The Developer's Guide to GraphRAG

Summary

You’ve built a RAG system and grounded it in your own data. Then you ask a complex question that needs to draw from multiple sources. Your heart sinks when the answers you get are vague or plain wrong.  

How could this happen?

Traditional vector-only RAG bases its outputs on just the words you use in your prompt. It misses out on valuable context because it pulls from different documents and data structures. Basically, it misses out on the bigger, more connected picture.

Your AI needs a mental model of your data with all its context and nuances. A knowledge graph provides just that by mapping your data as connected entities and relationships. Pair it with RAG to create a GraphRAG architecture to feed your LLM information about dependencies, sequences, hierarchies, and deeper meaning.

Check out The Developer’s Guide to GraphRAG. You’ll learn how to:

• Prepare a knowledge graph for GraphRAG
• Combine a knowledge graph with native vector search
• Implement three GraphRAG retrieval patterns