GraphRAG Python Package: Accelerate GenAI with Knowledge Graphs

30 Minutes

With Graph-powered RAG (GraphRAG), you can build GenAI apps that deliver more accurate, relevant, and explainable results. We’ve streamlined end-to-end GraphRAG workflows to help you get started fast. 

In this on-demand webinar, we introduce the GraphRAG Python package, supported by Neo4j. This package provides developers with simple workflows to go from unstructured data to knowledge graph creation, retrieval, and end-to-end GraphRAG in GenAI applications. 

You’ll learn how to use the GraphRAG Python package to: 

  • Quickly build knowledge graphs from unstructured text documents 
  • Easily implement knowledge graph retrievers combining graph traversals and vector and full-text search 
  • Develop end-to-end GraphRAG workflows to boost RAG quality and effectiveness in knowledge assistants and other GenAI applications. 

Watch this 30-minute session to find out about an easier, faster way to power your next GenAI breakthrough.



SPEAKERS

Zachary Blumenfeld.jpeg

Zachary Blumenfeld
AI/ML Product Specialist

Zach is an AI/ML graph enthusiast who helps engineers, data scientists, and business leaders leverage graph technology for analytics and AI applications. His expertise spans several dynamic fields, including criminal fraud detection, identity resolution, and recommendation systems.


Estelle Scifo Image

Estelle Scifo
Senior Software Engineer

After a PhD in particle physics at CERN, Estelle started a career in data science across multiple human size companies. She discovered graphs and Neo4j shortly after and since then can't stop using graphs in many different projects. She's been an active member of the Neo4j community, producing content such as books, blog posts and participating at conferences such as PyConFr or NODES. In 2024, she joined Neo4j to work on GenAI-related topics: GraphRAG and KG construction.

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