vector database

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

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 PlatformMLOpsPerformance Optimization

0 likes · 18 min read

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

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.

LLMMilvusRAG

0 likes · 11 min read

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

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

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 AgentDeploymentObservability

0 likes · 12 min read

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

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 AgentFunction CallingLLM

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

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 DatabasesMySQLPostgreSQL

0 likes · 5 min read

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

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

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

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 IntelligenceDeepSearcherGraphRAG

0 likes · 20 min read

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

Java Architecture Diary

Feb 26, 2025 · Databases

Build a Private LLM Knowledge Base with Redis and DeepSeek4J in 10 Minutes

This tutorial shows how to harness Redis's dual role as a high‑performance cache and a vector database, guiding you through Docker setup, vector storage methods, and Java Lettuce integration to build a private large‑language‑model knowledge base with DeepSeek4J.

AIDeepSeekJava

0 likes · 6 min read

Build a Private LLM Knowledge Base with Redis and DeepSeek4J in 10 Minutes

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.

LLMLangChainPython

0 likes · 10 min read

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

macrozheng

Jan 20, 2025 · Artificial Intelligence

How Redis’s New Multithreaded Query Engine Boosts Vector Search for Real‑Time AI Apps

Redis has introduced a multithreaded query engine that dramatically lowers latency and multiplies throughput for vector‑based retrieval, enabling real‑time RAG applications to approach the 100 ms response target while scaling vertically to billions of documents.

AI performanceRAGRedis

0 likes · 6 min read

How Redis’s New Multithreaded Query Engine Boosts Vector Search for Real‑Time AI Apps

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.

LangChain4jRAGembedding

0 likes · 35 min read

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

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

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 governancelarge language models

0 likes · 16 min read

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

DataFunSummit

Nov 20, 2024 · Artificial Intelligence

How Data Lakes Empower AI: Expert Insights on Feature Management, Columnar Storage, and Vector Formats

In a panel discussion, experts explain how data‑lake‑warehouse integration, columnar formats like Apache Iceberg, and emerging variant types enable efficient feature engineering, support large‑language‑model workloads, and provide flexible vector storage, thereby driving the evolution of AI from traditional ML to the GenAI era.

Apache IcebergArtificial IntelligenceData Lake

0 likes · 6 min read

How Data Lakes Empower AI: Expert Insights on Feature Management, Columnar Storage, and Vector Formats

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

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 chatbotRAGdata analysis

0 likes · 22 min read

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

DataFunSummit

Oct 30, 2024 · Databases

Design and Implementation of Vector Databases: Architecture, Indexing, and AI Optimizations

This article introduces vector databases as the foundation for efficient high‑dimensional data retrieval in generative AI, covering their background, Milvus’s cloud‑native architecture, key indexing techniques, performance‑trade‑offs, AI‑driven optimizations, and a Q&A session.

AIIndexingMilvus

0 likes · 15 min read

Design and Implementation of Vector Databases: Architecture, Indexing, and AI Optimizations