ADD RAG TO YOUR ELASTIC STACK

Summary from the TLDR DevOps newsletter

Your data is already in Elasticsearch — use Elastic Cloud [5] 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.