage of 30+ RAG techniques, including foundational, query enhancement, context enrichment, advanced retrieval, iterative, and architectural patterns.
• Detailed explanations and implementation guides for each technique, often referencing specific libraries like LangChain and LlamaIndex.
• Includes advanced concepts like Hypothetical Prompt Embeddings (HyPE) for improved retrieval alignment and reduced runtime overhead.
• Features evaluation methods using libraries like DeepEval and GroUSE, and advanced architectures like Knowledge Graph Integration (Graph RAG).

Maintenance & Community

The project emphasizes community contributions and fosters discussion via a Discord community. Regular updates with the latest advancements are planned.

Licensing & Compatibility

Licensed under a custom non-commercial license. This restricts commercial use and linking within proprietary, closed-source applications.

Limitations & Caveats

The non-commercial license is a significant restriction for many professional use cases. While comprehensive, the repository is a collection of techniques, and integrating them into a production-ready system requires significant engineering effort.