TLDR DevOps 2026-10-28

Your data is already in Elasticsearch — use Elastic Cloud to power Retrieval Augmented Generation (RAG). Skip the new database and add vector storage and LLM-powered search without changing your stack.

• Reuse your data layer: Store vector embeddings directly in Elasticsearch indices alongside your existing data — no separate vector database required
• Wire up RAG end to end: Connect Elastic Cloud, LangChain, and Amazon Bedrock with a working Python pipeline from document ingestion to LLM query
• Access models through a unified API: Use Elasticsearch's Open Inference API to call Amazon Bedrock models for chat completion and text embeddings

Follow the step-by-step tutorial and start asking questions of your data today.

Build a RAG pipeline on AWS >

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