RAG / LLM Application Architecture
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The prompt behind this diagram
A retrieval-augmented generation (RAG) application architecture: user query goes to a web app, backend embeds the query with an embedding model, searches a vector database (with documents previously chunked, embedded and indexed from a document store), retrieved context plus query sent to an LLM API, response streamed back to the user. Include an ingestion pipeline branch showing document upload, chunking, embedding and indexing.
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