What is GraphRAG? The Next Step in AI Accuracy

Guy Korland, CEO & Co-Founder of FlakorDB

If you are building a Generative AI application, you have likely encountered Retrieval-Augmented Generation (RAG). The standard blueprint - storing documents in a vector database to answer questions - is the most common pattern today. However, as many developers are discovering, this approach struggles when moving from simple demos to production systems that require high accuracy.

According to a Stanford research paper, legal GenAI applications based on this model failed to cross 65% accuracy. Another paper from Microsoft showed their own vector search solution barely crossed 76% on complex questions.

The reason for this accuracy ceiling is fundamental. Vector search is great for finding similar text, but it falls short on questions that require aggregating data and understanding deep relationships. This is the problem that GraphRAG solves. Based on an in-depth presentation by Guy, CEO of FalcoDB, this article explains exactly what GraphRAG is and how it works.

The Core Problem: Why Standard RAG Is Not Enough

To understand the need for GraphRAG, consider this question:

"Which Italian artist has the largest number of paintings on display currently in the Louvre?"

With a standard RAG system, your application would search its vector database and retrieve a collection of text chunks that are semantically similar to the question. The Language Model (LLM) is then asked to generate an answer from these fragmented pieces. Even if you retrieve 20 relevant documents, the model only sees a small part of the overall picture. It cannot accurately count and aggregate the data to definitively name the artist. It can only make an educated guess.

This is the core limitation: to answer complex questions, you need to see the bigger picture.

What is GraphRAG? The Solution for High-Accuracy AI

GraphRAG is an approach that structures enterprise knowledge as a Knowledge Graph before feeding it to an LLM. Instead of storing isolated chunks of text, it identifies the key entities and the relationships between them. This creates a highly structured and interconnected map of your data.

• Entities are the key nouns (e.g., "Leonardo da Vinci," "the Louvre").

• Relationships are the connections between them (e.g., "painted," "on display in").

With this structure, the LLM’s job changes. Instead of trying to piece together an answer from fragmented text, it generates a precise query to the knowledge graph. The graph database then does the heavy lifting of traversing relationships and aggregating data, returning a clean, accurate answer.

How Does GraphRAG Work in Practice?

While building knowledge graphs was traditionally difficult, modern LLMs have made the process much simpler. The workflow is as follows:

1. Entity and Relationship Extraction: An LLM reads through your source documents (PDFs, transcripts, etc.). Instead of just breaking them into chunks, it extracts key "triplets"—a subject, a relationship, and an object (e.g., (I, love, GraphRAG)).

2. Knowledge Graph Population: These extracted triplets are used to build and grow the knowledge graph in a graph database. Over time, this creates a comprehensive and interconnected view of your data.

3. Intelligent Querying: When a user asks a question, the LLM is made aware of the graph's structure (its ontology or schema). It then translates the user's natural language question into a formal query language (like Cypher) to retrieve the specific information needed from the graph.

This method is not only more accurate but also more efficient. Instead of stuffing the LLM's context window with dozens of text chunks, you provide it with a concise and precise piece of knowledge.

Where Can You Use GraphRAG? Real-World Examples

This approach is particularly powerful in domains where relationships and context are critical. The following examples were highlighted as prime use cases:

• Source Code Analysis: By representing a complex codebase as a graph, developers can ask questions like, "What is the most commonly used method in this entire project?" This is nearly impossible with vector search alone. An open-source project called CodeGraph on GitHub demonstrates this capability.

• Text-to-SQL on Complex Schemas: Many enterprise databases have thousands of tables, making it impossible for an LLM to see the whole schema. With GraphRAG, you can create a "semantic layer" by loading the database schema itself into a graph. The LLM can then query this small, smart graph to understand table relationships and generate a much more accurate SQL query.

• Fraud Detection and Network Analysis: The data in these fields is often a natural graph (transactions, bank accounts, network connections). GraphRAG allows you to analyze these relationships to uncover hidden patterns and security threats.

• Financial Data and Policy Analysis: GraphRAG can take unstructured financial documents and policies, run them through the entity extraction process on the fly, and build a knowledge graph that can be queried for deep insights.

Final Thoughts

In conclusion, while standard RAG provides a foundation, GraphRAG is the necessary next step for building enterprise-ready AI applications that demand high accuracy, data aggregation, and a true understanding of the relationships within your knowledge base.