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Vector Database

254 articles · Page 2 of 3
Shuge Unlimited
Shuge Unlimited
Feb 11, 2026 · Operations

How to Easily Manage Operations of 10 Milvus Clusters with an Agent Skill

This article walks through the real‑world pain points of monitoring dozens of Milvus collections across multiple clusters, then details a Python‑based Skill that automates connection handling, aggregates collection metadata, evaluates index health with a three‑state model, and provides unified health checks, performance testing, and capacity analysis for reliable large‑scale vector database operations.

Index managementMilvusMonitoring
0 likes · 18 min read
How to Easily Manage Operations of 10 Milvus Clusters with an Agent Skill
Java Architecture Diary
Java Architecture Diary
Feb 10, 2026 · Artificial Intelligence

Boost RAG Accuracy with LangChain4j 1.11.0 Hybrid Search on PgVector

This guide explains why pure vector retrieval often fails for version‑specific queries, introduces hybrid search that combines semantic and keyword matching, and provides step‑by‑step code and SQL examples for enabling PgVector hybrid search in LangChain4j 1.11.0.

Full-text SearchHybrid SearchLangChain4j
0 likes · 11 min read
Boost RAG Accuracy with LangChain4j 1.11.0 Hybrid Search on PgVector
Architecture and Beyond
Architecture and Beyond
Feb 8, 2026 · Artificial Intelligence

Designing Scalable Long-Term Memory for AI Agents: Capture, Compress, Retrieve

This article explains how to build a controllable, editable, and cost‑effective long‑term memory system for AI agents by categorizing memory types, structuring a three‑stage pipeline of capture, AI‑driven compression, and smart retrieval, and choosing appropriate storage back‑ends such as files, knowledge bases, or databases.

Agent designArtificial IntelligenceKnowledge Base
0 likes · 18 min read
Designing Scalable Long-Term Memory for AI Agents: Capture, Compress, Retrieve
Amazon Cloud Developers
Amazon Cloud Developers
Jan 14, 2026 · Databases

How OpenSearch Service Boosts Vector Database Build Speed by Up to 10× and Cuts Costs by 75%

Amazon OpenSearch Service now offers serverless GPU‑accelerated vector indexing and automatic optimization, enabling users to build billion‑scale vector databases up to ten times faster, reduce indexing costs to one‑quarter, and balance latency, quality, and memory without manual tuning.

AWS CLIAmazon OpenSearch ServiceGPU Acceleration
0 likes · 9 min read
How OpenSearch Service Boosts Vector Database Build Speed by Up to 10× and Cuts Costs by 75%
Sohu Tech Products
Sohu Tech Products
Jan 7, 2026 · Artificial Intelligence

Master Retrieval-Augmented Generation (RAG): Concepts, Benefits, Implementation

This article explains Retrieval‑Augmented Generation (RAG), its dual‑stage architecture that combines parametric LLM knowledge with external non‑parametric data, outlines its technical evolution, discusses why it outperforms pure LLMs, and provides a step‑by‑step guide with toolchain choices, evaluation metrics, and future challenges.

AIKnowledge BaseLLM
0 likes · 14 min read
Master Retrieval-Augmented Generation (RAG): Concepts, Benefits, Implementation
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 26, 2025 · Artificial Intelligence

How to Build a Fully Automated Knowledge‑Extraction Pipeline for AI Agents with Python

This article presents a complete end‑to‑end pipeline that automatically extracts, generalizes, incrementally updates, and vector‑syncs knowledge from diverse sources such as tickets, documents, and SQL code, turning the traditionally labor‑intensive knowledge‑base construction for agents into a low‑effort, continuously maintainable Python‑driven solution.

LLMPythonRAG
0 likes · 15 min read
How to Build a Fully Automated Knowledge‑Extraction Pipeline for AI Agents with Python
Zhuanzhuan Tech
Zhuanzhuan Tech
Dec 24, 2025 · Artificial Intelligence

Building an ASR+LLM+Vector Knowledge Base for Precise Video Ad Category Detection

This article presents a layered ASR‑LLM‑vector‑knowledge‑base pipeline that cleans speech transcripts, semantically repairs text, performs hierarchical exact and fuzzy matching, and iteratively refines mappings to accurately identify product categories in video advertisements, while detailing module functions, technical choices, and LLM parameter tuning.

ASRKnowledge BaseLLM
0 likes · 11 min read
Building an ASR+LLM+Vector Knowledge Base for Precise Video Ad Category Detection
Architects' Tech Alliance
Architects' Tech Alliance
Dec 17, 2025 · Artificial Intelligence

Mastering Retrieval‑Augmented Generation: From Theory to Scalable Deployment

This guide explains how Retrieval‑Augmented Generation (RAG) overcomes LLM knowledge staleness, hallucination, and domain‑adaptation challenges by combining external knowledge bases with real‑time retrieval, and provides detailed architecture, optimization techniques, engineering practices, monitoring, cost‑control, and future trends for building production‑grade RAG systems.

AICloudflareLLM
0 likes · 15 min read
Mastering Retrieval‑Augmented Generation: From Theory to Scalable Deployment
Xiaolong Cloud Tech Team
Xiaolong Cloud Tech Team
Dec 16, 2025 · Product Management

Mapping the AI Product Manager Knowledge Tree

The article outlines a knowledge‑tree framework for AI product managers, covering foundational concepts, data strategy, privacy, vector databases, the distinct AI product lifecycle, user‑experience challenges, and a step‑by‑step learning roadmap.

AI lifecycleAI product managementVector Database
0 likes · 4 min read
Mapping the AI Product Manager Knowledge Tree
Architect
Architect
Dec 14, 2025 · Artificial Intelligence

Mastering RAG with Spring AI: Build a Retrieval‑Augmented Generation System from Scratch

This article explains the background, principles, and step‑by‑step implementation of Retrieval‑Augmented Generation (RAG) using Spring AI, covering embedding models, vector databases, chunking strategies, indexing algorithms, similarity metrics, re‑ranking, prompt templates, and a complete Java code example.

EmbeddingRAGSpring AI
0 likes · 32 min read
Mastering RAG with Spring AI: Build a Retrieval‑Augmented Generation System from Scratch
Coder Life Journal
Coder Life Journal
Dec 14, 2025 · Artificial Intelligence

RAG Learning Journey Part 1: What Is Retrieval‑Augmented Generation?

This article explains Retrieval‑Augmented Generation (RAG), describing its definition, why it complements large language models, the end‑to‑end workflow, its four core components—embedding, vector database, retriever, generator—and the value it adds by providing up‑to‑date knowledge and reducing model hallucinations.

AIEmbeddingRAG
0 likes · 3 min read
RAG Learning Journey Part 1: What Is Retrieval‑Augmented Generation?
HyperAI Super Neural
HyperAI Super Neural
Dec 12, 2025 · Artificial Intelligence

AI Open‑Source Forum Recap: Video Generation, Vision, Vector DBs, AI‑Native Language

The AI Open‑Source Forum brought together researchers from Peking University, Tsinghua, Zilliz and MoonBit to share open‑source advances in audio‑synchronized video generation, vector database architecture, lightweight vision backbones, and an AI‑native programming language, highlighting datasets, system designs, and future collaborative directions.

AIAI‑Native ProgrammingVector Database
0 likes · 12 min read
AI Open‑Source Forum Recap: Video Generation, Vision, Vector DBs, AI‑Native Language
macrozheng
macrozheng
Dec 3, 2025 · Databases

How Redis’s New Multithreaded Query Engine Boosts Vector Search Performance

Redis has introduced a multithreaded query engine that dramatically reduces latency and increases throughput—up to 16×—for vector similarity searches, enabling vertical scaling and better support for real‑time RAG applications compared to traditional single‑threaded architectures and competing vector databases.

Database ScalingRAGRedis
0 likes · 6 min read
How Redis’s New Multithreaded Query Engine Boosts Vector Search Performance
Raymond Ops
Raymond Ops
Nov 23, 2025 · Databases

How to Install and Run Milvus Vector Database with Docker Compose

This guide introduces Milvus, an open‑source vector database for AI workloads, outlines its key features and common use cases, and provides step‑by‑step Docker‑Compose commands to set up Milvus, its storage backend MinIO, and the Attu management UI.

AttuDocker ComposeMilvus
0 likes · 8 min read
How to Install and Run Milvus Vector Database with Docker Compose
Data Party THU
Data Party THU
Nov 9, 2025 · Artificial Intelligence

Mastering Chunking Strategies for Effective RAG: Fixed, Recursive, Semantic, Structured, and Delayed

This article walks through the core RAG pipeline, explains why chunking is the linchpin of retrieval quality, and provides detailed definitions, trade‑offs, and implementation examples for five chunking techniques—fixed, recursive, semantic, structure‑aware, and delayed—so you can choose the right approach for any document‑heavy AI application.

AIChunkingLLM
0 likes · 10 min read
Mastering Chunking Strategies for Effective RAG: Fixed, Recursive, Semantic, Structured, and Delayed
dbaplus Community
dbaplus Community
Nov 3, 2025 · Artificial Intelligence

How RAG Turns Natural Language Queries into Accurate SQL for Data Platforms

This article explains how Retrieval‑Augmented Generation (RAG) combines vector databases with large language models to let non‑technical users ask natural‑language questions and receive precise SQL statements, detailing the workflow, architecture, chunking methods, performance gains, and remaining challenges.

LLMRAGSQL Generation
0 likes · 17 min read
How RAG Turns Natural Language Queries into Accurate SQL for Data Platforms
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Oct 9, 2025 · Artificial Intelligence

How Short‑Term and Long‑Term Memory Power LLM‑Based Agents

This article explains the definitions, technical implementations, functions, limitations, and collaborative workflow of short‑term and long‑term memory in large‑language‑model agents, detailing context windows, attention mechanisms, vector storage, retrieval strategies, and future research directions for building personalized, continuously learning AI agents.

Agent MemoryArtificial IntelligenceLLM
0 likes · 11 min read
How Short‑Term and Long‑Term Memory Power LLM‑Based Agents
DataFunSummit
DataFunSummit
Oct 6, 2025 · Artificial Intelligence

Why Vector Lakes Are the Next Frontier for AI Data Management

This article explains how Zilliz's Vector Lake extends traditional data lakes with a unified storage‑compute architecture optimized for massive unstructured and vector data, detailing its background, key data types, autonomous‑driving use case, data flow, architecture, and deployment options.

AI data managementData LakeVector Database
0 likes · 13 min read
Why Vector Lakes Are the Next Frontier for AI Data Management
JD Tech Talk
JD Tech Talk
Sep 28, 2025 · Artificial Intelligence

What Is Retrieval‑Augmented Generation (RAG) and How Does It Power Modern AI?

This article explains Retrieval‑Augmented Generation (RAG), an AI framework that combines traditional information retrieval with large language models, detailing its core workflow—from knowledge preparation, chunking, and embedding to vector database storage and the question‑answering stage—while highlighting key challenges, tools, and optimization strategies.

AIChunkingEmbedding
0 likes · 15 min read
What Is Retrieval‑Augmented Generation (RAG) and How Does It Power Modern AI?
Bilibili Tech
Bilibili Tech
Sep 26, 2025 · Artificial Intelligence

How RAG Transforms Natural Language Queries into Accurate SQL for Business Users

This article explains how Retrieval‑Augmented Generation (RAG) combines large language models with vector databases to let non‑technical staff query massive membership data using plain language, detailing the workflow, technical architecture, optimization challenges, and real‑world impact on data‑driven decision making.

AILLMNL-to-SQL
0 likes · 17 min read
How RAG Transforms Natural Language Queries into Accurate SQL for Business Users
AI Large Model Application Practice
AI Large Model Application Practice
Sep 23, 2025 · Artificial Intelligence

How MindsDB Turns Any Data Source into an AI‑Powered Query Engine

This article walks through installing MindsDB, configuring its unified data access layer, and demonstrates how to query across relational databases, files, and vector stores while injecting AI models—including traditional ML, LLMs, and embedding models—directly into SQL for intelligent data retrieval and analysis.

AI data integrationLLMMindsDB
0 likes · 16 min read
How MindsDB Turns Any Data Source into an AI‑Powered Query Engine
DataFunTalk
DataFunTalk
Sep 20, 2025 · Artificial Intelligence

Why Chroma’s Context Engineering Is Redefining AI Search Infrastructure

Jeff Huber, founder of Chroma, discusses the startup’s mission to turn AI demos into production‑grade applications, critiques the hype around RAG, emphasizes the importance of Context Engineering, and explains how Chroma’s open‑source vector database and cloud service aim to simplify AI search for developers.

AIChromaContext Engineering
0 likes · 32 min read
Why Chroma’s Context Engineering Is Redefining AI Search Infrastructure
Data STUDIO
Data STUDIO
Sep 18, 2025 · Artificial Intelligence

Build a RAG App from Scratch: Master Text Chunking, Vector Retrieval, and Coreference Resolution

This tutorial walks through building a Retrieval‑Augmented Generation (RAG) system from the ground up, covering document parsing, text chunking strategies, vector store creation with ChromaDB, semantic search, prompt engineering for LLMs, conversation memory, coreference handling, and practical optimization tips, all illustrated with complete Python code.

ChromaDBPythonRAG
0 likes · 19 min read
Build a RAG App from Scratch: Master Text Chunking, Vector Retrieval, and Coreference Resolution
Data Thinking Notes
Data Thinking Notes
Sep 7, 2025 · Artificial Intelligence

Unlocking AI Agent Memory: How LLMs Use Retrieval and Planning to Stay Smart

This article explains the core architecture of AI agents powered by large language models, detailing how planning, short‑term and long‑term memory, and tool integration work together through vector databases, retrieval‑augmented generation, and summarization to enable stateful, intelligent interactions across multiple sessions.

AI AgentLLMMemory
0 likes · 10 min read
Unlocking AI Agent Memory: How LLMs Use Retrieval and Planning to Stay Smart
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
Hailey Says
Hailey Says
Jul 6, 2025 · Artificial Intelligence

How Retrieval‑Augmented Generation Lets LLMs Actively Gather Quotes Before Responding

The article explains Retrieval‑Augmented Generation (RAG), detailing its three‑step workflow—retrieval, augmentation, generation—along with architecture components, data indexing, vector‑database choices, prompt construction, and challenges such as noise, token limits, and model accuracy, illustrating how RAG enables LLMs to fetch relevant quotes before answering.

Knowledge RetrievalLLMPrompt Engineering
0 likes · 9 min read
How Retrieval‑Augmented Generation Lets LLMs Actively Gather Quotes Before Responding
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 2025Vector Database
0 likes · 6 min read
BlendHouse: The Award‑Winning Cloud‑Native Vector Database Redefining Search
Ubiquitous Tech
Ubiquitous Tech
May 25, 2025 · Artificial Intelligence

Understanding RAG: The Core Capability Behind AI Customer Service

This article explains why Retrieval‑Augmented Generation (RAG) is essential for AI‑driven customer service, outlines the limitations of large language models, details the three‑stage RAG workflow (indexing, retrieval, generation), and shows how to implement it with FastGPT, vector databases, and LangChain.

AI Customer ServiceFastGPTKnowledge Base
0 likes · 16 min read
Understanding RAG: The Core Capability Behind AI Customer Service
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 infrastructureVector Databasehybrid indexing
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 platformMLOpsVector Database
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.

LLMRAGRetrieval-Augmented Generation
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 AgentTool IntegrationVector Database
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 trendsMySQL
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.

LLMRAGVector Database
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.

Artificial IntelligenceDeepSearcherGraph Database
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.

EmbeddingLangChain4jRAG
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.

EmbeddingLangChain4jRAG
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.

Fine-tuningLLMRAG
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.

EmbeddingFinancial AIPAI
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 GovernanceVector Database
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.

AIPostgreSQLRAG
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 chatbotData AnalysisKnowledge Base
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 AgentLangChainQdrant
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.

AIEmbeddingLLM
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 NativeGraph Database
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 ArchitectureIndustry Insights
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 DatabaseMultimodal DataVector Database
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
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.

AIVector Databaseexam generation
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 embeddingsVector DatabaseVector 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 AgentArtificial IntelligenceFine-tuning
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.

AIIndexingRetrieval
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.jsVector Database
0 likes · 9 min read
Optimizing Image Search System Architecture with Client‑Side Feature Extraction Using MobileNet