Today we’re excited to feature the Elastic team to share more about their Semantic Kernel Elasticsearch connector. Read the entire announcement here. I’ll turn it over to Srikanth Manvi to dive into it.
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.
Microsoft Semantic Kernel and Elasticsearch
Semantic Kernel offers developers a flexible framework for building AI agents that enhance LLMs with custom workflows and data. It enables developers to build context-aware, intelligent agents by providing tools for memory storage, skill management, and orchestration across various tasks. With its support for modular and extensible plugins, Semantic Kernel can be adapted to a wide range of applications, making it a great choice for creating robust, scalable AI agents.
The Elasticsearch vector database is essential for developers building AI agents with Microsoft Semantic Kernel as it provides efficient storage, retrieval, and similarity search for high-dimensional data, such as embeddings. In Semantic Kernel — which enables AI agents to process and interpret complex text data — Elasticsearch allows for quick access to similar or related concepts, boosting relevance in search and retrieval tasks. This is critical for applications like recommendation engines, question-answering, or context-aware responses, where AI agents need to handle large amounts of unstructured data and serve accurate results in real time. Elasticsearch vector database allows developers building Semantic Kernel-powered agents to manage large data sets with robust indexing and scalability
Elasticsearch has strong roots in the open source community, recently adding the AGPL license. With the open source availability of Microsoft Semantic Kernel, this creates a powerful combination of enterprise-ready tools. This setup supports building AI agents for production workloads that are adaptable to various deployment and licensing needs.
We are excited to collaborate with Elasticsearch to empower enterprises by integrating their cutting-edge technology with Microsoft Semantic Kernel. This partnership enables current Elasticsearch customers to leverage their existing investments and rapidly harness the power of AI with Semantic Kernel.
Evan Chaki, GM, AI Innovation at Microsoft
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.
What’s next?
- Stay tuned for upcoming Semantic Kernel Elasticsearch connectors for Python and Java in the coming months.
- We’re thrilled to partner with Microsoft to bring features like hybrid search and advanced retrieval strategies to Semantic Kernel developers in the near future.
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