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vector search

241 articles · Page 3 of 3
Alibaba Cloud Developer
Alibaba Cloud Developer
Sep 21, 2023 · Artificial Intelligence

How Vector Search Powers AI: From Embeddings to Real‑World Applications

This article explains how vector search converts unstructured data such as speech, images, video, and text into high‑dimensional embeddings, explores common algorithms like Brute‑Force, ANN, and HNSW, and presents optimization techniques that dramatically improve recall and query‑per‑second performance for large‑scale AI retrieval systems.

AIANNHNSW
0 likes · 27 min read
How Vector Search Powers AI: From Embeddings to Real‑World Applications
ZhongAn Tech Team
ZhongAn Tech Team
Sep 4, 2023 · Artificial Intelligence

Embedding Technology for FAQ Retrieval: Cases, Evaluation Metrics, and Model Comparison

This article introduces the evolution of embedding techniques, presents real‑world case studies of embedding‑based FAQ retrieval, explains evaluation metrics such as Recall and MRR, and compares the performance of a proprietary ZhongAn embedding model with OpenAI and Sentence‑BERT models on Chinese FAQ datasets.

Artificial IntelligenceFAQ Retrievalembedding
0 likes · 18 min read
Embedding Technology for FAQ Retrieval: Cases, Evaluation Metrics, and Model Comparison
Java High-Performance Architecture
Java High-Performance Architecture
Aug 18, 2023 · Databases

Redis 7.2 Unified Release: Boost AI, Vector Search, and Real‑Time Functions

Redis 7.2, the first Unified Redis Release, introduces AI‑ready vector indexing, hybrid semantic search, scalable RAG support, server‑side Triggers and Functions, enhanced geospatial queries, and a preview of high‑performance searchable indexes, while expanding client library support and integrating Redis Data Integration for seamless enterprise data pipelines.

AIRAGRedis
0 likes · 8 min read
Redis 7.2 Unified Release: Boost AI, Vector Search, and Real‑Time Functions
Architect
Architect
Aug 17, 2023 · Backend Development

Design and Implementation of Bilibili's New Customer Service System

This article details Bilibili's transition from a purchased customer‑service platform to a self‑developed system, describing the background, architectural design, core modules such as intelligent QA, seat scheduling, workbench, permission management, the use of Faiss for vector search, and future explorations with large language models, highlighting the technical challenges and solutions across backend development and AI integration.

AIBackendCustomer Service
0 likes · 22 min read
Design and Implementation of Bilibili's New Customer Service System
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jul 10, 2023 · Artificial Intelligence

Enhancing Large Language Models with LangChain: Prompt Engineering, Chains, Agents, and Node.js Implementation

This article explains the limitations of large language models, introduces prompt engineering as a remedy, and provides a comprehensive guide to using the LangChain framework—including models, prompts, chains, agents, vector search, and practical Node.js code examples—to enable LLMs to interact with external tools and data sources.

AI developmentAgentsLLM
0 likes · 35 min read
Enhancing Large Language Models with LangChain: Prompt Engineering, Chains, Agents, and Node.js Implementation
21CTO
21CTO
May 16, 2023 · Databases

How Cassandra’s New Vector Search Transforms AI Applications

This article explains how Cassandra’s newly added vector data type and ANN search capabilities empower AI developers to store, index, and query high‑dimensional embeddings at scale, enabling use cases such as image retrieval, recommendation, and large‑language‑model integration.

AIANNCassandra
0 likes · 10 min read
How Cassandra’s New Vector Search Transforms AI Applications
High Availability Architecture
High Availability Architecture
Apr 27, 2023 · Artificial Intelligence

Design and Optimization of Bilibili's Large‑Scale Video Duplicate Detection System

This article describes the design, algorithmic improvements, and engineering performance optimizations of Bilibili's massive video duplicate detection (collision) system, covering challenges of low‑edit‑degree reposts, two‑stage retrieval, self‑supervised feature extraction, GPU‑accelerated preprocessing, and the resulting gains in accuracy and throughput.

BilibiliLarge-Scale Retrievaldeep learning
0 likes · 17 min read
Design and Optimization of Bilibili's Large‑Scale Video Duplicate Detection System
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Mar 21, 2023 · Artificial Intelligence

From Daily to Minute-Level Updates: Real-Time Recommendation System Enhancements at Xiaohongshu

Xiaohongshu transformed its recommendation pipeline from daily to minute‑level updates by redesigning recall, ranking and feature‑joining components, deploying a base‑plus‑incremental training scheme, migrating Spark to Flink, rewriting services in C++, and optimizing RocksDB, which yielded over 10% longer dwell time, 15% more interactions and roughly 50% higher new‑note efficiency.

Real-time Traininglarge-scale systemsmodel serving
0 likes · 20 min read
From Daily to Minute-Level Updates: Real-Time Recommendation System Enhancements at Xiaohongshu
Alimama Tech
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.

HNSWRecalllarge-scale
0 likes · 13 min read
Evolution of Recall Indexes in Alibaba Advertising: From Quantization to Graph-based HNSW
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Jan 10, 2023 · Big Data

How Alibaba’s Dolphin Engine Uses Flink + Hologres for Real‑Time Big Data

The Dolphin engine, built by Alibaba’s Data Engine team, combines Flink and Hologres to deliver ultra‑large‑scale OLAP, streaming, batch, and AI capabilities for real‑time advertising analytics, offering smart materialization, intelligent indexing, and vector recall while supporting millions of advertisers and petabyte‑level data.

AIFlinkHologres
0 likes · 13 min read
How Alibaba’s Dolphin Engine Uses Flink + Hologres for Real‑Time Big Data
HomeTech
HomeTech
Dec 16, 2022 · Artificial Intelligence

Building and Optimizing a Milvus‑Based Vector Search Platform

This article describes the background, technical selection, architecture, deployment, performance tuning, and operational practices of a Milvus‑driven vector retrieval platform, including cloud‑native deployment, index choices, capacity planning, and real‑world application cases that improve recall latency and resource efficiency.

AIMilvusPerformance Optimization
0 likes · 12 min read
Building and Optimizing a Milvus‑Based Vector Search Platform
DeWu Technology
DeWu Technology
Nov 25, 2022 · Databases

Milvus Vector Database Performance Testing and Architecture Analysis

The author stress‑tested Milvus 2.1.4’s cloud‑native, micro‑service architecture—detailing its write and search paths, evaluating FLAT index performance across 100 K to 10 M 512‑dim vectors, uncovering scaling, scheduler, segment‑rebalance, and upgrade issues, and concluding the system is robust but benefits from graph‑based indexes and Helm‑driven scaling.

Milvusdatabase architectureperformance testing
0 likes · 10 min read
Milvus Vector Database Performance Testing and Architecture Analysis
Alimama Tech
Alimama Tech
Aug 24, 2022 · Artificial Intelligence

Distributed High‑Performance Vector Retrieval with gpdb‑faiss‑vector Plugin on Dolphin Engine

The gpdb‑faiss‑vector plugin embeds Facebook’s Faiss library into the Dolphin (Greenplum‑compatible) engine, exposing SQL functions for distributed, high‑performance approximate nearest‑neighbor vector retrieval with caching, parallel search, configurable indexes, and sub‑millisecond latency, enabling scalable recommendation and advertising workloads.

AIFAISSSQL
0 likes · 15 min read
Distributed High‑Performance Vector Retrieval with gpdb‑faiss‑vector Plugin on Dolphin Engine
ITPUB
ITPUB
Jul 16, 2022 · Artificial Intelligence

How Huya Live Uses Vector Search and Fine‑Ranking to Power Real‑Time Recommendations

This article explains Huya Live's recommendation architecture, covering business background, system design, vector retrieval challenges and solutions with ScaNN, and the fine‑ranking pipeline, while highlighting performance optimizations, scalability, and future directions for their live‑streaming platform.

FAISSHuya LiveScaNN
0 likes · 11 min read
How Huya Live Uses Vector Search and Fine‑Ranking to Power Real‑Time Recommendations
ITPUB
ITPUB
Jun 25, 2022 · Artificial Intelligence

How We Revamped a Content Community’s Recommendation Engine for Real‑Time, Personalized Results

This article details the evolution of the ‘逛逛’ content community’s recommendation system, comparing the legacy rule‑based Hive workflow with a new algorithm‑driven architecture that leverages Elasticsearch, Redis, multi‑stage recall, coarse‑ and fine‑ranking, re‑ranking, exposure filtering, cold‑start handling, performance tuning, and future plans for vector‑based recall and platformization.

algorithmic rankingcold startreal-time
0 likes · 18 min read
How We Revamped a Content Community’s Recommendation Engine for Real‑Time, Personalized Results
Laiye Technology Team
Laiye Technology Team
Apr 29, 2022 · Artificial Intelligence

Using Faiss for Efficient Vector Similarity Search: Installation, Index Construction, and Performance Optimization

This tutorial explains what Faiss is, how to install it, construct various indexes such as IndexFlatL2, IndexIVFFlat, and IndexIVFPQ, and demonstrates code examples for building and querying vector similarity search pipelines while discussing speed‑accuracy trade‑offs.

AIApproximate Nearest NeighborFAISS
0 likes · 11 min read
Using Faiss for Efficient Vector Similarity Search: Installation, Index Construction, and Performance Optimization
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
IEG Growth Platform Technology Team
IEG Growth Platform Technology Team
Mar 21, 2022 · Backend Development

Optimization of Local Vector Retrieval: Filtering, Storage, and Sorting Strategies

This article presents a comprehensive study of local vector retrieval optimization, covering memory‑based filtering techniques, Redis‑backed vector storage designs, and various sorting algorithms—including radix and heap‑based approaches—to achieve lower latency and higher throughput for large‑scale ad recommendation systems.

FilteringRedisbackend optimization
0 likes · 13 min read
Optimization of Local Vector Retrieval: Filtering, Storage, and Sorting Strategies
DataFunTalk
DataFunTalk
Mar 2, 2022 · Artificial Intelligence

Huya Live Streaming Recommendation Architecture: Business Background, System Design, Vector Retrieval, and Ranking

This article presents a comprehensive overview of Huya Live's recommendation system, covering business background, system architecture, vector retrieval techniques, ranking pipeline, technical challenges, implementation details, and future outlook, highlighting scalability and performance optimizations.

AIHuyalive streaming
0 likes · 14 min read
Huya Live Streaming Recommendation Architecture: Business Background, System Design, Vector Retrieval, and Ranking
Baidu Geek Talk
Baidu Geek Talk
Feb 14, 2022 · Artificial Intelligence

How Baidu’s PUCK Dominated the First BigANN Vector Search Competition

The inaugural BigANN competition, organized by NeurIPS, showcased large‑scale ANN research, and Baidu's self‑developed PUCK algorithm secured top scores across all four tracks by leveraging multi‑layer quantization, two‑level inverted indexing, and extensive system‑level optimizations.

ANNApproximate Nearest NeighborBigANN
0 likes · 8 min read
How Baidu’s PUCK Dominated the First BigANN Vector Search Competition
Code DAO
Code DAO
Dec 26, 2021 · Artificial Intelligence

Building a Vector‑Based Movie Recommendation System with Transformers

This tutorial walks through constructing a movie recommendation engine by downloading a dataset, cleaning and de‑duplicating entries, encoding plot summaries into vectors with transformer models, and performing nearest‑neighbor searches using scikit‑learn, while handling misspellings with Levenshtein distance.

Levenshtein distanceTransformersmovie recommendation
0 likes · 8 min read
Building a Vector‑Based Movie Recommendation System with Transformers
Laravel Tech Community
Laravel Tech Community
Dec 9, 2021 · Backend Development

Apache Lucene 9.0 Released – New Features and Improvements

Apache Lucene 9.0, a high‑performance Java full‑text search library, introduces high‑dimensional vector indexing, new language analyzers, faster faceting and sorting, updated file formats, and several performance optimizations, providing developers with a richer, more efficient search toolkit.

Apache LuceneJavafull-text search
0 likes · 3 min read
Apache Lucene 9.0 Released – New Features and Improvements
Kuaishou Tech
Kuaishou Tech
Nov 29, 2021 · Artificial Intelligence

Starry Vector Retrieval Platform: Architecture, Features, and Performance

The article describes the design, challenges, architecture, key features, algorithm optimizations, and future roadmap of Kuaishou's Starry vector retrieval platform, which delivers high‑performance, high‑reliability, and easy‑to‑use large‑scale ANN search for diverse business scenarios.

AI platformANNDistributed Architecture
0 likes · 14 min read
Starry Vector Retrieval Platform: Architecture, Features, and Performance
iQIYI Technical Product Team
iQIYI Technical Product Team
Aug 20, 2021 · Artificial Intelligence

Engineering Practice of Online Vector Recall Service at iQIYI

iQIYI’s engineering team built an online vector‑recall service on Milvus, wrapping it with a Dubbo‑gRPC interface to serve 6 M 64‑dimensional embeddings at roughly 3 k QPS and 20 ms p99 latency, integrating query‑embedding generation, simplifying recommendation pipelines, and demonstrating the performance and operational advantages of a platformized ANN‑based recall layer.

AIMilvusengineering
0 likes · 14 min read
Engineering Practice of Online Vector Recall Service at iQIYI
DataFunTalk
DataFunTalk
Aug 2, 2021 · Databases

From Text Search to Vector Search: Generalizing Unstructured Data Retrieval

The article explains why traditional text‑based search engines like ElasticSearch struggle with modern multimodal data, introduces vector databases that store implicit semantic embeddings, and proposes a generalized search architecture that decouples data‑to‑vector mapping from the engine while leveraging clustering or graph indexes for similarity search.

AIInformation Retrievalembedding
0 likes · 12 min read
From Text Search to Vector Search: Generalizing Unstructured Data Retrieval
DataFunTalk
DataFunTalk
Jul 2, 2021 · Artificial Intelligence

Vector Retrieval for Community Forum Search Using Milvus at Dingxiangyuan

This article describes how Dingxiangyuan's algorithm team adopted Milvus for distributed vector indexing to improve semantic search in their community forum, detailing the background, retrieval workflow, various embedding models—including Bi‑Encoder, Spherical Embedding, and Knowledge Embedding—and summarizing the benefits and future applications.

MilvusNLPembedding
0 likes · 10 min read
Vector Retrieval for Community Forum Search Using Milvus at Dingxiangyuan
Baidu Geek Talk
Baidu Geek Talk
May 10, 2021 · Industry Insights

How Baidu’s GNOIMI Powers Billion‑Scale Rich Media Retrieval

Baidu’s rich‑media retrieval system combines CNN‑based feature extraction with an Approximate Nearest Neighbor engine called GNOIMI, employing hierarchical clustering, product quantization, and optimized indexing to achieve sub‑millisecond search over billions of images, videos and audio, supporting anti‑spam, recommendation and risk‑control across dozens of services.

ANNGNOIMIHNSW
0 likes · 16 min read
How Baidu’s GNOIMI Powers Billion‑Scale Rich Media Retrieval
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 4, 2021 · Artificial Intelligence

How Alibaba’s Proxima Engine Revolutionizes Vector Search for AI Applications

Alibaba’s Damo Academy unveils Proxima, a high‑performance vector search engine that powers e‑commerce, video, and payment services, detailing its core capabilities, large‑scale indexing, distributed construction, real‑time updates, and challenges such as algorithm diversity, scalability, and multi‑modal retrieval.

AIAlibaba Proximalarge-scale indexing
0 likes · 17 min read
How Alibaba’s Proxima Engine Revolutionizes Vector Search for AI Applications
58 Tech
58 Tech
Mar 3, 2021 · Artificial Intelligence

Design and Implementation of a Faiss‑Based Vector Search Platform

The article describes the design, architecture, and key components of a vector search platform built on Faiss that supports full‑index construction, incremental and distributed indexing, online retrieval, city‑level search, and vector update/delete operations to meet large‑scale AI application needs.

AIKubernetesLarge-Scale Retrieval
0 likes · 10 min read
Design and Implementation of a Faiss‑Based Vector Search Platform
System Architect Go
System Architect Go
Jun 4, 2020 · Artificial Intelligence

Evolution and Underlying Principles of the Billion‑Scale Image Search System at Youpai Image Manager

This article describes the two‑generation evolution of Youpai Image Manager's billion‑scale image search system, explaining the mathematical representation of images, the limitations of MD5, the first‑generation pHash‑ElasticSearch solution, and the second‑generation CNN‑Milvus approach for robust, large‑scale visual similarity search.

CNNMilvusimage search
0 likes · 9 min read
Evolution and Underlying Principles of the Billion‑Scale Image Search System at Youpai Image Manager
System Architect Go
System Architect Go
Mar 30, 2020 · Artificial Intelligence

Overview of Image Search System

This article explains the fundamentals of building an image‑by‑image search system, covering image feature extraction methods such as hashing, traditional descriptors, CNN‑based vectors, and the use of vector search engines like Milvus for similarity retrieval.

CNNMilvusfeature extraction
0 likes · 6 min read
Overview of Image Search System
DataFunTalk
DataFunTalk
Oct 24, 2019 · Artificial Intelligence

Evolution and Engineering Practices of the 360 Display Advertising Recall System

This article details the 360 display advertising system's architecture and the progressive evolution of its recall module, covering business overview, overall pipeline, various recall strategies—including Boolean, vectorized, and deep‑tree approaches—and the performance optimizations applied to meet real‑time constraints.

Advertisingdeep learningrecall system
0 likes · 14 min read
Evolution and Engineering Practices of the 360 Display Advertising Recall System
360 Quality & Efficiency
360 Quality & Efficiency
Aug 23, 2019 · Artificial Intelligence

High‑Performance High‑Dimensional Vector KNN Search Using FAISS

This article introduces the background of vector representations in machine learning, explains the K‑Nearest Neighbors algorithm and its key parameters, reviews traditional tree‑based and modern high‑performance search solutions, and demonstrates how FAISS can achieve microsecond‑level KNN queries on large‑scale high‑dimensional data.

FAISSKNNhigh-dimensional
0 likes · 5 min read
High‑Performance High‑Dimensional Vector KNN Search Using FAISS
vivo Internet Technology
vivo Internet Technology
Nov 16, 2018 · Artificial Intelligence

Efficient Vector Search with Deep Learning Embeddings in Elasticsearch

The article explains how to replace keyword matching with deep‑learning document embeddings in Elasticsearch by applying PCA dimensionality reduction, indexing vectors using Lucene’s KD‑tree structures via a custom plugin, and leveraging FAISS‑style nearest‑neighbour techniques to achieve fast, semantically aware similarity search.

ElasticsearchFAISSKD-Tree
0 likes · 7 min read
Efficient Vector Search with Deep Learning Embeddings in Elasticsearch
Xianyu Technology
Xianyu Technology
Aug 31, 2018 · Artificial Intelligence

Personalized Recommendation for Xianyu Small Item Pools: Challenges and Solutions

Xianyu’s personalized recommendation system struggles with tiny, fast‑turnover item pools because traditional X2I matrices provide insufficient recall, so the team introduced pool‑specific pre‑filtering, high‑dimensional vector search, and a real‑time search‑engine recall, the latter boosting clicks by 14 % and transactions by 0.14 %.

Xianyuengineeringpersonalization
0 likes · 15 min read
Personalized Recommendation for Xianyu Small Item Pools: Challenges and Solutions