Build a knowledge graph without an LLM for your RAG applications

Bridge the gap and unlock hidden potential within your unstructured data

New features include community summaries, local and global retrievers, running retrievers in parallel, and custom prompt instructions.

When (not) to use GraphRAG

Explore its functionality and how it leverages the power of LLMs and Neo4j graph databases to improve data analysis and knowledge discovery.

David Hughes presented “Unleashing the Power of Multimodal GraphRAG: Integrating Image Features for Deeper Insights” at Data Day Texas 2025.

Discover Neo4j's 2024 product innovations in cloud-native graph technology, enhancing performance, scalability, AI innovation, and developer productivity.

What is really Graph RAG? Inspired by "From Local to Global: A Graph RAG Approach to Query-Focused Summarization" paper from Microsoft! How do you combine… | 12 comments on LinkedIn

UChicago Genie is now open source! How we built a zero-hallucination AI chatbot that answered over 10000 questions of students at the University of… | 25 comments on LinkedIn

As technology advances, large language models (LLMs) and information retrieval/management methods evolve at breakneck speed. Knowledge…

Graph Retrieval Augmented Generation (Graph RAG) is emerging as a powerful addition to traditional vector search retrieval methods. Graphs are great at repre...

A RAG System Built for Both Semantic Search and Structured Data Queries

Ashley Faith hosted Amy Hodler and me on a recent episode about “2025 Predictions” — here are notes I prepared, to review in 2026…

LlamaIndex is a simple, flexible framework for building knowledge assistants using LLMs connected to your enterprise data.

While retrieval-augmented generation is effective for simpler queries, advanced reasoning questions require deeper connections between information that exist across documents. They require a knowledge graph.

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

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

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

A Conceptual Overview for Building Robust Solutions.

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…

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