🤖 Build a RAG Application End-to-End

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This open-source ❤️ repo gives you everything you need to learn and build a Retrieval-Augmented Generation (RAG) application from scratch.

It’s a complete, hands-on resource that walks through the entire RAG pipeline covering both the fundamentals and advanced techniques like multi-querying, routing, and custom retrieval workflows.

Each notebook is designed as a practical guide, helping you move step-by-step from understanding the basics to experimenting with real-world implementations.

What it covers:

Query Construction - Learn how to translate natural language into structured queries across SQL, Cypher, or vector search. (Text-to-SQL, Text-to-Cypher, Self-Query Retriever)
Query Translation - Improve retrieval quality through decomposition and rephrasing. (Multi-query, RAG-Fusion, Hypothetical Docs)
Routing - Dynamically select the most relevant database or embedding context for each query.
Retrieval - Use advanced techniques like Re-Rank, RankGPT, RAG-Fusion, or CRAG to refine results - even pull live data from external sources.
Indexing - Explore multi-representation embeddings, hierarchical summarization, and optimization methods. (RAPTOR, CoLBERT, fine-tuning)
Generation - Enhance response quality with iterative reasoning and retrieval loops using Self-RAG and RRR.

If you want to understand RAG inside out and build your own system from the ground up this repo is the perfect starting point.

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