Using Graph Databases to Implement GraphRAG

As AI models continue to advance, there’s a growing need to manage and retrieve data more intelligently. For those diving into query-focused summarization, GraphRAG (Graph Retrieval-Augmented Generation) offers an effective approach. But how do you implement GraphRAG in a way that scales well with complex data relationships? That’s where graph databases like Neo4j come into play.

Why Use Graph Databases for GraphRAG?

GraphRAG is a hybrid approach combining the strengths of large language models (LLMs) with structured data from a graph. It works by constructing a knowledge graph from your documents or data, then leveraging this graph to generate context-specific responses. This method shines when handling domain-specific queries, where a linear document search would fall short.

A traditional database isn’t well-suited for capturing the intricate relationships between pieces of information. However, a graph database excels at this because it treats data as nodes and relationships between them as edges. This structure allows for more intuitive querying and rapid retrieval of contextually relevant data.

Why Neo4j?

Neo4j is one of the most popular graph databases today, known for its scalability and efficiency in handling highly connected data. It uses Cypher, a powerful query language specifically designed for graph databases, allowing you to easily retrieve, manipulate, and analyze complex data structures.

For example, imagine you’re building a document summarization service for legal documents. Each document can be broken down into different sections (nodes), with relationships (edges) to relevant clauses, case references, or legal precedents. With Neo4j, you can quickly navigate this web of data, identifying the most relevant information to pass to the LLM (such as GPT-4o) for summarization.

Implementation Outline: GraphRAG with Neo4j

Here’s a high-level overview of how you could implement GraphRAG with Neo4j:

1. Ingest Data: First, transform your documents into nodes. For each document, create a node for each section, clause, or reference that might be important. You’ll also want to create relationships between these nodes to represent the structure of the document.

2. Querying the Graph: When a query is made, use Cypher to traverse the graph and retrieve the relevant sections or clauses based on the relationships. The relationships in the graph allow you to quickly surface contextually relevant data.

3. Generating a Summary: Once you have the relevant context from the graph, pass this information to the LLM (in this case, GPT-4o) for the summarization task. The output will be more accurate and focused because you’ve guided the model with relevant contextual data.

4. Reindexing: Every time a new document is added, the graph needs to be reindexed. This can be done by adding new nodes and updating relationships, ensuring the graph stays relevant for future queries.

Example Query

Let’s say you’re asked to summarize a specific legal clause. You can write a Cypher query like this:

MATCH (c:Clause)-[:REFERENCES]->(r:LegalPrecedent)
WHERE c.title CONTAINS 'non-compete'
RETURN c, r

This query finds all clauses that reference non-compete agreements and the legal precedents they are associated with. Once retrieved, this data can be fed into GPT-4o for the final summarization.

Learn More

In my latest blog post, I’ve written a detailed tutorial that walks through building a GraphRAG pipeline using Neo4j and GPT-4o. Whether you’re a developer looking to build a scalable summarization tool or someone curious about graph-based approaches to AI, this guide will get you started.

Read the full tutorial here