GraphRAG is an advanced version of retrieval-augmented generation (RAG) that incorporates graph-structured data, such as knowledge graphs (KGs).1 Unlike baseline RAG systems that rely on vector search to retrieve semantically similar text, GraphRAG leverages the relational structure of graphs to retrieve and process information based on domain-specific queries.
GraphRAG was introduced by Microsoft research in 2024 to address the limitations of large language models (LLMs).2 Traditional LLMs often struggle with complex workflows, especially in reasoning private or structured data, because they lack the ability to understand relationships between entities. GraphRAG solves this issue by using graph databases to model these relationships, enabling it to handle complex queries, retrieve contextual information and improve accuracy in generative AI (gen AI) applications.
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Retrieval-augmented generation (RAG) is a technique that retrieves relevant information by using similarity search from vector databases, external knowledge sources and internal knowledge bases. It then combines this retrieved information with LLMs to generate accurate and context-aware outputs. While traditional RAG applications enhance the functionality of LLMs in generative AI applications, it lacks the ability to capture complex data relationships in data. It struggles to perform tasks such as multihop reasoning (combining information from multiple sources to derive answers through logical connections and indirect inferences), relational context and understanding hierarchical data. For example, a traditional RAG approach might struggle with a query like, "Who developed the theory of relativity?" because it requires reasoning over relationships between entities.
GraphRAG overcomes this issue by incorporating graph-structured data, which organizes information as a network of nodes (entities like people or places), edges (relationships between those entities) and labels (attributes that define the category of a node and edge). For example, a knowledge graph might represent "Albert Einstein—developed—the theory of relativity." as a graph-structured pieces of information, making it easier for GraphRAG to retrieve and process this information. In this example, the nodes are ‘Albert Einstein’ and ‘theory of relativity’, and the edge is ‘developed’.
GraphRAG works through four main components:
The user’s query is preprocessed to identify key entities and relationships relevant to the graph structure. Techniques such as named-entity recognition (NER) and relational extraction from machine learning are used to map the query to nodes and edges within the graph. For example, a query like "Who developed the theory of relativity?" identifies "Albert Einstein" as a node and "developed " as the relationship to be searched in the graph. Tools like Cypher, a graph query language, are used to fetch domain-specific data from knowledge graphs.
The retriever locates and extracts relevant content from external graph data sources based on the processed query. Unlike traditional RAG systems that rely on vector embeddings for text or images, GraphRAG retrievers handle graph-structured data by leveraging both semantic and structural signals. They use techniques such as graph traversal algorithms (methods like breadth-first search (BFS) or depth-first search (DFS) that explore the graph to locate relevant nodes and edges). Additional techniques include graph neural networks (GNNs) (advanced AI models that learn the structure of graphs to retrieve data effectively), adaptive retrieval (dynamically adjusts how much of the graph to search, reducing irrelevant information or noise) and embedding models. For the query "Who developed the theory of relativity?", the retriever locates the node "theory of relativity" in the graph and follows the "developed by" relationship to find "Albert Einstein."
The retrieved graph data is refined to remove irrelevant or noisy information through techniques like graph pruning, reranking and augmentation. The organizer helps ensure the retrieved graph is clean, compact and ready for processing while preserving critical contextual information. For the query "Who developed the theory of relativity?" the organizer refines the retrieved graph data by removing irrelevant nodes and edges, helping ensure only the relevant relationship, "Albert Einstein—developed—theory of relativity," is retained.
The cleaned graph data is then used to produce the final output. This can involve generating text-based answers using LLMs or creating new graph structures for scientific tasks, such as molecule design or knowledge graph expansion. For the query "Who developed the theory of relativity?", GraphRAG retrieves "Albert Einstein" from the graph and generates the answer: "Albert Einstein developed the theory of relativity." Generative AI techniques are used to synthesize the final response.
GraphRAG is transformative across industries, combining graph-based reasoning, vector search and generative AI to handle domain-specific tasks that demand deep contextual information. Below, we explore some of the key applications of GraphRAG:
GraphRAG can be used for query-focused text summarization. It focuses on answering specific user queries by retrieving and synthesizing information from a graph-structured representation of the text. A study demonstrated GraphRAG’s effectiveness in answering global, exploratory questions over large datasets, such as podcast transcripts and news articles.3 It outperformed traditional vector-based RAG systems in tasks requiring comprehensive and diverse insights. For example, GraphRAG was tested on a podcast dataset (~1M tokens) featuring conversations with tech leaders and a news dataset (~1.7M tokens) covering health, business and technology topics. Questions included “How do tech leaders view privacy laws?” and “What are the key public health priorities?”
GraphRAG processes these datasets by building a knowledge graph with entities (for example, “privacy laws”) and relationships (for example, “impact on tech”), organizing them into hierarchical communities (group of connected nodes organizing high-level topics to specific sub-topics). Pregenerated community summaries allow the system to retrieve and combine relevant insights efficiently. Compared to traditional RAG, GraphRAG achieved higher comprehensiveness (72–83%) and diversity (62–82%) in generated answers while requiring up to 97% fewer tokens for root-level summaries. This ability makes GraphRAG an ideal tool for sense-making tasks in domains like journalism, education and research.
In domains like e-commerce and entertainment, GraphRAG enables chatbots and recommendation engines to deliver personalized experiences. For example, in e-commerce, the past interactions between users and products can form a graph. GraphRAG helps manage the growing volume of user interaction data by extracting key subgraphs that reveal user preferences and behaviors. Research has demonstrated that using multiple retrievers to extract relevant subgraphs enhances user action prediction, while retrieving subgraphs of similar past issues improves the quality of customer service question-answering systems.4
In healthcare, GraphRAG assists doctors in diagnosing patients with complex symptoms by analyzing relationships between diseases, symptoms and treatments within a graph database. It retrieves relevant medical studies, case reports and drug information to suggest possible diagnoses, highlight effective treatment options and even warn of potential drug interactions. This capability allows healthcare professionals to make more informed decisions, reduce diagnostic errors and provide personalized care to patients.
For example, a recent study introduced MedGraphRAG, a framework designed for medical applications.5 It organizes medical data into three levels: private user data (for example, medical reports), recent peer-reviewed medical literature and foundational medical dictionaries, helping ensure accuracy, traceability and relevance. Using a hierarchical graph structure and a "U-retrieve" strategy, it efficiently retrieves and synthesizes information for user queries, improving the performance of LLMs by generating reliable, evidence-based responses with source citations. This framework demonstrates the potential for secure, transparent and efficient clinical workflows, aiding healthcare professionals with grounded, actionable insights.
GraphRAG identifies unusual patterns that deviate from expected behavior. For example, in financial services, it can detect suspicious transaction patterns to prevent fraud or uncover cross-selling opportunities by analyzing customer behavior. By connecting multiple small transactions across accounts, GraphRAG can reveal larger fraudulent schemes, helping banks enhance risk management and provide more personalized services.
GraphRAG can enhance knowledge management by organizing and retrieving documents in a way that makes knowledge more accessible and tailored to specific queries. It analyzes the context and relationships between various documents and helps extract the most relevant information quickly and effectively. For example, one prominent use case of GraphRAG is in law firms, where it excels at managing vast collections of legal documents. By analyzing the relationships and context within thousands of legal documents, GraphRAG can efficiently retrieve relevant case precedents or legal references, streamlining research workflows and significantly improving accuracy.
GraphRAG systems present challenges such as managing complex data relationships, helping ensure efficient retrieval and integrating with language models. These challenges can be addressed through careful graph schema design, optimized query strategies and leveraging robust tools. The primary challenges associated with GraphRAG are:
As the data volume increases, scaling GraphRAG systems is difficult. Challenges include managing unstructured data, efficient graph storage, optimizing graph queries, subgraph sampling, responsive generation, organizing retrieved components, training and fine-tuning. Implementing advanced hardware solutions, such as GPU acceleration, model compression and maintenance adds further complexity.
Designing a cohesive GraphRAG system requires seamless interaction between the query processor, retriever, organizer and generator components. Ensuring these components operate harmoniously while maintaining efficiency and accuracy is a complex challenge.
Ensuring low error rates across multistep reasoning is challenging due to the accumulation of errors in multihop retrieval and generation.
The relational structure of graphs introduces significant risks of sensitive information leakage, as connections and patterns within the graph can reveal private data. Protecting such information across the entire GraphRAG pipeline requires robust privacy-preserving techniques. GraphRAG systems are susceptible to adversarial attacks, including the exploitation of graph structures and manipulation of prompts, further emphasizing the need for enhanced security measures.
While GraphRAG offers enhanced explainability through explicit relationships between nodes, generating clear, interpretable reasoning paths or explanations remains a challenge. Ensuring these explanations are both comprehensive and faithful to the system’s logic is critical for trust in high-stakes domains like healthcare, law and finance.
GraphRAG systems can be implemented by using various tools and frameworks, including open source options, to support document processing, knowledge graph creation, semantic search and LLM integration. Popular tools include LangChain, LlamaIndex, Neo4j and OpenAI, with additional resources and tutorials available on platforms such as GitHub.
LlamaIndex is used for indexing documents, extracting entities and relationships to create knowledge graphs, generating vector embeddings and integrating with LLMs like GPT. Neo4j serves as the database for storing and managing graph structures, enabling efficient retrieval through graph traversal and semantic relationships.
These tools work together to enable semantic search by using vector embeddings, metadata handling for transparency and context-aware response generation. LLMs including OpenAI GPT models, integrated through APIs, help produce accurate and relevant answers based on retrieved graph data.
GraphRAG is a big step forward from traditional RAG systems, which are limited by linear retrieval methods. It combines the power of knowledge graphs, semantic search and advanced language models. As industries demand deeper understanding and interconnected insights, GraphRAG is set to become a key technology. It will enable smarter, more dynamic and highly adaptive information systems in the future.
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1 Han, H., Wang, Y., Shomer, H., Guo, K., Ding, J., Lei, Y., ... & Tang, J. (2024). Retrieval-augmented generation with graphs (graphrag). arXiv preprint arXiv:2501.00309.
2 Larson, J., & Truitt, S. (2024). GraphRAG: Unlocking LLM discovery on narrative private data. Microsoft Research Blog. https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
3 Edge, D., Trinh, H., Cheng, N., Bradley, J., Chao, A., Mody, A., ... & Larson, J. (2024). From local to global: A graph rag approach to query-focused summarization. arXiv preprint arXiv:2404.16130.
4 Peng, B., Zhu, Y., Liu, Y., Bo, X., Shi, H., Hong, C., ... & Tang, S. (2024). Graph retrieval-augmented generation: A survey. arXiv preprint arXiv:2408.08921.
5 Wu, J., Zhu, J., Qi, Y., Chen, J., Xu, M., Menolascina, F., & Grau, V. (2024). Medical graph rag: Towards safe medical large language model via graph retrieval-augmented generation. arXiv preprint arXiv:2408.04187.