Demystifying ChatGPT: Different methods for building AI search
In this blog, we look at how ChatGPT works, and consider three approaches to build generative AI like search experiences for specific domains.
How to create customized connectors for Elasticsearch
Learn how to create customized connectors for Elasticsearch to simplify your data ingestion process.
How to get the best of lexical and AI-powered search with Elastic’s vector database
Elastic has all you should expect from a vector database — and much more! You get the best of both worlds: traditional lexical and AI-powered search, including semantic search out of the box with Elastic’s novel Learned Sparse Encoder model.
Introducing Elasticsearch Relevance Engine™ — Advanced search for the AI revolution
Elasticsearch Relevance Engine™ (ESRE) powers generative AI solutions for private data sets with a vector database and machine learning models for semantic search that bring increased relevance to more search application developers.
Relativity uses Elasticsearch and Azure OpenAI to build futuristic search experiences, today
Elasticsearch Relevance Engine is a set of tools for developers to build AI-powered search applications. Relativity, the eDiscovery and legal search tech company, is building next-generation search experience with Elastic and Microsoft Azure Open AI.
Retrieval vs. poison — Fighting AI supply chain attacks
In this post, learn about the supply chain vulnerabilities of artificial intelligence large language models and how the AI retrieval techniques of search engines can be used to fight misinformation and intentional tampering of AI.
Vector search in Elasticsearch: The rationale behind the design
There are different ways to implement a vector database, which have different trade-offs. In this blog, you'll learn more about how vector search has been integrated into Elastisearch and the trade-offs that we made.