Why Knowledge Graphs are Critical to Agent Context
How should we organize knowledge to provide the best context for agents? We show how knowledge graphs could play a key role in enhancing context for agents.
Find out how to combine a knowledge graph with RAG for GraphRAG. Provide more complete GenAI outputs.
Youโve 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.ย ย
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 Developerโs Guide to GraphRAG. Youโll learn how to:
Prepare a knowledge graph for GraphRAG
Combine a knowledge graph with native vector search
Implement three GraphRAG retrieval patterns
Announcing general availability of Amazon Bedrock Knowledge Bases GraphRAG with Amazon Neptune Analytics | Amazon Web Services
Today, Amazon Web Services (AWS) announced the general availability of Amazon Bedrock Knowledge Bases GraphRAG (GraphRAG), a capability in Amazon Bedrock Knowledge Bases that enhances Retrieval-Augmented Generation (RAG) with graph data in Amazon Neptune Analytics. In this post, we discuss the benefits of GraphRAG and how to get started with it in Amazon Bedrock Knowledge Bases.
Build your hybrid-Graph for RAG & GraphRAG applications using the power of NLP | LinkedIn
Build a graph for RAG application for a price of a chocolate bar! What is GraphRAG for you? What is GraphRAG? What does GraphRAG mean from your perspective? What if you could have a standard RAG and a GraphRAG as a combi-package, with just a query switch? The fact is, there is no concrete, universal