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The Knowledge Graph is an interactive visualization of the people, organizations, concepts, and relationships that RelayHub automatically extracts from your uploaded documents. Instead of searching through individual files, the knowledge graph lets you see how information connects across your entire document library.

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What Gets Extracted

When a file is processed, an AI model reads the content and identifies:
  • Entities — Named things like people, companies, products, locations, dates, financial figures, and domain-specific concepts
  • Relationships — How entities relate to each other, such as “works at”, “acquired by”, “depends on”, or “located in”
These are stored as nodes (entities) and edges (relationships) that build up over time as you upload more documents. A single company name mentioned across ten different files becomes one node with connections to everything associated with it.

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Entity Types

The knowledge graph recognizes several categories of entities:

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Exploring the Graph

Open the knowledge graph from the Knowledge in the sidebar. The default view shows a network visualization with nodes representing entities and lines representing relationships.

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Relationship Visualization

Edges in the graph are labeled with the relationship type. The visualization uses color and thickness to convey information:
  • Edge labels describe the relationship (“reports to”, “supplies”, “references”)
  • Node size reflects how many connections an entity has — heavily referenced entities appear larger
  • Clustering groups related entities together spatially, making it easy to spot organizational structures or topic clusters
Zoom in to dense areas of the graph to read individual relationship labels. You can also drag nodes to rearrange the layout manually.

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Filtering the Graph

For large document libraries, the full graph can be dense. Use the filter controls to narrow what is displayed:
  • By entity type — Show only people, or only organizations, to simplify the view
  • By source file — Limit the graph to entities from a specific document or set of documents
  • By scope — Filter to workspace-level or company-level knowledge

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How the AI Uses the Knowledge Graph

The knowledge graph is not just a visualization tool. When you ask questions in chat, the AI can query the graph to find relationships and context that go beyond simple text search. For example:
  • “Who are the key contacts at Acme Corp?” — The AI finds all Person nodes connected to the Acme Corp organization node
  • “What products are mentioned alongside our Q3 revenue report?” — The AI traverses relationships from the report to connected product entities
Knowledge graph data is rebuilt when you reprocess a file. If you delete a file, its entities and relationships are also removed from the graph.

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Graph Growth Over Time

The knowledge graph becomes more valuable as you upload more documents. Early uploads create isolated clusters of entities. As more files are added, connections form between clusters — a person mentioned in a contract might also appear in meeting notes and a project plan, weaving a rich web of organizational knowledge.