In collaboration with

Skip the new database. If your data is already in Elasticsearch, you can add vector storage and large language model (LLM)-powered search without changing your stack.

Elastic Cloud combines with LangChain and Amazon Bedrock to give you a complete RAG implementation path:

• Reuse your data layer: Store vector embeddings directly in Elasticsearch indices alongside existing search data — no separate vector database to provision or manage
• 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

Build a RAG pipeline on AWS ›

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