May 13, 2025
Mapping embeddings to Elasticsearch field types: semantic_text, dense_vector, sparse_vector
Discussing how and when to use semantic_text, dense_vector, or sparse_vector, and how they relate to embedding generation.
April 23, 2025
How to implement Better Binary Quantization (BBQ) into your use case and why you should
Exploring why you would implement Better Binary Quantization (BBQ) in your use case and how to do it.
April 15, 2025
Elasticsearch BBQ vs. OpenSearch FAISS: Vector search performance comparison
A performance comparison between Elasticsearch BBQ and OpenSearch FAISS.
April 7, 2025
Speeding up merging of HNSW graphs
Explore the work we’ve been doing to reduce the overhead of building multiple HNSW graphs, particularly reducing the cost of merging graphs.
March 20, 2025
Scaling late interaction models in Elasticsearch - part 2
This article explores techniques for making late interaction vectors ready for large-scale production workloads, such as reducing disk space usage and improving computation efficiency.
March 19, 2025
Exploring GPU-accelerated Vector Search in Elasticsearch with NVIDIA
Powered by NVIDIA cuVS, the collaboration looks to provide developers with GPU-acceleration for vector search in Elasticsearch.
March 18, 2025
Searching complex documents with ColPali - part 1
The article introduces the ColPali model, a late-interaction model that simplifies the process of searching complex documents with images and tables, and discusses its implementation in Elasticsearch.
March 13, 2025
Semantic Text: Simpler, better, leaner, stronger
Our latest semantic_text iteration brings a host of improvements. In addition to streamlining representation in _source, benefits include reduced verbosity, more efficient disk utilization, and better integration with other Elasticsearch features. You can now use highlighting to retrieve the chunks most relevant to your query. And perhaps best of all, it is now a generally available (GA) feature!
March 12, 2025
Unifying Elastic vector database and LLM functions for intelligent query
Leverage LLM functions for query parsing and Elasticsearch search templates to translate complex user requests into structured, schema-based searches for highly accurate results.