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.Keep reading
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.