HippoRAG: Neurobiologically inspired RAG using Amazon Bedrock, Amazon Neptune, and personalized PageRank

This article demonstrates implementing HippoRAG, a neurobiologically inspired RAG framework that improves multi-hop reasoning by using knowledge graphs and personalized PageRank, leveraging Amazon Bedrock, Neptune, and Neptune Analytics.

• HippoRAG mimics hippocampal memory indexing to connect information across multiple documents, addressing limitations of standard RAG approaches
• Architecture uses Amazon Bedrock for LLM capabilities, Neptune for graph storage, Neptune Analytics for Personalized PageRank, and Titan Embeddings for vectors
• Data pipeline converts HotpotQA JSON to knowledge graph triples, generates CSV files, uploads to S3, and bulk-loads into Neptune
• Personalized PageRank enables single-step multi-hop retrieval by ranking entities and passages based on graph-theoretic importance to queries
• Demonstrates superior performance on complex reasoning tasks like connecting Stanford professors to Alzheimer's neuroscience research
• Fully managed AWS services provide scalability, security, and operational advantages for enterprise applications

HippoRAG with AWS services enables sophisticated multi-document question answering by combining graph-based retrieval with advanced relevance ranking algorithms.