GraphRAG: Complete Guide to Knowledge Graph-Enhanced Retrieval

GraphRAG (Graph Retrieval-Augmented Generation) is an architecture that combines knowledge graphs with retrieval-augmented generation (RAG) to give large language models richer, relationship-aware context at inference time. While standard RAG retrieves text chunks that are semantically similar to a query, GraphRAG retrieves structured graph relationships — entities, their connections, and the communities of related concepts they form — enabling LLMs to reason about complex, interconnected information that isolated document chunks cannot represent.

What Is RAG?

Retrieval-augmented generation (RAG) is a technique that augments an LLM's response by retrieving relevant information from an external knowledge base at query time and including that information in the model's context window. Instead of relying solely on knowledge encoded in model weights during training, a RAG system retrieves current, specific information and provides it alongside the user's question.

Standard RAG pipelines work in two phases. First, documents are chunked into passages and embedded into vector representations, which are stored in a vector database. At query time, the user's question is embedded and compared against stored vectors using cosine similarity; the most similar chunks are retrieved and prepended to the LLM prompt as context. The LLM then generates a response grounded in that retrieved context.

RAG has become a standard pattern for enterprise LLM applications because it keeps responses grounded in specific, up-to-date sources rather than relying on potentially outdated or incorrect model knowledge. However, standard RAG has a fundamental limitation: it retrieves isolated text chunks without understanding the relationships between entities mentioned across documents. This makes it poorly suited to questions that require synthesising information across multiple connected concepts — exactly the kind of questions enterprise users ask most frequently.

Knowledge Graphs

A knowledge graph is a structured representation of entities and the relationships between them, stored as a graph where nodes represent entities (people, organisations, products, concepts) and edges represent relationships (works_for, part_of, related_to, caused_by). Unlike relational databases that enforce rigid schemas, knowledge graphs accommodate heterogeneous entity types and relationship types in a flexible structure that can be extended as new knowledge is added.

Knowledge graphs excel at representing the kind of connected information that is hard to retrieve with vector search: organisational hierarchies, product dependency chains, regulatory relationships, event causal chains, and scientific concept networks. The graph structure makes it possible to traverse relationships — starting from one entity and following edges to reach related entities — enabling queries that require multi-hop reasoning rather than single-step similarity matching.

How GraphRAG Combines Them

GraphRAG constructs a knowledge graph from source documents during the indexing phase, rather than just creating vector embeddings of text chunks. Using an LLM to extract entities and relationships from source text, GraphRAG builds a graph in which nodes represent named entities (people, organisations, locations, concepts) and edges represent extracted relationships between them. This graph is then used at retrieval time instead of (or in addition to) vector similarity search.

The indexing phase in GraphRAG also applies community detection algorithms — typically hierarchical Leiden clustering — to identify groups of closely related entities within the graph. For each community, GraphRAG generates a summary that captures the key themes and relationships within that community. These community summaries become the retrieval units for global queries: instead of retrieving individual text chunks, the system retrieves community summaries that represent synthesised knowledge about clusters of related entities.

At query time, GraphRAG uses the graph structure to retrieve context that is relationship-aware rather than just semantically similar. For a question about a specific entity, GraphRAG traverses the graph to find the entity's connections and the community it belongs to, providing the LLM with both the directly relevant information and the broader context of related entities and themes. See also: data lineage for how graph structures track data relationships, and active metadata for metadata graphs in enterprise data platforms.

Microsoft GraphRAG

The term GraphRAG was popularised by Microsoft Research, which published the paper "From Local to Global: A Graph RAG Approach to Query-Focused Summarisation" in 2024 and released an open-source Python implementation. Microsoft's GraphRAG system became widely adopted because it directly addressed a well-known limitation of standard RAG: the inability to answer "global" questions about large document corpora — questions like "What are the main themes in this collection?" or "How do these events relate to each other?" — which require synthesising information across the entire corpus rather than retrieving locally relevant chunks.

Microsoft's implementation uses an LLM to extract entities and relationships from source text during indexing, builds a graph from those extractions, applies hierarchical community detection to the graph, and generates community summaries at multiple granularity levels. These summaries are used for global queries (answering questions about the whole corpus) while direct graph traversal is used for local queries (answering questions about specific entities).

Local vs Global Search

Local search in GraphRAG is used for questions about specific named entities — a person, organisation, product, or event. The system finds the relevant entity in the graph, retrieves its community and immediate relationships, and uses that relationship-enriched context in the LLM prompt. This produces answers that are grounded in the network of connections around the queried entity, rather than just in text passages that happen to mention its name.

Global search is used for questions about broad themes, patterns, or summaries across a large document collection. Standard RAG retrieves individual chunks for these queries and often produces incomplete or hallucinated answers because no single chunk captures the full picture. GraphRAG's global search uses the hierarchical community summaries generated during indexing to provide the LLM with a structured synthesis of key themes across the corpus, enabling comprehensive answers to global questions.

The choice between local and global search is typically made automatically based on query classification, or exposed as a user-configurable parameter. Some implementations use a hybrid approach: retrieving community summaries for global context alongside specific entity subgraphs for local detail, giving the LLM both breadth and depth.

GraphRAG vs Standard RAG

The practical differences between GraphRAG and standard RAG become apparent in real enterprise Q&A scenarios:

• Multi-hop queries: "What are the supply chain implications of Company X's acquisition of Company Y for Product Z?" Standard RAG struggles because the answer requires connecting information across multiple documents and entity types. GraphRAG traverses the graph from Company X to Company Y to their shared product relationships, retrieving the connected context the LLM needs.
• Global summaries: "What are the key risks identified across all our contract documents?" Standard RAG retrieves individual contract chunks but cannot synthesise across the full corpus. GraphRAG's community summaries capture cross-document themes explicitly.
• Entity disambiguation: Documents often mention entities by multiple names or pronouns. GraphRAG's entity extraction resolves these to canonical identities in the graph, preventing the LLM from treating references to the same entity as separate entities.
• Relationship reasoning: "Who reports to the CTO and what projects are they leading?" This requires relationship traversal that vector similarity cannot perform but graph traversal handles naturally.

The tradeoff is indexing cost and complexity. Standard RAG indexing is fast and simple: chunk, embed, store. GraphRAG indexing requires multiple LLM calls to extract entities and relationships, community detection computation, and summary generation — making it significantly more expensive and time-consuming. GraphRAG is therefore best suited to document corpora where the relationship structure is worth the indexing investment: regulatory documents, enterprise knowledge bases, scientific literature, and contract repositories.

Enterprise Use Cases

GraphRAG has found strong adoption in several enterprise use cases where relationship-aware retrieval provides meaningful advantages over standard RAG:

• Regulatory and compliance Q&A: compliance teams need to answer questions about how regulations interact — which rules apply to a specific scenario given the regulatory relationships between jurisdictions, frameworks, and entity types. GraphRAG's graph traversal enables this relationship-based reasoning.
• Enterprise knowledge bases: large organisations have complex internal knowledge distributed across wikis, documents, and databases. GraphRAG enables employees to ask questions that span departments, product lines, and time periods in ways that standard RAG cannot handle.
• Scientific and technical research: research organisations use GraphRAG to query across large bodies of academic literature, finding connections between concepts, methodologies, and findings that isolated document retrieval misses.
• Contract analysis: legal teams use GraphRAG to analyse portfolios of contracts, asking questions about obligations, rights, and risks across the entire portfolio rather than individual documents.
• Data catalog Q&A: data teams use graph-enhanced retrieval over metadata catalogs, asking questions like "Which tables are derived from this source and which models depend on them?" — a natural graph traversal problem. This is where metadata graphs and data lineage graphs intersect with GraphRAG.

Metadata Graphs and Dawiso

Enterprise data platforms generate knowledge graphs naturally as part of their metadata management: data lineage graphs connect tables, columns, and transformations; business glossaries create concept hierarchies; classification taxonomies create category trees; and ownership structures create organisational graphs. These metadata graphs are well suited to GraphRAG-style retrieval because users' questions about data are inherently relationship-based: "Where does this metric come from?", "Which datasets contain customer PII?", "What would break if we changed this table?"

Dawiso builds and exposes exactly this kind of metadata graph. Its data lineage graph captures column-level relationships across transformation chains. Its business glossary links technical assets to business concepts. Its classification and governance metadata creates a rich entity network across the data estate. This graph structure is the foundation for AI-powered data discovery and Q&A that goes beyond what keyword search or vector similarity can provide.

Dawiso's MCP server enables AI agents and LLMs to query the metadata graph directly, traversing lineage relationships, following classification links, and understanding the connections between data assets in ways that require graph awareness rather than text similarity. This positions Dawiso as the metadata graph layer for enterprise GraphRAG applications built on top of the data catalog — enabling questions like "What are the lineage implications of this upstream change?" to be answered by an LLM with graph-enriched context from Dawiso's metadata graph.