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vector-search

219 articles · Page 2 of 3
DataFunSummit
DataFunSummit
Sep 4, 2025 · Artificial Intelligence

Unlocking Elasticsearch Vector Search: From Basics to RAG Implementation

This article explores the evolving search demands of the intelligent era, explains dense and sparse vector concepts, details Elasticsearch's vector search capabilities and recent performance breakthroughs, introduces hybrid and relevance‑tuning techniques, and demonstrates RAG principles and real‑world enterprise use cases.

AIElasticsearchHybrid Search
0 likes · 14 min read
Unlocking Elasticsearch Vector Search: From Basics to RAG Implementation
Xiaolei Talks DB
Xiaolei Talks DB
Aug 28, 2025 · Databases

How AI Is Transforming Databases: Highlights from China’s DTCC2025

At DTCC2025 in Beijing, industry leaders showcased AI-driven innovations, vector database advances, RAG techniques, and distributed database performance breakthroughs, illustrating how databases are evolving from passive data stores into intelligent, autonomous systems that boost efficiency, scalability, and business value across sectors.

AIDatabasesRAG
0 likes · 10 min read
How AI Is Transforming Databases: Highlights from China’s DTCC2025
Mingyi World Elasticsearch
Mingyi World Elasticsearch
Aug 5, 2025 · Artificial Intelligence

Enterprise Semantic Search: Key Q&A on Scoring, Recall, LSH, Chunking, and Embedding Dimensions

This article answers practical questions about enterprise semantic search, explaining how Reciprocal Rank Fusion normalizes mixed scoring, how to control vector result size, the trade‑offs of LSH parameters, word‑ and sentence‑based chunking strategies with version‑specific defaults, and flexible embedding dimensionality.

ChunkingElasticsearchLSH
0 likes · 8 min read
Enterprise Semantic Search: Key Q&A on Scoring, Recall, LSH, Chunking, and Embedding Dimensions
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 5, 2025 · Databases

How PolarDB IMCI Unifies Vector Search and Embedding in One SQL Engine

This article explains how PolarDB IMCI integrates vector indexing and embedding directly into the database kernel, offering a unified, transactional, and real‑time vector lifecycle management service that lets developers build RAG knowledge bases and AI applications using only standard SQL, dramatically reducing development and operational complexity.

AIPolarDBRAG
0 likes · 11 min read
How PolarDB IMCI Unifies Vector Search and Embedding in One SQL Engine
Mingyi World Elasticsearch
Mingyi World Elasticsearch
Jul 30, 2025 · Backend Development

From Keyword Matching to Semantic Understanding: Building an Intelligent E‑Commerce Search Engine

The article analyzes the semantic gap in e‑commerce search, compares traditional keyword matching with vector‑based retrieval, and provides a step‑by‑step implementation using Elasticsearch/Easysearch pipelines, embedding models, and a hybrid search strategy to improve user intent understanding.

EasysearchElasticsearchHybrid Search
0 likes · 11 min read
From Keyword Matching to Semantic Understanding: Building an Intelligent E‑Commerce Search Engine
Sohu Tech Products
Sohu Tech Products
Jul 23, 2025 · Artificial Intelligence

Boosting Video Moderation with Multimodal CLIP and Efficient Vector Search

This article describes how a video review system combines multimodal CLIP models, image‑text feature alignment, and optimized vector‑search databases such as RedisSearch and Elasticsearch to detect prohibited content in real time and perform large‑scale historical recall, while addressing challenges of generalization, storage cost, and inference speed.

AICLIPmodel fine‑tuning
0 likes · 18 min read
Boosting Video Moderation with Multimodal CLIP and Efficient Vector Search
Mingyi World Elasticsearch
Mingyi World Elasticsearch
Jul 18, 2025 · Artificial Intelligence

Video: Building an Intelligent Knowledge‑Base Q&A System with Large Models and Elasticsearch (RAG)

The video walks through the differences between traditional keyword search and vector search, explains the core concept of Retrieval‑Augmented Generation, and demonstrates how to construct a knowledge‑base Q&A system using a large language model integrated with Elasticsearch.

ElasticsearchKnowledge BaseQ&A system
0 likes · 1 min read
Video: Building an Intelligent Knowledge‑Base Q&A System with Large Models and Elasticsearch (RAG)
DataFunSummit
DataFunSummit
Jul 15, 2025 · Artificial Intelligence

Unlocking Semantic Search: Elasticsearch Vector Search & RAG Applications

This article explains why traditional keyword search falls short, introduces Elasticsearch's vector search and hybrid retrieval capabilities, and shows how combining it with large language models enables Retrieval‑Augmented Generation (RAG) for more accurate, context‑aware AI-driven search across text and multimedia data.

AIElasticsearchLarge Language Models
0 likes · 5 min read
Unlocking Semantic Search: Elasticsearch Vector Search & RAG Applications
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 19, 2025 · Artificial Intelligence

Build Efficient Multimodal Text‑Image Search with Alibaba Cloud Milvus

This guide explains how to use Alibaba Cloud Milvus to create a scalable, high‑performance multimodal search system that supports text‑to‑image, image‑to‑image, and cross‑modal queries across various business scenarios, detailing architecture, deployment steps, validation, and resource cleanup.

AICloud DeploymentMilvus
0 likes · 8 min read
Build Efficient Multimodal Text‑Image Search with Alibaba Cloud Milvus
Sohu Tech Products
Sohu Tech Products
Jun 11, 2025 · Artificial Intelligence

How DeepSeek and TiDB AI Are Redefining Data Engines for the Large‑Model Era

This article explores DeepSeek's open‑source large‑model breakthroughs, PingCAP's AI‑enhanced database roadmap, TiDB.AI's retrieval‑augmented generation framework, the unified TiDB data engine, and practical Q&A insights on knowledge‑graph construction, vector search, and AI‑driven SQL generation.

AIDatabaseDeepSeek
0 likes · 15 min read
How DeepSeek and TiDB AI Are Redefining Data Engines for the Large‑Model Era
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
May 22, 2025 · Artificial Intelligence

Scalable Overload-Aware Graph-Based Index Construction for 10‑Billion‑Scale Vector Similarity Search (SOGAIC)

The paper introduces SOGAIC, a scalable overload‑aware graph‑based index construction system for billion‑scale vector similarity search that uses adaptive overlapping partitioning and load‑balanced distributed scheduling to cut construction time by 47.3% while maintaining high recall.

ANNdistributed schedulinggraph index
0 likes · 13 min read
Scalable Overload-Aware Graph-Based Index Construction for 10‑Billion‑Scale Vector Similarity Search (SOGAIC)
Mingyi World Elasticsearch
Mingyi World Elasticsearch
May 16, 2025 · Artificial Intelligence

Easysearch Vector Search: From Theory to Hands‑On Implementation

This article explains the principles of vector search, compares Easysearch's approximate (LSH) and exact kNN APIs, and walks through a complete hands‑on example using Stanford's 50‑dimensional GloVe embeddings to index, import, and query semantically similar words.

Approximate SearchEasysearchExact Search
0 likes · 9 min read
Easysearch Vector Search: From Theory to Hands‑On Implementation
Architecture & Thinking
Architecture & Thinking
May 15, 2025 · Databases

Redis 8.0 Unveiled: New AGPLv3 License, Vector Search, JSON & More

Redis 8.0, released on May 1 2025, introduces a major license shift to AGPL‑v3, adds eight native data structures—including vector sets, JSON, and time‑series—enhances the query engine with up to 16× performance gains, improves scalability, security, and cloud‑native support, and provides extensive code examples for AI and real‑time analytics.

DatabaseRedisjson
0 likes · 15 min read
Redis 8.0 Unveiled: New AGPLv3 License, Vector Search, JSON & More
Alibaba Cloud Native
Alibaba Cloud Native
May 9, 2025 · Artificial Intelligence

Build a Retrieval‑Augmented Generation (RAG) App with LangChain, Higress, and Elasticsearch

This tutorial walks through building a Retrieval‑Augmented Generation (RAG) system by combining LangChain for document processing, Elasticsearch’s vector store with the ELSER v2 model for semantic search, and Higress as a cloud‑native AI gateway, complete with deployment scripts, code examples, and query testing.

AIHigressLangChain
0 likes · 15 min read
Build a Retrieval‑Augmented Generation (RAG) App with LangChain, Higress, and Elasticsearch
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Apr 24, 2025 · Big Data

Boosting Product Recommendations with Serverless Spark and Milvus: A Real‑World Case Study

蝉妈妈 migrated its recommendation platform to Alibaba Cloud Serverless Spark and Milvus, replacing traditional vector search and Spark clusters, achieving 40% faster offline tasks, 80% lower failure rates, significant cost savings, and scalable, low‑latency similar‑product retrieval for personalized marketing.

Big DataMilvusrecommendation system
0 likes · 8 min read
Boosting Product Recommendations with Serverless Spark and Milvus: A Real‑World Case Study
dbaplus Community
dbaplus Community
Apr 7, 2025 · Databases

How Do LLMs Tackle Oracle Bad Block Errors? A Hands‑On Evaluation

This article presents a hands‑on evaluation of several large language models—including Mistral‑Small, Deepseek‑r1, Llama 3.3 and ChatGPT‑4‑go—on Oracle database bad‑block errors, RAG‑based document retrieval, and log‑driven reasoning, revealing performance gaps, scoring results, and practical DBA implications.

AIDatabaseLLM evaluation
0 likes · 11 min read
How Do LLMs Tackle Oracle Bad Block Errors? A Hands‑On Evaluation
Ubiquitous Tech
Ubiquitous Tech
Mar 28, 2025 · Artificial Intelligence

Building a RAG‑Powered AI Customer Service for Tongcheng Travel FAQs with FastGPT

The article walks through creating a Retrieval‑Augmented Generation (RAG) based AI chatbot for Tongcheng Travel by preparing FAQ data, formatting it with L1/L2/Q/A tags, importing it into FastGPT knowledge bases, configuring a workflow with intent classification and prompt engineering, and validating the system with multiple test cases.

AI chatbotFastGPTKnowledge Base
0 likes · 15 min read
Building a RAG‑Powered AI Customer Service for Tongcheng Travel FAQs with FastGPT
Sohu Tech Products
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 ApplicationsHNSWHybrid Retrieval
0 likes · 17 min read
Redis Vector Search Technology for AI Applications: Implementation and Best Practices
Mingyi World Elasticsearch
Mingyi World Elasticsearch
Mar 6, 2025 · Artificial Intelligence

Smart Q&A Knowledge Base Powered by Qwen2.5‑14B and Elasticsearch RAG

This article details a smart Q&A knowledge‑base system that integrates the Qwen2.5‑14B large language model with Elasticsearch vector search via RAG, covering data ingestion with FSCrawler, Chinese sentence embedding, Gradio UI, performance tests on a 483‑page book, architecture diagrams, code walkthroughs, and suggested enhancements.

Chinese EmbeddingElasticsearchFSCrawler
0 likes · 11 min read
Smart Q&A Knowledge Base Powered by Qwen2.5‑14B and Elasticsearch RAG
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Mar 4, 2025 · Artificial Intelligence

Deploy a High‑Performance RAG Service with Hologres, DeepSeek, and PAI‑EAS

This guide walks you through building a Retrieval‑Augmented Generation (RAG) system by integrating Alibaba Cloud's Hologres vector store, the Proxima high‑performance vector engine, and DeepSeek large language models via PAI‑EAS, covering prerequisites, deployment steps, configuration, and inference verification.

AI deploymentDeepSeekHologres
0 likes · 12 min read
Deploy a High‑Performance RAG Service with Hologres, DeepSeek, and PAI‑EAS
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Feb 25, 2025 · Artificial Intelligence

Build a RAG‑Powered Smart Q&A Assistant with Milvus, DeepSeek, and PAI LangStudio

This step‑by‑step guide shows how to assemble a Retrieval‑Augmented Generation (RAG) system using Alibaba Cloud Milvus vector search, the DeepSeek large language model, and PAI LangStudio, covering instance creation, data upload, model deployment, connection setup, flow design, and service invocation.

AI TutorialDeepSeekLLM
0 likes · 9 min read
Build a RAG‑Powered Smart Q&A Assistant with Milvus, DeepSeek, and PAI LangStudio
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.

ElasticsearchHNSWStorage Reduction
0 likes · 32 min read
Understanding Vector Storage and Optimization in Elasticsearch 8.16.1
Java Architecture Diary
Java Architecture Diary
Feb 13, 2025 · Artificial Intelligence

Create a Java RAG System Using DeepSeek R1, Milvus, and Spring

This guide walks through building a Java RAG system with DeepSeek R1, Milvus, and Spring, covering environment setup, vector model integration via OpenAI protocol, Maven dependencies, data embedding, and a chat endpoint that combines semantic retrieval with LLM generation.

AI integrationDeepSeekMilvus
0 likes · 11 min read
Create a Java RAG System Using DeepSeek R1, Milvus, and Spring
StarRocks
StarRocks
Feb 11, 2025 · Databases

How StarRocks Supercharges Vector Search: 7× Faster Queries and 1/3 Cost

This article explains the principles and practical implementation of vector retrieval in StarRocks, covering approximate nearest‑neighbor algorithms, index design, query planning, performance optimizations, real‑world case studies, and future challenges, showing how query latency dropped from 15 seconds to 2 seconds while cutting costs to a third.

ANNApproximate Nearest NeighborHNSW
0 likes · 25 min read
How StarRocks Supercharges Vector Search: 7× Faster Queries and 1/3 Cost
Architecture Digest
Architecture Digest
Jan 16, 2025 · Artificial Intelligence

Redis Introduces Multi‑Threaded Query Engine to Boost Vector Search Performance for Generative AI

Redis has unveiled a multi‑threaded query engine that dramatically increases query throughput and lowers latency for vector similarity searches, offering up to 16× performance gains and enabling real‑time Retrieval‑Augmented Generation (RAG) workloads in generative AI applications.

Database PerformanceRAGgenerative AI
0 likes · 7 min read
Redis Introduces Multi‑Threaded Query Engine to Boost Vector Search Performance for Generative AI
Bilibili Tech
Bilibili Tech
Jan 7, 2025 · Cloud Native

Design and Implementation of Bilibili's Large-Scale Recall System

Bilibili’s large‑scale recall system separates online processing into a two‑tier merge service and an index service, supports multi‑channel text, item‑to‑item and vector indexes with real‑time updates, uses horizontal sharding, robust CI/CD, monitoring and degradation mechanisms, and is being extended toward model‑based recall and greater automation.

BilibiliSearch Architecturecloud-native
0 likes · 16 min read
Design and Implementation of Bilibili's Large-Scale Recall System
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Dec 18, 2024 · Artificial Intelligence

Can GPU Graph Algorithms Boost Vector Search Performance by 10×?

This article explains how OpenSearch's GPU‑accelerated vector search leverages parallel graph algorithms to achieve up to tenfold speed improvements over CPU solutions, detailing ANNS techniques, performance benchmarks, and practical GPU specifications for high‑QPS AI applications.

Approximate Nearest NeighborGPU AccelerationOpenSearch
0 likes · 11 min read
Can GPU Graph Algorithms Boost Vector Search Performance by 10×?
21CTO
21CTO
Dec 15, 2024 · Databases

Why Antirez Is Returning to Redis: Insights on Licensing, AI, and Vector Search

Redis Labs announces the return of its founder Antirez, who shares his reasons for rejoining, discusses the recent licensing shift, reflects on his past projects, and outlines future plans for AI integration, vector indexing, and community engagement within the Redis ecosystem.

AIDatabaseLicensing
0 likes · 16 min read
Why Antirez Is Returning to Redis: Insights on Licensing, AI, and Vector Search
Sohu Tech Products
Sohu Tech Products
Nov 27, 2024 · Artificial Intelligence

RAG Technology and Practical Application in Multi-Modal Query: Using Chinese-CLIP and Redis Search

The article explains how Retrieval‑Augmented Generation (RAG) outperforms direct LLM inference by enabling real‑time knowledge updates and lower costs, and demonstrates a practical multi‑modal RAG pipeline that uses Chinese‑CLIP for vector encoding, various chunking strategies, and Redis Search for fast vector storage and retrieval.

Chinese-CLIPChunkingLLM
0 likes · 17 min read
RAG Technology and Practical Application in Multi-Modal Query: Using Chinese-CLIP and Redis Search
Baidu Tech Salon
Baidu Tech Salon
Nov 22, 2024 · Artificial Intelligence

How GPU‑Accelerated ANN Search Cuts Costs and Boosts Throughput in High‑Volume Retrieval

This article analyzes a GPU‑based approximate nearest neighbor (ANN) retrieval solution built on NVIDIA's RAFT library, detailing algorithm selection, offline indexing tricks, batch online search design, performance results on a 25‑million‑vector workload, and cost‑saving implications for large‑scale search services.

ANNGPUIVF_INT8
0 likes · 21 min read
How GPU‑Accelerated ANN Search Cuts Costs and Boosts Throughput in High‑Volume Retrieval
Baidu Geek Talk
Baidu Geek Talk
Nov 20, 2024 · Artificial Intelligence

Boosting ANN Search with GPU: Inside RAFT’s IVF_INT8 Implementation

This article examines how Baidu and NVIDIA leveraged the open‑source RAFT library to build a GPU‑accelerated approximate nearest neighbor (ANN) retrieval system, detailing algorithm choices, offline indexing, online batch processing, performance results, and practical guidelines for deploying ANN on GPUs.

ANNGPUIVF_INT8
0 likes · 20 min read
Boosting ANN Search with GPU: Inside RAFT’s IVF_INT8 Implementation
DataFunSummit
DataFunSummit
Nov 19, 2024 · Databases

From DIKW to Distributed Data Warebase: Evolution of Data Systems and AI‑Driven Architecture

The article traces the progression from the human DIKW information hierarchy to its computer‑world counterpart, illustrates how a homestay platform’s data architecture evolves through relational, NoSQL, search, and data‑warehouse layers, and introduces the next‑generation distributed Data Warebase that unifies structured, semi‑structured, and vectorized knowledge to meet modern AI‑driven business demands.

AIDIKW ModelDatabase Systems
0 likes · 26 min read
From DIKW to Distributed Data Warebase: Evolution of Data Systems and AI‑Driven Architecture
AntData
AntData
Nov 18, 2024 · Databases

Modern Data Paradigms: From Relational Databases to Vector Retrieval and AI

This article surveys the evolution of modern data technologies—from the 4V characteristics of big data and the limitations of traditional relational databases, through the rise of NoSQL and polyglot persistence, to embedding‑driven vector search, hybrid retrieval and RAG, illustrating how each paradigm frees applications from data constraints.

Artificial IntelligenceBig DataDatabases
0 likes · 30 min read
Modern Data Paradigms: From Relational Databases to Vector Retrieval and AI
DaTaobao Tech
DaTaobao Tech
Oct 9, 2024 · Artificial Intelligence

Building a Vertical Domain QA Bot with Vector Search, RAG, and SFT

This guide walks entry‑level developers through building a logistics‑focused QA bot by first embedding documents for vector similarity search, then adding retrieval‑augmented generation, fine‑tuning a small model, integrating hybrid checks, and optimizing deployment with feedback loops to achieve fast, accurate, out‑of‑scope‑aware answers.

AIChatbotFine-tuning
0 likes · 15 min read
Building a Vertical Domain QA Bot with Vector Search, RAG, and SFT
21CTO
21CTO
Oct 3, 2024 · Databases

MongoDB 8.0 Unveiled: Massive Performance Gains and New Vector Support

MongoDB 8.0 launches with up to 36% higher read throughput, 56% faster batch writes, 200% faster time‑series processing, 50‑fold faster sharding distribution, quantized vector search, and queryable encryption, delivering significant performance, cost, and security improvements for modern workloads.

DatabaseMongoDBSharding
0 likes · 4 min read
MongoDB 8.0 Unveiled: Massive Performance Gains and New Vector Support
DataFunSummit
DataFunSummit
Sep 4, 2024 · Artificial Intelligence

How Elasticsearch Powers Retrieval‑Augmented Generation (RAG) Applications

This article explains how Elasticsearch’s advanced search capabilities—including vector and semantic search, hardware acceleration, hybrid retrieval, model re‑ranking, multi‑vector support, and integrated security—enable robust RAG implementations and outlines future directions such as a new compute engine, stronger vector engines, and cloud‑native serverless deployment.

AIElasticsearchHybrid Search
0 likes · 9 min read
How Elasticsearch Powers Retrieval‑Augmented Generation (RAG) Applications
DataFunTalk
DataFunTalk
Sep 4, 2024 · Artificial Intelligence

Data+AI Data Lake Technologies: Challenges, Apache Iceberg Overview, and Vector Table Implementations with PyIceberg

This article explores the evolution of data lakes for AI, discusses the challenges of AI-era data management, introduces Apache Iceberg and its architecture, demonstrates PyIceberg-based AI training and inference pipelines, and presents vector table designs with LSH indexing and performance optimizations.

AIApache IcebergBig Data
0 likes · 22 min read
Data+AI Data Lake Technologies: Challenges, Apache Iceberg Overview, and Vector Table Implementations with PyIceberg
Selected Java Interview Questions
Selected Java Interview Questions
Aug 18, 2024 · Backend Development

Redis Introduces a Multi‑Threaded Query Engine to Boost Vector Search Performance for Generative AI

Redis has launched a multi‑threaded query engine that vertically scales its in‑memory database, dramatically increasing query throughput and lowering latency for vector similarity searches, thereby addressing the performance demands of real‑time retrieval‑augmented generation in generative AI applications.

RAGRedisbackend
0 likes · 9 min read
Redis Introduces a Multi‑Threaded Query Engine to Boost Vector Search Performance for Generative AI
AI Large Model Application Practice
AI Large Model Application Practice
Aug 16, 2024 · Artificial Intelligence

How to Query a Microsoft GraphRAG Knowledge Graph with Neo4j: Local and Global Modes

This guide explains how to query a Microsoft GraphRAG knowledge graph using the official CLI, API, and a custom Neo4j implementation, covering both local and global retrieval modes, vector index creation, Cypher query customization, and integration with LangChain for end‑to‑end RAG pipelines.

LangChainMicrosoft GraphRAGNeo4j
0 likes · 13 min read
How to Query a Microsoft GraphRAG Knowledge Graph with Neo4j: Local and Global Modes
21CTO
21CTO
Jul 30, 2024 · Databases

What Goes Around: 20‑Year Evolution of Database Systems and Future Trends

This article reviews two decades of database research, analyzing the rise and decline of various data models—from hierarchical and relational to NoSQL, vector, and graph databases—while highlighting how AI, cloud, and hardware advances are reshaping DBMS architecture and predicting which approaches will dominate tomorrow’s data landscape.

DBMS EvolutionData ModelingNoSQL
0 likes · 30 min read
What Goes Around: 20‑Year Evolution of Database Systems and Future Trends
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Jul 24, 2024 · Artificial Intelligence

How Alibaba Cloud’s Milvus Service Boosted E‑commerce Search Stability and Scalability

This case study details how ShiHuo, an e‑commerce recommendation platform, overcame rapid product growth, cluster instability, and high operational overhead by adopting Alibaba Cloud’s fully managed Milvus vector search service, achieving higher performance, better availability, and reduced management costs.

AIMilvuse-commerce
0 likes · 8 min read
How Alibaba Cloud’s Milvus Service Boosted E‑commerce Search Stability and Scalability
DataFunTalk
DataFunTalk
Jun 26, 2024 · Big Data

Evolution of the Big Data + AI Development Paradigm and Alibaba Cloud’s Integrated Architecture

This article examines how the big‑data AI development paradigm has shifted from model‑centric to data‑centric workflows, outlines the challenges of integrating data and AI teams, and details Alibaba Cloud’s end‑to‑end, serverless big‑data platform—including MaxCompute, Hologres, MaxFrame, Object Table, and vector search—designed to accelerate large‑scale AI applications.

AI integrationBig DataMaxCompute
0 likes · 20 min read
Evolution of the Big Data + AI Development Paradigm and Alibaba Cloud’s Integrated Architecture
DataFunSummit
DataFunSummit
Jun 20, 2024 · Big Data

Data+AI Data Lake Technologies: Apache Iceberg, PyIceberg, and Vector Table Solutions

This article presents a comprehensive overview of modern Data+AI data lake challenges and solutions, covering the evolution of data lakes, an introduction to Apache Iceberg, practical use of PyIceberg for AI training and inference pipelines, and advanced vector table and indexing techniques for efficient similarity search.

AI trainingApache IcebergBig Data
0 likes · 22 min read
Data+AI Data Lake Technologies: Apache Iceberg, PyIceberg, and Vector Table Solutions
AI Large Model Application Practice
AI Large Model Application Practice
Jun 7, 2024 · Artificial Intelligence

Mastering Advanced Retrieval: Fusion and Recursive Strategies for RAG

This article explores two advanced retrieval paradigms—Fusion Retrieval, which merges results from multiple retrievers using re‑ranking, and Recursive Retrieval, which builds hierarchical chunk‑to‑chunk or chunk‑to‑retriever links—to boost the quality and flexibility of Retrieval‑Augmented Generation pipelines.

Fusion RetrievalLLMLangChain
0 likes · 12 min read
Mastering Advanced Retrieval: Fusion and Recursive Strategies for RAG
21CTO
21CTO
May 6, 2024 · Databases

How Oracle’s New 23ai Database Brings AI-Powered Vector Search to Enterprises

Oracle’s latest release, Database 23ai, upgrades its 23c platform with AI-driven vector search, RAG capabilities, and enhanced JSON and graph querying, positioning the database as a unified, secure, and scalable solution for handling structured, semi‑structured, and unstructured data across cloud and on‑premises environments.

AIDatabaseOracle
0 likes · 7 min read
How Oracle’s New 23ai Database Brings AI-Powered Vector Search to Enterprises
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Mar 22, 2024 · Artificial Intelligence

Improving Document Search with Vector Search: From Elasticsearch Limitations to Milvus Integration

This article explains how traditional keyword search with Elasticsearch often yields inaccurate or incomplete results for document retrieval, introduces vectorization and semantic search using NLP embeddings, and demonstrates a practical workflow that combines these techniques with the Milvus vector database to achieve more accurate and efficient document search.

AIDocument RetrievalElasticsearch
0 likes · 13 min read
Improving Document Search with Vector Search: From Elasticsearch Limitations to Milvus Integration
Sohu Tech Products
Sohu Tech Products
Mar 13, 2024 · Databases

DingoDB Multi-Modal Vector Database: Design Philosophy, Architecture and Applications

DingoDB is a multi‑modal vector database that unifies storage and analysis of structured, semi‑structured and unstructured data through a Raft‑based distributed architecture, offering MySQL‑compatible SQL, high‑performance APIs, automatic sharding, real‑time index optimization, and hybrid scalar‑vector queries for enterprise knowledge bases, LLM memory, and real‑time decision‑making.

DingoDBLLM applicationsMulti-Modal Database
0 likes · 11 min read
DingoDB Multi-Modal Vector Database: Design Philosophy, Architecture and Applications
Baidu Geek Talk
Baidu Geek Talk
Jan 24, 2024 · Artificial Intelligence

Building AI‑Native Applications with Baidu Cloud AppBuilder

Sun Ke’s keynote at the 2023 Baidu Cloud Intelligence Conference explains how AI‑native development has shifted from model selection to building practical applications, and introduces Baidu Cloud AppBuilder—a three‑layer, low‑code‑and‑code platform that provides multimodal, LLM, and infrastructure services, enabling rapid prototyping of solutions such as automated resume screening and interview preparation.

AIAppBuilderNL2SQL
0 likes · 12 min read
Building AI‑Native Applications with Baidu Cloud AppBuilder
JD Cloud Developers
JD Cloud Developers
Jan 10, 2024 · Artificial Intelligence

Boosting Elasticsearch with Generative AI: Relevance Engine & Vector Search

This article explores the rise of generative AI, outlines popular models like ChatGPT, DALL‑E, and Google Bard, examines their limitations, and then delves into Elasticsearch’s Relevance Engine and vector capabilities, demonstrating how to store, index, and query dense embeddings with practical code examples.

ElasticsearchLLM Integrationgenerative AI
0 likes · 17 min read
Boosting Elasticsearch with Generative AI: Relevance Engine & Vector Search
Tongcheng Travel Technology Center
Tongcheng Travel Technology Center
Dec 27, 2023 · Big Data

Recap of Tongcheng Travel’s 7th Big Data Technology Salon – Talks on StarRocks, Paimon, Iceberg, Data+AI, Vector Retrieval, Real‑Time Computing, and Hotel Ranking

The 7th Tongcheng Travel Big Data Technology Salon in Beijing featured a series of expert talks covering StarRocks architecture evolution, lake‑house solutions with Paimon, Iceberg real‑time upsert, Data+AI for travel recommendation, vector retrieval in AI, JD Logistics real‑time computing governance, and multi‑task hotel ranking modeling, providing deep technical insights and future roadmaps.

AIBig DataLakehouse
0 likes · 10 min read
Recap of Tongcheng Travel’s 7th Big Data Technology Salon – Talks on StarRocks, Paimon, Iceberg, Data+AI, Vector Retrieval, Real‑Time Computing, and Hotel Ranking
dbaplus Community
dbaplus Community
Nov 27, 2023 · Artificial Intelligence

Build an Image‑Search Engine with Elasticsearch 8.x and CLIP

This guide explains how to implement reverse image search by extracting visual features with a multilingual CLIP model, storing the vectors in Elasticsearch 8.x, and using its k‑NN plugin to retrieve similar images, covering architecture, tools, code snippets, and results.

CLIPdeep learningimage search
0 likes · 9 min read
Build an Image‑Search Engine with Elasticsearch 8.x and CLIP
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Nov 27, 2023 · Artificial Intelligence

How OpenSearch Supercharges Vector Search for Large‑Model Applications

This article explains how Alibaba Cloud OpenSearch leverages vector retrieval, engineering and algorithmic optimizations, heterogeneous CPU‑GPU computing, and dense‑sparse hybrid memory to deliver billion‑scale, high‑throughput search performance and enable conversational AI use cases such as intelligent Q&A and SmartArXiv.

AIOpenSearchRetrieval
0 likes · 16 min read
How OpenSearch Supercharges Vector Search for Large‑Model Applications
Baidu Intelligent Cloud Tech Hub
Baidu Intelligent Cloud Tech Hub
Nov 1, 2023 · Databases

How BES Powers Large-Scale Vector Search for AI Applications

This article explains the principles of vector databases, outlines the engineering practices of Baidu Intelligent Cloud BES for large‑scale vector retrieval, discusses optimization techniques such as HNSW, IVF and filter integration, and presents real‑world AI use cases and future development directions.

AIBESElasticsearch
0 likes · 16 min read
How BES Powers Large-Scale Vector Search for AI Applications
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Oct 19, 2023 · Artificial Intelligence

How to Build a Retrieval‑Augmented LLM Knowledge Base on Alibaba Cloud

This guide details a complete end‑to‑end solution for constructing a large‑language‑model knowledge‑base chatbot on Alibaba Cloud, covering background, modular architecture, vector database selection, text preprocessing, embedding models, LLM fine‑tuning, prompt engineering, deployment with PAI‑EAS and BladeLLM, and real‑world results.

AILLMLangChain
0 likes · 37 min read
How to Build a Retrieval‑Augmented LLM Knowledge Base on Alibaba Cloud
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.

AIANNEmbedding
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 IntelligenceEmbeddingFAQ Retrieval
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.

AIDatabaseRAG
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.

AICustomer ServiceFAISS
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.

HNSWlarge-scalerecall
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.

AIBig DataFlink
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.

Database ArchitectureMilvusPerformance 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
NetEase Cloud Music Tech Team
NetEase Cloud Music Tech Team
Mar 31, 2022 · Industry Insights

How Implicit Relationship Chains Solve Cold‑Start Problems at NetEase Cloud Music

This article details NetEase Cloud Music's technical approach to building implicit user relationship chains—using SimHash, Item2Vec, and MetaPath2Vec embeddings, large‑scale vector search, and a unified service architecture—to address cold‑start challenges across multiple business scenarios.

Item2VecMetaPath2VecRecommendation Systems
0 likes · 20 min read
How Implicit Relationship Chains Solve Cold‑Start Problems at NetEase Cloud Music