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'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
Resources:
---Your Documents Are Trying to Tell You What’s Relevant: Better RAG Using Links
https://towardsdatascience.com/your-d...
--How Knowledge Graph RAG Boosts LLM Results
https://www.datastax.com/blog/how-kno...
--A Guide to Graph RAG, a New Way to Push the Boundaries of GenAI Apps
/ a-guide-to-graph-rag-a-new-way-to-push-the...
--The examples above use the open source connector, if you’re using Astra you can use the data api as well:
https://python.langchain.com/api_refe...
--Scaling knowledge graphs by eliminating edges
https://www.datastax.com/blog/scaling...
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