GraphRAG infrastructure for generative AI using Vertex AI and Spanner Graph | Cloud Architecture Center | Google Cloud
Design infrastructure for a generative AI application with GraphRAG using Spanner Graph.
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Why Knowledge Graphs are Critical to Agent Context
How should we organize knowledge to provide the best context for agents? We show how knowledge graphs could play a key role in enhancing context for agents.
The Developer's Guide to GraphRAG
Find out how to combine a knowledge graph with RAG for GraphRAG. Provide more complete GenAI outputs.
You’ve built a RAG system and grounded it in your own data. Then you ask a complex question that needs to draw from multiple sources. Your heart sinks when the answers you get are vague or plain wrong.
How could this happen?
Traditional vector-only RAG bases its outputs on just the words you use in your prompt. It misses out on valuable context because it pulls from different documents and data structures. Basically, it misses out on the bigger, more connected picture.
Your AI needs a mental model of your data with all its context and nuances. A knowledge graph provides just that by mapping your data as connected entities and relationships. Pair it with RAG to create a GraphRAG architecture to feed your LLM information about dependencies, sequences, hierarchies, and deeper meaning.
Check out The Developer’s Guide to GraphRAG. You’ll learn how to:
Prepare a knowledge graph for GraphRAG
Combine a knowledge graph with native vector search
Implement three GraphRAG retrieval patterns
Want to explore the Anthropic Transformer-Circuit's as a queryable graph?
Want to explore the Anthropic Transformer-Circuit's as a queryable graph?
Wrote a script to import the graph json into Neo4j - code in Gist.
https://lnkd.in/eT4NjQgY
https://lnkd.in/e38TfQpF
Next step - write directly from the circuit-tracer library to the graph db.
https://lnkd.in/eVU_t6mS
Want to explore the Anthropic Transformer-Circuit's as a queryable graph?
Announcing general availability of Amazon Bedrock Knowledge Bases GraphRAG with Amazon Neptune Analytics | Amazon Web Services
Today, Amazon Web Services (AWS) announced the general availability of Amazon Bedrock Knowledge Bases GraphRAG (GraphRAG), a capability in Amazon Bedrock Knowledge Bases that enhances Retrieval-Augmented Generation (RAG) with graph data in Amazon Neptune Analytics. In this post, we discuss the benefits of GraphRAG and how to get started with it in Amazon Bedrock Knowledge Bases.
Why GraphRAG Feels Hard (and What I Learned Trying to Make It Work)
Integrating Neo4j, DuckDB, and Kestra for Metadata-Driven Graph Design
Graphiti: Knowledge Graph Memory for a Post-RAG Agentic World
An open-source Python framework for real-time, graph-based knowledge in Neo4j
Google Cloud & Neo4j: Teaming Up at the Intersection of Knowledge Graphs, Agents, MCP, and Natural Language Interfaces - Graph Database & Analytics
We’re thrilled to announce new Text2Cypher models and Google’s MCP Toolbox for Databases from the collaboration between Google Cloud and Neo4j.
Everything a Developer Needs to Know About the Model Context Protocol (MCP) - Graph Database & Analytics
The Model Context Protocol gives users a wide range of services they can connect to from the comfort of their AI client.
Using Spanner Graph with LangChain for GraphRAG | Google Cloud Blog
Learn how to use Spanner Graph with LangChain workflows to build advanced, relationship-aware RAG systems, a.k.a. GraphRAG.
Talk To Your Graph 2.0 - A Partner’s View
Read about how our partners from Semantic Partners explore the latest iteration of the GraphDB's Talk To Your Graph feature
Build your hybrid-Graph for RAG & GraphRAG applications using the power of NLP | LinkedIn
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
Introduction to the Neo4j LLM Knowledge Graph Builder
Bridge the gap and unlock hidden potential within your unstructured data
LLM Knowledge Graph Builder — First Release of 2025
New features include community summaries, local and global retrievers, running retrievers in parallel, and custom prompt instructions.
Should you be using GraphRAG?
When (not) to use GraphRAG
Introduction to the Neo4j LLM Knowledge Graph Builder
Explore its functionality and how it leverages the power of LLMs and Neo4j graph databases to improve data analysis and knowledge discovery.
Chatting with your knowledge graph
Enabling an LLM to chat directly with structured graph data
Multimodal Graph RAG (mmGraphRAG): Incorporating Vision in Search and Analytics - Enterprise Knowledge
David Hughes presented “Unleashing the Power of Multimodal GraphRAG: Integrating Image Features for Deeper Insights” at Data Day Texas 2025.
GraphRAG interview — Alonso
As technology advances, large language models (LLMs) and information retrieval/management methods evolve at breakneck speed. Knowledge…
LangGraph AI Agents with Neo4j Knowledge Graph
A RAG System Built for Both Semantic Search and Structured Data Queries
Introducing the Neo4j Text2Cypher (2024) Dataset
Authors: Makbule Gulcin Ozsoy, Leila Messallem, Jon Besga
Building Knowledge Graphs with LLM Graph Transformer
A deep dive into LangChain’s implementation of graph construction with LLMs
Turn Your CSVs Into Graphs Using LLMs
Learn how to turn CSV files into graph models using LLMs, simplifying data relationships, enhancing insights, and optimizing workflows.
Implementing GraphReader with Neo4j and LangGraph
Elevating RAG accuracy and performance by structuring long documents into explorable graphs and implementing graph-based agent systems
Ontotext mentioned in more than 10 Gartner Hype Cycle reports
😎 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…
Ontotext mentioned in more than 10 Gartner Hype Cycle reports
Microsoft GraphRAG with an RDF Knowledge Graph — Part 3
Using SPARQL and the Knowledge Graph for RAG
Using knowledge graphs to build GraphRAG applications with Amazon Bedrock and Amazon Neptune | Amazon Web Services
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.
loading Microsoft Research GraphRAG data into Neo4j
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
loading Microsoft Research hashtag#GraphRAG data into Neo4j
GitHub - SynaLinks/HybridAGI: 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
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
This notebook converts CSV data into a Neo4j Graph Database
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…