Multimodal RAG with Amazon Bedrock Data Automation & Knowledge Bases

Part 8: Hands-on Tutorials · Hands-on Tutorials

Read the full tutorial: Multimodal RAG with Amazon Bedrock Data Automation & Knowledge Bases ↗

Link verified 2026-08-23.

This tutorial builds a RAG pipeline where Bedrock Data Automation extracts text and structure from multimodal documents (PDFs, images), a Bedrock Knowledge Base chunks and embeds them into a vector store, and an LLM answers questions grounded in that retrieved context instead of its training data alone.

It’s the working implementation behind Architecting for AI: Feature Stores, Vector Databases, RAG & Governance Guardrails — the same embeddings-and-retrieval architecture, deployed rather than described.

flowchart LR
    A[Multimodal documents] --> B[Bedrock Data Automation - extract/parse]
    B --> C[Bedrock Knowledge Base - chunk + embed]
    C --> D[(Vector store)]
    E[User query] --> F[Retrieve relevant chunks]
    D --> F
    F --> G[LLM generates grounded answer]

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