Category: Vector Database
Articles tagged Vector Database
How we built a persistent agent memory layer on Elasticsearch with 0.89 recall and zero tenant leaks
Discover the architecture behind a persistent, multi-tenant agent memory layer on Elasticsearch: three indices, hybrid retrieval with RRF and a reranker, supersession, decay, and per-user DLS isolation. R@10 0.89 across 168 questions. Full open-source implementation included.
Your AI agent reads the fine print: building a RAG pipeline over EU regulations with Elasticsearch and OGX
Learn how to configure Elasticsearch as an OGX vector store, ingest EU regulation PDFs and build a Python RAG agent that runs hybrid BM25 and vector search with source-level citations.
Best practices for building a modern app with vector search
Exploring six vector search tips for building modern AI search applications entirely on Elasticsearch, with an opinionated rationale at each architectural decision.
Elasticsearch simdvec deep-dive: Walking the memory tightrope to 2x better vector throughput
A deep dive into four optimizations (cascade unrolling, batch prefetching, dim-axis unrolling, a structural refactor) that pushed Elasticsearch simdvec to 2x vector throughput by working with the CPU, not against it.
Elasticsearch DiskBBQ: 40% faster vector scoring with native SIMD Blocks
A deep dive into how DiskBBQ's block layout, doc ID compression modes and native SIMD kernels combine to deliver 40% improved vector scoring throughput for DiskBBQ in 9.4.
Multilingual image search with Jina CLIP v2 and Elasticsearch
Build a multilingual image search system using Jina CLIP v2 and Elasticsearch. Query your image collection in 89 languages with no translation pipeline, and use Matryoshka Representations to cut index size by 75%
How we doubled vector search throughput on Elasticsearch Serverless
How we brought Elasticsearch's native SIMD scoring engine to serverless, and why serverless is where vector search innovation happens next.
Cutting Elasticsearch DiskBBQ query quantization time by 5x
See how asymmetric quantization cuts DiskBBQ query quantization overhead from about 20% to 4% with little recall impact.
Up to 3x faster stored-vector queries in Elasticsearch
Elasticsearch 9.4 provides a simpler way to search with vectors stored in an Elasticsearch index, with up to 3x lower latency.
Elasticsearch Vector DiskBBQ filter search is now 3–5x faster
Learn how Elasticsearch 9.4 makes restrictive filtered DiskBBQ vector search 3–5x faster and more stable by avoiding wasted centroid and postings-list work when selectivity is high.
Elasticsearch's BBQ vs. TurboQuant: 10–40× faster on CPU and lower ranking noise
A head-to-head look at Elasticsearch BBQ and TurboQuant, including throughput, ranking accuracy, and why uniform quantization wins for CPU vector search with up to 40× faster comparisons and smaller ranking noise.
