NirDiamant/RAG_Techniques resource

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29232 stars · 3567 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A community-driven collection of 42+ runnable Jupyter notebook tutorials covering Retrieval-Augmented Generation (RAG) techniques from foundational to advanced, built with tools like LangChain and Llama-Index. Each notebook explains the intuition, provides working code, and links references for building accurate, context-rich retrieval systems.

Use cases

• learn how to build a rag pipeline from scratch
• improve retrieval accuracy for llm question answering over documents
• compare rag chunking and embedding strategies with runnable code
• implement agentic rag and query routing techniques
• evaluate rag systems with metrics like faithfulness and relevance
• find tutorials on semantic search with vector databases
• study production rag patterns before building a chatbot over my docs

When to choose

• you want hands-on, notebook-based learning of RAG techniques with code you can run and adapt
• you need a broad survey from basic to cutting-edge RAG methods in one place
• you are prototyping a retrieval-augmented system and want reference implementations in LangChain or Llama-Index

When to avoid

• you need a production-ready RAG framework or library to drop into your app rather than tutorials
• you want a non-Python or non-notebook-based solution
• you need a maintained software package with API stability guarantees - this is educational material, not a library

Facets

learning-resource · maturity active

rag nlp machine-learning search-engine vector-database prompt-engineering large-language-models artificial-intelligence tutorials python cross-platform jupyter-notebooks langchain llama-index embeddings semantic-search agentic-rag educational retrieval-augmented-generation natural-language-processing search

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