A session showing how to build smarter AI-powered apps by combining SurrealDB's graph and vector capabilities with LangChain. We'll walk through a complete example: a chatbot that analyses symptoms and recommends appointment scheduling based on semantic similarity and structured graph relationships. Learn how to set up SurrealDB as both a graph and vector store in a single system, use LangChain to query structured knowledge alongside embeddings, chain together document ingestion, graph construction, and AI-driven Q&A, and deploy an architecture that scales from prototype to production.
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Building AI Apps with GraphRAG: A Practical Guide Using SurrealDB + LangChain
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