GraphRAG

Standard retrieval-augmented generation works well for questions whose answers sit in one or two passages of text, such as what the travel policy says about hotels. It struggles with questions that depend on connections across many sources. Which teams are working on initiatives that support our top strategic goal, and what are they spending? What are the main themes across thousands of customer complaints? Which suppliers would be affected if this component became unavailable? Answering these requires following relationships and seeing the bigger picture, not just finding similar passages. GraphRAG addresses this by using a knowledge graph as part of retrieval.

The term gained wide attention in 2024, when Microsoft Research published its GraphRAG approach and released it as open source. Microsoft's method uses a language model to extract entities and relationships from documents into a graph, groups related entities into communities and creates summaries that help answer broad questions about a whole collection. The term is also used more generally for any approach that combines knowledge graphs with retrieval-augmented generation, including querying an existing enterprise knowledge graph alongside documents. For AI engineers, data architects, knowledge teams and leaders deploying AI on complex information, GraphRAG is an important extension of RAG.

Definition of GraphRAG

GraphRAG is a retrieval-augmented generation approach in which a knowledge graph, either built from source documents using language models or drawn from an existing graph of business entities, is used to retrieve relevant entities, relationships, paths and summaries, which are provided to a language model as context for generating grounded answers.

It extends retrieval-augmented generation (RAG), depends on a knowledge graph and is often powered by an enterprise knowledge graph in business settings.

How GraphRAG Works

Graph construction. Entities and relationships are extracted from documents using language models, or an existing knowledge graph is connected.

Enrichment. Related entities may be grouped into communities, and summaries are generated for entities and groups.

Retrieval. For each question, the system identifies relevant entities and follows their relationships, retrieving connected facts, paths and summaries, often alongside text passages.

Generation. The language model answers using the retrieved graph context, citing entities and sources.

Maintenance. The graph is updated as documents and data change.

GraphRAG vs. Standard RAG

Retrieval unit. Standard RAG retrieves text chunks, while GraphRAG retrieves entities, relationships and summaries as well.

Question types. Standard RAG suits specific factual questions, while GraphRAG is stronger on questions that require connecting information or summarizing across a collection.

Explainability. GraphRAG can show the entities and relationships behind an answer, making reasoning easier to trace.

Cost and complexity. GraphRAG requires building and maintaining a graph, which adds effort and cost.

Importance of GraphRAG in Enterprise Operations

It answers connected questions. Questions about dependencies, ownership, impact and themes across many sources become answerable. These are often the questions leaders care about most.

It improves accuracy for complex queries. Explicit relationships reduce the chance that the model connects facts incorrectly. The model works from stated relationships rather than inferring them from loosely related passages.

It also uses existing investments. Organizations with enterprise knowledge graphs can use them directly to ground AI.

GraphRAG and Financial Management

Building graphs from documents with language models can be expensive for large collections, since every document must be processed.

Using an existing enterprise knowledge graph avoids much of this cost and provides higher-quality relationships.

GraphRAG queries may use more tokens than standard RAG because they include richer context, which affects running costs. Careful selection of the most relevant parts of the graph keeps prompts focused.

The approach is most cost-effective for high-value questions that standard RAG cannot answer well.

Common Challenges

Automatically extracted graphs contain errors and duplicates that reduce answer quality. A graph with the same customer listed three times will give incomplete answers about that customer.

Graph construction and updates are slow and costly for large, frequently changing content. Incremental updates, rather than rebuilding the whole graph, help keep costs and delays manageable.

Teams apply GraphRAG to simple questions where standard RAG would be cheaper and just as good. Routing simple questions to standard retrieval and complex ones to graph retrieval keeps costs down.

Integrating graph retrieval with existing search and access controls takes significant engineering effort. Permissions must apply to entities and relationships as well as documents.