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Comprehensive guide to learn RAG from basics to advanced.

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KalyanKS-NLP/rag-zero-to-hero-guide

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This repository serves as a comprehensive guide to learn RAG from basics to advanced.

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Quick links

🧱 RAG Basics Course🚀 RAG Toolkit🩸 RAG Survey Papers
✅ RAG Evaluation Course

RAG Basics Course

TopicDescriptionLink
What is RAG?Explain RAG in with a simple example.Link
Why RAG?Explain the drawbacks of LLMs and how RAG addresses them.Link
How does RAG work?Explain the different steps in RAG - Indexing, Retrieval, Augmentation and Generation.Link
RAG Benefits and ChallengesDiscusses the benefits and challenges of RAG.Link
RAG Must Know TermsDefinitions of RAG must know terms.Link
RAG RoadmapDetailed roadmap to learn RAG from basics to advanced.Link
RAG Developer's StackCovers the various libraries used to build RAG systemsLink
RAG from ScratchRAG implementation from scratch without any frameworks.Link
RAG with LangChainRAG implementation using LangChain framework.Link
Website RAGRAG over a website implemented using LangChain framework.Link
YouTube Video RAGRAG over a YouTube video transcript implemented using LangChain framework.Link
Agentic RAGAgentic RAG system implemented using CrewAI framework.Link

RAG Evaluation Course

TopicDescriptionLink
RAG Evaluation Metrics IntroBrief overview of RAG evaluation metricsLink
RAG Retriever Evaluation MetricsDetailed explanation of RAG retriever evaluation metricsLink
RAG Generator Evaluation MetricsDetailed explanation of RAG generator evaluation metricsLink
RAG Evaluation with RAGASImplementation of RAG evaluation metrics with RAGAS libraryLink
RAG Evaluation with DeepEvalImplementation of RAG evaluation metrics with DeepEval libraryLink
Detect Hallucination in RAGDetection of hallucination in RAG using LLMs and LettuceDetect libraryLink

RAG Toolkit

🔴Frameworks🔴

LibraryDescriptionLink
LangChainLangChain is a framework for developing applications powered by large language models (LLMs).Link
Llama IndexLlamaIndex is a data framework for your LLM applicationsLink
HaystackHaystack is an end-to-end LLM framework that allows you to build applications powered by LLMs, Transformer models, vector search and more.Link
fastRAGResearch framework for efficient and optimized retrieval augmented generative pipelines, incorporating state-of-the-art LLMs and Information Retrieval.Link
LlmwareUnified framework for building enterprise RAG pipelines with small, specialized modelsLink

🟠Research🟠

LibraryDescriptionLink
FlashRAGA Python Toolkit for Efficient RAG Research. This toolkit includes 36 pre-processed benchmark RAG datasets and 16 state-of-the-art RAG algorithms.Link

🟡Data Extraction - Web Scraping🟡

LibraryDescriptionLink
Crawl4AI (Web Scraping)Open-source LLM Friendly Web Crawler & ScrapperLink
ScrapeGraphAI (Web & Document)A web scraping Python library that uses LLM and direct graph logic to create scraping pipelines for websites and local documents (XML, HTML, JSON, Markdown, etc.).Link
Crawlee (Web Scraping)A web scraping and browser automation libraryLink

🟢Data Extraction - Documents🟢

LibraryDescriptionLink
Docling (Document)Docling parses documents and exports them to the desired format with ease and speed.Link
Llama Parse (Document)GenAI-native document parser that can parse complex document data for any downstream LLM use case (RAG, agents).Link
PyMuPDF4LLM (Document)PyMuPDF4LLM library makes it easier to extract PDF content in the format you need for LLM & RAG environments.Link
MegaParse (Document)Parser for every type of documentsLink
ExtractThinker (Document)Document Intelligence library for LLMsLink

🔵Vector Database🔵

LibraryDescriptionLink
SQLite-VecA vector search SQLite extension that runs anywhere!Link
FAISSA library for efficient similarity search and clustering of dense vectors.Link
PGVectorOpen-source vector similarity search for PostgresLink
ChromaThe AI-native open-source embedding database. The fastest way to build Python or JavaScript LLM apps with memory!Link
QdrantHigh-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI.Link
PinconeThe vector database for machine learning applications.Link
WeaviateWeaviate is a cloud-native, open source vector database that is robust, fast, and scalable.Link
MilvusMilvus is a high-performance, cloud-native vector database built for scalable vector ANN searchLink

🟣Chunking🟣

LibraryDescriptionLink
ChonkieRAG chunking library that is lightweight, lightning-fast, and easy to use. The no-nonsense RAG chunking library. This library supports seven different chunking strategies.Link

🟤Rerankers🟤

LibraryDescriptionLink
RerankersA lightweight, low-dependency, unified API to use all common reranking and cross-encoder models. Any new reranking models can be added with very little knowledge of the codebase.Link

🟠Agentic RAG🟠

LibraryDescriptionLink
CrewAIFramework for orchestrating role-playing, autonomous AI agents.Link
AgnoBuild AI Agents with memory, knowledge, tools and reasoning. Chat with them using a beautiful Agent UI.Link
LangGraphBuild resilient language agents as graphs.Link
AutoGenAn open-source framework for building AI agent systems.Link
R2RAgentic Retrieval-Augmented Generation (RAG) with a RESTful API. R2R offers multimodal content ingestion, hybrid search functionality, knowledge graphs, and comprehensive user and document management.Link
VectaraBuild Agentic RAG applications.Link

🟢Graph RAG🟢

LibraryDescriptionLink
GraphRAGA modular graph-based Retrieval-Augmented Generation (RAG) system.Link
Nano GraphRAGA simple, easy-to-hack GraphRAG implementation.Link
FastGraph RAGStreamlined and promptable Fast GraphRAG framework designed for interpretable, high-precision, agent-driven retrieval workflows.Link

🔴Evaluation🔴

LibraryDescriptionLink
RAGCheckerA Fine-grained Framework For Diagnosing RAG.Link
BeyondLLMBeyond LLM offers an all-in-one toolkit for experimentation, evaluation, and deployment of Retrieval-Augmented Generation (RAG) systemsLink
RAGASRagas is your ultimate toolkit for evaluating and optimizing Large Language Model (LLM) applications.Link
GiskardOpen-Source Evaluation & Testing for ML & LLM systems.Link
DeepEvalThe LLM (RAG) Evaluation Framework.Link

RAG Survey Papers

PaperCategoryLink
Retrieval-Augmented Generation for Large Language Models: A SurveyGeneralLink
Retrieval-Augmented Generation for Natural Language Processing: A SurveyGeneralLink
A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future DirectionsGeneralLink
Retrieval-Augmented Generation for AI-Generated Content: A SurveyGeneralLink
A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language ModelsGeneralLink
A Survey on Retrieval-Augmented Text Generation for Large Language ModelsGeneralLink
Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More WiselyGeneralLink
Graph Retrieval-Augmented Generation: A SurveyGraph RAGLink
Agentic Retrieval-Augmented Generation: A Survey on Agentic RAGAgentic RAGLink
Evaluation of Retrieval-Augmented Generation: A SurveyEvaluationLink
Searching for Best Practices in Retrieval-Augmented GenerationRAG Best PracticesLink

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