What is GraphRAG?
GraphRAG (Graph-based Retrieval Augmented Generation) extends traditional RAG by adding a knowledge graph layer. Instead of treating documents as isolated chunks of text, GraphRAG extracts entities and relationships to build a connected graph of your organization's knowledge.
This graph enables a fundamentally different class of queries — ones that require understanding relationships between concepts, not just finding similar text.
How It Works
Step 1: Entity Extraction
When documents are ingested, AI extracts named entities: people, organizations, products, processes, locations, dates, and concepts. Each entity becomes a node in the graph.
Step 2: Relationship Mapping
The system identifies relationships between entities: "Person X approved Document Y", "Product A depends on Component B", "Policy X applies to Department Z". These become edges in the graph.
Step 3: Community Detection
Graph algorithms identify clusters of related entities — communities that represent topics, projects, or organizational units. This enables high-level summarization and navigation.
Step 4: Graph-Augmented Retrieval
When a user asks a question, the system:
- Identifies relevant entities in the query
- Traverses the graph to find connected entities and documents
- Combines graph results with vector and keyword search results
- Returns a comprehensive answer drawing from multiple connected sources
What GraphRAG Can Answer That Vector Search Can't
Relationship Queries
"Which teams are working with the same vendor?"
Vector search finds documents that mention vendors. GraphRAG traverses: Team → uses → Vendor ← used by ← Team, finding connections across documents that never directly reference each other.
Aggregation Queries
"Summarize all the decisions made about Project Alpha"
GraphRAG finds all entities connected to "Project Alpha" — meetings, decisions, approvals, milestones — even when scattered across dozens of documents.
Impact Analysis
"What would be affected if we change our data retention policy?"
By traversing the graph from the data retention policy node, GraphRAG identifies all connected systems, processes, teams, and compliance requirements.
Building the Graph Automatically
The key insight is that the knowledge graph should be built automatically from your existing documents — not manually curated. Manual knowledge graphs are expensive to create and impossible to maintain.
Courdx uses LLM-powered entity extraction to build and maintain the graph as documents are ingested and updated. The graph grows organically with your knowledge base.
When to Use GraphRAG
GraphRAG adds the most value when:
- Your knowledge spans many interconnected documents
- Users ask relationship and impact questions
- You need to find connections that aren't explicitly stated
- Your documents reference shared entities (people, projects, products)
For simple factual lookups, vector search is often sufficient. But for the complex questions that enterprise teams actually need answered, GraphRAG is transformative.
Courdx automatically builds knowledge graphs from your documents with entity extraction and community detection. See it in action.