HNSW | BestHub

Sohu Tech Products

Mar 19, 2025 · Databases

Redis Vector Search Technology for AI Applications: Implementation and Best Practices

The article explains how Redis vector search, powered by RedisSearch’s FLAT and HNSW algorithms and supporting various data types and precisions, enables fast AI-driven similarity queries for text, image, and audio, and provides implementation guidance, optimization tips, and a real‑world customer‑service use case.

AI applicationsDatabase OptimizationHNSW

0 likes · 17 min read

Redis Vector Search Technology for AI Applications: Implementation and Best Practices

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

Sohu Tech Products

Mar 27, 2024 · Artificial Intelligence

Building a RAG Application with Baidu Vector Database and Qianfan Embedding

This tutorial walks through building a Retrieval‑Augmented Generation application by setting up Baidu’s Vector Database and Qianfan embedding service, configuring credentials, creating a document database and vector table, loading and chunking PDFs, generating embeddings, storing them, and performing scalar, vector and hybrid similarity searches, ready for integration with Wenxin LLM for answer generation.

AI applicationsBaidu QianfanHNSW

0 likes · 11 min read

Building a RAG Application with Baidu Vector Database and Qianfan Embedding

Architects Research Society

Jul 24, 2023 · Artificial Intelligence

Neural Search in Apache Solr: Dense Vector Fields, HNSW Graphs, and K‑Nearest Neighbor Implementation

This article explains how Apache Solr implements neural search using dense vector fields, K‑Nearest Neighbor algorithms, and Hierarchical Navigable Small World graphs, detailing the underlying Lucene support, configuration options, query syntax, and integration with AI‑driven vector representations.

AIApache SolrDense Vectors

0 likes · 15 min read

Neural Search in Apache Solr: Dense Vector Fields, HNSW Graphs, and K‑Nearest Neighbor Implementation

Alimama Tech

Feb 8, 2023 · Artificial Intelligence

Evolution of Recall Indexes in Alibaba Advertising: From Quantization to Graph-based HNSW

Alibaba’s advertising pipeline progressed from low‑dimensional quantization partitions to hierarchical tree indexes, then to graph‑based HNSW structures—including multi‑category, multi‑level graphs and a BlazeOp‑driven scoring service—dramatically boosting recall efficiency, scalability and maintainability while meeting strict latency constraints.

HNSWIndexingLarge Scale

0 likes · 13 min read

Evolution of Recall Indexes in Alibaba Advertising: From Quantization to Graph-based HNSW

DeWu Technology

Jul 27, 2022 · Artificial Intelligence

Overview of Nearest Neighbor Search Algorithms

The article reviews how high‑dimensional vector representations in deep‑learning applications require efficient approximate nearest‑neighbor search, comparing K‑d trees, hierarchical k‑means trees, locality‑sensitive hashing, product quantization, and HNSW graphs, and discusses practical FAISS implementations and how algorithm choice depends on data size, recall, latency, and resources.

HNSWKD-TreeLSH

0 likes · 8 min read

Overview of Nearest Neighbor Search Algorithms

Architects Research Society

Jun 6, 2022 · Artificial Intelligence

Neural Search in Apache Solr: Dense Vector Fields, HNSW Graphs, and K‑Nearest Neighbor Implementation

This article explains how Apache Solr and Lucene implement neural search using dense vector fields, hierarchical navigable small‑world (HNSW) graphs, and approximate K‑nearest neighbor algorithms, covering configuration, custom codecs, indexing formats, and query parsers for vector‑based retrieval.

Apache SolrDense VectorsHNSW

0 likes · 15 min read

IEG Growth Platform Technology Team

Jan 17, 2022 · Artificial Intelligence

Introduction to Vector Retrieval, Distance Metrics, and Fundamental Algorithms

This article introduces the concept of vector retrieval, outlines its diverse application scenarios, explains common distance metrics for both floating‑point and binary vectors, and surveys fundamental approximate nearest‑neighbor algorithms including tree‑based, graph‑based, quantization, and hashing methods.

HNSWKD-TreeLSH

0 likes · 22 min read

Introduction to Vector Retrieval, Distance Metrics, and Fundamental Algorithms

Laiye Technology Team

Jan 7, 2022 · Artificial Intelligence

Understanding Vector Retrieval: Principles, Applications, and High‑Performance Algorithms

This article explains how deep learning transforms unstructured data into dense vectors, defines vector retrieval, outlines its many use cases such as product, video, and text search, discusses challenges in learning effective embeddings, and reviews high‑performance algorithms like LSH, neighbor graphs, and product quantization.

AI applicationsHNSWLSH

0 likes · 21 min read

Understanding Vector Retrieval: Principles, Applications, and High‑Performance Algorithms