Understanding GraphRAG: AI Retrieval with Knowledge Graphs and Cypher

Did you know that traditional vector search methods are no longer the only option for complex data retrieval? Enter Graph Retrieval Augmented Generation (GraphRAG), which leverages knowledge graphs for deeper, context-rich queries.

Overview of Graph Retrieval Augmented Generation (GraphRAG)

GraphRAG, sometimes referred to as Graphrag, is an emerging AI-driven approach that offers an alternative to established vector search methods. Instead of indexing embedded vectors, GraphRAG systems store information in a knowledge graph within a graph database, such as Neo4j. This method places as much importance on the connections—called edges—as on the entities themselves, known as vertices or nodes. By relying on the graph’s structured relationships, GraphRAG delivers richer context and a deeper understanding of complex networks that vector embeddings alone cannot match.

Creating and Populating the Knowledge Graph

The first step in any GraphRAG workflow is building your knowledge graph. Start by feeding unstructured text into a Language Learning Model (LLM) that extracts key entities and relationships. The LLM transforms this raw text into a structured format, defining nodes and edges that represent real-world connections. For example, you might parse an employee roster to identify person nodes, title nodes, and group nodes, while also mapping relationships such as holdsTitle and collaboratesWith. Once the data is structured, you insert these elements into the graph database to establish the foundational knowledge graph. In code, you use LangChain’s Document class to wrap your text inputs, then define allowed node types and relationships in an LLMGraphTransformer. This transformer call instructs the model on expected entity labels and edge semantics. Once generated, the GraphDocument output shows JSON-like structures that map automatically into Cypher CREATE statements. The entire workflow—from entity extraction to graph insertion—is handled predominantly by LLM-driven APIs, dramatically reducing manual engineering overhead.

Setting Up the Environment for the Project

To run a GraphRAG proof of concept, you need a graph database like Neo4j, accessible via containerization tools such as Docker or Podman. After initializing and starting your container, configure it with credentials and include plugins like APOC for advanced graph operations. In addition to Neo4j credentials, you’ll need API keys and project IDs for your AI provider. LangChain’s IBM and ibm-watsonx.ai modules make it straightforward to set authentication in your Python notebook. Use environment variables or the getpass module for secure key management. After configuring the API endpoint URL, verify connectivity by sending a simple test prompt to your LLM. Next, create a dedicated Python virtual environment—Python 3.11.3 is a good choice—and install libraries such as LangChain for LLM integration and graph-database connectors. Proper setup ensures seamless interaction between your code, the LLM, and the knowledge graph.

Transforming Unstructured Text Data

The magic of GraphRAG lies in converting raw text into graph-ready data. Within your Python environment, configure the LLM to extract specific entities (e.g., person, title, group) and allowed relationships (e.g., holdsTitle, collaboratesWith). By constraining the output schema, you improve accuracy and reduce hallucinations. When configuring the LLM for graph transformation, it’s best to set a low temperature (e.g., 0.2–0.3) and a higher maximum token count to capture all relevant entities without introducing spurious nodes. By explicitly constraining the model to a predefined schema of nodes and edges, you minimize the risk of hallucinations and ensure that domain-specific terms are correctly identified. The LLM graph transformer then outputs graph documents that list nodes and edges in a format suitable for insertion. Finally, use methods like addGraphDocuments to populate your Neo4j instance with this structured data.

Querying the Knowledge Graph Using Natural Language

Once your graph is populated, users can submit natural language queries that the LLM translates into Cypher, the query language for graph databases. A two-part prompt guides this process: the first prompt generates precise Cypher syntax, using example-driven constraints to avoid extra commentary, and the second transforms the query results back into clear, natural language. By chaining the Cypher prompt, QA prompt, knowledge graph, and LLM, you create a streamlined question-answering pipeline that handles both straightforward lookups and complex relationship traversals.

Practical Example: Employee Directory Query

Imagine querying an employee directory stored in your knowledge graph. When a user asks, “What is John’s title?”, the LLM constructs a Cypher query such as:

MATCH (p:Person {name: "John"})-[:holdsTitle]->(t:Title)
RETURN t.name AS title;

The database returns “Director of Digital Marketing,” which the LLM then formats as: “John serves as the Director of the Digital Marketing Group.” For a more complex question like, “Who does John collaborate with?”, the LLM generates:

MATCH (p:Person {name: "John"})-[:collaboratesWith]->(colleague:Person)
RETURN colleague.name AS collaborator;

The result might list “Jane” and “Sharon,” and the LLM replies, “John collaborates with Jane and Sharon.” This concrete example illustrates how GraphRAG bridges natural language, AI, and Cypher to deliver precise, context-rich answers.

Differences Between GraphRAG and VectorRAG Systems

GraphRAG and VectorRAG systems diverge primarily in how they store and retrieve data:

• Data Storage: GraphRAG builds a structured knowledge graph, while VectorRAG computes embeddings stored in a vector database.
• Retrieval Process: GraphRAG navigates relationships with Cypher queries, whereas VectorRAG relies on semantic search over vector embeddings. GraphRAG’s use of graph indexes allows summarization over entire groupings of nodes, providing holistic insights within a single query. GraphRAG can also support graph-level analytics like centrality measures or shortest-path queries for advanced AI-driven insights. In real-world applications, many teams adopt a hybrid RAG approach that combines the best of both graph and vector retrieval techniques.

Conclusion

• Takeaway: Integrate GraphRAG into your data ecosystem to unlock context-aware retrieval and richer insights through knowledge graphs and Cypher.

By adopting GraphRAG, you can improve query precision and harness AI to navigate complex relationships more effectively than semantic search alone. How might you integrate graph databases into your data strategy to optimize retrieval and insights? Explore the possibilities of GraphRAG today!

For further information, check out the GraphRAG code or learn more about GraphRAG. Stay updated with the latest advancements in AI from IBM.