Category: Vector Database
Articles tagged Vector Database
How Elasticsearch's batched query phase improves search performance at scale
The batched query phase can cut search execution time in half by reducing transport overhead and better distributing reduction work across the cluster.
The mystery stress your heap chart can't see: AutoOps now watches vector off-heap memory
Dense vectors use off-heap memory your heap chart never shows. AutoOps detects memory pressure before vector RAM stress causes OOM.
One field, every modality: how Elasticsearch's semantic field indexes and searches images, audio, video and PDFs automatically
The semantic field turns images, audio, video, PDFs and text into multimodal embeddings at ingest time. Describe a scene and find the matching image or use a video frame to surface related clips, all from one Elasticsearch field.
17% faster search, zero config: auto-calibrating vector quantization in Elasticsearch
Automatic calibration at merge time picks vector quantization parameters for each segment by predicting recall from a small sample. Here's how we built it into Elasticsearch's merge path.
How Elasticsearch auto-tunes vector quantization to hit your recall target
Learn the geometric model that lets Elasticsearch predict recall with R² > 0.98 accuracy and auto-select vector quantization parameters from a small data sample.
4 NVIDIA AI tasks, 1 Elasticsearch API: Embeddings, chat, completion, and rerank
Set up NVIDIA hosted models in Elasticsearch with one API key and a model ID. No custom integration code needed.
A picture is worth 1.5x the words: What we learned benchmarking product search embeddings
We benchmarked two embedding models on 5,000 real products and found that combining image and text beats either alone by up to 50%. Here's the data and the model that won.
The disk that never woke up: what actually decided our Qdrant vector search benchmark rematch
On the same hardware, Elasticsearch and Qdrant land in the same range at 56 QPS. The io_uring disk scorer and memory claims turned out to be the two things that mattered least.
How BBQ shrinks Jina v5 embeddings by 29x without losing recall in Elasticsearch
A hands-on test comparing BBQ and float32 vector indices in Elasticsearch, measuring memory, disk and recall@10 across five languages.
Short queries, formal documents: how HyDE improved semantic search precision by 50% in Elasticsearch
HyDE boosts semantic search precision and recall by 50% on short queries. Here's how to implement it in Elasticsearch with the Inference API and semantic_text.
A simdvec deep-dive: How Elasticsearch uses neural-net and video-codec CPU instructions for vector search
Four ways Elasticsearch's vector search engine reuses neural-network, video-codec and cryptography CPU instructions for up to 6x speedups; with the math, the failed attempts and the benchmarks.
