GraphRAG uses semantic search to find relevant text, entities, or summaries, then uses graph relationships to gather connected context for a language model.
This lets a retrieval system answer questions that require both meaning-based matching and relationship-aware evidence.
Short Answer
GraphRAG combines semantic search with graph retrieval.
Semantic search finds content or entities that are close in meaning to the user query. Graph retrieval follows relationships from those starting points to collect connected entities, source chunks, relationship summaries, and community context.
The result is richer RAG context than a simple top-k vector search over independent chunks.
Why Relationships Matter
Many questions cannot be answered by one similar chunk.
The answer may depend on who is connected to whom, which document cites another, which product depends on which service, or which event caused another event.
Graph relationships make those connections retrievable.
What Semantic Search Does
Semantic search retrieves by meaning rather than exact wording.
It uses embeddings to compare a query with embedded chunks, documents, entities, or summaries.
This helps GraphRAG find good starting points even when the user does not use the exact labels stored in the graph.
What the Graph Does
The graph stores entities and relationships.
Entities might be people, organizations, products, documents, concepts, policies, symptoms, assets, events, or locations.
Relationships describe how those entities connect.
Naive RAG Limitation
Naive RAG often retrieves independent chunks by vector similarity.
That works well when the answer is contained in one or a few semantically similar chunks.
It works less well when the answer depends on relationships across multiple chunks, documents, or entities.
GraphRAG Difference
GraphRAG adds a relationship layer to retrieval.
Instead of treating every chunk as isolated, it can retrieve the connected structure around a query.
This can include entities, relationships, source text, summaries, and communities.
The Basic Retrieval Flow
A common GraphRAG flow looks like this:
- interpret the user query
- find relevant entities or chunks with semantic search
- map results to graph nodes
- follow useful relationships
- collect source-backed context
- rank and trim the context
- send the context to the language model
Entity Recognition
GraphRAG often starts by identifying entities in the user query.
The system may detect names, organizations, concepts, products, places, events, dates, or domain-specific terms.
These detected entities help choose where graph retrieval should begin.
Semantic Entity Search
Sometimes the user does not name the exact entity.
Semantic entity search can retrieve graph nodes by meaning using entity names, aliases, descriptions, or summaries.
This is useful when the query is vague, conversational, or uses different terminology from the corpus.
Chunk Search
GraphRAG can also search source chunks directly.
Vector search over chunks finds text that is semantically related to the query.
The retrieved chunks can then point to entities and relationships in the graph.
Entity Entry Points
An entity entry point is a node selected as a starting point for graph retrieval.
It may come from exact entity matching, semantic entity search, chunk retrieval, or query classification.
Good entry points are critical because they shape the graph neighborhood that gets retrieved.
Relationship Traversal
Relationship traversal follows graph edges from entry points.
For example, the system may move from a contract to its parties, from parties to related contracts, from contracts to obligations, and from obligations to source clauses.
This lets GraphRAG collect evidence that is connected, not merely similar.
Local Graph Search
Local graph search retrieves a neighborhood around relevant entities.
It is useful for questions about a specific person, product, organization, policy, disease, document, or event.
The local neighborhood may include linked entities, relationship summaries, and chunks that mention those entities.
Global Graph Search
Global graph search is useful for broad questions across a corpus.
Instead of focusing on one entity neighborhood, it may use community summaries or topic-level graph structure.
This helps when the answer requires synthesis rather than one direct path.
Relationship Types
Relationship types matter.
“Owns,” “depends on,” “caused by,” “cites,” “signed,” “approved,” “belongs to,” and “contradicts” all mean different things.
GraphRAG can prioritize relationship types that match the user’s intent.
Relationship Summaries
GraphRAG systems may summarize relationships extracted from many source chunks.
A relationship summary can explain how two entities are connected without forcing the model to read every mention.
These summaries should still link back to source evidence.
Entity Summaries
Entity summaries consolidate information about one entity across documents.
They help the retrieval system provide a compact description of a person, organization, product, concept, or event.
They are useful when an entity is mentioned in many places.
Community Summaries
Some GraphRAG systems group densely connected entities into communities.
A community summary gives a higher-level view of that group.
This can help answer broad questions that require context from many related entities.
Source Chunks
Source chunks remain important.
Graph nodes and summaries are useful, but the language model needs grounded evidence from the original corpus.
A good GraphRAG system links every answer back to source chunks or source documents.
Semantic Search Over Summaries
GraphRAG can embed entity summaries, relationship summaries, and community summaries.
This makes graph-level knowledge searchable with natural language.
It is especially useful when the relevant answer is distributed across many documents.
Hybrid Search
Hybrid search combines semantic similarity with keyword matching.
It helps GraphRAG because exact entity names, IDs, acronyms, citations, and codes often matter.
Semantic search handles meaning, while keyword search catches precise terms.
Metadata Filters
Metadata filters keep retrieval within the right scope.
Filters may include tenant, user role, product, region, document type, language, date, status, confidentiality, or source system.
GraphRAG should enforce these filters before context reaches the language model.
Reranking
GraphRAG usually retrieves more candidates than it can send to the model.
Reranking helps choose the most useful chunks, entities, paths, and summaries.
Good ranking combines semantic score, graph distance, relationship type, source quality, freshness, and access eligibility.
Context Assembly
The final context must be compact.
It may include the best source chunks, entity summaries, relationship summaries, graph paths, and community summaries.
The goal is to include enough connected evidence without filling the prompt with noisy graph neighbors.
Provenance
Provenance is essential in GraphRAG.
Each entity, relationship, and summary should point back to the source documents or chunks that support it.
This makes generated answers easier to verify, cite, and debug.
Why GraphRAG Can Improve Answers
GraphRAG can improve answers because it retrieves context based on both similarity and structure.
It can find relevant chunks, then add connected entities and relationships that the chunks alone do not expose.
This is useful when the answer needs multi-document or multi-hop context.
Example Retrieval Path
Suppose a user asks, “Which teams are affected if this service is deprecated?”
Semantic search may find chunks about the service. The graph can then follow relationships from service to dependencies, owners, customers, teams, runbooks, and incidents.
The final answer can cite both the service documentation and the connected evidence.
When It Helps Most
GraphRAG helps most when data is relationship-rich.
Examples include contracts, research papers, legal corpora, compliance systems, support knowledge, product dependencies, supply chains, customer accounts, medical knowledge, and organizational records.
It is less useful when a single self-contained chunk usually answers the question.
Indexing Requirements
GraphRAG requires more indexing work than basic vector RAG.
The system must extract entities, extract relationships, normalize duplicates, generate summaries, link graph facts to sources, and keep everything updated.
This extra work is justified only when relationship-aware retrieval improves answers.
Freshness Challenge
Graph summaries can become stale.
If new documents change an entity or relationship, the graph and summaries may need updates.
Production systems need an update strategy for embeddings, extracted relationships, summaries, and source links.
Scalability Challenge
Highly connected nodes can create noisy retrieval.
Generic entities may connect to thousands of documents and dominate results.
GraphRAG systems should cap traversal depth, filter generic nodes, and rank paths carefully.
Common Failure Modes
Common failure modes include:
- wrong entity recognition
- missing aliases
- duplicate graph nodes
- incorrect extracted relationships
- overly broad graph traversal
- stale summaries
- missing source provenance
- ranking by graph popularity instead of relevance
- leaking restricted context through graph edges
Evaluation
Evaluate GraphRAG with real questions, not only graph statistics.
Measure whether the system retrieves the right entities, relationships, source chunks, and final answer evidence.
Useful checks include Recall@K, precision, citation support, answer faithfulness, path relevance, and human review of difficult queries.
Summary
GraphRAG uses semantic search to find relevant chunks, entities, or summaries, and relationships to expand from those results into connected context.
Semantic search handles meaning. Graph relationships handle structure.
Together, they help RAG systems answer questions that depend on both relevant text and the connections between entities.