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Star history, last 90 days
Date	Stars
2026-07-24	1314
2026-07-25	1314
2026-07-28	1314
2026-07-30	1314
2026-07-31	1318
2026-08-06	1318

Star history, last 90 days

Date

Stars

2026-07-24

1314

2026-07-25

1314

2026-07-28

1314

2026-07-30

1314

2026-07-31

1318

2026-08-06

1318

# 😎 Awesome Retrieval Augmented Generation (RAG) [![Awesome](https://awesome.re/badge-flat.svg)](https://awesome.re) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/Danielskry/Awesome-RAG) [![Awesome-RAG Agent Plugin](https://img.shields.io/badge/Agent_Plugin-Available-blueviolet)](https://github.com/Danielskry/Awesome-RAG-Agent-Plugin) A curated resource map of tools, frameworks, techniques, and learning materials for building Retrieval-Augmented Generation (RAG) systems. This repository catalogs the RAG ecosystem and provides links to authoritative sources, tutorials, and implementations to help you explore and build RAG applications. Also [available as an Agent Plugin](https://github.com/Danielskry/Awesome-RAG-Agent-Plugin) for VS Code, GitHub Copilot CLI, and Claude Code. ## Overview **Retrieval-Augmented Generation (RAG)** is a sophisticated technique in Generative AI that enhances Large Language Models (LLMs) by dynamically retrieving and incorporating relevant context from external knowledge sources during the generation process. Unlike traditional LLMs that rely solely on pre-trained knowledge, RAG systems enable models to access up-to-date, domain-specific, or proprietary information, significantly improving accuracy, reducing hallucinations, and enabling real-time knowledge integration. ### Key Benefits - **Reduced Hallucinations**: Grounds responses in retrieved factual information - **Domain Adaptation**: Enables LLMs to work with specialized knowledge without fine-tuning - **Real-time Updates**: Incorporates latest information without model retraining - **Cost Efficiency**: More economical than fine-tuning for domain-specific tasks - **Transparency**: Provides source attribution for generated content - **Privacy & Security**: Keeps sensitive data in private knowledge bases ## Content - [ℹ️ General Information on RAG](#ℹ%EF%B8%8F-general-information-on-rag) - [🏗️ Architecture Patterns](#%EF%B8%8F-architecture-patterns) - [🎯 Advanced Approaches](#-advanced-approaches) - [🧰 Frameworks that Facilitate RAG](#-frameworks-that-facilitate-rag) - [🐍 Python Ecosystem for RAG](#-python-ecosystem-for-rag) - [🛠️ Techniques](#-techniques) - [📊 Metrics & Evaluation](#-metrics--evaluation) - [💾 Databases](#-databases) - [🔌 Platform-Specific RAG Implementations](#-platform-specific-rag-implementations) - [🚀 Production Considerations](#-production-considerations) - [💡 Best Practices](#-best-practices) ## ℹ️ General Information on RAG RAG addresses a fundamental limitation of LLMs: their static knowledge cutoff and inability to access external information. Traditional RAG implementations employ a retrieval pipeline that enriches LLM prompts with contextually relevant documents from a knowledge base. For example, when querying about renovation materials for a specific house, the LLM may have general renovation knowledge but lacks details about that particular property. An RAG system can retrieve relevant documents (e.g., blueprints, material specifications, local building codes) to provide accurate, context-aware responses. ### Implementation Resources #### Python Tutorials & Examples - Complete basic [RAG implementation in Python](https://github.com/Danielskry/LangChain-Chroma-RAG-demo-2024): Full-stack RAG example with LangChain and Chroma - [LangChain RAG Tutorial](https://python.langchain.com/docs/use_cases/question_answering/): Comprehensive guide to building RAG applications - [LlamaIndex RAG Tutorial](https://docs.llamaindex.ai/en/stable/getting_started/starter_example/): Getting started with LlamaIndex for RAG - [Haystack RAG Pipeline](https://docs.haystack.deepset.ai/docs/retrieval-augmented-generation): Building RAG pipelines with Haystack - [RAG Techniques](https://github.com/NirDiamant/RAG_Techniques): A comprehensive open-source collection of advanced Retrieval-Augmented Generation techniques as runnable Jupyter notebooks. - [RAG Interview System](https://github.com/ather-techie/rag-i