A novel RAG framework called HippoRAG, inspired by the hippocampal memory system, addresses limitations in standard Retrieval Augmented Generation (RAG) methods that struggle with multi-hop reasoning tasks requiring connecting information from separate documents. The implementation uses a comprehensive AWS stack: 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. HippoRAG draws inspiration from the hippocampal indexing theory, where the neocortex processes perceptual inputs and the hippocampus creates an index of associations between memories. It addresses standard RAG limitations by building a knowledge graph to represent relationships between entities, using the Personalized PageRank algorithm for efficient graph traversal and relevance ranking, and enabling single-step multi-hop retrieval. The solution architecture includes Amazon Bedrock for extracting knowledge graph triples, answering questions, and identifying named entities; Amazon Neptune Database for storing the knowledge graph structure; Amazon Neptune Analytics for executing Personalized PageRank; and Amazon Titan Embeddings for vector representations. Prerequisites include an AWS account with access to Amazon Bedrock and Neptune services, an Amazon Neptune cluster, an Amazon Neptune Analytics graph, AWS CLI and Python 3.8+, and appropriate IAM permissions.