How To
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.
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.
Go-ing 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. Join us as we use Go to hunt for gophers in Elasticsearch using traditional keyword search.
How to create customized connectors for Elasticsearch
Learn how to create customized connectors for Elasticsearch to simplify your data ingestion process.