Announcing the Microsoft Semantic Kernel Elasticsearch connector

In collaboration with the Microsoft Semantic Kernel team, we are announcing the availability of Semantic Kernel Elasticsearch Vector Store connector for Microsoft Semantic Kernel (.NET) users. Semantic Kernel simplifies building enterprise-grade AI agents, including the capability to enhance large language models (LLMs) with more relevant, data-driven responses from a vector store. Semantic Kernel provides an abstraction layer for interacting with various vector stores like Elasticsearch, offering essential features, such as creating, listing, and deleting records.

With Elasticsearch now available as an out-of-the-box connector, Semantic Kernel developers can seamlessly plug in the Elasticsearch vector database with their new or existing AI agents using Semantic Kernel Elasticsearch vector store connector.

You can quickly get started with Elasticsearch locally using start-local for experimentation and move to Elastic Cloud for low-cost infrastructure or on-prem deployment. Whether you’re working with a local, self-hosted, or cloud hosted instance of Elasticsearch, Semantic Kernel’s integration makes using Elasticsearch with Semantic kernel effortless.

The Elasticsearch Connector can be used against Serverless or 8.x versions (signup for Elastic cloud). The connector is valuable not only for basic storage and retrieval use cases but also potentially for future advanced applications. Elasticsearch users have had access to great hybrid search, such as RRF in retrievers (now GA in 8.16). As Semantic Kernel expands its support for sophisticated features in the future, the full suite of what Elastic has to offer will be fully manifested in the Semantic Kernel experience.

The release and timing of any features or functionality described in this post remain at Elastic's sole discretion. Any features or functionality not currently available may not be delivered on time or at all.

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