HippoRAG: Neurobiologically inspired RAG using Amazon Bedrock, Amazon Neptune, and personalized PageRank
AWS just showed how to build RAG systems that scale to enterprise. Here's the stack.
Why it matters
AWS is positioning Bedrock + Neptune + Titan as a production RAG stack for enterprises. This is a deployment pattern that matters to teams building retrieval-augmented applications at scale.
The key facts
9 to knowHippoRAG implementation using Amazon Bedrock (LLM), Amazon Neptune (graph DB), Neptune Analytics (Personalized PageRank), and Titan Embeddings
Focus on enterprise-scale deployment and production readiness
Neurobiologically-inspired RAG architecture (HippoRAG approach)
AWS managed services stack for end-to-end RAG pipeline
HippoRAG implementation using Amazon Bedrock (LLM), Amazon Neptune (graph DB), Neptune Analytics (graph algorithms), Amazon Titan Embeddings (vectors)
Neurobiologically-inspired retrieval approach using Personalized PageRank
Enterprise-scale deployment architecture on AWS
Integrated AWS stack approach: LLM + graph database + embeddings + analytics in single platform
Published July 1, 2026 on AWS ML blog
Go to the source
AWS Machine Learning Blogaws.amazon.com
Publisher excerpt: In this post, we demonstrate how to implement HippoRAG using a comprehensive AWS stack. We use Amazon Bedrock for LLM capabilities, Amazon Neptune for graph database functionality, Amazon Neptune Analytics for advanced graph algorithms including Personalized PageRank, and Amazon Titan Embeddings…