What is Graph RAG?

What Is Graph RAG?

Graph RAG, short for Graph Retrieval-Augmented Generation, is an advanced approach to helping AI language models access and reason over your organisation's data. Developed and open-sourced by Microsoft Research, Graph RAG improves upon standard RAG by first organising your documents into a knowledge graph, a structured map of entities and their relationships, before using that graph to retrieve the most relevant information for answering questions.

Think of it this way: standard RAG is like searching a library by looking at individual pages in isolation. Graph RAG is like having a librarian who understands how every book, author, and topic in the library connects to every other, and can pull together a comprehensive answer by following those connections. When a business leader asks a complex question that spans multiple documents, departments, or data sources, Graph RAG can trace the relationships between concepts and deliver a far more complete response.

How Graph RAG Works

Standard RAG systems split documents into small text chunks, store them in a vector database, and retrieve the most similar chunks when a user asks a question. This works well for straightforward lookups but struggles with questions that require synthesising information across many documents or understanding relationships between entities.

Graph RAG adds several important steps to this process:

• Entity extraction: The system reads through your documents and identifies key entities such as people, organisations, products, locations, and concepts using a large language model.
• Relationship mapping: It then identifies and records how these entities relate to each other, creating a structured knowledge graph. For example, it might map that "Company A partnered with Company B on Project C in Market D."
• Community detection: The knowledge graph is analysed to find clusters of closely related entities, called communities. Each community is summarised to create high-level overviews of topics within your data.
• Hierarchical retrieval: When a user asks a question, Graph RAG can retrieve information at multiple levels, from specific entity relationships to broad community summaries, depending on what the question requires.

This layered approach means Graph RAG excels at answering questions like "What are the major trends across all our customer feedback?" or "How do our supply chain partners connect to each other?" where the answer requires synthesising information spread across dozens or hundreds of documents.

Why Graph RAG Matters for Business

For business leaders, the distinction between standard RAG and Graph RAG becomes critical as organisations try to build AI systems that can reason over their entire knowledge base rather than just search it. The limitations of standard RAG become apparent quickly in enterprise settings:

• Complex queries fail silently. When an executive asks "What are the key risks across our portfolio?", standard RAG may only find one or two relevant passages rather than synthesising risk information from all portfolio documents. Graph RAG connects the dots across the entire dataset.
• Cross-departmental insights emerge. By mapping relationships between entities across different business units, Graph RAG can surface connections that no single team would have identified. A customer mentioned in a support ticket might also appear in a sales pipeline and a partnership discussion.
• Summarisation at scale. Graph RAG's community summaries enable AI systems to provide meaningful overviews of large document collections, something standard RAG cannot do effectively because it retrieves individual chunks rather than holistic summaries.
• Reduced hallucination. Because Graph RAG grounds its responses in structured relationships rather than loosely similar text passages, the AI is less likely to fabricate connections or misrepresent information.

For Southeast Asian enterprises dealing with multilingual documents, diverse market data, and complex regional partnerships, Graph RAG provides a framework for making sense of information that spans languages, markets, and organisational boundaries.

Key Examples and Use Cases

Due diligence and investment analysis. Private equity firms and venture capital funds in Singapore and Jakarta can use Graph RAG to analyse hundreds of documents about a target company, automatically mapping relationships between the company's partners, customers, competitors, and market conditions. Rather than reading every document, analysts can ask questions and receive answers that synthesise information across the entire corpus.

Regulatory compliance. Financial institutions operating across ASEAN markets face overlapping and sometimes contradictory regulations. Graph RAG can map the relationships between regulatory requirements, internal policies, and business processes, making it easier to identify compliance gaps and understand how a regulatory change in one market affects operations across the region.

Customer intelligence. Companies like Grab and Sea Group, which operate across multiple business lines and markets, can use Graph RAG to connect customer data, feedback, and behavioural patterns across their ecosystem. A customer who uses ride-hailing, food delivery, and financial services creates data across multiple systems that Graph RAG can unify into a coherent picture.

Knowledge management. Large professional services firms and consultancies can deploy Graph RAG over their historical project documents, enabling consultants to quickly find relevant past work, methodologies, and client insights even when they do not know the exact terminology used in those documents.

Getting Started with Graph RAG

Adopting Graph RAG requires more upfront investment than standard RAG but delivers significantly better results for complex use cases. Here is a practical path forward:

1. Evaluate your use case. Graph RAG is most valuable when your questions require synthesising information across many documents or understanding entity relationships. If your use case involves simple document lookup, standard RAG may be sufficient.
2. Start with Microsoft's open-source implementation. Microsoft released GraphRAG as an open-source project, providing a well-documented starting point that your engineering team can deploy and customise.
3. Prepare your data. Graph RAG works best with well-structured text documents. Begin with a focused document collection, such as all reports from a specific department or project, rather than trying to index everything at once.
4. Budget for LLM costs. The entity extraction and relationship mapping steps require significant LLM processing during the indexing phase. This is a one-time cost per document set but can be substantial for large collections.
5. Iterate on your graph structure. The quality of Graph RAG outputs depends heavily on how well entities and relationships are extracted. Plan to review and refine the extraction prompts and parameters based on your specific domain and document types.

Graph RAG represents the next evolution in enterprise AI search and reasoning. As organisations move beyond simple chatbot deployments to AI systems that can genuinely reason over complex business data, the structured approach of Graph RAG will become increasingly essential.