Much has been discussed about how Large Language Models, Knowledge Graphs and Search Engines can be combined in a synergistic manner. A dimension largely absent from current academic discourse is...

Lettria, an AWS Partner, demonstrated that integrating graph-based structures into RAG workflows improves answer precision by up to 35% compared to vector-only retrieval methods. In this post, we explore why GraphRAG is more comprehensive and explainable than vector RAG alone, and how you can use this approach using AWS services and Lettria.

Examples to help you understand the GraphRAG mechanism

Authors: Makbule Gulcin Ozsoy, Leila Messallem, Jon Besga

We're excited to publicly release the Diffbot GraphRAG LLM! With larger and larger frontier LLMs, we realized that they would eventually hit a limit in terms… | 48 comments on LinkedIn

𝐌𝐲 𝐛𝐨𝐥𝐝 𝐚𝐧𝐝 𝐨𝐧𝐥𝐲 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝟐𝟎𝟐𝟓: By December, everyone, their chatbot, and their agents will finally agree that ontologies… | 80 comments on LinkedIn

Ontologies Prevent Hallucinations in LLM-based Applications

Using knowledge graphs and AI to retrieve, filter, and summarize medical journal articles

Join us for the first GraphGeeks Talk of 2025!! 🌐 "Exploring News at Scale with On-the-Fly Graphs" 🗓️ January 15th at 9:00 AM PT | 17:00 London Join…

Task planning in language agents is emerging as an important research topic alongside the development of large language models (LLMs). It aims to break down complex user requests in natural...

A Flask app running GraphRAG for healthcare, made with Vertex AI and Neo4j, to be deployed in a container (Cloud Run or ECS). - RubensZimbres/Graph_RAG

Build a model-native app using knowledge graphs and the open source Modus API framework

🧱Building Knowledge Graphs with LLM Graph Transformer A deep dive into LangChain’s implementation of graph construction with LLMs If you want to try out… | 32 comments on LinkedIn

SimGRAG is a novel method for knowledge graph driven RAG, transforms queries into graph patterns and aligns them with candidate subgraphs using a graph…

How we used Kùzu in combination with LLMs in multiple stages of the Graph RAG pipeline to build a QA chatbot for the Connected Data London Knowledge Graph Challenge

codes explained with example, Generative AI

Why someone in a regulated industry should invest in #GraphRAG is something we have already discussed here: https://lnkd.in/d5ykdD7u With the associated…

GraphRAG is a powerful retrieval mechanism that improves Generative AI applications by taking advantage of the rich context in graph data structures.

A Conceptual Overview for Building Robust Solutions.

Graphs + Transformers = the best of both worlds 🤝 The same models powering breakthroughs in natural language processing are now being adapted for graphs…

Agentic Graph Intelligence 🕳️ Agentic graph reasoning represents a sophisticated cognitive architecture that integrates multiple advanced components into a…

Enterprise GraphRAG: Building Production-Grade LLM Applications with Knowledge Graphs Let’s dive into the numbers: Real-World Results Implementing GraphRAG…

Want better results from your RAG? GraphRAG takes it to the next level. GraphRAG is a powerful approach to retrieval augmented generation (RAG). It… | 46 comments on LinkedIn

Turn your Pandas Data Frame into a Knowledge Graph using LLMs. Build your own LLM graph-builder and QA your KG.

Introducing a new approach to graph-enabled RAG. LazyGraphRAG needs no prior summarization of source data, avoiding prohibitive up-front indexing costs. It’s inherently scalable in cost and quality across multiple methods and search mechanisms:

Parsing through hype to understand how to think about Knowledge Graph creation processes

Graph is increasingly driving the Agentic space, which I see as being a very good sign. Recently, a programmer asked why graphs would be superior to using…

Watch the first podcast of Paco Nathan's Graph Power Hour. This week's topic - Understanding Graph Rag: Enhancing LLM Applications Through Knowledge Graphs.

Working with RDF on LLMs ================== The following is a quick reference list of things I've found when trying to work with the RDF stack on LLMs *… | 14 comments on LinkedIn