An open-source Python framework for real-time, graph-based knowledge in Neo4j

We’re thrilled to announce new Text2Cypher models and Google’s MCP Toolbox for Databases from the collaboration between Google Cloud and Neo4j.

The Model Context Protocol gives users a wide range of services they can connect to from the comfort of their AI client.

Learn how to use Spanner Graph with LangChain workflows to build advanced, relationship-aware RAG systems, a.k.a. GraphRAG.

Read about how our partners from Semantic Partners explore the latest iteration of the GraphDB's Talk To Your Graph feature

Build a graph for RAG application for a price of a chocolate bar! What is GraphRAG for you? What is GraphRAG? What does GraphRAG mean from your perspective? What if you could have a standard RAG and a GraphRAG as a combi-package, with just a query switch? The fact is, there is no concrete, universal

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.

Enabling an LLM to chat directly with structured graph data

Today, I conducted a comparison between ChatGPT and DeepSeek chat capabilities by providing them with a schema and a natural language question. I tasked them…

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.

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

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

Authors: Makbule Gulcin Ozsoy, Leila Messallem, Jon Besga

Authors: Makbule Gulcin Ozsoy, Leila Messallem, Jon Besga

A deep dive into LangChain’s implementation of graph construction with LLMs

Learn how to turn CSV files into graph models using LLMs, simplifying data relationships, enhancing insights, and optimizing workflows.

Elevating RAG accuracy and performance by structuring long documents into explorable graphs and implementing graph-based agent systems

😎 Pretty proud to see Ontotext mentioned in more than 10 Gartner Hype Cycle reports over the last couple months! This demonstrates the success of our…

Using SPARQL and the Knowledge Graph for RAG

Retrieval Augmented Generation (RAG) is an innovative approach that combines the power of large language models with external knowledge sources, enabling more accurate and informative generation of content. Using knowledge graphs as sources for RAG (GraphRAG) yields numerous advantages. These knowledge bases encapsulate a vast wealth of curated and interconnected information, enabling the generation of responses that are grounded in factual knowledge. In this post, we show you how to build GraphRAG applications using Amazon Bedrock and Amazon Neptune with LlamaIndex framework.

Many people have asked about loading Microsoft Research #GraphRAG data into Neo4j. I wrote a quick notebook last night to import Documents, Chunks (TextUnit)… | 27 comments on LinkedIn

The Programmable Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected - SynaLinks/HybridAGI

End to end solution for migrating CSV data into a Neo4j graph using an LLM for the data discovery and graph data modeling stages. - a-s-g93/neo4j-runway

This notebook converts CSV data into a Neo4j Graph Database. All you do is describe your data. Have you wanted to see what your data looked like as a graph…

How SP used Ontotext GraphDB’s vector capabilities to build a simple RAG retriever over a Knowledge Graph, using open, offline LLM models

How we used Ontotext GraphDB’s vector capabilities to build a simple RAG retriever over a Knowledge Graph, using open, offline LLM models