Guide
Graphs in Chat
Knowledge graphs help AI find better answers by following relationships between concepts.
Knowledge graphs don’t just create pretty visualizations. They make your AI chat smarter. When you ask a question, the graph helps find relevant information that traditional search might miss.
The Problem with Traditional Search
Traditional search works by matching keywords. If you search for “CEO compensation”, it finds documents containing those words.
But what if a document says:
“John Smith, who leads the company, received a $2M bonus last year.”
Traditional search might miss this because it doesn’t contain “CEO” or “compensation”. The knowledge graph knows that “John Smith” → “leads” → “the company”, connecting it to your question.
How Graph-Enhanced Search Works
You Ask a Question
Type your question in the chat panel.
Entity Recognition
AI identifies entities in your question. For “What’s the relationship between Apple and Google?”, it recognizes “Apple” and “Google” as entities.
Graph Traversal
The system looks up these entities in your knowledge graph and follows their relationships to find connected entities and concepts.
Combined Retrieval
Results from graph traversal are combined with traditional search (semantic + keyword) to find the most relevant passages.
Answer Generation
AI generates a response using passages found through all methods, with citations.
What Graph Retrieval Adds
Finding Indirect Connections
Without graph: Search finds documents mentioning your exact query.
With graph: Search also finds documents about related entities.
Example:
- Question: “What are the risks of Project Aurora?”
- Graph knows: “Project Aurora” → “depends on” → “Cloud Provider X”
- Graph also knows: “Cloud Provider X” → “has history of” → “Security incidents”
- Result: Finds passages about cloud provider risks even if they don’t mention “Aurora”
Cross-Document Discovery
When entities appear in multiple documents, the graph connects them:
- Document A: Mentions “Sarah Chen” is VP of Engineering
- Document B: Discusses engineering team challenges
- Graph connects: Questions about “Sarah Chen” can surface relevant passages from Document B
Relationship-Aware Answers
The graph preserves relationship types, so AI understands not just that things connect, but how:
| Relationship | What AI Learns |
|---|---|
| ”causes” | Causality for “why” questions |
| ”before/after” | Sequence for timeline questions |
| ”manages” | Hierarchy for organizational questions |
| ”uses” | Dependencies for technical questions |
When Graph Retrieval Helps Most
Complex Questions
Questions involving multiple concepts or relationships:
- “How does X affect Y?”
- “What’s the connection between A and B?”
- “Who is responsible for…?”
Research Across Many Documents
When you have many sources about related topics:
- Academic papers in a field
- Company documents about a project
- News articles about an industry
Discovering Non-Obvious Connections
Finding relationships you didn’t know existed:
- Shared dependencies
- Common personnel
- Hidden causal chains
Seeing Graph Retrieval in Action
Thinking Stages
When you ask a question, watch the thinking stages:
| Stage | What It Means |
|---|---|
| Graph Search | Finding entities related to your question |
| Traversal | Following relationships in the graph |
These stages show that graph retrieval is being used.
Citations
Responses may include passages that don’t obviously match your keywords. This often means graph retrieval found them through relationships.
Best Practices
Build Rich Graphs
The more sources you add, the more connections the graph can find.
- Add multiple documents about the same topic
- Include different perspectives (papers, reports, articles)
- Let the graph consolidate entities across sources
Ask Relationship Questions
Graph retrieval excels at relationship questions:
| Less Effective | More Effective |
|---|---|
| ”Tell me about Company X" | "How does Company X relate to Market Y?" |
| "What is machine learning?" | "How does machine learning connect to our product?" |
| "Who is John Smith?" | "What’s John Smith’s role in Project Z?” |
Use Entity Names
When you mention specific entities from your sources, the graph can look them up directly:
- “What challenges does Project Aurora face?”
- “How has Sarah Chen contributed to the engineering team?”
- “What are the risks of Cloud Provider X?”
Technical Details
How Results Are Combined
Graph results are merged with traditional search using Reciprocal Rank Fusion (RRF):
- Vector search finds semantically similar passages
- Full-text search finds keyword matches
- Graph retrieval finds relationship-connected passages
- RRF combines rankings from all three methods
This ensures you get the best of all approaches.
Traversal Depth
By default, the graph follows relationships two hops deep:
- Direct: Entities you mention
- 1 hop: Entities directly connected to those
- 2 hops: Entities connected to the connected entities
Deeper traversal finds more connections but may introduce noise.
Scoring by Distance
Passages are scored based on how they were found:
- Direct mention: Highest relevance (1.0)
- 1 hop away: High relevance (0.8)
- 2 hops away: Moderate relevance (0.6)
This ensures directly relevant passages rank higher than distantly connected ones.
Limitations
Graph Needs Data
Graph retrieval only works if:
- Your sources have been processed
- Knowledge graph building completed successfully
- Relevant entities were extracted
Not All Questions Benefit
Simple factual questions may not need graph retrieval:
- “What date was the report published?” (keyword search is enough)
- “What’s the definition of X?” (semantic search handles this)
Graph retrieval adds most value for relationship and connection questions.