Graph RAG Explained: How Knowledge Graphs Improve AI Retrieval

Key takeaways

• GraphRAG relies on knowledge graphs to organize and extract data points and their relationships, making it a more advanced version of retrieval-augmented generation (RAG).

• GraphRAG builds a knowledge graph by extracting entities and their relationships from multiple data sources and storing them as connected nodes and edges; it retrieves relevant subgraphs upon query and injects that structured context into LLM prompts to produce more accurate responses. 

• While baseline RAG retrieves information via vector similarity across text chunks, GraphRAG combines vector search with a knowledge graph to incorporate connections between entities, thereby improving accuracy and context awareness for complex queries.

A traditional RAG can identify relevant information, but it could miss the connections between data points. GraphRAG increases accuracy by capturing explicit relationships between entities, improves reasoning, response relevance, and explainability while reducing hallucinations to help artificial intelligence (AI models) provide more accurate, in-depth answers. Explore a detailed comparison of GraphRAG and traditional RAG, as well as domain-specific use cases and benefits.

If you’re ready to start building expertise in AI systems, enroll in IBM’s AI Foundations for Everyone Specialization. You’ll have the opportunity to learn fundamental AI concepts like natural language processing, large language models, generative AI, prompt patterns, machine learning, and more in as little as four weeks. Upon completion, you’ll earn a career certificate for your resume.

What is GraphRAG?

GraphRAG is an advanced type of retrieval augmented generation (RAG) that uses knowledge graphs to map and extract data points and their relationships. Traditional vector-based RAG systems retrieve text fragments based on semantic similarity with the search query, often failing to capture the context and relationships between entities. While large language models (LLMs) based on traditional RAG systems excel at retrieving specific information from relevant knowledge sources, they often struggle to understand the relationships and logic connecting the data points, which reduces their accuracy when answering queries that require complex, multi-step reasoning. 

In contrast, GraphRAG uses graphs to structure data into a knowledge graph, representing information as nodes (entities), edges (relationships), and properties (defining features of a node or edge’s category). This allows LLMs to extract more contextually rich data, understand complex relationships among interconnected entities, and provide more in-depth, well-rounded answers, especially for domain-specific queries. For example, traditional RAG is like pulling out a few random chapters from a book and hoping you’ll grasp the story from that, while GraphRAG is like having a map of all the characters, events, and regions, as well as their connections, to obtain a full picture of the story [1].

Origins of GraphRAG: Microsoft GraphRAG

Microsoft’s research team first introduced the concept of GraphRAG in 2024 to improve LLM outputs [2]. In their study, they fed a private data set containing information like business communications and proprietary research to an LLM and tasked with mapping all the relationships between entities to create a knowledge graph. Then, using graph machine learning and prompt augmentation, the researchers showed that GraphRAG significantly outperformed baseline RAG models when answering questions that required connecting different pieces of information based on shared characteristics, as well as questions that involved a holistic understanding of contextual themes in the data set.

What is the difference between GraphRAG and baseline RAG?

The key difference is that baseline RAG retrieves text chunks via vector similarity, while GraphRAG adds a knowledge graph to capture entity relationships and support multi-step reasoning.

GraphRAG vs. agentic RAG

Agentic RAG involves using AI agents in the RAG process to make autonomous decisions, independently evaluate and adapt retrieval strategies, learn and optimize its own responses, and automatically call tools if necessary. However, GraphRAG uses a knowledge graph to map relationships between data points, facilitating advanced reasoning when handling multi-step queries. Agentic RAG is best suited to automating complex, multi-step, dynamic retrieval tasks, while GraphRAG is best for understanding relationships between data through multi-level reasoning and obtaining more in-depth answers.

How does GraphRAG work?

The GraphRAG process first entails collecting information from diverse data sources and processing it with an LLM to extract entities and their relationships. Using a graph database tool, you map entities as nodes and relationships as edges, add detailed contextual information, and store the graph in a database like Neo4j or AWS Neptune.

Once you query the LLM, the following steps are set in motion:

1. Query processing: The LLM extracts relevant entities and relationships from the query and expands the search across the entire graph, matching the query to connected entities and nodes.

2. Graph retrieval: The GraphRAG’s retriever uses mechanisms such as graph traversal to identify relevant entities and nodes and retrieve a subgraph of relationships with contextual information.

3. Context augmentation: The graph data is then converted into a structured format and added to the LLM’s prompt to improve its contextual understanding.

4. Response generation: The improved context helps the LLM generate more accurate and logical responses either as text or as new graph structures.

Why are the benefits of GraphRAG?

GraphRAG improves on traditional RAG by capturing entity relationships, enabling multi-step reasoning, and reducing hallucinations. Other advantages of GraphRAG over traditional RAG include:

• Improved accuracy: By understanding relationships between data points, GraphRAG can retrieve the most relevant data and provide a more complete response than traditional RAG. It also ensures answers are accurate and grounded in real-world context, which is critical for domains like finance and health care. In a study comparing the accuracy of GraphRAG with traditional RAG, GraphRAG achieved 90 percent accuracy in answering complex real-world questions, when considering both “correct” and “acceptable” answers, compared to 67.5 percent for traditional RAG [3].

• Enhanced reasoning: Structured graph data enables the model to perform multi-step reasoning and connect distinct pieces of information to derive new insights, a valuable ability for areas like scientific research. 

• Information relevance: By filtering and ranking the retrieved information based on your query and task, GraphRAG ensures you receive concise, targeted, and relevant information, reducing information redundancy and improving output quality.

• Explainability: You can clearly trace the path through the graph that the model used to arrive at a decision, helping you better understand its reasoning and decision-making approach, a critical feature for building transparent and trustworthy AI models.

• Reduced hallucinations: By grounding their responses in knowledge graphs and contextual relationships, GraphRAG models lower the risk of AI hallucinations that can arise from semantically derived answers alone.

Is Neo4j a GraphRAG?

No, Neo4j is actually a NoSQL graph database written in Java and Scala. It, however, offers extensive support for building GraphRAG systems and generative AI (genAI) applications. For example, the Neo4j GraphRAG package is a specialized Python package for building knowledge graph-enabled AI applications.

Who is using GraphRAG?

Professionals across a range of domains, from health care to e-commerce, use GraphRAG to inform their decision-making. GraphRAG architecture helps explore not just the “what” of things but also the “how” they relate to each other, making it suitable for understanding domain-specific knowledge and data trends.

Health care 

Doctors can use GraphRAG models to enhance patient diagnosis accuracy and treatment efficiency by connecting and analyzing links between symptoms and diseases, correlating patient history, genetics, and tracking disease spread, treatment pathways, and possible drug reactions through knowledge graphs. 

For example, if a physician queried the GraphRAG: “What could be causing patient X’s chest pain and shortness of breath?” The model would then connect the patient’s symptoms, previous diagnoses, lab results, and family history with medical research and similar cases to help the physician quickly uncover patterns in the data.

Read more: AI in Health Care: Applications, Benefits, and Examples

Law 

Legal professionals can speed up research and document analysis by using a GraphRAG with statutes, case law, regulations, and contracts as nodes, and references, dependencies, and contradictions as relationships.

When queried with something like: “What safety incidents resulted from compliance violations related to product X?” The GraphRAG model can not only retrieve direct matches but also employ multi-hop reasoning to return more concise and specific data that relates safety incidents involving product X with recorded compliance violations for the same product.

Finance 

Finance professionals, especially those working in fraud prevention, can use GraphRAG models to analyze complex relationships between accounts, transactions, and users to uncover suspicious patterns indicative of money laundering or other fraudulent activities.

If you ask the GraphRAG model: “Are any of the new accounts connected to previously flagged accounts?” The model will correlate account holder names, phone numbers, email addresses, and transaction histories with the details of flagged accounts to reveal potential fraudulent schemes. 

E-commerce

GraphRAG can enhance the recommendation engines that e-commerce platforms use to suggest products to customers. By representing customer behaviors, product interactions, and purchases as knowledge graphs, e-commerce platforms can better understand user preferences, provide more personalized and accurate recommendations, and improve customer satisfaction.

For example, if a user looks at running shoes on an e-commerce website, the recommendation system can look at what other buyers also bought with these shoes, the target user’s previous purchases, and reviews for the running shoes to understand what product to suggest next, which could be running socks or athletic wear. 

Limitations of GraphRAG

GraphRAG can pose challenges in data quality, volume, and integration, ultimately affecting the efficiency of data retrieval and response generation. For example, it is only as effective as the quality of the knowledge graph itself. Limitations to consider include:

• Data quality: If the knowledge graph incorporates outdated, irrelevant, incomplete, or incorrect data, the GraphRAG can draw an inaccurate understanding of the interconnections and provide misleading responses.

• Scalability: Scaling GraphRAG systems becomes difficult as data volume and graph size increase. As the knowledge graph’s nodes and interconnections grow, the graph traversal and retrieval speed for complex multi-step queries decreases.

• Implementation complexity: Building and maintaining a GraphRAG system requires technical expertise in areas like natural language processing, entity recognition, graph databases, and schema design, as well as significant data modeling and ETL efforts.

Support your professional growth with actionable learning tools

Subscribe to our Career Chat newsletter on LinkedIn for industry insights, skill-building tips, and networking opportunities. Then, explore our free resources to learn more about AI:

• Chart your learning journey: AI Learning Roadmap: From Beginner to Expert

• Hear from an expert: Becoming an AI Engineer: 7 Questions with an IBMer

• Watch on YouTube: How Does GenAI Work? | Generative AI 101

Whether you want to develop a new skill, get comfortable with an in-demand technology, or advance your abilities, keep growing with a Coursera Plus subscription. You’ll get access to over 10,000 flexible courses. 

Build job-ready skills with Coursera Plus

Start 7-day free trial

• 
• 
• 
• 

Start 7-day free trial