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When you treat a knowledge graph as a generative model rather than a lookup table, something interesting happens: Graph RAG starts to look a lot like active inference. This piece unpacks the convergence.

Getting Graph RAG to work in a notebook is the easy part. This post digs into the real engineering challenges: graph consistency at scale, query latency, and maintenance costs in live systems.

Vector search finds semantic neighbors; graph search finds relational paths. Combining them outperforms either approach alone — and it's changing how AI systems retrieve and reason over knowledge.

Microsoft GraphRAG: Architecture and Lessons Learned Series: Graph RAG | Part: 6 of 10 In April 2024, Microsoft Research released GraphRAG—an open-source implementation of knowledge graph-based retrieval for AI agents. It wasn't the first Graph RAG system, but it was the first production-quality reference architecture from a major

Most questions worth asking can't be answered in one lookup. Multi-hop reasoning chains facts across a knowledge graph — and it turns out the geometry of that graph determines what you can and can't know.

A pile of documents is information. A knowledge graph is structured knowledge — entities named, relationships labeled, facts connected. This is how you build one: pipelines that identify entities and relations, resolve ambiguities, and assemble a graph machines can actually reason over.

Facts don't live in isolation. Marie Curie discovered polonium; polonium is an element; elements have atomic numbers. Knowledge graphs make those connections explicit: nodes for things, labeled edges for relationships, a structure a machine can traverse and reason over.