Level Up Your GenAI Chatbot with Graph RAG & LangChain
You've built a GenAI Chatbot with RAG and it works for easy questions, but as more complex questions come in… so do hallucinations often addressed with "I can't help you with that" guardrails. It's impossible to handle every edge case before you go live–you need a way to improve responses over time.
You've built a GenAI Chatbot with RAG and it works for easy questions, but as more complex questions come in… so do hallucinations often addressed with "I can't help you with that" guardrails. It's impossible to handle every edge case before you go live–you need a way to improve responses over time.
You're in luck! This livestream is about a technique that helps your app get smarter over time. Graph RAG can enable you to enhance responses all without needing to be an expert in Graph techniques or databases–that is if you’re using our contributed LangChain GraphVectorStore.
During this session, you’ll learn:
- What you can do to improve hallucinations
- Why combining the connectivity of knowledge graphs with semantic understanding improves RAG
- How to leverage a hybrid graph/vector retrieval algorithm in LangChain
Dr. Charna Parkey
VP, Product & Ops, Innovation
David Jones-Gilardi
Developer Relations Engineer