FullStackRetrieval-com/RetrievalTutorials resource

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1482 stars · 236 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter Notebook tutorials and guides teaching retrieval and Retrieval Augmented Generation (RAG) for LLM applications, from FullStackRetrieval.com. It covers the full retrieval landscape with code, videos, and evaluations, mostly in Python using OpenAI and popular libraries like LangChain and LlamaIndex.

Use cases

• learn how to build a RAG pipeline from scratch
• understand chunking strategies for documents before embedding
• improve retrieval quality for my LLM chatbot
• compare LangChain vs LlamaIndex for retrieval
• evaluate whether retrieved context actually helps my model
• get an overview of the advanced retrieval landscape
• tutorials on embeddings and vector search for beginners

When to choose

• you are learning RAG and retrieval concepts from the ground up
• you want framework-agnostic explanations with runnable Python notebooks
• you need a mental model of the retrieval process before picking tools

When to avoid

• you need a production-ready RAG framework or library rather than educational material
• you want a maintained tool with a license and active releases
• you need non-Python examples or fully open-source model coverage

Facets

learning-resource · maturity maintenance

rag nlp machine-learning llm-inference data-science large-language-models artificial-intelligence tutorials python cross-platform retrieval rag-tutorials jupyter-notebooks education llm-context embeddings chunking openai retrieval-augmented-generation natural-language-processing

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