Introducing Retrievers - Search All the Things!
Learn about Elasticsearch retrievers, including Standard, kNN, text_expansion, and RRF. Discover how to use retrievers with examples.
How to choose the best k and num_candidates for kNN search
Learn strategies for selecting the optimal values for `k` and `num_candidates` parameters in kNN search, illustrated with practical examples.
Using Elasticsearch as a vector database for Azure OpenAI On Your Data
Learn how to set up and ingest data into Elasticsearch for use as a vector database with Azure OpenAI On Your Data, allowing you to chat with your private data.
Elasticsearch open inference API adds support for Azure OpenAI embeddings
Elasticsearch open inference API adds support for Azure OpenAI embeddings to be stored in the world's most downloaded vector database.
Elasticsearch open inference API adds Azure AI Studio support
Elasticsearch open inference API now supports Azure AI Studio. Learn how to use Azure AI Studio capabilities with Elasticsearch in this blog.
Elasticsearch delivers performance increase for users running the Elastic Search AI Platform on Arm-based architectures
Benchmarking in preview provides Elasticsearch up to 37% better performance on Azure Cobalt 100 Arm-based VMs.
Elastic Cloud adds Elasticsearch Vector Database optimized profile to Microsoft Azure
Elasticsearch added a new vector search optimized profile to Elastic Cloud on Microsoft Azure. Get started and learn how to use it here.
How to choose between exact and approximate kNN search in Elasticsearch
Learn more about exact and approximate kNN search in Elasticsearch, and when to use each one.
Search relevance tuning: Balancing keyword and semantic search
This blog offers practical strategies for tuning search relevance that can be complementary to semantic search.
Vector similarity measures and scoring
Explore vector similarity measures and scoring in Elasticsearch, including L1 & L2 distance, cosine similarity, dot product similarity and max inner product similarity.
Retrieval of originating information in multi-vector documents
Learn about multi-vector documents in Elasticsearch, their use cases, and how to link original context to a multi-vector document.
Red Hat & Elastic: Red Hat OpenShift AI integration with Elasticsearch
Red Hat OpenShift users can now implement Elasticsearch for vector search & RAG applications via the Red Hat Ecosystem Catalog. Explore this integration here.