Connect the Dots in Your RAG Pipeline with Graph RAG and GraphRetriever

You’ve built a GenAI app using RAG, but your answers still feel shallow, especially when users ask questions that require context from multiple documents. The problem isn’t your vector search. The problem is that your documents are disconnected.

Join us for a practical walkthrough of GraphRetriever, a lightweight approach to Graph RAG that enriches LLM responses with structured context, without needing a knowledge graph or graph database.

In this session, you’ll learn:

--Why basic vector search misses the full picture
--How to use metadata to define edges and relationships at query time
--How GraphRetriever connects reviews and movie metadata to answer real-world queries like “What are some good family movies?”

This session is based on a real use case using Rotten Tomatoes reviews. You’ll leave with runnable code and a better way to make your RAG pipeline feel smarter.

Livestream Resources:
Free Astra Account: dtsx.io/3U7f1RW
Blog: Graph RAG & Movie Reviews: dtsx.io/4lkMZPa
Graph RAG Movie Review Examples: dtsx.io/3IkQfeQ
Graph RAG Colab: dtsx.io/44UDbFC


Additional Resources:
DataStax Developer Hub: https://dtsx.io/devhub
DataStax Blog: https://dtsx.io/howto
Try Langflow: https://dtsx.io/trylangflow
Try Astra DB: https://dtsx.io/40kQpI6
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