For the past few years, Retrieval-Augmented Generation (RAG) has become one of the most popular techniques for improving the quality of AI responses. Instead of relying solely on what a Large Language Model learned during training, RAG retrieves relevant information from an external knowledge source and includes that information as additional context before generating a response. This approach helps reduce hallucinations, allows AI to work with private organizational data, and gives models access to information that changes over time. Traditional RAG has proven extremely effective, but it also has limitations.
Most RAG systems retrieve individual documents or text chunks based on semantic similarity. If the information needed to answer a question is spread across multiple documents or requires understanding how different pieces of information relate to one another, a traditional RAG pipeline can struggle. The relevant information may be retrieved, but the relationships between those pieces of information are often lost.
This is exactly the problem Microsoft Research set out to address with GraphRAG.
Rather than treating documents as isolated chunks of text, GraphRAG extracts entities, relationships, and higher-level concepts from unstructured information to build a knowledge graph. That graph then becomes part of the retrieval process, allowing the AI to reason over connected information rather than simply matching documents based on similar wording.
What Is Microsoft GraphRAG?
Microsoft GraphRAG is an open-source project from Microsoft Research that provides a graph-based approach to Retrieval-Augmented Generation. Rather than replacing Large Language Models, GraphRAG improves how information is prepared and retrieved before the model generates its response.
During indexing, GraphRAG analyzes unstructured documents and extracts structured information from them. It identifies important entities, determines the relationships between those entities, detects communities of related information, generates summaries of those communities, and creates vector embeddings that support retrieval. The resulting graph provides an additional layer of context that complements traditional semantic search rather than replacing it.
One of the key differences is that GraphRAG is designed as a configurable data pipeline rather than simply a retrieval engine. The indexing process is made up of workflows, prompts, adapters, and transformation steps that convert raw text into structured knowledge. This allows organizations to adapt the extraction process to their own data rather than relying on a single fixed approach.
Why Traditional RAG Isn’t Always Enough
Traditional RAG works extremely well when the answer exists within a relatively small number of documents. For example, if you ask:
“How do I configure Conditional Access?”
A semantic search can usually identify the relevant documentation, provide it to the language model, and generate an accurate answer. Enterprise questions are often far more complicated. Imagine asking:
“Which members of the Cloud Security team are working on projects supporting European customers, and which compliance frameworks apply to those projects?”
Answering that question requires understanding multiple relationships. The AI needs to know:
- Which employees belong to the Cloud Security team.
- Which projects those employees support.
- Which customers those projects serve.
- Which countries those customers operate within.
- Which compliance frameworks apply.
- Whether the user is authorized to access that information.
Those relationships are rarely contained within a single document. They are distributed across employee records, project documentation, customer information, compliance guidance, and organizational knowledge. Traditional RAG retrieves documents. GraphRAG retrieves relationships. That distinction becomes increasingly important as enterprise AI systems become more sophisticated.
How GraphRAG Works
Although the internal processing performed by GraphRAG is quite sophisticated, the overall workflow is relatively easy to understand.
It begins with a collection of unstructured documents such as reports, policies, meeting notes, technical documentation, or internal knowledge bases. During indexing, GraphRAG divides those documents into smaller text units before using a Large Language Model to identify important entities, relationships, and key claims. Those entities are then grouped into communities, summaries are generated, and vector embeddings are created for retrieval. The outputs are stored as structured data, with embeddings written to the configured vector store.
By the time indexing completes, the original documents have been transformed into something much richer than a document collection. The AI no longer sees isolated files.
It sees people connected to departments, departments connected to projects, projects connected to customers, customers connected to regulations, and technologies connected to business processes.
That network of relationships becomes part of the context used during retrieval.
Different Ways of Retrieving Information
One of the strengths of GraphRAG is that it supports multiple retrieval strategies depending on the type of question being asked.
Local Search is designed for questions centered around specific entities. It combines information extracted from the knowledge graph with relevant text chunks from the original documents. This makes it well suited for questions such as “Who owns Project Falcon?” or “What technologies are associated with Microsoft Sentinel?”
Global Search approaches the problem differently. Rather than focusing on individual entities, it searches across AI-generated community reports to answer broader questions about the dataset as a whole. Although this approach is more computationally expensive, it often performs well for questions that require understanding themes, trends, or relationships across an entire collection of information.
GraphRAG also supports additional query methods, allowing developers to choose retrieval strategies that best match the questions their users are asking.
Why Organizations Should Consider GraphRAG
GraphRAG becomes particularly valuable when AI systems need to work across multiple business systems and large collections of interconnected information.
Instead of simply retrieving documents, the AI gains the ability to understand how information is connected. This often produces more contextual responses because the model can reason across people, projects, technologies, customers, and policies rather than treating each document independently.
This also benefits AI agents. As organizations begin deploying autonomous agents capable of completing business tasks rather than simply answering questions, understanding relationships becomes increasingly important. Agents need to understand dependencies between systems, resource ownership, business processes, and organizational structure before making decisions or performing actions. GraphRAG helps provide that connected understanding.
Preparing for Your First GraphRAG Project
One of the biggest mistakes people make is attempting to index an entire organization’s knowledge base on day one. Microsoft specifically recommends starting with the tutorial dataset and experimenting with smaller, less expensive models until you understand how the indexing pipeline behaves. GraphRAG can consume significant Large Language Model resources during indexing because it performs entity extraction, relationship discovery, community detection, summarization, and embedding generation.
Before building your first GraphRAG environment, it is worth preparing a small set of well-understood documents that describe a fictional organization or a limited business scenario. Working with a controlled dataset makes it much easier to validate the extracted entities, verify the discovered relationships, and understand how the resulting graph influences retrieval.
Equally important is deciding what questions you expect the completed graph to answer. Rather than indexing documents simply because they are available, think about the business problems you are trying to solve. A well-designed graph begins with understanding the relationships that matter, not simply collecting more data.
Looking Ahead
GraphRAG represents an important evolution in Retrieval-Augmented Generation. Rather than relying solely on semantic similarity between documents, it introduces structure, relationships, and community understanding into the retrieval process. The result is an AI system that can reason across connected information instead of treating every document as an isolated source of knowledge.
Understanding these concepts is the first step. The next step is seeing how they work in practice. In the following article, we’ll build a GraphRAG environment from scratch, configure the indexing pipeline, create a small knowledge graph, and explore how GraphRAG transforms unstructured documents into a connected knowledge model that can be queried by AI.
Learn more about GraphRAG