GraphRAG: Combining Knowledge Graphs with Retrieval-Augmented Generation

Why Vector Search Alone Misses Relationships

Standard vector RAG retrieves chunks based on semantic similarity to the query. This works well for localized factual questions where the answer is contained within one or a few chunks. But it struggles with queries that require understanding how entities relate to each other across the corpus.

Consider the query: "Which engineering teams have dependencies on the billing service, and what would break if we took it offline for maintenance?" The answer is not in any single chunk. It is scattered across architecture documents, incident reports, deployment configurations, and team wikis. Each chunk mentions the billing service in a different context, but no chunk explicitly lists all the dependencies. A vector search retrieves the 5 most similar chunks to the query text, which might include a billing service overview and a couple of incident reports, but it misses the complete dependency picture because that picture is implicit in the relationships across dozens of documents.

A knowledge graph captures those relationships explicitly. During ingestion, entity extraction identifies mentions of services, teams, and dependencies across all documents. The graph stores "Team Alpha depends on billing service," "Team Beta calls billing API," "Payment processing uses billing service for invoice generation," and dozens of similar relationships as edges between entity nodes. When the user asks about billing service dependencies, the graph traversal retrieves all connected entities and their relationships, providing a complete answer that no vector search could assemble from individual chunks.

This is the core value proposition of GraphRAG: it captures structural information, the relationships and connections between entities, that is present implicitly in the corpus but invisible to vector similarity search. The existing knowledge graphs pillar covers the foundational concepts of graph data structures; this guide focuses specifically on integrating graphs with RAG.

How GraphRAG Is Constructed

Building a GraphRAG system requires two parallel ingestion pipelines: the standard vector pipeline (chunk, embed, index) and a graph construction pipeline (extract entities, extract relationships, build graph). Both pipelines process the same source documents, and the outputs are queried together at retrieval time.

Entity extraction identifies named entities in the text: people, organizations, products, services, locations, events, concepts, and domain-specific entities. In 2026, the most effective approach is LLM-based extraction, where you prompt a model like GPT-4o or Claude to identify entities and their types from each chunk. The prompt specifies the entity types relevant to your domain ("Extract all software services, teams, APIs, and infrastructure components mentioned in this text") and the model returns structured output listing each entity with its type and the text span where it appears. This is more accurate than traditional NER models for domain-specific entities because the LLM understands context.

Relationship extraction identifies how entities relate to each other. Using the same LLM-based approach, you prompt the model to identify relationships between the entities found in each chunk: "For each pair of entities, describe their relationship (depends on, manages, integrates with, reports to, etc.)." The output is a list of triples: (entity A, relationship type, entity B). Each triple becomes an edge in the knowledge graph. Relationship extraction is harder than entity extraction because relationships are often implicit ("The checkout service calls the billing API to process refunds" contains the implicit relationship "checkout service depends on billing service for refunds") and the LLM must infer the relationship type.

Entity resolution merges references to the same entity across different documents. "AWS S3," "Amazon S3," "S3 bucket," and "our object store" might all refer to the same entity. Without resolution, the graph contains four separate nodes instead of one, and traversal misses connections. Resolution uses a combination of string matching (edit distance, acronym expansion), embedding similarity (are the entity names semantically similar?), and contextual cues (do they appear in similar contexts?). LLM-based resolution, where you prompt the model with a list of candidate entity names and ask it to identify which ones refer to the same entity, is the most accurate approach for ambiguous cases.

Community detection is a technique introduced in Microsoft's GraphRAG paper (2024) that groups densely connected entities into communities and generates summaries of each community. These community summaries provide a hierarchical view of the knowledge base: at the top level, you have broad topic clusters, and within each cluster, you have specific entity groups and their relationships. Community summaries are particularly useful for global queries like "What are the main technology risks across the organization?" that require synthesizing information across the entire corpus rather than retrieving specific chunks.

Graph storage uses a graph database. Neo4j is the most widely used option and has native support for property graphs, Cypher queries, and graph algorithms. For smaller graphs (under 1 million nodes), an in-memory graph using NetworkX (Python) is sufficient for development and moderate production loads. Amazon Neptune, Azure Cosmos DB (Gremlin API), and MemGraph are alternatives for managed deployments. The graph should store entity nodes with properties (name, type, description, source documents) and relationship edges with properties (type, strength, source chunk, extraction confidence).

Graph-Enhanced Retrieval

At query time, GraphRAG combines vector retrieval with graph traversal to produce richer context than either approach alone. The combination works in several patterns.

Entity-anchored retrieval. The system first identifies entities mentioned in the query (using NER or the LLM), then retrieves the subgraph around those entities from the knowledge graph, including their relationships, neighboring entities, and associated chunks. This subgraph provides structural context that the vector search would miss. If the query mentions "billing service," the graph provides all related entities (dependent services, responsible teams, recent incidents) as context, regardless of whether those entities appear in chunks that are semantically similar to the query.

Path-based retrieval. For queries about relationships between two specific entities, the system finds paths between them in the graph. "How does the marketing team's data reach the analytics dashboard?" triggers a graph traversal from the "marketing team" node to the "analytics dashboard" node, following edges through intermediate entities (CRM, data pipeline, data warehouse). The path reveals the full chain of connections, which would be extremely difficult to assemble from vector retrieval alone.

Community-based retrieval. For broad, summarization-type queries, the system retrieves relevant community summaries rather than individual chunks. "What are the main compliance challenges we face?" retrieves summaries of entity communities related to compliance, each summarizing a cluster of related entities, regulations, processes, and known issues. This produces answers that are holistic and well-organized rather than fragmented.

Hybrid graph-vector retrieval. The most common production pattern runs vector retrieval and graph retrieval in parallel, then merges the results. Vector search returns chunks that are semantically similar to the query. Graph traversal returns chunks and entity information that are structurally related to entities in the query. The merged context combines semantic relevance with structural connectivity, giving the LLM both the most similar content and the most connected content.

The Microsoft GraphRAG Approach

Microsoft Research published the most influential GraphRAG architecture in 2024, and it has become the reference implementation. The approach has two key innovations beyond basic graph construction.

First, it uses hierarchical community detection (Leiden algorithm) to organize the graph into communities at multiple levels of granularity. Level 0 communities are small clusters of 3 to 10 tightly connected entities. Level 1 communities are larger clusters of related level 0 communities. Level 2 and above provide increasingly broad views. Each community gets an LLM-generated summary that describes what the community is about, the key entities and relationships within it, and the main themes. This hierarchy enables answering questions at different levels of specificity.

Second, it defines two query modes. Local search starts from entities in the query, retrieves their local neighborhood and associated text, and generates an answer from that focused context. This is similar to entity-anchored retrieval and works best for specific questions about known entities. Global search retrieves community summaries across the entire graph at the appropriate level and generates an answer by synthesizing across communities. This works best for broad questions that require corpus-wide understanding, like "What are the emerging trends in our industry?" or "Summarize the key risks across all departments."

The trade-off with Microsoft's approach is the construction cost. Building the full GraphRAG index requires running LLM extraction on every chunk (entity extraction, relationship extraction, community summarization), which consumes significant tokens. For a corpus of 100,000 chunks, the graph construction can cost $50 to $200 in LLM API calls and take several hours. This is a one-time cost (with incremental updates for new documents), but it is substantially higher than the near-zero cost of building a vector index.

When GraphRAG Outperforms Standard RAG

GraphRAG is not universally better than vector-only RAG. It is specifically better for certain query types and corpus characteristics.

Multi-entity queries. Questions that involve relationships between multiple entities ("Which vendors supply components to both our US and European manufacturing plants?") benefit from graph traversal because the answer requires finding connections across the corpus. Vector search retrieves chunks about vendors, US plants, and European plants independently but cannot connect them.

Comparison queries. Questions that require comparing entities ("How does Team A's approach to testing differ from Team B's?") benefit from retrieving the graph neighborhoods of both entities and comparing the associated information. Vector search retrieves chunks similar to the query text but may not retrieve comparable information for both entities.

Summarization queries. Questions that require a high-level synthesis across the corpus ("What are the main themes in customer feedback this quarter?") benefit from community summaries that aggregate information across many documents. Vector search retrieves the most similar individual chunks, which are fragments rather than summaries.

Highly interconnected domains. Knowledge bases where entities have rich relationships (organizational structures, software architectures, supply chains, legal document networks) benefit more from GraphRAG than flat document collections (a library of independent articles, product specifications, or FAQ entries). If your documents rarely reference the same entities, a graph adds little value.

Standard vector RAG remains sufficient for simple factual lookups, single-document questions, homogeneous document collections, and applications where latency matters more than answer completeness. The RAG cost guide covers the cost implications of adding a graph layer.

Practical Implementation Considerations

The biggest challenge in building GraphRAG is entity extraction quality. LLM-based extraction is accurate but expensive. If the extraction misses an entity, the graph has a gap. If it misidentifies an entity, the graph has noise. If entity resolution fails, the graph has duplicates. All of these degrade retrieval quality. Start by extracting entities from a sample of your corpus, manually reviewing the results, and iterating on the extraction prompt until accuracy is acceptable before running the full extraction.

Graph maintenance is the second challenge. When source documents change, the entities and relationships they contain may change. Adding a new document requires extracting new entities and relationships, resolving them against existing nodes, and updating community structures. Deleting a document requires removing its entities and relationships, which can disconnect parts of the graph. These operations are more complex than the simple add/update/delete operations in a vector database, and most GraphRAG implementations handle them with periodic full re-extraction rather than incremental updates.

For teams starting with GraphRAG, the recommended approach is: build a standard vector RAG pipeline first, evaluate it on your query set, identify the query types where it fails (usually multi-entity and summarization queries), and add the graph layer specifically to handle those query types. Use the graph for entity-anchored retrieval and community summaries, and fall back to vector-only retrieval for simple queries. This hybrid approach gives you the benefits of GraphRAG where it matters without the cost and complexity of running graph construction on every query.