Graph RAG

Vector databases were supposed to solve AI's knowledge problem. Just embed your documents, store them, retrieve relevant chunks, and feed them to your language model. Simple, right?

Except it doesn't work for complex questions. Vector retrieval fails at multi-hop reasoning, misses contextual relationships, and can't connect dots across separate documents. Your AI agent can find individual facts but can't reason about how they relate.

Knowledge graphs solve this. By representing information as structured networks of entities and relationships, Graph RAG enables the kind of reasoning that naive vector search can't touch: following chains of inference, discovering unexpected connections, and maintaining coherent understanding across vast knowledge bases.

Why This Matters for Coherence

Knowledge isn't just a bag of facts. It's a structured web of relationships. Coherent reasoning requires maintaining that structure—tracking how concepts relate, how evidence accumulates, and how conclusions follow from premises. Graph RAG provides the infrastructure for exactly this kind of structured coherence in AI systems.

Understanding Graph RAG helps us understand what knowledge representation looks like when optimized for reasoning rather than just retrieval.

Articles in This Series

Beyond Vector Search: Why Graph RAG Is the Future of AI Retrieval

Introduction to Graph RAG—why knowledge graphs solve the limitations of naive vector retrieval.

IdeasthesiaRyan Collison

The Limits of Naive RAG: Why Your AI Agent Can't Reason

What goes wrong with basic retrieval augmented generation—missing context, multi-hop failure, and semantic gaps.

IdeasthesiaRyan Collison

Knowledge Graphs 101: Nodes, Edges, and Semantic Structure

Foundational concepts in knowledge graphs—how to represent structured knowledge.

IdeasthesiaRyan Collison

Building Knowledge Graphs from Documents: Extraction Pipelines

How to automatically construct knowledge graphs from unstructured text—the entity and relation extraction stack.

IdeasthesiaRyan Collison

Multi-Hop Reasoning: How Graphs Enable Complex Queries

The key advantage of graph retrieval—following chains of relationships to answer complex questions.

IdeasthesiaRyan Collison

Microsoft GraphRAG: Architecture and Lessons Learned

Deep dive into Microsoft's Graph RAG implementation—what works and what doesn't at scale.

IdeasthesiaRyan Collison

Hybrid Retrieval: Combining Vectors and Graphs

Best practices for combining vector and graph retrieval—getting the best of both approaches.

IdeasthesiaRyan Collison

Graph RAG at Scale: Production Engineering Challenges

What it takes to run Graph RAG in production—infrastructure, performance, and maintenance.

IdeasthesiaRyan Collison

Graph RAG Meets Active Inference: Knowledge as Generative Model

Bridging Graph RAG to active inference—how knowledge graphs serve as generative world models for agents.

IdeasthesiaRyan Collison

Synthesis: What Graph RAG Teaches About Structured Coherence

Integration showing how graph retrieval illuminates questions about how systems maintain coherent knowledge.

IdeasthesiaRyan Collison