AWS | Community | RAG with Elasticsearch and Amazon Bedrock| S02 EP27

Jeff Vestal from Elastic and Ayan Ray from AWS discussed the integration between Elasticsearch and Amazon Bedrock for generative AI applications. Elastic has built solutions for security and observability using retrieval augmented generation with large language models. They have now extended this capability by creating inference endpoints (_inference and _inference_completion) that integrate with Amazon Bedrock's foundation models. This allows any application built on Elasticsearch to easily incorporate natural language capabilities through generative AI.

A key feature is the "semantic text" field type in Elasticsearch, which automatically handles tasks like text chunking, generating embeddings (using built-in models or Amazon Bedrock), and enabling semantic search. This simplifies the process of building retrieval augmented generation applications directly within the Elastic platform. Jeff demonstrated building a basic restaurant recommendation app using this approach along with the Elastic Playground for iterating on prompts and model settings.

The partnership between Elastic and AWS aims to make the generative AI experience seamless for customers. Amazon Bedrock provides access to a wide range of foundation models optimised for different use cases like multi-lingual, cost vs performance tradeoffs, and specialised models like for analysing financial charts. Security and responsible AI are also priorities, with services like Amazon Bedrock's Guardrails to detect hallucinations and toxic outputs.