RAG | BestHub

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JD Tech

Jul 10, 2024 · Artificial Intelligence

Implementing Retrieval‑Augmented Generation (RAG) with LangChain4j in Java

This article provides a step‑by‑step guide for Java engineers on building a Retrieval‑Augmented Generation (RAG) application using the LangChain4j framework, covering RAG fundamentals, environment setup, Maven integration, document loading, splitting, embedding with OpenAI, vector store management with Chroma, and prompt‑based LLM interaction.

JavaLLMLangChain4j

0 likes · 35 min read

Implementing Retrieval‑Augmented Generation (RAG) with LangChain4j in Java

JD Tech

Jun 28, 2024 · Artificial Intelligence

An Overview of Large Language Models: History, Fundamentals, Prompt Engineering, Retrieval‑Augmented Generation, Agents, and Multimodal AI

This article provides a comprehensive introduction to large language models, covering their historical development, core architecture, training process, prompt engineering techniques, Retrieval‑Augmented Generation, agent frameworks, multimodal capabilities, safety challenges, and future research directions.

AI SafetyAI agentsDeep Learning

0 likes · 22 min read

An Overview of Large Language Models: History, Fundamentals, Prompt Engineering, Retrieval‑Augmented Generation, Agents, and Multimodal AI

JD Tech Talk

Sep 30, 2024 · Artificial Intelligence

Yunli XiaoZhi: An AI‑Powered Intelligent Assistant for Knowledge Q&A and Data Analysis in Logistics Operations

The document describes the design, implementation, and operational results of Yunli XiaoZhi, an AI‑driven portable knowledge‑base and data‑analysis chatbot that consolidates SOPs, manuals, and real‑time information for logistics staff, using LangChain‑based RAG, vector databases, and large‑model prompting to improve query efficiency, proactive alerts, and reporting across multiple user groups.

AIChatbotRAG

0 likes · 19 min read

Yunli XiaoZhi: An AI‑Powered Intelligent Assistant for Knowledge Q&A and Data Analysis in Logistics Operations

JD Tech Talk

Oct 8, 2024 · Artificial Intelligence

Building a Retrieval‑Augmented Generation (RAG) System with Rust and Qdrant

This article explains how to construct a Retrieval‑Augmented Generation pipeline in Rust, covering knowledge‑base creation with Qdrant, model loading and embedding using the candle library, data ingestion, and integration of a Rust‑based inference service based on mistral.rs, while also discussing resource usage and common pitfalls.

AILLMQdrant

0 likes · 16 min read

Building a Retrieval‑Augmented Generation (RAG) System with Rust and Qdrant

58 Tech

Aug 7, 2024 · Artificial Intelligence

Bridging Compute and Applications: 58.com AI Lab’s Large‑Model Platform and AI Agent Solutions

In this article, 58.com AI Lab senior director Zhan Kunlin explains how the company built a multi‑layer AI platform, created a vertical large‑language model called LingXi, and developed an AI Agent system with RAG capabilities to accelerate practical AI applications across various business scenarios.

AI PlatformAI agentsModel Deployment

0 likes · 10 min read

Bridging Compute and Applications: 58.com AI Lab’s Large‑Model Platform and AI Agent Solutions

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.

LLMOpen SourceRAG

0 likes · 18 min read

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

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

AntData

Oct 16, 2024 · Artificial Intelligence

Building a Data Assistant Application with DB‑GPT V0.6.0

This tutorial walks through the end‑to‑end process of creating a data‑assistant application using DB‑GPT V0.6.0, covering prerequisite deployment, knowledge‑base construction, sub‑agent creation, RAG‑based QA, AWEL workflow installation, intent‑recognition knowledge base, and unified multi‑agent orchestration.

AIDB-GPTData Assistant

0 likes · 12 min read

Building a Data Assistant Application with DB‑GPT V0.6.0

AntData

Sep 26, 2024 · Artificial Intelligence

DB-GPT: Open-Source AI-Native Data Application Development Framework

DB‑GPT is an open‑source AI‑native data‑application framework that provides multi‑model management, Text‑to‑SQL optimization, RAG, multi‑agent collaboration, and intelligent workflow orchestration, enabling developers to build scalable large‑model database applications, with proven enterprise adoption, community growth, and academic publications.

AIOpen SourceRAG

0 likes · 6 min read

DB-GPT: Open-Source AI-Native Data Application Development Framework

AntTech

Jul 2, 2024 · Artificial Intelligence

Design and Implementation of a Generalized Retrieval‑Augmented Generation (RAG) Framework with Graph RAG Support

This article surveys Retrieval‑Augmented Generation (RAG), analyzes the limitations of traditional vector‑based RAG, introduces Graph RAG that leverages knowledge graphs for more reliable context, proposes a universal RAG architecture compatible with vector, graph and full‑text indexes, and details its open‑source implementation, code components, testing, and future research directions.

AIEngineeringGraphRAGKnowledgeGraph

0 likes · 26 min read

Design and Implementation of a Generalized Retrieval‑Augmented Generation (RAG) Framework with Graph RAG Support

Zhihu Tech Column

Dec 9, 2024 · Artificial Intelligence

Large Model Application Engineering: ZhiLight Inference Framework and Zhihu Direct Answer System

The article details Zhihu's technical salon on large‑model engineering, covering the RAG‑based Zhihu Direct Answer system, the open‑source ZhiLight inference framework, prompt engineering, agent research, and future plans for integrating AI into product and community workflows.

AI EngineeringInference FrameworkPrompt Engineering

0 likes · 8 min read

Large Model Application Engineering: ZhiLight Inference Framework and Zhihu Direct Answer System

Zhihu Tech Column

Jan 17, 2025 · Artificial Intelligence

Zhihu Direct Answer: Product Overview and Technical Practices

This article summarizes the key technical insights from Zhihu Direct Answer, an AI-powered search product, covering its product overview, RAG framework, query understanding, retrieval strategies, chunking, reranking, generation techniques, evaluation methods, and engineering optimizations for cost and performance.

AI SearchChunkingEngineering Optimization

0 likes · 13 min read

Zhihu Direct Answer: Product Overview and Technical Practices

Aikesheng Open Source Community

Nov 12, 2024 · Artificial Intelligence

ChatDBA: An AI‑Powered Database Fault Diagnosis Assistant Using Large Language Models

ChatDBA is a conversational AI system built by Shanghai Aikesheng that employs large language models and Retrieval‑Augmented Generation to help database administrators diagnose faults, learn domain knowledge, and generate or optimize SQL, with a redesigned architecture that addresses early‑stage shortcomings and outlines future enhancements.

ChatDBADatabase AIFault Diagnosis

0 likes · 10 min read

ChatDBA: An AI‑Powered Database Fault Diagnosis Assistant Using Large Language Models

Aikesheng Open Source Community

Nov 11, 2024 · Databases

ChatDBA: An AI‑Powered Intelligent Assistant for Database Fault Diagnosis and Management

ChatDBA is an AI‑driven conversational system developed by Shanghai Aikesheng that assists DBAs with fault diagnosis, knowledge learning, SQL generation and optimization by leveraging large language models, RAG architecture, and advanced retrieval and document‑processing techniques.

ChatDBADatabase AIFault Diagnosis

0 likes · 10 min read

ChatDBA: An AI‑Powered Intelligent Assistant for Database Fault Diagnosis and Management

Rare Earth Juejin Tech Community

Feb 22, 2025 · Artificial Intelligence

Deploying DeepSeek Locally with Ollama, Building Personal and Organizational Knowledge Bases, and Integrating with Spring AI

This guide explains how to locally deploy the DeepSeek large‑language model using Ollama on Windows, macOS, and Linux, configure model storage and CORS, build personal and enterprise RAG knowledge bases with AnythingLLM and Open WebUI, and integrate the model into a Spring AI application via Docker and Docker‑Compose.

ContainerizationDeepSeekDocker

0 likes · 16 min read

Deploying DeepSeek Locally with Ollama, Building Personal and Organizational Knowledge Bases, and Integrating with Spring AI

Rare Earth Juejin Tech Community

Apr 11, 2024 · Artificial Intelligence

AI Hackathon Journey: Building the "Novel Jump" Bot on Coze Platform

This article recounts the author's participation in a Shenzhen AI Hackathon, detailing the development of an interactive novel‑character chatbot using the Coze platform, describing the workflow design, technical challenges, model choices, knowledge‑base construction, and the final demo and award outcomes.

AIChatbotCoze

0 likes · 12 min read

AI Hackathon Journey: Building the "Novel Jump" Bot on Coze Platform

Rare Earth Juejin Tech Community

Apr 8, 2024 · Artificial Intelligence

PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers

The article introduces PreFLMR, an open‑source, general‑purpose pre‑trained multimodal retriever that leverages fine‑grained late‑interaction to boost retrieval‑augmented generation for knowledge‑intensive visual tasks, describes its M2KR benchmark, training stages, and strong experimental results across multiple tasks.

AIFLMRRAG

0 likes · 11 min read

PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers

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

Architecture and Beyond

Feb 22, 2025 · Artificial Intelligence

Understanding Retrieval‑Augmented Generation (RAG) and Its Role in Enhancing Large Language Models

The article explains how the inherent knowledge‑staleness, hallucination, lack of private data, non‑traceable output, limited long‑text handling, and data‑security concerns of large language models can be mitigated by Retrieval‑Augmented Generation, which combines external retrieval, augmentation, and generation to provide up‑to‑date, reliable, and secure AI responses.

AILLMRAG

0 likes · 15 min read

Understanding Retrieval‑Augmented Generation (RAG) and Its Role in Enhancing Large Language Models

System Architect Go

Nov 19, 2024 · Artificial Intelligence

Retrieval Augmented Generation (RAG) System Overview and Implementation with LangChain, Redis, and llama.cpp

This article explains the concept, architecture, and step‑by‑step implementation of Retrieval Augmented Generation (RAG), covering indexing, retrieval & generation processes, a practical LangChain‑Redis‑llama.cpp example on Kubernetes, code snippets, test results, challenges, and references.

AILLMLangChain

0 likes · 6 min read

Retrieval Augmented Generation (RAG) System Overview and Implementation with LangChain, Redis, and llama.cpp