GraphRAG Python Package: Accelerate GenAI with Knowledge Graphs
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 webinar, we’ll introduce the GraphRAG Python package, supported by Neo4j. This package provides developers with simple workflows to gorom unstructured data to knowledge graph creation, retrieval, and end-to-end GraphRAG in GenAI applications.
- 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.
SPEAKERS
Zachary Blumenfeld
AI/ML Product Specialist, Neo4j
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
Senior Software Engineer, Neo4j
