Search

No. Elastic’s BM25 textual search algorithm, its scalable vector database, semantic search, and reciprocal rank fusion (RRF) hybrid scoring all come ready to use with Elastic Search. Elastic even has its own semantic search model, the Elastic Learned Sparse Encoder, that can be used out-of-the-box.

Yes. Elastic is the world’s most downloaded, scalable vector database that lets developers create, store, and search vector embeddings. But that’s not all. Elastic’s search platform also includes everything you need to build outstanding search experiences including aggregations, filtering and faceting, auto-complete, multiple retrieval methods, and the flexibility to integrate with your own or third-party transformer models.

You need a search product if you use a large language model because it’s a cost and time efficient approach for achieving more accurate results in your generative AI experience. By searching over your domain-specific data, you can minimize hallucinations from the large language model by providing highly relevant search results as additional context and limit the time it takes to fine tune the model. Using retrieval augmented generation (RAG), Elastic lets you query proprietary data to get more accurate, real-time results, requiring fewer compute and storage resources. Elastic also controls search access with its document-level security.

If you’re a developer, one of the best places to get technical and practical information about implementing Elastic in blogs, examples and apps is on Elastic Search Labs. It’s created and maintained by the technologists who work at Elastic for the technologists who use Elastic. Visit Elastic Search Labs to learn about the latest in generative AI, vector search, and machine learning research.