Graph-Powered Chatbots with LangChain

You’ve built a GenAI Chatbot with RAG, and it works well for simple questions. But as more complex queries come in, hallucinations follow—often met with the dreaded “I can’t help you with that” guardrails. Managing every edge case before launch is impossible—you need a way for your chatbot to learn and improve over time.

In this talk, we’ll dive into Graph RAG, a method that enhances your chatbot’s intelligence by leveraging the connectivity of knowledge graphs with semantic understanding. You’ll see how Astra DB vectors and hybrid graph/vector search can be used to boost retrieval accuracy.

Even better, you don’t need to be a graph database expert. We’ll explore how to use LangChain’s GraphVectorStore and LangFlow, a no-code canvas for building LLM-powered workflows, to simplify your development. Plus, we’ll demonstrate automating graph node and edge creation using LLMs, making complex knowledge management easier than ever.