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dbaplus Community
dbaplus Community
May 19, 2026 · Artificial Intelligence

From RAG to GraphRAG: How Huolala Raised Metadata Retrieval Accuracy from 56% to 78%

The article details Huolala's transition from a basic Retrieval‑Augmented Generation (RAG) system to a GraphRAG architecture, explaining the challenges of traditional RAG, the design of offline and online stages, multi‑index hybrid search, concrete performance metrics (accuracy up to 78%, knowledge recall 91%, Top‑K 90%, MRR 0.73), and future plans such as stronger hybrid retrieval, reranking, and Agentic RAG.

AIGraphRAGHybrid Search
0 likes · 15 min read
From RAG to GraphRAG: How Huolala Raised Metadata Retrieval Accuracy from 56% to 78%
Data Party THU
Data Party THU
May 17, 2026 · Artificial Intelligence

Personalizing AI Agents: Memory, Rolling Context, and Advanced Retrieval Techniques

The article explains how AI agents use memory to retain conversation context, why sending the full history to large language models is inefficient, and presents rolling context windows, inverted‑index pruning, semantic embedding retrieval, and GraphRAG as complementary strategies to build more accurate and personalized agents.

AI memoryGraphRAGLLM optimization
0 likes · 10 min read
Personalizing AI Agents: Memory, Rolling Context, and Advanced Retrieval Techniques
DataFunTalk
DataFunTalk
May 15, 2026 · Artificial Intelligence

Exploring Multimodal GraphRAG: Combining Document Intelligence, Knowledge Graphs, and Large Models

This article provides a comprehensive technical overview of multimodal GraphRAG, detailing document‑intelligence parsing pipelines, layout analysis, OCR‑pipeline vs OCR‑free approaches, knowledge‑graph integration for chunk relationships, multimodal indexing, retrieval‑generation workflows, and a comparative analysis of RAG, GraphRAG, and KG‑QA solutions.

Document IntelligenceGraphRAGKnowledge Graph
0 likes · 23 min read
Exploring Multimodal GraphRAG: Combining Document Intelligence, Knowledge Graphs, and Large Models
James' Growth Diary
James' Growth Diary
May 12, 2026 · Artificial Intelligence

GraphRAG Deep Dive: Boost Multi‑Hop Reasoning Accuracy from 50% to 85% with Knowledge Graphs

This article explains why traditional vector RAG loses relational information, how GraphRAG reconstructs entity‑relationship triples into a knowledge graph, and provides step‑by‑step code, performance benchmarks, retrieval modes, and practical tips that raise multi‑hop reasoning accuracy from around 50% to 85%.

GraphRAGKnowledge GraphLangChain
0 likes · 14 min read
GraphRAG Deep Dive: Boost Multi‑Hop Reasoning Accuracy from 50% to 85% with Knowledge Graphs
DeepHub IMBA
DeepHub IMBA
May 11, 2026 · Artificial Intelligence

2026 RAG Selection Guide: How to Choose Between Vector, Graph, and Vectorless

This article compares traditional Vector RAG, GraphRAG, and the newer Vectorless RAG, explains why Vector RAG fails on relational and structured queries, presents benchmark results, outlines each architecture's strengths and costs, and offers a decision framework and Adaptive RAG routing strategy for production systems.

Adaptive RetrievalGraphRAGKnowledge Graph
0 likes · 13 min read
2026 RAG Selection Guide: How to Choose Between Vector, Graph, and Vectorless
DataFunTalk
DataFunTalk
May 10, 2026 · Artificial Intelligence

Exploring Multimodal GraphRAG: Combining Document Intelligence, Knowledge Graphs, and Large Models

This article presents a detailed technical walkthrough of multimodal GraphRAG, covering document‑intelligence parsing pipelines, multimodal graph index construction, knowledge‑graph‑driven chunk linking, recent research progress, performance trade‑offs, and practical recommendations for deploying RAG solutions.

Document IntelligenceGraphRAGKnowledge Graph
0 likes · 23 min read
Exploring Multimodal GraphRAG: Combining Document Intelligence, Knowledge Graphs, and Large Models
DataFunSummit
DataFunSummit
May 3, 2026 · Artificial Intelligence

From Flawed to Production-Ready: Deep Dive into Building Enterprise-Grade RAG Systems

The article analyzes why early RAG deployments often fall short, dissects the most common technical pain points—from document parsing to vector overload—and presents a systematic roadmap that includes hybrid search, reranking, GraphRAG, Agentic RAG, model selection, scalability tricks, and security controls for robust B‑side production.

Agentic RAGEnterprise AIFine-tuning
0 likes · 20 min read
From Flawed to Production-Ready: Deep Dive into Building Enterprise-Grade RAG Systems
Spring Full-Stack Practical Cases
Spring Full-Stack Practical Cases
May 3, 2026 · Artificial Intelligence

9 Advanced Retrieval‑Augmented Generation (RAG) Architectures Explained

This article introduces Retrieval‑Augmented Generation (RAG) and systematically details nine distinct RAG architectures—standard, conversational with memory, corrective (CRAG), adaptive, self‑RAG, fusion, HyDE, agentic, and Graph RAG—highlighting their workflows, real‑world examples, advantages, and trade‑offs.

AI ArchitectureGraphRAGLLM
0 likes · 17 min read
9 Advanced Retrieval‑Augmented Generation (RAG) Architectures Explained
DeepHub IMBA
DeepHub IMBA
May 1, 2026 · Artificial Intelligence

How to Build Intelligent Contextual Memory for AI Agents

The article examines why naïvely feeding all dialogue history to large language models is costly and unreliable, and it walks through rolling context windows, inverted‑index pruning, semantic vector search, and GraphRAG as complementary techniques for creating efficient, reasoning‑capable AI agent memory.

AIAgent MemoryContext Window
0 likes · 11 min read
How to Build Intelligent Contextual Memory for AI Agents
DataFunTalk
DataFunTalk
Apr 24, 2026 · Artificial Intelligence

Exploring Multimodal GraphRAG: Document Intelligence, Knowledge Graphs, and Large‑Model Integration

This article presents a detailed technical walkthrough of multimodal GraphRAG, covering document‑intelligence parsing pipelines, layout‑analysis models, knowledge‑graph augmentation, multimodal indexing and retrieval, and a comparative analysis of RAG, GraphRAG, and KG‑QA approaches, with concrete examples, model sizes, benchmark scores, and research citations.

Document IntelligenceGraphRAGKnowledge Graph
0 likes · 25 min read
Exploring Multimodal GraphRAG: Document Intelligence, Knowledge Graphs, and Large‑Model Integration
AI Step-by-Step
AI Step-by-Step
Mar 29, 2026 · Artificial Intelligence

How RAG Quickly Gives Your Agent Real Business Knowledge

The article explains why agents often lack business understanding, describes Retrieval‑Augmented Generation (RAG) as the fastest way to provide correct, up‑to‑date business context, outlines eight practical RAG patterns, and offers a step‑by‑step checklist for building enterprise‑ready agents.

AgentEnterprise AIGraphRAG
0 likes · 10 min read
How RAG Quickly Gives Your Agent Real Business Knowledge
Huolala Tech
Huolala Tech
Mar 18, 2026 · Artificial Intelligence

Boosting LLM Accuracy: From RAG to GraphRAG for Enterprise Metadata Retrieval

This article explains the fundamentals of Retrieval‑Augmented Generation (RAG), introduces GraphRAG as an advanced architecture using knowledge graphs, details implementation pipelines, evaluates performance improvements, analyzes common pitfalls, and outlines future enhancements for enterprise metadata search.

AIGraphRAGKnowledge Graph
0 likes · 17 min read
Boosting LLM Accuracy: From RAG to GraphRAG for Enterprise Metadata Retrieval
AI Architecture Path
AI Architecture Path
Mar 16, 2026 · Artificial Intelligence

How MiroFish Turns Documents into Parallel AI Worlds for Future Simulation

MiroFish is an open‑source multi‑agent platform that automatically builds high‑fidelity digital societies from any text, enabling realistic opinion, policy, literary, and crisis simulations with a five‑step GraphRAG workflow, Docker or source deployment, and detailed reporting tools.

AI simulationGraphRAGMulti-Agent
0 likes · 12 min read
How MiroFish Turns Documents into Parallel AI Worlds for Future Simulation
PaperAgent
PaperAgent
Feb 3, 2026 · Artificial Intelligence

Relink: Turning GraphRAG into a Dynamic, Query‑Driven Knowledge Graph

Relink introduces a ‘reason‑and‑construct’ paradigm that builds knowledge‑graph paths during inference, combining a high‑precision factual graph with a high‑recall potential‑relation pool, using query‑driven dynamic path expansion and contrastive alignment to markedly improve multi‑hop QA performance and robustness to sparse knowledge.

Dynamic RetrievalGraphRAGKnowledge Graph
0 likes · 8 min read
Relink: Turning GraphRAG into a Dynamic, Query‑Driven Knowledge Graph
PaperAgent
PaperAgent
Jan 25, 2026 · Artificial Intelligence

How Deep GraphRAG Solves Retrieval’s Three‑Way Dilemma with Hierarchical Search

Deep GraphRAG tackles the three‑fold dilemma of traditional Retrieval‑Augmented Generation by introducing hierarchical global‑to‑local retrieval, a beam‑search dynamic reordering that cuts latency, and a DW‑GRPO reinforcement‑learning module that adaptively weights rewards, achieving near‑state‑of‑the‑art performance with up to 86% faster inference.

AI researchGraphRAGHierarchical Retrieval
0 likes · 5 min read
How Deep GraphRAG Solves Retrieval’s Three‑Way Dilemma with Hierarchical Search
PaperAgent
PaperAgent
Jan 6, 2026 · Artificial Intelligence

How Ontology‑Driven GraphRAG Eliminates Noise in AI Knowledge Graphs

This article examines the shortcomings of naïve GraphRAG implementations on clinical data and explains how an ontology‑driven, zero‑noise GraphRAG architecture can create self‑improving, conflict‑free knowledge graphs for AI applications.

AIData QualityGraphRAG
0 likes · 3 min read
How Ontology‑Driven GraphRAG Eliminates Noise in AI Knowledge Graphs
AI Architecture Hub
AI Architecture Hub
Dec 27, 2025 · Artificial Intelligence

How GraphRAG Turns Knowledge Graphs into Smarter Retrieval for LLMs

GraphRAG extends traditional Retrieval‑Augmented Generation by building a knowledge graph from documents, extracting entities and relationships, performing community detection, and supporting both local and global searches, offering detailed step‑by‑step guidance, code examples, configuration tips, and a comparison with classic RAG approaches.

GraphRAGKnowledge GraphLLM
0 likes · 28 min read
How GraphRAG Turns Knowledge Graphs into Smarter Retrieval for LLMs
Architect
Architect
Dec 25, 2025 · Artificial Intelligence

How GraphRAG Boosts Retrieval Accuracy with Knowledge Graphs – A Complete Guide

This article explains why traditional RAG suffers from hallucinations, introduces GraphRAG’s knowledge‑graph‑based approach, walks through its indexing and query pipelines—including text splitting, entity‑relation extraction, graph construction, community detection, and local vs. global retrieval—provides practical setup commands, Neo4j visualization steps, and compares its performance with classic RAG.

EmbeddingGraphRAGKnowledge Graph
0 likes · 27 min read
How GraphRAG Boosts Retrieval Accuracy with Knowledge Graphs – A Complete Guide
PaperAgent
PaperAgent
Dec 9, 2025 · Artificial Intelligence

How Code Graph Model (CGM) Redefines Repository‑Level Code Understanding

The Code Graph Model (CGM) introduced by Ant's multimodal code team integrates repository‑level graph structures into open‑source LLMs, achieving a 44% solve rate on SWE‑bench Lite, eliminating agent dependence, and demonstrating a novel graph‑enhanced code model through multi‑granular graph construction, dual‑modal alignment, and a lightweight GraphRAG framework.

AICode GraphGraphRAG
0 likes · 9 min read
How Code Graph Model (CGM) Redefines Repository‑Level Code Understanding
Instant Consumer Technology Team
Instant Consumer Technology Team
Sep 30, 2025 · Artificial Intelligence

What Makes Youtu-GraphRAG’s Engineering Stand Out? Inside the AI Blueprint

This article dissects the engineering of Tencent's Youtu-GraphRAG, covering its architectural challenges, real‑time FastAPI/WebSocket design, security measures, iterative retrieval chains, parallel processing, intelligent caching, schema‑driven knowledge handling, and performance tweaks, offering practical insights for AI system builders.

AI EngineeringFastAPIGraphRAG
0 likes · 7 min read
What Makes Youtu-GraphRAG’s Engineering Stand Out? Inside the AI Blueprint
DataFunTalk
DataFunTalk
Sep 19, 2025 · Artificial Intelligence

How Tencent’s Large Language Models Transform Business with RAG, GraphRAG, and Agents

This article examines Tencent's large language model deployments across diverse business scenarios, detailing how Retrieval‑Augmented Generation, GraphRAG, and autonomous agents boost model intelligence, improve user experience, and enable advanced content generation, understanding, and multi‑step reasoning.

Autonomous AgentsGraphRAGRetrieval Augmented Generation
0 likes · 4 min read
How Tencent’s Large Language Models Transform Business with RAG, GraphRAG, and Agents
DataFunSummit
DataFunSummit
Sep 14, 2025 · Artificial Intelligence

How Tencent’s Large Language Models Boost Business with RAG, GraphRAG, and AI Agents

This article examines Tencent's large language model deployments across various business scenarios, detailing the use of Retrieval‑Augmented Generation, GraphRAG for role‑playing, and Agent technologies, while also outlining core application areas and the three main technical approaches—SFT, RAG, and Agents.

AI AgentsAI applicationsGraphRAG
0 likes · 4 min read
How Tencent’s Large Language Models Boost Business with RAG, GraphRAG, and AI Agents
Data Party THU
Data Party THU
Aug 22, 2025 · Artificial Intelligence

How BAML Turns a 25% Success Rate into 99%+ for Knowledge‑Graph Extraction with Small LLMs

This article presents a systematic study of extracting knowledge graphs from unstructured news articles using small quantized LLMs, exposing the brittleness of LangChain's JSON‑based pipelines, evaluating prompt‑engineering fixes, and introducing the BAML framework whose fuzzy parsing and concise schema raise extraction success from roughly 25% to over 99% on a 344‑document benchmark.

BAMLGraphRAGLLM
0 likes · 33 min read
How BAML Turns a 25% Success Rate into 99%+ for Knowledge‑Graph Extraction with Small LLMs
Architect's Alchemy Furnace
Architect's Alchemy Furnace
Jul 12, 2025 · Artificial Intelligence

Why GraphRAG Is the Future of Retrieval‑Augmented Generation

This article explains how GraphRAG combines knowledge graphs with retrieval‑augmented generation to overcome the limitations of vector‑only RAG, delivering higher accuracy, better explainability, easier development, and stronger governance for generative AI applications across various domains.

AIGraphRAGKnowledge Graph
0 likes · 23 min read
Why GraphRAG Is the Future of Retrieval‑Augmented Generation
DataFunSummit
DataFunSummit
Jul 4, 2025 · Artificial Intelligence

Multimodal GraphRAG: Knowledge Graphs, Large Models, and Industry Use Cases

This guide introduces a series of cutting‑edge topics, including multimodal GraphRAG, the dual‑driven synergy of knowledge graphs and large models across finance, traditional Chinese medicine, private‑domain Q&A, knowledge management, and automotive manufacturing, plus trends and standards for large‑model‑based knowledge management.

AI applicationsGraphRAGKnowledge Graphs
0 likes · 2 min read
Multimodal GraphRAG: Knowledge Graphs, Large Models, and Industry Use Cases
Fun with Large Models
Fun with Large Models
Jun 23, 2025 · Artificial Intelligence

Boost RAG Answer Accuracy: Detailed Step‑by‑Step GraphRAG Knowledge‑Graph Construction

This article walks through the complete GraphRAG knowledge‑graph building pipeline—text splitting, entity extraction, relation mining, community clustering, and report generation—using a concrete example from the book “The Age of Big Data,” and explains why each step improves retrieval and answer quality.

GraphRAGKnowledge GraphRAG
0 likes · 20 min read
Boost RAG Answer Accuracy: Detailed Step‑by‑Step GraphRAG Knowledge‑Graph Construction
Fun with Large Models
Fun with Large Models
Jun 19, 2025 · Artificial Intelligence

How GraphRAG Boosts Answer Accuracy with Knowledge Graphs (Part 1)

This article explains GraphRAG’s architecture, compares it with traditional RAG, and presents experimental results showing that GraphRAG’s knowledge‑graph‑driven retrieval markedly improves answer accuracy, especially on low‑match, multi‑paragraph queries.

GraphRAGKnowledge GraphPerformance Evaluation
0 likes · 11 min read
How GraphRAG Boosts Answer Accuracy with Knowledge Graphs (Part 1)
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.

DeepSearcherGraphRAGKnowledge Retrieval
0 likes · 20 min read
Comparative Study of Traditional RAG, GraphRAG, and DeepSearcher for Knowledge Retrieval and Generation
Ma Wei Says
Ma Wei Says
Feb 25, 2025 · Artificial Intelligence

What Is GraphRAG? A Deep Dive into Next‑Gen Retrieval‑Augmented Generation and Open‑Source Implementations

GraphRAG, the next generation of Retrieval‑Augmented Generation, combines large language models, knowledge graphs, and graph databases to overcome traditional RAG’s knowledge gaps, hallucinations, and context limitations, and the article reviews its architecture, core modules, a recent 2025 paper, and six notable open‑source implementations.

GraphRAGRetrieval Augmented Generationartificial intelligence
0 likes · 9 min read
What Is GraphRAG? A Deep Dive into Next‑Gen Retrieval‑Augmented Generation and Open‑Source Implementations
NewBeeNLP
NewBeeNLP
Dec 16, 2024 · Artificial Intelligence

How Tencent Boosts LLM Power with RAG, GraphRAG, and Agent Technologies

This article examines Tencent's large language model deployments across content generation, intelligent customer service, and role‑playing scenarios, detailing the principles and practical implementations of Retrieval‑Augmented Generation (RAG), GraphRAG, and Agent techniques, and discusses challenges, optimization strategies, and real‑world use cases.

AIAgentGraphRAG
0 likes · 18 min read
How Tencent Boosts LLM Power with RAG, GraphRAG, and Agent Technologies
DataFunTalk
DataFunTalk
Dec 10, 2024 · Artificial Intelligence

Tencent Large Language Model Applications: RAG, GraphRAG, and Agent Technologies

This article explores Tencent's large language model deployments across various business scenarios, detailing the principles and practical implementations of Retrieval‑Augmented Generation (RAG), GraphRAG for role‑playing, and Agent technologies, while also covering model fine‑tuning, knowledge‑base construction, and evaluation methods.

AI applicationsAgentGraphRAG
0 likes · 15 min read
Tencent Large Language Model Applications: RAG, GraphRAG, and Agent Technologies
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
Nov 29, 2024 · Artificial Intelligence

How GraphRAG Transforms Global QA with Structured Retrieval

This article examines GraphRAG—a graph‑enhanced Retrieval‑Augmented Generation approach—detailing its core concepts, the practical challenges of deploying it in enterprise settings, and the engineering solutions and future directions that enable more accurate, efficient, and explainable global question‑answering systems.

Global QAGraphRAGLLM
0 likes · 16 min read
How GraphRAG Transforms Global QA with Structured Retrieval
DataFunSummit
DataFunSummit
Nov 9, 2024 · Artificial Intelligence

GraphRAG: Using Graph Structures to Enhance Retrieval‑Augmented Generation – Challenges, Methods, and Product Deployments

This article introduces GraphRAG, explains the limitations of traditional RAG, outlines four major challenges (fine‑grained retrieval, global context, similarity vs relevance, and macro‑level reasoning), describes GraphRAG’s graph‑based retrieval strategies, showcases comparative experiments, and presents NebulaGraph’s GenAI Suite and RAG products along with future research directions.

AIGraphRAGRetrieval Augmented Generation
0 likes · 16 min read
GraphRAG: Using Graph Structures to Enhance Retrieval‑Augmented Generation – Challenges, Methods, and Product Deployments
Full-Stack Cultivation Path
Full-Stack Cultivation Path
Sep 4, 2024 · Artificial Intelligence

Hot Open-Source RAG Tool for Document Chat: GraphRAG, Multimodal QA & Complex Reasoning

This article introduces Kotaemon, an open‑source Retrieval‑Augmented Generation platform that lets users chat with their documents, offering a self‑hosted web UI, support for local and API LLMs, hybrid retrieval, multimodal question answering, GraphRAG indexing, and advanced reasoning capabilities, along with step‑by‑step installation via App or Docker.

GraphRAGLLMMultimodal QA
0 likes · 6 min read
Hot Open-Source RAG Tool for Document Chat: GraphRAG, Multimodal QA & Complex Reasoning
AI Large Model Application Practice
AI Large Model Application Practice
Sep 4, 2024 · Artificial Intelligence

When to Use GraphRAG vs. Traditional RAG and How to Combine Them

This article compares GraphRAG with traditional RAG across seven dimensions—suitable scenarios, knowledge representation, retrieval, comprehensive queries, hidden‑relationship understanding, scalability, and performance‑cost trade‑offs—explains how they can be fused, and offers guidance on selecting the right approach for complex data‑driven applications.

GraphRAGLLMRAG
0 likes · 13 min read
When to Use GraphRAG vs. Traditional RAG and How to Combine Them
AI Large Model Application Practice
AI Large Model Application Practice
Aug 9, 2024 · Artificial Intelligence

How to Build and Index Microsoft GraphRAG with Neo4j: A Step‑by‑Step Guide

This article explains the fundamentals of Microsoft GraphRAG, details its indexing pipeline—including text chunking, entity‑relationship extraction, community detection, and description generation—shows how to set up the graphrag library, create adaptive prompts, build the index, and import the resulting graph into Neo4j for visualization and analysis.

AIGraphRAGNeo4j
0 likes · 13 min read
How to Build and Index Microsoft GraphRAG with Neo4j: A Step‑by‑Step Guide
AntTech
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