Large language models (LLMs) have transformed how we process and generate information, but they still struggle with effectively integrating knowledge across multiple sources. Standard Retrieval Augmented Generation (RAG) methods, although helpful, often fall short when tackling multi-hop reasoning tasks that require connecting information from separate documents. To address these limitations, we explore HippoRAG, a novel RAG framework inspired by the hippocampal memory system in human brains.

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 for vector representations. This implementation showcases how to build and deploy HippoRAG within AWS infrastructure for enterprise-scale applications.

HippoRAG draws inspiration from the hippocampal indexing theory of human long-term memory. In human brains, the neocortex processes perceptual inputs, whereas the hippocampus creates an index of associations between memories. This dual-component system allows humans to efficiently integrate information across different experiences.

Standard RAG approaches treat each document independently, struggling with questions that require connecting information across multiple sources. HippoRAG addresses this by:

This architecture allows us to use the full power of personalized PageRank while maintaining the scalability and reliability of AWS managed services.

A necessary first step in implementing HippoRAG is converting raw data into a knowledge graph structure suitable for Neptune. In this section, we walk through how we process HotpotQA data from JSON format. We extract knowledge-graph triples using Amazon Bedrock, generate Neptune bulk-load CSV files, upload them to Amazon S3, and load them into our Neptune cluster. Each of the following subsections corresponds to a stage of that pipeline.