Category: Vector Database
Articles tagged Vector Database
Preconditioning Vectors: Making Elasticsearch VectorDB Better Binary Quantization work for every vector
Modern quantization techniques can hurt recall when using older models or embeddings that aren’t normally distributed. Learn how preconditioning fixes these vectors through random orthogonal projection, making BBQ more effective and recovering recall.
How we built Elasticsearch simdvec to make vector search one of the fastest in the world
How we built Elasticsearch simdvec, the hand-tuned SIMD kernel library behind every vector search query in Elasticsearch.
Unsupervised document clustering with Elasticsearch + Jina embeddings
A practical, reproducible approach to unsupervised document clustering with Elasticsearch and Jina embeddings.
When TSDS meets ILM: Designing time series data streams that don't reject late data
How TSDS time bounds interact with ILM phases; and how to design policies that tolerate late-arriving metrics.
LINQ to Elasticsearch ES|QL: Write C#, query Elasticsearch
Exploring the new LINQ to Elasticsearch ES|QL provider in the Elasticsearch .NET client, which allows you to write C# code that’s automatically translated to ES|QL queries.
Fast vs. accurate: Measuring the recall of quantized vector search
Explaining how to measure recall for vector search in Elasticsearch with minimal setup.
Adaptive early termination for HNSW in Elasticsearch
Introducing a new adaptive early termination strategy for HNSW in Elasticsearch.
Elasticsearch vector search is up to 8x faster than OpenSearch
Exploring filtered vector search benchmarks of OpenSearch vs. Elasticsearch and why vector search performance is critical for context-engineered systems.
Elasticsearch 9.3 adds bfloat16 vector support
Exploring the new Elasticsearch element_type: bfloat16, which can halve your vector data storage.
How to defend your RAG system from context poisoning
How context engineering techniques prevent context poisoning in LLM responses.
