Traditional RAG works well when the answer can be found in a few relevant passages.
But many enterprise questions are not passage-local.
Examples:
Which suppliers are indirectly connected to facilities affected by the new regulation?
What themes appear across thousands of complaints, and which products, regions and policies connect to those themes?
How are Customer A, Account B, Complaint C and Policy D related?
A vector database can retrieve semantically similar text. It does not automatically understand the network of relationships connecting facts across documents.
GraphRAG combines retrieval-augmented generation with graph-structured knowledge so an AI system can retrieve not only similar passages, but related entities, relationships, communities and evidence paths.
GraphRAG does not replace ordinary RAG. It extends retrieval for questions where relationships, multi-hop reasoning or corpus-wide structure matter.
What is GraphRAG?
GraphRAG combines:
- knowledge graphs,
- entity relationships,
- document evidence,
- vector retrieval,
- graph traversal,
- large language models.
A knowledge graph represents entities and relationships explicitly. GraphRAG uses that structure to guide retrieval and generation.
GraphRAG vs traditional RAG
| Dimension | Traditional RAG | GraphRAG |
|---|---|---|
| Primary retrieval unit | Text chunks | Entities, relationships, communities + text |
| Main signal | Semantic similarity | Semantic similarity + graph structure |
| Strongest query type | Passage-level questions | Relational, multi-hop and corpus-wide questions |
| Index construction | Simpler | More complex |
| Relationships | Implicit in text | Explicitly modeled |
| Global understanding | Limited | Can be explicitly supported |
| Typical cost | Lower | Potentially higher |
GraphRAG solves a different class of retrieval problems.
Why vector RAG can fail on relational questions
If one document links Supplier A to Manufacturer B, another links Manufacturer B to Facility C, and a third says Facility C is affected by Regulation D, the answer requires a multi-hop relationship chain.
Graph traversal can provide another retrieval signal.
Indexing architecture
Documents
↓
Parsing
↓
Chunking
↓
Entity Extraction
↓
Relationship Extraction
↓
Entity Resolution
↓
Knowledge Graph
↓
Community Detection
↓
Community Summaries
↓
Embeddings / Search Indexes
Parse correctly
Poor PDF extraction, missing headings and broken tables reduce graph quality.
GraphRAG inherits standard RAG ingestion requirements.
Chunk the content
Chunking remains important because entity and relationship extraction depends on sufficient local context.
Poor boundaries can split relationships.
Extract entities and relationships
Common entities include people, companies, accounts, products, locations, policies, systems and events.
Relationships may include direction, dates, confidence, evidence and source.
Entity resolution
Enterprise data contains aliases and inconsistent names.
GraphRAG needs canonicalization so one business entity does not become several nodes.
Community detection
Large graphs contain clusters of strongly related entities.
Community summaries can support corpus-level questions.
Query modes
A GraphRAG system may support multiple retrieval strategies.
Local Search
Best for entity-centered questions.
Global Search
Best for corpus-wide themes and patterns.
DRIFT Search
Combines local retrieval with broader community context and iterative exploration.
Basic Search
A vector-RAG baseline for direct factual questions.
Many questions do not need graph traversal.
Query routing matters
A production system can route:
- simple factual query → Basic RAG,
- relationship query → Local GraphRAG,
- corpus-wide query → Global Search,
- complex exploratory query → DRIFT.
Do not use the most expensive retrieval mode for every question.
GraphRAG vs knowledge graph
A knowledge graph is a representation.
GraphRAG is:
Knowledge Graph + Retrieval + Source Evidence + LLM Generation
Bring-your-own graph
If the enterprise already has explicit customer, account, supplier, product or policy relationships, use those rather than reconstructing everything from text.
GraphRAG and vector databases work together
Vector Search
↓
Find likely entities
↓
Graph Traversal
↓
Expand relationships
↓
Retrieve source evidence
↓
LLM
Vectors and graphs solve different retrieval problems.
GraphRAG vs hybrid RAG
Hybrid RAG combines dense and sparse retrieval.
GraphRAG adds graph structure as another signal.
Enterprise retrieval can combine all three.
When GraphRAG is valuable
Strong candidates include:
- regulatory intelligence,
- financial intelligence,
- customer intelligence,
- supply chain,
- cybersecurity,
- enterprise knowledge.
GraphRAG becomes more useful as answers depend on connections rather than isolated passages.
When not to use GraphRAG
Traditional RAG may be better when the corpus is small, questions are direct, relationships add little value, data changes rapidly, cost must be low or latency is strict.
Structured databases may be better
Exact balances, counts and current structured facts may belong in SQL or APIs.
GraphRAG should augment authoritative systems, not replace them.
Graph construction quality
Measure entity precision/recall, relationship precision, duplicate rate, alias resolution, unsupported edges and source coverage.
A wrong edge can propagate errors.
Store evidence on every relationship
Attach source document, location, effective date and confidence where possible.
This supports explanation, audit and correction.
Temporal GraphRAG
Relationships change over time.
Represent valid_from and valid_to where appropriate.
Graph freshness
Measure source-change → graph-update → searchable delay.
Graph maintenance can be more complex than vector re-embedding.
Cost
Costs split into index-time extraction/summarization and query-time retrieval/generation.
Use cost-aware routing so expensive global search is used only when needed.
Evaluate against a strong RAG baseline
Compare GraphRAG with good chunking, embeddings, hybrid retrieval, metadata filters and reranking.
Adopt graph complexity only where it materially improves important queries.
Three levels of evaluation
- Graph quality.
- Retrieval quality.
- Answer quality.
Do not compress these into one score.
Query-type slices
Evaluate:
- single fact,
- multi-hop,
- relationship,
- global thematic,
- historical,
- cross-document.
GraphRAG may win only on specific slices.
Evaluate graph paths
For relational questions, measure whether the correct nodes and edges were retrieved.
Use node recall, edge recall, path precision and path completeness.
Source grounding
The graph should guide retrieval.
Authoritative source text should provide evidence.
Security
Graph structure itself can reveal sensitive relationships.
Apply permissions to nodes, edges, attributes, communities and summaries.
Permission-aware traversal
User / Agent Identity
↓
Authorized Graph View
↓
Graph Traversal
↓
Authorized Evidence
↓
LLM
Multitenancy
Avoid cross-tenant graph or community leakage.
GraphRAG for agents
Agents can route context requests among SQL, vector search, GraphRAG and APIs.
This is stronger than forcing every query through one retrieval technology.
Context engineering
GraphRAG is one context-selection mechanism.
It answers:
Which connected facts and relationships should be retrieved?
Domain ontology
Use entity and relationship types appropriate to the domain.
Start small and expand only when evaluation shows a need.
Enterprise reference architecture
ENTERPRISE SOURCES
↓
INGEST + PARSE
↓
CANONICAL ENTITIES
↓
TEXT/VECTOR + KNOWLEDGE GRAPH
↓
RETRIEVAL ROUTER
(Basic / Local / Global / DRIFT)
↓
RERANK
↓
SOURCE EVIDENCE
↓
LLM
↓
GROUNDED RESPONSE
↓
EVALUATION + FEEDBACK
Important GraphRAG KNOBS
- chunk size,
- overlap,
- entity types,
- relationship types,
- extraction model,
- extraction prompt,
- merge threshold,
- community algorithm,
- community level,
- summary model,
- query route,
- traversal depth,
- candidate count,
- embedding model,
- reranking,
- context limit.
Measure impact on correctness, recall, graph density, latency, tokens and cost.
Build incrementally
- Select a relationship-heavy use case.
- Build a strong RAG baseline.
- Define a small ontology.
- Extract the graph.
- Resolve entities.
- Evaluate graph quality.
- Add Local Search.
- Add Global/DRIFT only if needed.
- Add routing.
- Compare end-to-end outcomes.
Common mistakes
- Assuming GraphRAG replaces vector search.
- Using it for every question.
- Focusing on graph visualization rather than retrieval.
- Ignoring entity resolution.
- Storing relationships without evidence.
- Ignoring temporal validity.
- Allowing unrestricted traversal.
- Measuring only final answers.
- Rebuilding everything unnecessarily.
- Using default prompts without evaluation.
Where KREATE, KONTROLS and KNOBS fit
KREATE can transform documents, databases and APIs into governed entities, relationships and evidence.
KONTROLS can govern sources, sensitive attributes, relationship visibility, tenant isolation and agent access.
KNOBS can expose ontology, merge threshold, community level, retrieval mode and traversal depth as measurable variables.
Bottom line
GraphRAG extends RAG by giving retrieval an explicit model of entities and relationships.
Use:
- SQL for structured facts,
- Basic RAG for passage-level questions,
- Hybrid RAG when lexical + semantic retrieval is enough,
- GraphRAG when relationships and corpus structure matter.
Ask:
Which classes of questions improve enough with graph structure to justify the additional complexity?
Frequently asked questions
What is GraphRAG?
A retrieval-augmented architecture combining knowledge graphs, source documents, vector retrieval and LLMs so search can use explicit entities and relationships.
Is GraphRAG better than vector RAG?
Not universally. It is most useful for relational, multi-hop and corpus-wide questions; ordinary RAG is often simpler and cheaper for direct factual lookup.
Does GraphRAG still use embeddings?
Usually yes. Embeddings remain useful for semantic retrieval and entity matching while graph traversal adds structural signals.
KREATE
Turn documents, databases and APIs into governed entities, relationships, evidence and reusable intelligence for vector retrieval, GraphRAG, assistants and agents.