The Knowledge Graph Mullet: Trimming GraphRAG Complexity - William Lyon
Jun 3, 2025 · 32:52
William Lyon presents the 'Knowledge Graph Mullet' concept, combining property graph interfaces with RDF triple storage in Dgraph to optimize GraphRAG systems and AI agent development. Dgraph uses a property graph model for data modeling and DQL (inspired by GraphQL) for queries, while leveraging RDF triples for scalable storage and data interchange. Features include vector similarity search (e.g., finding articles about 'money laundering'), geospatial indexes, and a Model Context Protocol (MCP) server that exposes tools to models like Claude Desktop for auto-generating schema and querying. Lyon demonstrates building a news knowledge graph from the New York Times, chunking articles into nodes, and using graph traversal to surface related topics after vector search. He also showcases Hypermode agents, created from a prompt with access to GitHub and Notion via MCP servers, which can analyze repos like hyper-news and generate social media posts with code snippets, then save them to a Notion workspace. Hypermode agents are now in early access.
