Graph RAG — 4 Technical Articles

Nov 20, 2025 · Artificial Intelligence

From 9,874 Papers to 15,000 Structures: MOF‑ChemUnity Rebuilds MOF Knowledge for Explainable AI

MOF‑ChemUnity constructs a scalable, extensible knowledge graph that links millions of MOF names and synonyms to over 15,000 crystal structures using LLM‑driven entity matching, enabling accurate, explainable AI‑assisted material discovery, water‑stability prediction, expert recommendation validation, and graph‑enhanced retrieval across diverse applications.

Graph RAGKnowledge GraphLarge Language Model

0 likes · 17 min read

From 9,874 Papers to 15,000 Structures: MOF‑ChemUnity Rebuilds MOF Knowledge for Explainable AI

Ops Development Stories

Jul 14, 2025 · Artificial Intelligence

Mastering AIOps: Prompt Engineering, Function Calling, RAG, Graph RAG, and Local LLM Deployment

This comprehensive guide explores AIOps techniques such as prompt engineering, chat completions, memory management, function calling, fine‑tuning, retrieval‑augmented generation (RAG), graph‑based RAG, and practical steps for deploying open‑source large language models locally, providing code examples and best‑practice recommendations for modern DevOps environments.

Function CallingGraph RAGRAG

0 likes · 47 min read

Mastering AIOps: Prompt Engineering, Function Calling, RAG, Graph RAG, and Local LLM Deployment

AI Algorithm Path

Jul 3, 2025 · Artificial Intelligence

Exploring Advanced, Graph, and Agentic RAG: The Evolution of Retrieval‑Augmented Generation

This article examines how Retrieval‑Augmented Generation (RAG) has progressed from simple keyword‑based retrieval to advanced semantic methods, modular architectures, graph‑enhanced reasoning, and autonomous agentic systems, highlighting each approach's workflow, benefits, limitations, and the shift toward dynamic AI decision‑making.

AIAgentic RAGGraph RAG

0 likes · 7 min read

Exploring Advanced, Graph, and Agentic RAG: The Evolution of Retrieval‑Augmented Generation

AsiaInfo Technology: New Tech Exploration

Dec 13, 2024 · Artificial Intelligence

Optimizing Graph RAG: Boosting Global QA with Better Chunking, Prompts, and Entity Extraction

This article presents a comprehensive analysis of Graph RAG, detailing its implementation workflow, step‑by‑step execution guide, four targeted optimization strategies, and experimental validation that demonstrates significant improvements in global and local question answering for industry scenarios.

Graph RAGLLM optimizationRetrieval-Augmented Generation

0 likes · 18 min read

Optimizing Graph RAG: Boosting Global QA with Better Chunking, Prompts, and Entity Extraction