Vector Search (kNN) Implementation Guide - API Edition
Follow along with code examples and a Jupyter notebook to quickly get up and running with kNN vector search in Elasticsearch
Chunking Large Documents via Ingest pipelines plus nested vectors equals easy passage search
In this post we'll show how to easily ingest large documents and break them up into sentences via an ingest pipeline so that they can be text embedded along with nested vector support for searching large documents semantically. Generated image of a chonker.
Finding your puppy with Image Search
Have you ever been in a situation where you found a lost puppy on the street and didn’t know if it had an owner? Learn how to do it with vector search or image search.
Using hybrid search for gopher hunting with Elasticsearch and Go
Just like animals and programming languages, search has undergone an evolution of different practices that can be difficult to pick between. In the final blog of this series, Carly Richmond and Laurent Saint-Félix combine keyword and vector search to hunt for gophers in Elasticsearch using the Go client.
Finding gophers with vector search in Elasticsearch and Go
Just like animals and programming languages, search has undergone an evolution of different practices that can be difficult to pick between. Join us on part two of our journey hunting gophers in Go with vector search in Elasticsearch.
Elasticsearch as a GenAI Caching Layer
Explore how integrating Elasticsearch as a caching layer optimizes Generative AI performance by reducing token costs and response times, demonstrated through real-world testing and practical implementations.
Lexical and Semantic Search with Elasticsearch
In this blog post, you will explore various approaches to retrieving information using Elasticsearch, focusing specifically on text: lexical and semantic search.
Multilingual vector search with the E5 embedding model
In this post we'll introduce multilingual vector search. We'll use the Microsoft E5 multilingual embedding model, which has state-of-the-art performance in zero-shot and multilingual settings. We'll walk through how multilingual embeddings work in general and then how to use E5 in Elasticsearch.
Adding passage vector search to Lucene
Discover how we added passage vectors to Apache Lucene, the benefits of doing so, and how existing Lucene structures were used to create an efficient retrieval experience.
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.
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.
Enhancing chatbot capabilities with NLP and vector search in Elasticsearch
In this blog post, we will explore how vector search and NLP work to enhance chatbot capabilities and demonstrate how Elasticsearch facilitates the process. Let's begin with a brief overview of vector search.
How to deploy NLP: Text Embeddings and Vector Search
Taking Text Embeddings and Vector Similarity Search as the example task, this blog describes the process for getting up and running using deep learning models for Natural Language Processing, and demonstrates vector search capability in Elasticsearch
Text similarity search with vector fields
This post explores how text embeddings and Elasticsearch’s new dense_vector type could be used to support similarity search.