Glossary · 4 minute read
What Is Graph RAG? Retrieval Over Relationships, Not Just Text
Graph RAG is a retrieval-augmented generation approach that draws context from a knowledge graph of entities and their relationships, often alongside text passages, so a model can answer questions that require connecting facts across many documents. It helps with multi-hop questions, entity-centric queries, and global summaries, at the cost of building and maintaining the graph.
Standard retrieval finds passages that resemble the question, which works when the answer lives in one place. Many enterprise questions do not: which suppliers are connected to a delayed component, what a customer's contracts across subsidiaries allow, how a policy change affects the processes that reference it. Graph RAG adds a knowledge graph of entities and relationships to retrieval so a model can follow connections across documents. This explainer covers how it works, when it helps, and what it costs, drawing on FISTA Solutions' AI enablement practice. The build guide is in how to build a graph rag system and the underlying structure in what is a knowledge graph.
What is graph RAG?
Graph RAG is a retrieval-augmented generation design in which context is retrieved from a knowledge graph, nodes representing entities and edges representing relationships, often with attached text passages, rather than from passages alone. A query is mapped to entities, relevant subgraphs are traversed, connected facts and supporting passages are assembled, and the model answers with citations. Variants also build community summaries over the graph to answer whole-corpus questions. Standard retrieval is described in what is rag.
How does it compare with passage-based RAG?
| Dimension | Vector or hybrid RAG | Graph RAG |
|---|---|---|
| Retrieval unit | Passages | Entities, relationships, subgraphs, plus passages |
| Strong at | Single-passage answers, paraphrase | Multi-hop, entity-centric, corpus-wide questions |
| Weak at | Connecting facts across documents | Simple lookups; adds overhead |
| Build effort | Chunking and indexing | Extraction, entity resolution, graph modeling |
| Maintenance | Index refresh | Graph refresh and quality control |
| Explainability | Cited passages | Cited paths and passages |
Hybrid retrieval is in what is hybrid search.
What questions does graph RAG answer better?
Multi-hop questions that require chaining facts: which projects depend on a vendor whose certification lapsed. Entity-centric questions that aggregate everything known about a person, product, or account across sources. Global questions that ask for themes or summaries across a whole corpus. Impact questions that follow dependencies. Passage retrieval struggles with all of these because no single passage contains the answer.
How is the graph built?
Entities and relationships are extracted from documents with language models or specialized extractors, duplicates are resolved so one customer is one node, entities are linked to master data where it exists, relationships carry provenance to source passages, and the graph is stored in a graph database or graph-capable index. Quality control catches extraction errors, and refresh pipelines keep the graph current. Extraction patterns are in how to build an ai data extraction pipeline and lineage in what is data lineage in ai.
How does retrieval work at query time?
The query is analyzed for entities and intent; matching nodes are found through search; the neighborhood is traversed to a bounded depth along relevant relationship types; connected passages are collected; results are ranked and trimmed to context budgets; and the model answers with citations to paths and passages. Query analysis is in what is query rewriting.
How do hybrid designs combine graph and passage retrieval?
Most production systems run passage retrieval and graph traversal together: passages supply detail and phrasing, the graph supplies connections, and a reranker or the model itself integrates them. Simple questions take the passage path; relationship questions engage the graph. Reranking is in what is a reranker.
What does graph RAG cost?
Extraction and entity resolution effort, which is the largest share; graph storage and query infrastructure; refresh pipelines as documents change; and added retrieval complexity and latency. Against that stands the value of questions that passage retrieval cannot answer. Cost planning is in enterprise rag cost and the decision framework in when to use a knowledge graph.
How should graph RAG be evaluated?
With a golden set that includes multi-hop, entity-centric, and corpus-wide questions with expected facts and sources, compared against a standard RAG baseline on the same set, measuring groundedness, completeness, and latency. If the graph does not beat the baseline on the questions that motivated it, the investment is not justified. Evaluation practice is in what is groundedness in ai and what is a golden dataset.
What does graph RAG look like in practice?
A manufacturer builds a graph of products, components, suppliers, certifications, and plants from specifications, contracts, and quality records. When a supplier's certification lapses, the assistant traverses the graph to list affected components, products, and plants with supporting documents, a question no passage search could answer. Routine specification lookups still use passage retrieval. Supply chain context is in the AI for supply chain resilience whitepaper.
How FISTA Solutions builds graph RAG
FISTA Solutions adds graph retrieval where client questions are relationship-driven, builds extraction and entity resolution with quality control, combines graph traversal with passage retrieval and reranking, and evaluates against a standard RAG baseline before committing to the maintenance cost. The AI enablement practice delivers retrieval platforms, AI agents draw on them, and forward deployed engineers embed with client data and knowledge teams. The record behind the approach is 150+ projects with 99.9% uptime.
To answer questions that span your documents, message FISTA on WhatsApp, or read what is chunking in rag for the passage side that graph RAG builds on.
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01What is graph RAG in simple terms?
Standard RAG finds passages that look like the question. Graph RAG also keeps a map of who and what is connected to what, so when a question needs facts from several places, the system can follow the connections, gather them, and give the model a joined-up answer.
02How is graph RAG different from vector RAG?
Vector RAG retrieves passages by embedding similarity and works well when the answer sits in one or two passages. Graph RAG retrieves entities, relationships, and connected passages by traversing a graph, which handles questions whose answer spans documents or depends on how things relate.
03How is the knowledge graph built?
By extracting entities and relationships from documents with language models or specialized extractors, resolving duplicates, linking to existing master data, and storing the result in a graph database or graph-capable index, with quality checks and refresh as documents change.
04When is graph RAG worth it?
When questions are multi-hop, entity-centric, or require summaries across a corpus, when the domain has rich relationships such as products, parts, people, and contracts, and when a standard RAG baseline measurably fails on those questions. Simple lookup corpora rarely need it.
05What does graph RAG cost?
Entity and relationship extraction effort at ingestion, entity resolution to merge duplicates, graph storage and maintenance, refresh pipelines that keep the graph consistent with source documents, and added retrieval complexity that combines graph traversal with passage search. It is justified where relationship and multi-hop questions carry business value that passage retrieval alone cannot deliver, and unnecessary otherwise.
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