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RAG pipeline

9 shapes and 8 connections, sketched in isketch.

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RAG pipeline Documents PDFs, markdown, tickets Loader Parses and splits into chunks Embedder Batched API calls Vector store Chunks with embeddings Question From the chat UI Retriever Top-k similarity search Reranker Cheap cross-encoder pass LLM Answers with the retrieved context Citations Chunk ids shown to the user

The .flow source

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examples/templates/rag-pipeline.flow
title: RAG pipeline
note: Ingest documents, embed chunks, retrieve, answer with citations.

docs = document "Documents" -- PDFs, markdown, tickets
loader = process "Loader" -- Parses and splits into chunks
embedder = process "Embedder" -- Batched API calls
vector = database "Vector store" -- Chunks with embeddings
query = terminal "Question" -- From the chat UI
retriever = process "Retriever" -- Top-k similarity search
reranker = process "Reranker" -- Cheap cross-encoder pass
answer = process "LLM" -- Answers with the retrieved context
citations = document "Citations" -- Chunk ids shown to the user

docs -> loader
loader -> embedder
embedder -> vector
query -> retriever
vector -> retriever
retriever -> reranker
reranker -> answer
answer -> citations