RAG | BestHub

Collection size

151 articles

Page 1 of 8

DataFunSummit

Jun 12, 2025 · Artificial Intelligence

How Alibaba Cloud’s AI Search Evolves with Agentic RAG and Multi‑Model Innovations

This article details Alibaba Cloud AI Search’s development journey, covering its dual product lines, the evolution of Agentic RAG technology, multi‑agent architectures, vector retrieval breakthroughs, GPU‑accelerated indexing, NL2SQL capabilities, deployment models, and future directions for AI‑driven search solutions.

AI SearchGPU AccelerationLarge Models

0 likes · 33 min read

How Alibaba Cloud’s AI Search Evolves with Agentic RAG and Multi‑Model Innovations

TAL Education Technology

Jun 13, 2025 · Operations

How Large Language Models Are Revolutionizing Fault Localization

This article explores how the rapid rise of large language models and techniques like Retrieval‑Augmented Generation, Chain‑of‑Thought prompting, and multi‑agent architectures can dramatically improve the speed, accuracy, and automation of fault localization in modern operations environments.

CoTRAGagent architecture

0 likes · 14 min read

How Large Language Models Are Revolutionizing Fault Localization

Full-Stack Internet Architecture

Jun 4, 2025 · Artificial Intelligence

Key AI Concepts for Spring AI: Models, Prompts, Embeddings, Tokens, Structured Output, and RAG

This article introduces essential AI concepts—including models, prompts and prompt templates, embeddings, tokens, structured output, and Retrieval‑Augmented Generation—explaining their meanings and relevance for effectively using Spring AI in real‑world applications.

AIEmbeddingsRAG

0 likes · 7 min read

Key AI Concepts for Spring AI: Models, Prompts, Embeddings, Tokens, Structured Output, and RAG

37 Interactive Technology Team

Nov 4, 2024 · Artificial Intelligence

Developing RAG and Agent Applications with LangChain: A Case Study of an AI Assistant for Activity Components

The article outlines a step‑by‑step methodology for creating Retrieval‑Augmented Generation and custom Agent applications with LangChain, illustrated by an AI assistant for activity components that evolves from a rapid Dify prototype to a LangChain‑based RAG system and finally a hand‑crafted ReAct‑style agent, detailing LCEL chain composition, vector‑search integration, model performance trade‑offs, and a unified routing layer.

AI AssistantCloud-nativeData Warehouse

0 likes · 6 min read

Developing RAG and Agent Applications with LangChain: A Case Study of an AI Assistant for Activity Components

Youzan Coder

May 8, 2025 · Artificial Intelligence

Building and Optimizing a Store Smart Assistant with Aily: Architecture, Workflow, and Practical Lessons

The article details how Youzan’s Store Smart Assistant was built on the Feishu Aily platform, describing why Aily was chosen, the three‑stage development process, deep system integration, practical tips for knowledge‑base management and model stability, and the resulting efficiency gains such as handling 80% of routine queries.

AI AssistantAily platformKnowledge Base

0 likes · 24 min read

Building and Optimizing a Store Smart Assistant with Aily: Architecture, Workflow, and Practical Lessons

DeWu Technology

May 19, 2025 · Artificial Intelligence

AI-Powered Automated Test Case Generation: Design, Implementation, and Future Plans

This article presents a comprehensive AI-driven solution for automatically generating functional test cases, detailing the AI background, design scheme, core components such as PRD parsing, test‑point generation, test‑case creation, knowledge‑base construction, implementation results, and future development directions.

AIKnowledge BaseLLM

0 likes · 7 min read

AI-Powered Automated Test Case Generation: Design, Implementation, and Future Plans

Tencent Technical Engineering

May 19, 2025 · Artificial Intelligence

RAG, Agents, and Multimodal Large Models: Evolution, Challenges, and Future Trends

This article examines the evolution of large model technologies—including Retrieval‑Augmented Generation, AI agents, and multimodal models—detailing their technical foundations, practical challenges, industry applications, and future development trends, offering a comprehensive perspective for AI practitioners and researchers.

AI AgentRAGknowledge retrieval

0 likes · 14 min read

RAG, Agents, and Multimodal Large Models: Evolution, Challenges, and Future Trends

Java Tech Enthusiast

May 21, 2025 · Artificial Intelligence

How ChatGPT's New Memory Feature Works: Technical Analysis and Implementation Details

The article provides a detailed technical breakdown of OpenAI's new ChatGPT memory feature, explaining its two memory modes, underlying sub‑systems, possible implementation approaches using vector stores and scheduled jobs, and early user feedback highlighting both benefits and bugs.

AIChatGPTMemory Feature

0 likes · 8 min read

How ChatGPT's New Memory Feature Works: Technical Analysis and Implementation Details

DevOps

Apr 27, 2025 · Artificial Intelligence

Large Model Technologies: RAG, AI Agents, Multimodal Applications, and Future Trends

This article examines how Retrieval‑Augmented Generation (RAG), AI agents, and multimodal large‑model techniques are reshaping AI‑industry integration, discusses their technical challenges and practical implementations, and outlines future development directions across algorithms, products, and domain‑specific applications.

AI agentsArtificial IntelligenceLarge Models

0 likes · 14 min read

Large Model Technologies: RAG, AI Agents, Multimodal Applications, and Future Trends

DevOps

Apr 20, 2025 · Artificial Intelligence

Building a Medical Knowledge Base with RAG: A Step‑by‑Step Example

This article demonstrates how to construct an AI‑powered medical knowledge base for diabetes treatment by preprocessing literature, performing semantic chunking, generating BioBERT embeddings, storing them in a FAISS vector database, and using a RAG framework together with a knowledge graph to retrieve and generate accurate answers.

BioBERTMedical AIRAG

0 likes · 12 min read

Building a Medical Knowledge Base with RAG: A Step‑by‑Step Example

DataFunTalk

Apr 24, 2025 · Artificial Intelligence

Is Retrieval‑Augmented Generation (RAG) Dead Yet?

This article explains the original purpose of Retrieval‑Augmented Generation, why it remains essential despite advances in large‑context LLMs, and how combining RAG with fine‑tuning, longer context windows, and model‑context protocols yields more scalable, accurate, and privacy‑preserving AI systems.

AIContext WindowLarge Language Models

0 likes · 9 min read

Is Retrieval‑Augmented Generation (RAG) Dead Yet?

DataFunSummit

May 13, 2025 · Artificial Intelligence

Integrating Large Language Models and Knowledge Graphs for Financial Applications: Challenges, Solutions, and Future Directions

This talk explores the technical challenges of applying large language models and knowledge graphs in finance, discusses solutions such as RAG enhancements, graph‑guided retrieval, multimodal extensions, and presents future research directions including multimodal graph integration, agentic systems, and decision‑making applications.

AIAgentic SystemsFinance

0 likes · 33 min read

Integrating Large Language Models and Knowledge Graphs for Financial Applications: Challenges, Solutions, and Future Directions

DataFunSummit

May 9, 2025 · Artificial Intelligence

Practical Experience Building Zhihu Direct Answer: An AI‑Powered Search Product

This article presents a comprehensive overview of Zhihu Direct Answer, describing its AI‑driven search architecture, RAG framework, query understanding, retrieval, chunking, reranking, generation, evaluation mechanisms, engineering optimizations, and the professional edition, while sharing concrete performance‑boosting practices and future development plans.

AIGenerationProduct Development

0 likes · 14 min read

Practical Experience Building Zhihu Direct Answer: An AI‑Powered Search Product

Sohu Tech Products

May 28, 2025 · Artificial Intelligence

Introducing AIFlowy: An Open‑Source Java‑Based One‑Stop AI Application Development Platform

AIFlowy is a Java‑powered, open‑source, enterprise‑grade AI platform that offers a bot for natural‑language interaction, extensible plugins, a knowledge‑base with RAG support, and visual workflow automation, enabling developers to quickly build and customize AI applications for domestic B2B scenarios.

AIBotJava

0 likes · 10 min read

Introducing AIFlowy: An Open‑Source Java‑Based One‑Stop AI Application Development Platform

DevOps

May 28, 2025 · Artificial Intelligence

Google Proposes a “Sufficient Context” Framework to Strengthen Enterprise Retrieval‑Augmented Generation Systems

Google researchers introduce a “sufficient context” framework that classifies retrieved passages as adequate or inadequate for answering a query, enabling large language models in enterprise RAG systems to decide when to answer, refuse, or request more information, thereby improving accuracy and reducing hallucinations.

AI ReliabilityContext EvaluationLarge Language Models

0 likes · 9 min read

Google Proposes a “Sufficient Context” Framework to Strengthen Enterprise Retrieval‑Augmented Generation Systems

Didi Tech

Jun 5, 2025 · Artificial Intelligence

Unlocking Modern AI Application Architecture: From RAG to Agents and MCP

This article surveys the evolution of AI applications, explains large language model fundamentals, outlines architectural challenges, and introduces three core patterns—Retrieval‑Augmented Generation (RAG), autonomous Agents, and Model Context Protocol (MCP)—while providing practical LangChain code snippets and integration guidance.

AILLMLangChain

0 likes · 28 min read

Unlocking Modern AI Application Architecture: From RAG to Agents and MCP

IT Services Circle

Jun 6, 2025 · Artificial Intelligence

Master Retrieval‑Augmented Generation (RAG): From Basics to Advanced Practices

This article introduces Retrieval‑Augmented Generation (RAG), explains its core components—knowledge embedding, retriever, and generator—covers practical system construction, optimization techniques, evaluation metrics, and advanced paradigms such as GraphRAG and Multi‑Modal RAG, while highlighting a comprehensive guidebook for hands‑on implementation.

AILarge Language ModelsRAG

0 likes · 12 min read

Master Retrieval‑Augmented Generation (RAG): From Basics to Advanced Practices

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

Java Architecture Diary

May 21, 2025 · Artificial Intelligence

Spring AI 1.0 Launch: Production‑Ready Java AI Framework Unveiled

Spring AI 1.0, the first production‑grade Java AI framework, introduces ready‑to‑use APIs, seamless model integration, enterprise‑level RAG engine, smart tool calling, and three development modes, empowering developers to rapidly build, customize, and fully control AI applications with major model providers like OpenAI, Anthropic, DeepSeek.

AI FrameworkDeepSeekJava AI

0 likes · 13 min read

Spring AI 1.0 Launch: Production‑Ready Java AI Framework Unveiled

Java Architecture Diary

Apr 14, 2025 · Artificial Intelligence

How to Empower LLMs with a Private SearXNG Search Engine for Real‑Time Knowledge

This guide explains why large language models need private search capabilities, outlines the benefits of a self‑hosted SearXNG engine, provides step‑by‑step Docker deployment, and demonstrates Java integration using LangChain4j for both basic queries and retrieval‑augmented generation (RAG).

DockerLLMLangChain4j

0 likes · 6 min read