The short answer
Retrieval and graph relationships can work together. Start with the question.
Compare a passage lookup, a changed decision and a dependency query.How retrieval and graphs work together
Retrieval-augmented generation, or RAG, brings retrieved material into a model's work. A knowledge graph records entities or concepts and the relationships between them. The two can work together: retrieval may use text search, vectors, graph relationships or a combination.
Start by listing the evidence your task needs. Finding a warranty clause, tracing a changed decision and summarizing themes across an archive impose different demands. Choosing "a graph" before defining the task can add structure without improving the answer.
Match the question to the evidence
| Question | Evidence needed | First thing to test |
|---|---|---|
| What does contract A say about notice? | The relevant passage in a known source | Exact and paraphrased retrieval |
| Which decision changed Project North's date? | Original, amendment and their relationship | Current-state selection with provenance |
| Which projects depend on supplier Aster? | Correct project identities and dependency links | Relationship coverage and meaning |
| What themes recur across the archive? | Broad, representative coverage | Whether the method samples the corpus adequately |
Use these as tests for your own sources. No retrieval technique is guaranteed to pass them. A graph with missing edges can miss a dependency. A vector result can be semantically close yet refer to the wrong contract. Exact identifiers and source inspection remain useful.
Use a paired example
Create two fictional projects, North and South, with the same supplier. Give each a different delivery date. Add a later amendment for North only. Ask for South's date, North's current date and the source that changed North.
A useful result must keep the projects separate and apply the amendment narrowly. Merely retrieving both dates is insufficient. Record whether a failure came from an incorrect relationship, a missed passage or the final reasoning step.
Next, remove the amendment from the retrieval inputs according to the system's supported procedure. Test again. This helps check how the system behaves when evidence is incomplete. It is not a substitute for a full deletion audit.
Understand what GraphRAG research establishes
Microsoft's GraphRAG paper describes an approach using a graph and community summaries for query-focused summarization, including corpus-wide questions. Its findings concern that method and evaluation.
A set of backlinks does not automatically reproduce that system. Nor do its results prove that a graph is better for every lookup. When a product cites research, check whether its actual implementation and your task match the research claim.
Count the cost of maintaining relationships
Relationships need provenance, clear meanings and a way to handle changes. Two names may refer to one organization, or one name may refer to two projects. An extracted relationship can be syntactically valid while being factually unsupported.
- Require a source for consequential relationships.
- Keep unresolved identity matches visible.
- Check what happens when an original note changes.
- Test whether derived data can be rebuilt.
- Measure mistakes on your own questions before adding complexity.
Keep a simple retrieval baseline in the comparison. If adding relationships does not improve the cases you care about, investigate coverage and maintenance before expanding the graph.
What our current comparison actually measures
The phrase GraphRAG covers a family of retrieval approaches. The useful comparison is vector retrieval versus graph-assisted retrieval for a specific question and source set.
Our executed experiment compares lexical, hybrid, graph-reranked and graph-expanded configurations in one local service. It does not compare Gigabrain with other products. It does not include a pure vector-only baseline.
Use those results to inspect graph contributions within this implementation. They cannot establish a general performance advantage over vector RAG.
| Question | Evidence needed | Useful first test |
|---|---|---|
| What date appears in the Monday schedule? | One schedule passage. | Direct retrieval. |
| Which schedule governs Project Alder? | Project, review and schedule records. | A supported relationship path. |
| Did every project meet its deadline? | Complete obligations and completion records. | Check corpus completeness first. |
A graph does not supply missing completion records in the last case. A retrieved schedule also does not prove an inspection happened.
Compare maintenance as well as retrieval
Both approaches need current passages and source locations. A graph adds identity decisions and relationship support that must survive corrections.
Our connected-source demonstration shows a stale citation fail after its document changes. The graph path remains unavailable until the fixture supplies updated support.
Start with direct retrieval when questions resolve to one well-labelled document. Consider graph assistance when dependencies, ownership or changes repeatedly require connected sources.
Record those responsibilities in the cost worksheet. Deployment is a separate choice: graph systems can be local or hosted.
How to evaluate Gigabrain
Gigabrain combines semantic and lexical retrieval with typed graph relationships, graph reranking and source-verification tools. It does not claim to be Microsoft GraphRAG. Its graph explanation describes the supported model. The release evidence describes the recorded checks.
To put this knowledge infrastructure to work with your own agent, follow the architecture guide, check compatibility and compare packages. Choose based on how well the system answers questions you can check, rather than the architecture label.
Method note: Gigabrain Labs prepared this article with AI assistance. Linked records define the measured scope. Fictional exercises remain separate from executed results.
