Graph DB to LLM: Chat with Structured Data (NO Cypher/SQL)

This rapid prototype shows senior software developers, architects, and engineering leaders how to connect a Local LLM (like Llama 3 or DeepSeek) directly to a structured Knowledge Graph (Neo4j) for highly accurate, natural language querying—all without an internet connection or token charges. This is the future of data accessibility for large, complex datasets. The core problem for engineering leaders is making vast, structured data accessible without forcing every user to learn complex query languages. This video demonstrates a full Retrieval-Augmented Generation (RAG) pipeline that leverages graph embeddings and semantic search to answer complex, multi-step questions (like complex family tree traversal and nested calculations) with zero hallucination. 🔗 Transcript available on InfoQ: https://bit.ly/4nWdGLg #KnowledgeGraph #LocalLLM #SoftwareArchitecture 👉 What’s the most complex traversal or aggregation query in your current database that you'd like to test with this approach? Let us know in the comments! 👇