Category: Vector Database
Articles tagged Vector Database
Elasticsearch open Inference API adds support for Cohere’s Rerank 3 model
“Learn about Cohere reranking, how to use Cohere's Rerank 3 model with the Elasticsearch open inference API and Elastic's roadmap for semantic reranking.”
Using Cohere embeddings with Elastic-built search experiences
Elasticsearch now supports Cohere embeddings! This blog explains how to use Cohere embeddings with Elastic-built search experiences.
Scaling ML inference pipelines in Elasticsearch: How to avoid issues and troubleshoot bottlenecks
Learn strategies to run Machine Learning (ML) inference pipelines in Elasticsearch, troubleshoot bottlenecks and avoid issues when scaling.
Improving text expansion performance using token pruning
Learn about token pruning and how it boosts the performance of text expansion queries by making them more efficient without sacrificing recall.
Semantic search as service at a search center of excellence
Learn how to implement and scale semantic search as a service for a search Center of Excellence (COE) using ELSER.
How to deploy NLP: Text embeddings and vector search
Using text embeddings and vector similarity search, this blog explains how to run deep learning models for NLP & showcases Elasticsearch's vector search capability.
Avatar assisted & dialogue driven voice to RAG search
Create avatar-assisted voice search experience by integrating speech-to-text, semantic search, RAG and a synthesized avatar for responses.
Optimizing vector distance computations with the Foreign Function & Memory (FFM) API
Learn how to optimize vector distance computations using the Foreign Function & Memory (FFM) API to achieve faster performance.
AI plagiarism: Plagiarism detection with Elasticsearch
Here's how to check for AI plagiarism using Elasticsearch, focusing on use cases with NLP models and Vector Search.
Introducing kNN Query: An expert way to do kNN search
Explore how the kNN query in Elasticsearch can be used and how it differs from top-level kNN search, including examples.
Understanding fused multiply-add (FMA) within vector similarity computations in Lucene
Learn how to use fused multiply-add (FMA) within vector similarity computations in Lucene and discover how FMA can improve performance.
