Elastic has introduced hybrid search capabilities to Microsoft's Semantic Kernel project, becoming the first vector database to offer this functionality for .NET developers. The integration allows users to combine traditional keyword-based search with semantic search techniques to retrieve more relevant and accurate results.
The announcement marks a significant development for developers building enterprise AI applications, enabling them to incorporate advanced search strategies within the Semantic Kernel framework.
"What sets Elastic apart is our powerful hybrid search capabilities, which our customers can run at vast scale," said Ken Exner, chief product officer at Elastic.
"As the first vector database to bring hybrid search capability to Microsoft Semantic Kernel, our .NET users benefit from significantly improved retrieval quality, making it easier for them to build enterprise-grade AI applications."
Hybrid search is a method that combines lexical search—based on exact word matches—with semantic search, which understands the meaning behind queries.
This dual approach increases the likelihood of surfacing the most relevant documents, particularly in applications such as retrieval-augmented generation (RAG), where context and precision are essential.
Elastic's hybrid search approach uses an algorithm called Reciprocal Rank Fusion (RRF) to blend results from different search techniques.
The algorithm assigns a score to each document based on its ranking in both lexical and semantic result lists. The scores are then combined to produce a final, unified list of results, ordered by relevance.
In a demonstration provided by Elastic, two separate searches—one lexical and one semantic—returned different sets of results.
By applying the RRF algorithm, the final list prioritised documents that ranked highly in both methods. For instance, a document that appeared near the top in both searches received a higher overall score than those that were prominent in just one, resulting in more balanced and accurate outcomes.
Developers working within the .NET environment can now take advantage of this approach when using Elastic's integration with Semantic Kernel.
They can specify search keywords and semantic vectors in a single query, allowing both types of search to run concurrently. Results can also be filtered to include or exclude documents based on specific criteria, such as content relevance or metadata.
Elastic illustrated how this feature could be applied in a business context, using a hotel search example. Developers could query for hotels described as "downtown" or "luxury", while also providing a semantic vector to capture intent. They could then filter the results further to focus only on beachfront properties, refining the accuracy of the final output.
Recent updates to Microsoft Semantic Kernel have improved the way filters are applied, introducing support for more advanced conditions, such as comparisons and logical operations.
Elastic noted that while filtering capabilities have expanded, some complex operations are still being developed.
While the default combination method is RRF, Elastic also supports alternative retrieval strategies for developers who want to fine-tune their search performance. Options such as the Linear Retriever offer a simpler way to customise how search scores are merged, providing flexibility for different use cases.
Elasticsearch has supported hybrid search since version 8.8.0, but this marks the first time the capability is available in the .NET connector for Semantic Kernel. The integration is now generally available for use.
The collaboration with Microsoft's Semantic Kernel team reflects Elastic's ongoing investment in tools that support generative AI applications and improved information retrieval. The company confirmed that future updates will expand Semantic Kernel support further, continuing to enhance the developer experience.
