datawhalechina/all-in-rag resource

Health v2 · maintenance only

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Adoption not part of the score

10622 stars · 5260 forks observed · 2026-08-28

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

An open-source Chinese-language tutorial (Datawhale) providing a full-stack guide to Retrieval-Augmented Generation (RAG) for LLM applications, from fundamentals to advanced practice. It includes hands-on examples using tools like LangChain, LlamaIndex, Milvus, and Neo4j, readable online.

Use cases

• learn how to build RAG applications
• understand retrieval-augmented generation from basics to advanced
• find a hands-on RAG course with code examples
• learn to use LangChain and LlamaIndex for RAG
• build a knowledge base chatbot with vector databases
• study multimodal and graph RAG techniques

When to choose

• you want a structured, free curriculum for learning RAG end to end
• you prefer learning by doing with Python code examples
• you want coverage of the modern RAG stack including Milvus, Neo4j, and multimodal RAG

When to avoid

• you need a production-ready RAG framework or library rather than a tutorial
• you need software with a maintained license for redistribution
• you are looking for non-Chinese-language primary content (though an English README exists)

Facets

learning-resource · maturity active

rag llm-inference search-engine nlp large-language-models tutorials artificial-intelligence python cross-platform rag tutorial open-course langchain llama-index milvus embeddings vector-database chinese datawhale retrieval-augmented-generation

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