HippoRAG: A Neurobiological Approach to RAG

The evolution of large language models (LLMs) has revolutionized our approach to information processing and generation. However, they still face challenges when it comes to integrating knowledge from various sources effectively. Traditional Retrieval Augmented Generation (RAG) methods can struggle, particularly with multi-hop reasoning tasks that require linking information from different documents.

Introducing HippoRAG

To overcome these limitations, we introduce HippoRAG, an innovative RAG framework inspired by the hippocampal memory system found in humans. This framework not only enhances the retrieval process but also significantly improves the generation of information by better connecting disparate sources.

Implementation Using AWS

In this post, we will detail how to implement HippoRAG using a robust AWS stack. The architecture utilizes Amazon Bedrock for its LLM capabilities, Amazon Neptune for graph database functionality, and incorporates advanced algorithms such as Personalized PageRank via Amazon Neptune Analytics.

The Architecture of HippoRAG

HippoRAG mimics the dual-component indexing system of human memory, where the neocortex processes sensory inputs while the hippocampus indexes associations. This architecture allows HippoRAG to manage information more effectively than traditional RAG approaches, which often treat documents in isolation.

Key Components of HippoRAG

Amazon Bedrock: Provides LLM capabilities
Amazon Neptune: Acts as the graph database
Personalized PageRank: Enhances graph algorithms
Amazon Titan Embeddings: Generates vector representations

Building the Knowledge Graph

A crucial first step in deploying HippoRAG involves converting raw data into a knowledge graph structure that is compatible with Amazon Neptune. We will explore how we process HotpotQA data from JSON format to create this graph.

The process begins with the HotpotQANeptuneImporter class, which orchestrates the data pipeline. This class manages reading the JSON file, generating CSV outputs, and uploading these files to Amazon S3 for loading into Neptune.

class HotpotQANeptuneImporter:
    """Class to handle importing HotpotQA data into Neptune."""
    def __init__(self, hotpotqa_file_path: str, output_dir: str, s3_bucket: str, s3_prefix: str, neptune_endpoint: str, neptune_port: int, iam_role_arn: str, aws_region: str):
        self.hotpotqa_file_path = hotpotqa_file_path
        self.output_dir = output_dir
        self.s3_bucket = s3_bucket
        self.s3_prefix = s3_prefix
        self.neptune_endpoint = neptune_endpoint
        self.neptune_port = neptune_port
        self.iam_role_arn = iam_role_arn
        self.aws_region = aws_region
        self.client('s3', region_name=aws_region)
        self.Session()
        self.phrase_dict = {}
        self.phrase_embeddings = {}

Extracting Knowledge Graph Triples

A vital part of this pipeline involves using Amazon Bedrock’s LLM capabilities to extract structured knowledge from unstructured text. For each passage, the system generates subject-relation-object triples, which serve as edges within the knowledge graph.

def extract_triples_with_llm(self, text: str) -> List[Tuple[str, str, str]]:
    words = text.split()
    if len(words) < 5:
        return []
    triples = []
    for i in range(min(3, len(words) - 2)):
        subject = words[i]
        relation = 'related_to'
        obj = words[i + 2]
        triples.append((subject, relation, obj))
    return triples

Converting JSON to CSV

Once the triples are generated, we must serialize the data into CSV format suitable for Neptune’s bulk loader. This step processes the HotpotQA JSON records into multiple CSV files that accurately represent the knowledge graph structure.

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