Category: Vector Database
Articles tagged Vector Database
Lighter by default: Excluding vectors from source
Elasticsearch now excludes vectors from source by default, saving space and improving performance while keeping vectors accessible when needed.
Beyond similar names: How Elasticsearch semantic text exceeds OpenSearch semantic field in simplicity, efficiency, and integration
Comparing Elasticsearch semantic text and OpenSearch semantic field in terms of simplicity, configurability, and efficiency.
Using Direct IO for vector searches
Using rescoring for kNN vector searches improves search recall, but can increase latency. Learn how to reduce this impact by leveraging direct IO.
Elasticsearch now with BBQ by default & ACORN for filtered vector search
Explore how Elasticsearch's vector search now delivers better results faster, and at a lower cost.
Diversifying search results with Maximum Marginal Relevance
Implementing the Maximum Marginal Relevance (MMR) algorithm with Elasticsearch and Python. This blog includes code examples for vector search reranking.
Semantic text is all that and a bag of (BBQ) chips! With configurable chunking settings and index options
Semantic text search is now customizable, with support for customizable chunking settings and index options to customize vector quantization, making semantic_text more powerful for expert use cases.
K-means for building vector indices
We discuss optimizing k-means to efficiently create high quality vector indices
Elasticsearch open inference API adds support for IBM watsonx.ai rerank models
Explore how to use IBM watsonx™ reranking when building semantic search experiences in Elasticsearch.
Optimizing scalar quantization with sparse preconditioners
We discuss a sparse preconditioner to apply to vectors which results in more stable quantization performance with respect to data distribution.
Get set, build: Red Hat OpenShift AI applications powered by Elasticsearch vector database
Learn how to use Elasticsearch with the ‘AI Generation’ Validated Pattern to rapidly deploy secure, GitOps-driven RAG applications on Red Hat OpenShift.
Mapping embeddings to Elasticsearch field types: semantic_text, dense_vector, sparse_vector
Discussing how and when to use semantic_text, dense_vector, or sparse_vector, and how they relate to embedding generation.
