GraphRAG Developer Guide

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Graph databases provide unmatched data organization, flexibility, and standardization, making it easy to write diverse queries and answer more questions. GraphRAG (retrieval-augmented generation with graphs) extends these benefits to AI, enabling developers to build more accurate, agile, and extensible GenAI apps and agentic systems. Use these resources to get started today!

How to Get Started

  1. For a high level overview, have a look at neo4j.com/genai

  2. Use the LLM-Knowledge Graph Builder to turn your own documents into a knowledge graph or see the example projects below for building graphs from mixed structured & unstructured data.

  3. If you want to learn more take one of the GenAI GraphAcademy courses

  4. Pick your GenAI framework of choice and start building your own GenAI applications with Neo4j

GraphAcademy Courses

If you want to learn how LLMs and Knowledge Graphs combine to improve GenAI applications, check out the Neo4j & LLM courses on GraphAcademy.

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Example Projects & Use Case Applications

Explore these projects to see how GraphRAG is applied in GenAI & agent workflows, various use cases, and integrations with specific cloud service providers.

Neo4j GenAI Product Features

Neo4j has a number of features & product supported tools to make it easier to build GenAI applications and integrate LLMs with knowledge graphs.

GraphRAG Ecosystem Tools

In Neo4j Labs we built a number of tools to demonstrate the power of combining graphs with LLMs. These tools are open source, you can use and contribute to them or learn and build your own.

GenAI Frameworks

Neo4j and our community have contributed integrations to many GenAI development frameworks. You can find overviews of these integrations here, as well as code examples, tutorials and more. Popular frameworks include:

Additional Resources