BestHub
Sign in
Tagged articles

vector database

224 articles · Page 2 of 3
Amazon Cloud Developers
Amazon Cloud Developers
Sep 5, 2025 · Artificial Intelligence

Cut Search Time by 30% and Boost Accuracy 80% with Amazon Bedrock for Financial Data Retrieval

Amazon Finance built an AI assistant that combines Amazon Bedrock, Claude 3 Sonnet, and Amazon Kendra to let analysts query financial data in natural language, achieving a 30% reduction in search time, an 80% increase in accuracy, and high precision and recall across data‑discovery and document‑search tasks.

AI assistantAmazon BedrockAmazon Kendra
0 likes · 20 min read
Cut Search Time by 30% and Boost Accuracy 80% with Amazon Bedrock for Financial Data Retrieval
Data Thinking Notes
Data Thinking Notes
Aug 31, 2025 · Artificial Intelligence

Embedding's Role in Retrieval‑Augmented Generation: Basics, Challenges & Future

This article explains how embedding technology converts unstructured data into vector representations, powers precise retrieval in Retrieval‑Augmented Generation (RAG), outlines the evolution of embedding models, discusses current challenges such as long‑text handling and domain adaptation, and highlights emerging solutions.

AIEmbeddingRAG
0 likes · 12 min read
Embedding's Role in Retrieval‑Augmented Generation: Basics, Challenges & Future
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 21, 2025 · Artificial Intelligence

Why Your AI Defect Deduplication Returns Mixed Data and How to Fix It

This article details the challenges of building an AI‑powered defect deduplication system using Retrieval‑Augmented Generation, explains why LLMs produce composite (spliced) results, diagnoses the root cause as information loss in the RAG pipeline, and presents a step‑by‑step solution that restores atomicity of records for reliable duplicate detection.

AI debuggingKnowledge BaseLLM
0 likes · 14 min read
Why Your AI Defect Deduplication Returns Mixed Data and How to Fix It
TAL Education Technology
TAL Education Technology
Jul 31, 2025 · Databases

How Milvus Powers Billion-Scale Vector Search for AI at TAL Education

This article explains how TAL Education leverages the open‑source Milvus vector database—covering its architecture, features, cloud‑native deployment, monitoring, and real‑world AI applications such as intelligent grading and multimodal search—to handle billions of vectors with millisecond‑level similarity retrieval.

AICloud NativeEducation Technology
0 likes · 14 min read
How Milvus Powers Billion-Scale Vector Search for AI at TAL Education
DeWu Technology
DeWu Technology
Jul 30, 2025 · Databases

Why Milvus Outperforms Traditional Databases: Deep Dive into Vector DB Architecture

This article explores the evolution, architecture, and operational challenges of vector databases like Milvus and Zilliz, comparing them with traditional databases, detailing indexing strategies such as HNSW and DiskANN, migration plans, performance benchmarks, and future directions for large‑scale AI‑driven search systems.

AIIndexingMilvus
0 likes · 26 min read
Why Milvus Outperforms Traditional Databases: Deep Dive into Vector DB Architecture
AI Algorithm Path
AI Algorithm Path
Jun 26, 2025 · Artificial Intelligence

The 10 Essential Components of a Retrieval‑Augmented Generation (RAG) System

This guide breaks down the ten core building blocks of a production‑ready RAG pipeline—from input handling and vector stores to prompt engineering, LLM inference, observability, and evaluation—showing why each piece matters, common pitfalls, and practical best‑practice recommendations.

LLMObservabilityPrompt engineering
0 likes · 9 min read
The 10 Essential Components of a Retrieval‑Augmented Generation (RAG) System
ByteDance Data Platform
ByteDance Data Platform
Jun 11, 2025 · Databases

BlendHouse: The Award‑Winning Cloud‑Native Vector Database Redefining Search

ByteHouse’s BlendHouse, a cloud‑native vector database system presented at ICDE 2025, won the Best Industry and Application Paper Award, showcasing a high‑performance, universally designed framework with deep mixed‑query optimization that outperforms dedicated vector databases in read/write speed and supports large‑scale multimodal retrieval.

BlendHouseICDE 2025cloud-native
0 likes · 6 min read
BlendHouse: The Award‑Winning Cloud‑Native Vector Database Redefining Search
AntData
AntData
May 20, 2025 · Artificial Intelligence

How Vector Retrieval Powers AI: Challenges, Solutions, and VSAG’s Open‑Source Breakthrough

The article examines the rapid growth of unstructured data, explains the fundamentals and resource‑intensive nature of vector retrieval, presents Ant Group’s engineering practices—including hybrid HNSW‑DiskANN indexing, performance tricks like BSA pruning and memory prefetching, sparse‑vector and feedback‑driven recall improvements—and outlines the open‑source VSAG roadmap and ecosystem integrations.

AI InfrastructurePerformance OptimizationVector Retrieval
0 likes · 18 min read
How Vector Retrieval Powers AI: Challenges, Solutions, and VSAG’s Open‑Source Breakthrough
DeWu Technology
DeWu Technology
May 9, 2025 · Artificial Intelligence

Growth Story of a Technical Lead: Building a One‑Stop Large‑Model Training and Inference Platform at Dewu

Meng, a former Tencent and Alibaba engineer, led Dewu’s one‑stop large‑model training and inference platform, cutting integration costs, creating a shared GPU pool and CI/CD pipeline, building a Milvus vector‑database, and driving self‑directed learning that boosted business value, user experience, and set a roadmap for future RAG and cloud‑native optimizations.

AI platformMLOpscareer development
0 likes · 18 min read
Growth Story of a Technical Lead: Building a One‑Stop Large‑Model Training and Inference Platform at Dewu
Fun with Large Models
Fun with Large Models
Apr 18, 2025 · Artificial Intelligence

How RAG Works: From Data Prep to LLM Generation Explained

This article breaks down Retrieval‑Augmented Generation (RAG) into its three core stages—data preparation, data retrieval, and LLM generation—showing how document chunking, embedding, vector databases, similarity search, and optional re‑ranking combine to let large language models produce more accurate, knowledge‑grounded answers.

EmbeddingLLMRAG
0 likes · 9 min read
How RAG Works: From Data Prep to LLM Generation Explained
Spring Full-Stack Practical Cases
Spring Full-Stack Practical Cases
Apr 10, 2025 · Artificial Intelligence

Build a RAG-Powered Knowledge Base with Spring Boot, Milvus, and Ollama

This guide walks through creating a Retrieval‑Augmented Generation (RAG) system using Spring Boot 3.4.2, Milvus vector database, and the bge‑m3 embedding model via Ollama, covering environment setup, dependency configuration, vector store operations, and integration with a large language model to deliver refined, similarity‑based answers.

EmbeddingLLMMilvus
0 likes · 11 min read
Build a RAG-Powered Knowledge Base with Spring Boot, Milvus, and Ollama
Big Data Technology & Architecture
Big Data Technology & Architecture
Apr 3, 2025 · Artificial Intelligence

Understanding Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), and Vector Databases for LLM Integration

This article explains the Model Context Protocol (MCP) as a standard for LLM‑data integration, describes Retrieval‑Augmented Generation (RAG) techniques to reduce hallucinations, and introduces vector databases like Milvus that store high‑dimensional embeddings for efficient AI retrieval tasks.

LLMMCPMilvus
0 likes · 7 min read
Understanding Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), and Vector Databases for LLM Integration
Architect
Architect
Mar 29, 2025 · Artificial Intelligence

How Non‑AI Developers Can Build Powerful LLM Apps: Prompt Engineering, RAG, and AI Agents Explained

This article guides developers without an AI background through the fundamentals of building large‑language‑model applications, covering prompt engineering, multi‑turn interaction, function calling, retrieval‑augmented generation, vector databases, code assistants, and the MCP protocol for AI agents.

AI AgentEmbeddingFunction Calling
0 likes · 51 min read
How Non‑AI Developers Can Build Powerful LLM Apps: Prompt Engineering, RAG, and AI Agents Explained
DaTaobao Tech
DaTaobao Tech
Mar 19, 2025 · Artificial Intelligence

Retrieval Augmented Generation (RAG): Principles, Challenges, and Implementation Techniques

Retrieval‑augmented generation (RAG) enhances large language models by integrating a preprocessing pipeline—cleaning, chunking, embedding, and vector storage—with a query‑driven retrieval and prompt‑injection workflow, leveraging vector databases, multi‑stage recall, advanced prompting, and comprehensive evaluation metrics to mitigate knowledge cut‑off, hallucinations, and security issues.

EvaluationLLMRAG
0 likes · 27 min read
Retrieval Augmented Generation (RAG): Principles, Challenges, and Implementation Techniques
Architect
Architect
Mar 18, 2025 · Artificial Intelligence

2025 AI Agent Technology Stack: Layers, Core Functions, and Future Directions

The article outlines the 2025 AI Agent technology stack, detailing its five layered architecture—model serving, storage & memory, tooling, framework orchestration, and deployment—while discussing current trends, challenges, and future directions such as tool ecosystem expansion, self‑evolution, and edge‑cloud hybrid deployments.

AI AgentObservabilityTool Integration
0 likes · 12 min read
2025 AI Agent Technology Stack: Layers, Core Functions, and Future Directions
Tencent Technical Engineering
Tencent Technical Engineering
Mar 10, 2025 · Artificial Intelligence

How Non‑AI Developers Can Build LLM Apps: Prompt Engineering, RAG, and Function Calling Explained

This guide shows non‑AI developers how to create large‑model applications by mastering prompt engineering, multi‑turn interactions, Retrieval‑Augmented Generation, function calling, and AI‑Agent integration, with practical code examples, tool design patterns, and deployment tips.

AI AgentEmbeddingFunction Calling
0 likes · 48 min read
How Non‑AI Developers Can Build LLM Apps: Prompt Engineering, RAG, and Function Calling Explained
DevOps
DevOps
Mar 9, 2025 · Artificial Intelligence

A Beginner's Guide to Building Large Language Model Applications: Prompt Engineering, Retrieval‑Augmented Generation, Function Calling, and AI Agents

This article provides a comprehensive introduction to developing large language model (LLM) applications, covering prompt engineering, zero‑ and few‑shot techniques, function calling, retrieval‑augmented generation (RAG) with embedding and vector databases, code assistants, and the MCP protocol for building AI agents, all aimed at non‑AI specialists.

AI AgentEmbeddingFunction Calling
0 likes · 48 min read
A Beginner's Guide to Building Large Language Model Applications: Prompt Engineering, Retrieval‑Augmented Generation, Function Calling, and AI Agents
IT Services Circle
IT Services Circle
Mar 8, 2025 · Databases

PostgreSQL Overtaking MySQL: Cloud Adoption, Vector DB Advantage, and Future Database Landscape

The article analyzes recent industry data and expert observations showing PostgreSQL surpassing MySQL in cloud instance counts, CPU usage, and ecosystem support, especially in vector‑database and serverless contexts, while highlighting MySQL's strategic shortcomings and predicting PostgreSQL's dominance in the coming years.

Cloud DatabasesDatabase TrendsPostgreSQL
0 likes · 5 min read
PostgreSQL Overtaking MySQL: Cloud Adoption, Vector DB Advantage, and Future Database Landscape
Cognitive Technology Team
Cognitive Technology Team
Mar 4, 2025 · Artificial Intelligence

Deep Searcher: An Open‑Source Agentic RAG Framework for Enterprise‑Level Search and Knowledge Retrieval

The article introduces Deep Searcher, an open‑source Agentic Retrieval‑Augmented Generation system that combines large language models, Milvus vector databases, and multi‑step reasoning to deliver enterprise‑grade search, reporting, and complex query capabilities, and compares its performance against traditional RAG and Graph RAG approaches.

LLMRAGagentic
0 likes · 18 min read
Deep Searcher: An Open‑Source Agentic RAG Framework for Enterprise‑Level Search and Knowledge Retrieval
Tencent Cloud Developer
Tencent Cloud Developer
Mar 4, 2025 · Artificial Intelligence

A Practical Guide to Building Large Language Model Applications: Prompt Engineering, Retrieval‑Augmented Generation, Function Calling and AI Agents

The guide teaches non‑AI developers how to build practical LLM‑powered applications by mastering prompt engineering, function calling, retrieval‑augmented generation, and AI agents, and introduces the Modal Context Protocol for seamless tool integration, offering a clear learning path to leverage large language models without deep theory.

AI AgentFunction CallingLLM
0 likes · 48 min read
A Practical Guide to Building Large Language Model Applications: Prompt Engineering, Retrieval‑Augmented Generation, Function Calling and AI Agents
Cognitive Technology Team
Cognitive Technology Team
Feb 28, 2025 · Artificial Intelligence

Comparative Study of Traditional RAG, GraphRAG, and DeepSearcher for Knowledge Retrieval and Generation

This article examines why Retrieval‑Augmented Generation (RAG) is needed, compares traditional RAG, GraphRAG, and the DeepSearcher framework across architecture, data organization, retrieval mechanisms, result generation, efficiency and accuracy, and provides step‑by‑step implementation guides and experimental results using vector and graph databases.

DeepSearcherGraphRAGRAG
0 likes · 20 min read
Comparative Study of Traditional RAG, GraphRAG, and DeepSearcher for Knowledge Retrieval and Generation
iKang Technology Team
iKang Technology Team
Feb 7, 2025 · Artificial Intelligence

Retrieval‑Augmented Generation (RAG) with LangChain: Concepts and Python Implementation

Retrieval‑Augmented Generation (RAG) using LangChain lets developers enhance large language models by embedding user queries, fetching relevant documents from a vector store, inserting the context into a prompt template, and generating concise, source‑grounded answers, offering low‑cost, up‑to‑date knowledge while reducing hallucinations and fine‑tuning expenses.

LLMLangChainRAG
0 likes · 10 min read
Retrieval‑Augmented Generation (RAG) with LangChain: Concepts and Python Implementation
Architect
Architect
Jan 27, 2025 · Artificial Intelligence

How to Build a Retrieval‑Augmented Generation QA Assistant for an Open Platform

This article details a step‑by‑step design of a RAG‑based intelligent Q&A assistant for the DeWu Open Platform, covering background, RAG fundamentals, system architecture, technology selection, prompt engineering with CO‑STAR, data preprocessing, vector store setup, LangChain.js implementation, similarity search, runnable chaining, debugging, and future prospects.

AILLMLangChain
0 likes · 28 min read
How to Build a Retrieval‑Augmented Generation QA Assistant for an Open Platform
JD Tech Talk
JD Tech Talk
Jan 9, 2025 · Artificial Intelligence

Practical Guide to Building Retrieval‑Augmented Generation (RAG) Applications with LangChain4j in Java

This article provides a step‑by‑step tutorial for Java engineers on using the LangChain4j framework to implement Retrieval‑Augmented Generation (RAG) with large language models, covering concepts, environment setup, code integration, document splitting, embedding, vector‑store operations, and prompt engineering.

EmbeddingJavaLangChain4j
0 likes · 35 min read
Practical Guide to Building Retrieval‑Augmented Generation (RAG) Applications with LangChain4j in Java
JD Cloud Developers
JD Cloud Developers
Jan 9, 2025 · Artificial Intelligence

Boost Your Java Apps with LangChain4j: A Hands‑On RAG Guide

This article walks Java developers through the fundamentals of Retrieval‑Augmented Generation (RAG), explains the LangChain4j framework, compares large‑model development with traditional Java coding, and provides step‑by‑step code examples for environment setup, document splitting, embedding, vector‑store operations, and LLM interaction.

EmbeddingJavaLangChain4j
0 likes · 34 min read
Boost Your Java Apps with LangChain4j: A Hands‑On RAG Guide
DeWu Technology
DeWu Technology
Jan 6, 2025 · Artificial Intelligence

Design and Implementation of a Retrieval‑Augmented Generation (RAG) Answering Assistant for the Dewu Open Platform

The paper describes building a Retrieval‑Augmented Generation assistant for the Dewu Open Platform that leverages GPT‑4o‑mini, OpenAI embeddings, Milvus vector store, and LangChain.js to semantically retrieve API documentation, structure user queries, and generate accurate, JSON‑formatted answers, thereby reducing manual support and hallucinations.

AILLMLangChain
0 likes · 28 min read
Design and Implementation of a Retrieval‑Augmented Generation (RAG) Answering Assistant for the Dewu Open Platform
Baobao Algorithm Notes
Baobao Algorithm Notes
Dec 15, 2024 · Artificial Intelligence

What Are the Best Practices for Retrieval‑Augmented Generation (RAG)?

This comprehensive study evaluates various components of Retrieval‑Augmented Generation pipelines—including query classification, chunking, embedding models, vector databases, retrieval, re‑ranking, summarization, and generator fine‑tuning—identifies optimal configurations, and proposes best‑practice guidelines for both performance‑maximizing and efficiency‑balanced RAG systems.

LLMRAGRetrieval-Augmented Generation
0 likes · 17 min read
What Are the Best Practices for Retrieval‑Augmented Generation (RAG)?
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Dec 5, 2024 · Artificial Intelligence

How to Build a Financial RAG Solution with Alibaba PAI: Step-by-Step Guide

Learn how to create a Retrieval‑Augmented Generation (RAG) system for financial scenarios using Alibaba’s PAI platform—covering knowledge‑base construction with PAI‑Designer, template creation in PAI‑LangStudio, deployment of LLM and embedding models, and linking vector stores for accurate, context‑aware answers.

EmbeddingPAIRAG
0 likes · 17 min read
How to Build a Financial RAG Solution with Alibaba PAI: Step-by-Step Guide
DataFunSummit
DataFunSummit
Nov 27, 2024 · Artificial Intelligence

Applying Large Language Models in Data Management and Risk Control at Ping An One Wallet

This presentation details how Ping An One Wallet leverages large language models across five key areas—current application status, data management, risk control, technical architecture, and a Q&A session—highlighting strategies such as vectorized rule storage, prompt engineering, RAG enhancements, and workflow agents to improve efficiency and accuracy in data governance and fraud detection.

AI ArchitectureData Governancerisk control
0 likes · 16 min read
Applying Large Language Models in Data Management and Risk Control at Ping An One Wallet
21CTO
21CTO
Nov 19, 2024 · Databases

Why Vector Databases Like Milvus Outperform Elasticsearch in Hybrid Search

This article explains how combining dense vector‑based semantic search with traditional keyword matching using a unified vector database such as Milvus delivers superior performance, scalability, and simplicity compared to maintaining separate Elasticsearch and vector‑search stacks.

ElasticsearchHybrid SearchMilvus
0 likes · 9 min read
Why Vector Databases Like Milvus Outperform Elasticsearch in Hybrid Search
ITPUB
ITPUB
Nov 15, 2024 · Databases

Why Vector Databases Matter: Deploying PgVector on PostgreSQL for Scalable AI Retrieval

This article explains the need for vector databases in the AI era, reviews PostgreSQL's extensible ecosystem, compares vector‑database options, provides step‑by‑step PgVector installation and usage, shares operational best practices, performance tuning tips, and real‑world Qunar & Tujia case studies.

AIPerformance TuningPostgreSQL
0 likes · 27 min read
Why Vector Databases Matter: Deploying PgVector on PostgreSQL for Scalable AI Retrieval
Architects' Tech Alliance
Architects' Tech Alliance
Nov 12, 2024 · Artificial Intelligence

How Retrieval‑Augmented Generation Boosts Enterprise AI with Intel Optimizations

This article explains the fundamentals of Retrieval‑Augmented Generation (RAG), its four‑step workflow, architecture, and how Intel’s hardware and software optimizations—including vector search, quantized embeddings, and advanced inference extensions—enhance performance, security, and scalability for enterprise LLM applications.

AI inferenceEmbedding QuantizationIntel Optimization
0 likes · 14 min read
How Retrieval‑Augmented Generation Boosts Enterprise AI with Intel Optimizations
Baidu Tech Salon
Baidu Tech Salon
Nov 11, 2024 · Cloud Native

Baidu Cloud Native Data Platform: Empowering Enterprise AI in the LLM Era

To empower enterprise AI in the LLM era, Baidu Cloud unveils a cloud‑native data platform featuring upgraded databases—PegaDB, GaiaDB 5.0, Vector DB 2.0, Palo 2.0—and integrated services like DBSC 2.0, EDAP 2.0, and DBStack, delivering high‑performance, cost‑effective handling of structured, unstructured, and vector data for fine‑tuning and Enterprise RAG.

DBStackData LakehouseEDAP
0 likes · 10 min read
Baidu Cloud Native Data Platform: Empowering Enterprise AI in the LLM Era
JD Tech
JD Tech
Oct 31, 2024 · Artificial Intelligence

Design and Implementation of the Logistics Intelligent Robot “Yunli XiaoZhi” Powered by Large Language Models

The article details the development of Yunli XiaoZhi, an AI‑driven logistics chatbot that combines knowledge‑base Q&A, data‑analysis, proactive alerts and report‑pushing to streamline SOP access, reduce manual query effort, and improve operational efficiency for operators, carriers and drivers.

AI ChatbotKnowledge BaseRAG
0 likes · 22 min read
Design and Implementation of the Logistics Intelligent Robot “Yunli XiaoZhi” Powered by Large Language Models
JavaEdge
JavaEdge
Oct 15, 2024 · Artificial Intelligence

Build a Real‑Time Search & Bazi AI Agent with LangChain & FastAPI

This tutorial walks through creating a LangChain tool‑calling agent that combines a real‑time web search tool, a Qdrant vector store for local knowledge retrieval, and a custom Bazi fortune‑telling service, all wrapped in a FastAPI application for interactive use.

AI AgentFastAPILangChain
0 likes · 15 min read
Build a Real‑Time Search & Bazi AI Agent with LangChain & FastAPI
JD Cloud Developers
JD Cloud Developers
Sep 29, 2024 · Artificial Intelligence

Build a Local AI Q&A System with Java, Ollama, and LangChain4J

This article walks through building a local AI question‑answer system using Java, Ollama, LangChain4J, embeddings, and a Chroma vector database, covering LLM fundamentals, embedding techniques, RAG architecture, setup steps, Maven dependencies, and sample code to retrieve and answer queries.

AIEmbeddingJava
0 likes · 19 min read
Build a Local AI Q&A System with Java, Ollama, and LangChain4J
DaTaobao Tech
DaTaobao Tech
Sep 20, 2024 · Databases

Database Technology Evolution: From Hierarchical to Vector Databases

The article chronicles the evolution of database technology from early hierarchical and network models through relational, column‑store, document, key‑value, graph, time‑series, HTAP, and finally vector databases, detailing each system’s architecture, strengths, limitations, typical uses, and future trends toward specialization, distributed cloud‑native designs, and AI‑driven applications.

HBaseHTAPInfluxDB
0 likes · 52 min read
Database Technology Evolution: From Hierarchical to Vector Databases
DataFunTalk
DataFunTalk
Sep 20, 2024 · Databases

Technical Paper Summaries on Graph Databases, Vector Databases, and Real-Time Data Warehousing

This article compiles concise English summaries of several technical papers covering Xiaohongshu's REDgraph graph database, DingoDB vector database, Tianqiong autonomous data platform, Douyin's real‑time data warehouse, financial‑grade data warehousing, Alibaba Cloud ClickHouse Serverless offering, best practices in financial data governance, and 58.com user‑profile data warehouse construction.

Big DataData Warehousegraph database
0 likes · 5 min read
Technical Paper Summaries on Graph Databases, Vector Databases, and Real-Time Data Warehousing
DataFunTalk
DataFunTalk
Sep 19, 2024 · Databases

Technical Topics Overview from DataFun Summit: Graph Database, Vector Database, Real-time Data Warehouse, and Cloud‑Native Solutions

The article presents a collection of technical overviews—including a graph database for distributed queries, a next‑generation vector database, real‑time data warehouse architectures at Douyin and Ant Group, a cloud‑native ClickHouse service, and best practices for financial data warehousing—while also explaining how to obtain the related e‑book.

Big DataCloud NativeReal-Time Data Warehouse
0 likes · 4 min read
Technical Topics Overview from DataFun Summit: Graph Database, Vector Database, Real-time Data Warehouse, and Cloud‑Native Solutions
ITPUB
ITPUB
Sep 18, 2024 · Databases

Why Vector Databases Are the Next Big Thing in GenAI Applications

The article examines how vector databases have become the most popular database type in the past three years, why they are essential for handling unstructured data in GenAI, compares proprietary and multi‑model solutions, and outlines future trends and practical deployment considerations.

AI ApplicationsDatabase TrendsGenAI
0 likes · 10 min read
Why Vector Databases Are the Next Big Thing in GenAI Applications
DataFunTalk
DataFunTalk
Sep 17, 2024 · Databases

Overview of Recent Advances in Graph, Vector, and Real-Time Data Warehouse Technologies

This article presents a collection of technical abstracts covering graph database parallel query optimization, next‑generation vector databases, real‑time data warehouse architectures, and cloud‑native analytics solutions, while also providing instructions for obtaining the full e‑book via a WeChat public account.

Big DataCloud NativeData Warehouse
0 likes · 5 min read
Overview of Recent Advances in Graph, Vector, and Real-Time Data Warehouse Technologies
Code Mala Tang
Code Mala Tang
Sep 12, 2024 · Artificial Intelligence

Boost LLM Accuracy with Retrieval‑Augmented Generation Using LangChain.js

This article explains the core concepts of Retrieval‑Augmented Generation (RAG), walks through its implementation steps with LangChain.js—including text chunking, embedding, storage, retrieval, and generation—and showcases practical use cases, challenges, and best practices for building reliable AI‑powered applications.

AI ApplicationsEmbeddingLLM
0 likes · 16 min read
Boost LLM Accuracy with Retrieval‑Augmented Generation Using LangChain.js
Baidu Geek Talk
Baidu Geek Talk
Sep 11, 2024 · Databases

Why Vector Databases Are the Next Big Thing in AI: A Deep Dive into RAG and Baidu’s VectorDB

This article examines the 70‑year evolution of databases, explains how large‑model AI drives the rise of vector databases and Retrieval‑Augmented Generation (RAG), outlines the four‑stage RAG workflow, compares Baidu’s self‑built VectorDB with open‑source alternatives, and showcases real‑world deployments that highlight performance, scalability, and enterprise benefits.

AIDatabase ArchitectureRAG
0 likes · 16 min read
Why Vector Databases Are the Next Big Thing in AI: A Deep Dive into RAG and Baidu’s VectorDB
21CTO
21CTO
Sep 7, 2024 · Artificial Intelligence

Why AI Databases Are the Next Big Leap for Vector Search and Multimodal Data

The article explains how AI databases combine structured, unstructured, and vector data, integrate machine‑learning, NLP, and generative models, and why platforms like Vespa are emerging as open‑source solutions to meet the performance and scalability demands of modern generative AI applications.

AI DatabaseGenerative AIMultimodal Data
0 likes · 8 min read
Why AI Databases Are the Next Big Leap for Vector Search and Multimodal Data
DaTaobao Tech
DaTaobao Tech
Aug 30, 2024 · Artificial Intelligence

Overview of Large Model Application Development Platforms: LangChain, Dify, Flowise, and Coze

The article reviews open‑source and commercial large‑model development platforms—LangChain, Dify, Flowise, and Coze—detailing their architectures, low‑code visual tools, model integrations, extensibility, and a step‑by‑step Dify example, and concludes they are essential infrastructure for rapid AI application deployment.

AI application developmentDifyFlowise
0 likes · 13 min read
Overview of Large Model Application Development Platforms: LangChain, Dify, Flowise, and Coze
Qunar Tech Salon
Qunar Tech Salon
Aug 28, 2024 · Databases

Why Vector Databases Are Needed, PgVector Installation, Usage, and Operational Practices in PostgreSQL

This article explains the necessity of vector databases for AI workloads, reviews the PostgreSQL ecosystem, compares vector database options, provides detailed PgVector installation and usage steps, shares operational best‑practices, performance tuning tips, and real‑world deployment cases at Qunar and Tujia.

AIPerformance TuningPostgreSQL
0 likes · 24 min read
Why Vector Databases Are Needed, PgVector Installation, Usage, and Operational Practices in PostgreSQL
JD Tech
JD Tech
Aug 23, 2024 · Artificial Intelligence

AI-Powered Automated Exam Generation for Aviation Maintenance Training

This article describes an AI-driven solution that uses vector databases and large language models to automatically generate, evaluate, and maintain training exam questions for aviation maintenance personnel, addressing high document volume, frequent updates, and low training effectiveness.

AIexam generationtraining automation
0 likes · 14 min read
AI-Powered Automated Exam Generation for Aviation Maintenance Training
Volcano Engine Developer Services
Volcano Engine Developer Services
Aug 20, 2024 · Databases

How Vector Databases Power RAG: Scaling, Algorithms, and Real‑World Trade‑offs

RAG technology leverages vector databases to provide context‑aware answers without updating model parameters, and this article explores how cloud search teams integrate multiple vector algorithms, balance cost, stability and latency, and adopt open‑source solutions like OpenSearch to build scalable, enterprise‑grade retrieval systems.

AIDiskANNOpenSearch
0 likes · 21 min read
How Vector Databases Power RAG: Scaling, Algorithms, and Real‑World Trade‑offs
21CTO
21CTO
Aug 17, 2024 · Artificial Intelligence

Vector Store vs Vector Database: Which Powers Your AI Apps Better?

This guide explains the differences between vector stores and vector databases, covering vector embeddings, performance, scalability, integration, and ideal use‑cases, helping developers choose the right tool—or a hybrid approach—for AI applications.

AI embeddingsScalable ArchitectureVector Store
0 likes · 12 min read
Vector Store vs Vector Database: Which Powers Your AI Apps Better?
Alibaba Cloud Native
Alibaba Cloud Native
Aug 13, 2024 · Cloud Native

How to Build an AI Cache WASM Plugin for Higress Gateway

This guide explains how to set up a Higress gateway, compile a WebAssembly AI cache plugin, integrate Redis and DashVector for semantic caching of large‑model requests, and provides complete configuration and code examples for end‑to‑end deployment.

AI CacheHigressRedis
0 likes · 16 min read
How to Build an AI Cache WASM Plugin for Higress Gateway
Open Source Tech Hub
Open Source Tech Hub
Jul 31, 2024 · Artificial Intelligence

Understanding LLMs, AI Agents, and Retrieval-Augmented Generation: Key Concepts and Challenges

This article explains the fundamentals of large language models, artificial general intelligence, AI-generated content, AI agents, retrieval‑augmented generation, knowledge bases, multimodal processing, fine‑tuning, alignment, tokens, vectors, and related tools, highlighting their capabilities, limitations, and practical considerations.

AI AgentLLMRAG
0 likes · 14 min read
Understanding LLMs, AI Agents, and Retrieval-Augmented Generation: Key Concepts and Challenges
AntData
AntData
Jul 12, 2024 · Databases

Recent Advances in Vector Databases Presented at SIGMOD 2024

This article reviews the latest vector database research showcased at SIGMOD 2024, covering system designs such as Starling, Vexless, RaBitQ, and ACORN, and discusses current academic hotspots including query processing, index structures, optimization techniques, and hardware acceleration for large‑scale similarity search.

AIIndexingSIGMOD 2024
0 likes · 20 min read
Recent Advances in Vector Databases Presented at SIGMOD 2024
System Architect Go
System Architect Go
Jul 4, 2024 · Artificial Intelligence

Optimizing Image Search System Architecture with Client‑Side Feature Extraction Using MobileNet

This article explains the architecture of an image‑search system that extracts feature vectors, stores them in a vector database, and performs similarity queries, then proposes an optimized design that offloads feature extraction to a lightweight MobileNet model running in the browser, reducing latency, server load, and component complexity.

MobileNetTensorFlow.jsclient-side AI
0 likes · 9 min read
Optimizing Image Search System Architecture with Client‑Side Feature Extraction Using MobileNet
Data Thinking Notes
Data Thinking Notes
Jun 20, 2024 · Artificial Intelligence

Leveraging LLMs for Data: Embedding Search, Knowledge Bases, Text2SQL, and EDA

This article explores how large language models can transform data workflows by using embeddings for semantic search, building private domain knowledge bases, generating SQL code from natural language with visualized results, and enhancing exploratory data analysis, outlining practical steps and benefits for enterprises.

EDAEmbeddingKnowledge Base
0 likes · 7 min read
Leveraging LLMs for Data: Embedding Search, Knowledge Bases, Text2SQL, and EDA
21CTO
21CTO
Jun 7, 2024 · Artificial Intelligence

10 Essential Tools for Building a Modern AI Data Lake Architecture

This article outlines ten critical components of a modern data lake reference architecture for AI/ML, detailing each function, the supporting vendor tools and open‑source libraries, and how they enable scalable storage, MLOps, distributed training, model hubs, vector search, and data visualization.

AIData LakeMLOps
0 likes · 14 min read
10 Essential Tools for Building a Modern AI Data Lake Architecture
Sohu Tech Products
Sohu Tech Products
Jun 5, 2024 · Artificial Intelligence

Retrieval Augmented Generation (RAG): Concepts, Workflow, and LangChain Implementation

The article outlines LLM issues such as hallucination, outdated knowledge, and data privacy, then explains Retrieval‑Augmented Generation—detailing its data‑preparation and query‑time retrieval workflow, demonstrates a full LangChain implementation, and contrasts RAG with fine‑tuning as complementary strategies for up‑to‑date, grounded responses.

LLMLangChainPrompt engineering
0 likes · 15 min read
Retrieval Augmented Generation (RAG): Concepts, Workflow, and LangChain Implementation
JD Retail Technology
JD Retail Technology
Jun 4, 2024 · Databases

How to Deploy and Query JD’s Open‑Source Vearch Vector Database for LLM Retrieval

This article walks through the practical use of JD’s self‑developed Vearch vector database—covering cluster creation, space setup, data insertion, and both text and vector search—illustrating how it integrates with LangChain and OpenAI embeddings to enable retrieval‑augmented generation for large language models.

EmbeddingLLM RetrievalLangChain
0 likes · 16 min read
How to Deploy and Query JD’s Open‑Source Vearch Vector Database for LLM Retrieval
JD Tech
JD Tech
May 31, 2024 · Artificial Intelligence

Understanding Large Language Models, Retrieval‑Augmented Generation, and AI Agents: Concepts, Engineering Practices, and Applications

This article explains the fundamentals and engineering practices of large language models (LLM), retrieval‑augmented generation (RAG) and AI agents, compares small and large embedding models, provides Python code for vector‑database RAG with Chroma, and discusses integration, use cases, and future challenges in AI development.

AI EngineeringAI agentsLLM
0 likes · 41 min read
Understanding Large Language Models, Retrieval‑Augmented Generation, and AI Agents: Concepts, Engineering Practices, and Applications
JD Retail Technology
JD Retail Technology
May 27, 2024 · Artificial Intelligence

Automating Test Case Generation with Large Language Models and LangChain

This article describes how large language models and the LangChain framework can be combined with PDF parsing, text chunking, memory management, and a vector database to automatically generate software test cases, achieving significant efficiency gains while outlining implementation details, results, and future challenges.

AILangChainLarge Language Model
0 likes · 10 min read
Automating Test Case Generation with Large Language Models and LangChain
Code Ape Tech Column
Code Ape Tech Column
May 25, 2024 · Artificial Intelligence

Introducing Spring AI: Integrating Artificial Intelligence into Spring Boot Applications

Spring AI brings artificial‑intelligence capabilities to the Spring Boot ecosystem, offering model support, vector‑database integration, SQL‑like filtering, and easy Maven configuration, enabling Java developers to add generative AI, semantic search, and AI‑driven image generation to their backend applications.

Generative AIJavaSpring AI
0 likes · 6 min read
Introducing Spring AI: Integrating Artificial Intelligence into Spring Boot Applications
DataFunTalk
DataFunTalk
May 9, 2024 · Databases

ByteHouse Vector Search Technical Guide: Architecture, Design, and Performance Optimizations

This guide explains ByteHouse’s high‑performance vector search capabilities, covering the background of vector retrieval for LLMs, the limitations of its existing skip‑index architecture, the new vector‑index design with HNSW and IVF, query‑time optimizations, performance benchmarks against Milvus, and future development plans.

ByteHouseIndexingLLM
0 likes · 8 min read
ByteHouse Vector Search Technical Guide: Architecture, Design, and Performance Optimizations
AntTech
AntTech
Apr 26, 2024 · Databases

Data Processing Technologies in the AI Era: Trends and Integration of Vector and Relational Databases

The talk explores how the rapid growth of multimodal data and large language models is reshaping data processing, highlighting three key trends—online‑offline integration, vector‑relational database convergence, and the fusion of data processing with AI computation—while presenting practical solutions and future visions for unified data‑AI ecosystems.

AIBig DataDatabases
0 likes · 12 min read
Data Processing Technologies in the AI Era: Trends and Integration of Vector and Relational Databases
Sohu Tech Products
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 QianfanEmbedding
0 likes · 11 min read
Building a RAG Application with Baidu Vector Database and Qianfan Embedding
Eric Tech Circle
Eric Tech Circle
Mar 24, 2024 · Artificial Intelligence

Running Local LLMs: Ollama vs Hugging Face – A Hands‑On Comparison

This guide compares Ollama and Hugging Face for running large language models locally, detailing API and local execution methods, installation steps, model selection, resource requirements, integration with AnythingLLM, container deployment, embedding and vector store setup, and practical observations on performance and limitations.

AnythingLLMDockerEmbedding
0 likes · 15 min read
Running Local LLMs: Ollama vs Hugging Face – A Hands‑On Comparison
Alimama Tech
Alimama Tech
Mar 20, 2024 · Artificial Intelligence

Dolphin VectorDB: A High-Performance Vector Database for AI Applications

Dolphin VectorDB, created by Alibaba’s Alimama team, is a high‑performance, scalable vector database that delivers fast, cost‑effective AI‑driven vector storage and real‑time updates, supporting multiple query modes and powering applications such as content risk control, marketing Q&A, and audience selection, with ongoing enhancements for multimodal computing.

AI ApplicationsReal-time Updatescontent risk control
0 likes · 13 min read
Dolphin VectorDB: A High-Performance Vector Database for AI Applications
Ops Development & AI Practice
Ops Development & AI Practice
Mar 16, 2024 · Databases

Why ChromaDB Is Becoming the Go-To Vector Store for AI Applications

ChromaDB is an open‑source, AI‑native vector database that efficiently stores, indexes, and retrieves high‑dimensional embeddings, offering fast similarity search, easy integration via flexible APIs, strong scalability, and active community support, making it suitable for recommendation systems, NLP, and image‑recognition workloads.

AIChromaDBembeddings
0 likes · 5 min read
Why ChromaDB Is Becoming the Go-To Vector Store for AI Applications
Baidu Geek Talk
Baidu Geek Talk
Mar 13, 2024 · Artificial Intelligence

Understanding Retrieval-Augmented Generation (RAG) and Building a Personal Knowledge Base with ERNIE SDK and LangChain

The article explains Retrieval-Augmented Generation (RAG), its workflow, advantages, comparison with fine-tuning, and provides a step-by-step implementation using Baidu's ERNIE SDK, LangChain, and ChromaDB to build a personal knowledge base that answers queries with retrieved context.

AIERNIE SDKKnowledge Base
0 likes · 13 min read
Understanding Retrieval-Augmented Generation (RAG) and Building a Personal Knowledge Base with ERNIE SDK and LangChain
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Feb 25, 2024 · Artificial Intelligence

Pinecone Vector Database and Embedding Model Summary from DeepLearning.AI’s AI Course

This article reviews the author’s hands‑on experience with Pinecone’s serverless vector database, various embedding and generation models such as all‑MiniLM‑L6‑v2, text‑embedding‑ada‑002, clip‑ViT‑B‑32, and GPT‑3.5‑turbo‑instruct, and demonstrates how they are applied to semantic search, RAG, recommendation, hybrid, and facial similarity tasks using Python code examples.

AIEmbedding ModelsPinecone
0 likes · 9 min read
Pinecone Vector Database and Embedding Model Summary from DeepLearning.AI’s AI Course
Baidu Geek Talk
Baidu Geek Talk
Feb 7, 2024 · Artificial Intelligence

Design and Implementation of a Knowledge-Base Intelligent Q&A System for Database Operations Using Large Models

The paper details Baidu Intelligent Cloud’s design and deployment of a domain‑specific knowledge‑base Q&A system for database operations, combining prompt‑engineered LLMs with hybrid vector‑search using LangChain, BES vector store, and custom ingestion, addressing recall, token limits, and hallucination challenges across dashboard and IM bot interfaces.

AIDatabase operationsKnowledge Base
0 likes · 16 min read
Design and Implementation of a Knowledge-Base Intelligent Q&A System for Database Operations Using Large Models
Baidu Intelligent Cloud Tech Hub
Baidu Intelligent Cloud Tech Hub
Jan 31, 2024 · Artificial Intelligence

How Baidu Built an 80% Accurate AI-Powered Database Ops Knowledge Base

This article details Baidu Intelligent Cloud's database operations team’s end‑to‑end design of an AI‑driven knowledge‑base Q&A system, covering background, architecture, technical choices, module implementation, key challenges such as vector‑search recall and token limits, and real‑world deployment scenarios.

AIPrompt engineeringvector database
0 likes · 18 min read
How Baidu Built an 80% Accurate AI-Powered Database Ops Knowledge Base
DataFunTalk
DataFunTalk
Dec 29, 2023 · Artificial Intelligence

Enterprise Knowledge Assistant: Leveraging Vector Databases and Large Language Models

This article explores the emerging enterprise knowledge assistant paradigm in the era of large models, detailing traditional knowledge management challenges, solution architecture using vector databases and LLMs, core technologies such as ETL pipelines, reranking, secure fine‑tuning, and future prospects for intelligent enterprise applications.

Enterprise AIKnowledge ManagementLLM fine-tuning
0 likes · 11 min read
Enterprise Knowledge Assistant: Leveraging Vector Databases and Large Language Models
Baidu Geek Talk
Baidu Geek Talk
Dec 11, 2023 · Industry Insights

How AI and Cloud Are Redefining the Database Landscape – Baidu’s Journey and Future Trends

This article traces the 70‑year evolution of databases, examines how the rise of AIGC, cloud computing and AI native architectures are reshaping the industry, and details Baidu Smart Cloud's historical milestones, flagship products such as GaiaDB and PegaDB, and the emerging trends that will drive the next generation of database solutions.

AICloud ComputingCloud Native
0 likes · 22 min read
How AI and Cloud Are Redefining the Database Landscape – Baidu’s Journey and Future Trends
Architect
Architect
Nov 19, 2023 · Artificial Intelligence

Why AutoGPT Abandoned Vector Databases – A Deep Dive into Simpler Memory Strategies

The article examines AutoGPT's shift away from vector databases, detailing the original vision of using embeddings for long‑term memory, the performance calculations that exposed unnecessary complexity, the adoption of JSON‑based storage, and the emerging trend of specialized multi‑agent architectures.

AI agentsAutoGPTMemory Management
0 likes · 9 min read
Why AutoGPT Abandoned Vector Databases – A Deep Dive into Simpler Memory Strategies
DataFunTalk
DataFunTalk
Nov 17, 2023 · Databases

Cost as the Primary Driver of Vector Database Industry Development

Vector databases gain traction because they dramatically reduce storage, learning, scaling, and large‑model limitations costs by enabling semantic similarity search, RAG‑based prompt optimization, efficient high‑dimensional indexing, and cloud‑native architectures, making them essential for modern AI applications despite the promotional context.

AIBig DataRAG
0 likes · 11 min read
Cost as the Primary Driver of Vector Database Industry Development
DataFunTalk
DataFunTalk
Oct 30, 2023 · Databases

Engineering Practices and Evolution of Douyin’s Cloud‑Native Vector Database

This article outlines Douyin’s step‑by‑step engineering evolution of its cloud‑native vector database, covering the background of vector search, core concepts, algorithmic optimizations, storage‑compute separation, streaming updates, multi‑tenant orchestration, and future applications such as large language model integration.

ANNCloud NativeDouyin
0 likes · 17 min read
Engineering Practices and Evolution of Douyin’s Cloud‑Native Vector Database
Volcano Engine Developer Services
Volcano Engine Developer Services
Sep 19, 2023 · Databases

Unlocking AI with Vector Databases: Architecture, Optimization, and Real-World Cases

This article explores how vector databases serve as the memory layer for large AI models, detailing their distributed, compute‑separated architecture, performance optimizations, hybrid vector‑scalar retrieval, and practical deployments across TikTok’s ecosystem such as image search, intelligent Q&A, and multimodal AI services.

AIKnowledge Basedistributed architecture
0 likes · 11 min read
Unlocking AI with Vector Databases: Architecture, Optimization, and Real-World Cases
phodal
phodal
Sep 17, 2023 · Artificial Intelligence

How Chocolate Factory’s Codebase AI Assistant Boosts Code Search with RAG

This article explains the design and implementation of the Codebase AI Assistant in the Chocolate Factory framework, covering its problem‑solving DSL, retrieval‑augmented generation pipeline, indexing and querying stages, prompt strategies, and code‑splitting rules that together enable efficient semantic code search.

AI assistantCode searchKotlin
0 likes · 11 min read
How Chocolate Factory’s Codebase AI Assistant Boosts Code Search with RAG