Connect the Dots in Your RAG Pipeline with Graph RAG and GraphRetriever скачать в хорошем качестве

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 ____________________ Stay in touch: Join our Discord Community:   / discord   Follow us on X: https://x.com/DataStaxDevs _________