HippoRAG on AWS: Brain-inspired RAG boosts multi-hop reasoning with Neptune and PageRank

HippoRAG, a neurobiologically-inspired retrieval-augmented generation (RAG) framework, addresses the critical limitation of standard RAG in multi-hop reasoning. Traditional RAG treats each document independently, failing to connect related facts across multiple sources. HippoRAG mimics the hippocampal indexing theory of human long-term memory by building a knowledge graph (KG) of entities and relationships, then using Personalized PageRank (PPR) for efficient graph traversal and relevance ranking. This enables single-step multi-hop retrieval instead of the iterative, costly processes required by conventional methods.

Amazon’s implementation integrates HippoRAG with a full AWS stack: Amazon Bedrock for LLM-powered knowledge graph triple extraction and question answering, Amazon Neptune Database for storing the KG, Amazon Neptune Analytics for executing PPR algorithms, and Amazon Titan Embeddings for vector representations. The pipeline processes data from formats like HotpotQA JSON into Neptune bulk-load CSV files, uploaded to S3 and loaded into a Neptune cluster. This architecture provides enterprise-scale scalability, reliability, and access to advanced graph algorithms, making it practical for real-world applications where connecting disparate information is crucial.