BestHub
Sign in
Tagged articles

dense_vector

9 articles · Page 1 of 1
Mingyi World Elasticsearch
Mingyi World Elasticsearch
Sep 21, 2026 · Databases

Easysearch 2.4.0 Vector Search: Verified Guide to Indexing, Querying & Hybrid Search Pitfalls

This article provides a step-by-step verified guide to implementing vector search in Easysearch 2.4.0, covering index creation with dense_vector fields, data ingestion with embeddings, k-NN query pitfalls including the mandatory 'k' parameter, and the critical distinction between compound queries and RRF-based hybrid search for combining keyword and semantic search.

EasysearchHNSWRRF
0 likes · 10 min read
Easysearch 2.4.0 Vector Search: Verified Guide to Indexing, Querying & Hybrid Search Pitfalls
Tech Freedom Circle
Tech Freedom Circle
Nov 5, 2025 · Artificial Intelligence

Elasticsearch: BM25, TF‑IDF, Dense Vectors, kNN, L2 & Cosine Distances, RRF

This article provides a comprehensive technical guide to Elasticsearch’s core retrieval models—BM25 and TF‑IDF—while detailing modern vector‑based search using dense_vector, kNN, L2 and cosine distances, and demonstrates how to combine keyword and semantic results through hybrid search and Reciprocal Rank Fusion (RRF) with practical configuration examples.

BM25ElasticsearchKNN
0 likes · 42 min read
Elasticsearch: BM25, TF‑IDF, Dense Vectors, kNN, L2 & Cosine Distances, RRF
Mingyi World Elasticsearch
Mingyi World Elasticsearch
Apr 8, 2025 · Backend Development

Boost Elasticsearch 8.x Search with Vector Embeddings

This article explains how vector embeddings enhance Elasticsearch 8.x search, walks through the concepts of dense vectors, shows step‑by‑step Python and Logstash pipelines for generating and storing embeddings, compares their pros and cons, and offers guidance on selecting the right approach for large‑scale log data.

ElasticsearchKNNLogstash
0 likes · 12 min read
Boost Elasticsearch 8.x Search with Vector Embeddings
Tencent Technical Engineering
Tencent Technical Engineering
Feb 21, 2025 · Databases

Understanding Vector Storage and Optimization in Elasticsearch 8.16.1

The article explains how Elasticsearch 8.16.1 stores dense and sparse vectors using various file extensions, compares flat and HNSW index formats, shows how disabling doc‑values removes redundant column‑store copies, and demonstrates scalar and binary quantization—including a quantization‑only mode—that can cut storage to roughly 9 percent while preserving search accuracy.

ElasticsearchHNSWIndex Optimization
0 likes · 32 min read
Understanding Vector Storage and Optimization in Elasticsearch 8.16.1
System Architect Go
System Architect Go
Apr 15, 2022 · Artificial Intelligence

Elasticsearch Vector Search: script_score and _knn_search Methods

This article explains Elasticsearch's vector search capabilities, detailing two approaches—script_score using dense_vector fields for exact similarity scoring and the experimental _knn_search for approximate nearest neighbor queries—along with data modeling examples, code snippets, performance considerations, and usage guidelines.

Elasticsearch_knn_searchdense_vector
0 likes · 6 min read
Elasticsearch Vector Search: script_score and _knn_search Methods