Discover the recent enhancements and optimizations that notably improve vector search performance in Elasticsearch & Lucene vector database.

Elasticsearch and Lucene report card: noteworthy speed and efficiency investments

Harnessing Lucene's architecture for multi-threaded search

Accelerating multi-graph vector search

Harnessing Java's latest advancements for ludicrous speed

Maximizing memory efficiency with scalar quantization

Achieving seamless compression with minimal impact on recall

Paving the way for binary quantization

Multi-vector integration in Lucene and Elasticsearch

Wrapping up (for now)

Making Elasticsearch and Lucene the best vector database

Making Elasticsearch and Lucene the best vector database: up to 8x faster and 32x efficient

Using text embeddings and vector similarity search, this blog explains how to run deep learning models for NLP & showcases Elasticsearch's vector search capability.

Deploying NLP Models, generating text embeddings & running vector search

1. Deploy a text embedding model

2. Loading initial data

3. Creating pipeline for text embeddings

4. Reindex data through text embeddings pipeline

5. Vector similarity search

6. Quick verification

Trying it out

What is vector similarity search?

How to deploy NLP: Text embeddings and vector search

Explore how the kNN query in Elasticsearch can be used and how it differs from top-level kNN search, including examples.

kNN search as a top-level section

Introducing kNN query in Elasticsearch

kNN query API examples

Elasticsearch kNN Query: An expert way to do kNN search

Introducing kNN Query: An expert way to do kNN search

Explore multi-graph vector search in Lucene and discover how sharing information between segment searches enhances search speed.

The previous state of multi-graph vector search in Lucene

Speeding up multi-graph vector search by sharing information between segment searches

Author

Mayya Sharipova

Articles

Making Elasticsearch and Lucene the best vector database: up to 8x faster and 32x efficient

How to deploy NLP: Text embeddings and vector search

Introducing kNN Query: An expert way to do kNN search

Speeding Up Multi-graph Vector Search