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RAG pipeline

Separate preparing the knowledge from answering a question.

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RAG pipeline with documents prepared into a searchable index above, and a question retrieving context for a model below. — Use this starter to explain a document-answering feature without squeezing every implementation detail into one diagram.

Make it fit your system.

Rename Documents and Search index for your own knowledge sources.
Add your retrieval method: keyword, vector or hybrid search.
Label the checks or citations your application uses before showing an answer.

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Preparation divides source documents into useful chunks and adds them to a searchable index. At question time, retrieval selects relevant content and the application supplies it with the question to a language model. The model generates an answer using that context. Vector search commonly adds embeddings; keyword and hybrid retrieval are also possible. Retrieved material can improve grounding, but does not guarantee a correct answer.

Reference: Microsoft: Retrieval-augmented generation.

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RAG pipeline

Make it fit your system.

Watch the explanation unfold.

Try it with your own idea.