Partner highlight: Exploring OpenSearch’s vector database capabilities

Many organizations are turning to machine learning (ML) tools to enhance their search applications. Among those tools are ML embedding models, which can encode the meaning and context of documents, images, and audio into vectors. Those vectors can be stored and indexed within a vector database, then searched to identify similarities. Ultimately, this functionality can be used to augment search with artificial intelligence.

In a recent blog post, OpenSearch partner and contributor Amazon Web Services takes an in-depth look at the vector database capabilities built into OpenSearch and explores how Amazon OpenSearch Service can be used to implement semantic search, Retrieval Augmented Generation (RAG) with large language models (LLMs), recommendation engines, and search rich media. Take a look.