About vs. Demo: This demo folder now uses a standardized launch page. Start with About Demo, then choose the run mode that fits your class.

RAG & NLP • Browser • 35-45 min

Graph RAG

Browser-based demonstration of Knowledge Graph-based Retrieval-Augmented Generation.

Open About Demo Read README View Course Path

🔗 Knowledge Graph

Graph visualization will appear here...

❓ Query Node

Path will be shown here...

🤖 Graph-Based Answer

Select nodes and explore relationships to generate answers...

📖 How Graph RAG Works

  1. Build Graph: Entities as nodes, relationships as edges
  2. Traverse: Find relevant paths between query and data
  3. Reason: Use graph structure for inference
  4. Generate: LLM answers from graph context

Standard demo guide

Use this demo in a logical learning sequence

Starts in browser, but first load may take 10-30 seconds if heavier assets initialize.

What this demo is about

Browser-based demonstration of Knowledge Graph-based Retrieval-Augmented Generation.

Learning objectives

  • Explain the main rag/nlp decision that Graph RAG is designed to support.
  • Change input assumptions and predict how the output should respond before running the demo.
  • Interpret the result in plain language, not just as a number, chart, or AI recommendation.

Run mode and expectations

  • Supported modes: Browser
  • Starts in browser, but first load may take 10-30 seconds if heavier assets initialize.

Step 1: Inputs

  • Start with the default assumptions, then change one variable at a time so students can isolate cause and effect.
  • Treat each input as a lever that changes the scenario, baseline, or business context behind the result.

Step 2: Decision buttons

  • Use the main run or simulate action to compute the scenario after inputs are set.
  • Use export or reset actions, when present, to compare runs or return to a classroom-safe baseline.

Step 3: Outputs and what to notice

  • Read the top-line result first, then look for supporting metrics, tables, or narratives that explain why it changed.
  • Students should explain whether the output is descriptive, predictive, simulated, or recommended.
  • Look for entities, relationships, graph context, and generated answer
  • Observe how graph connections improve answers that need relationship reasoning

Available run modes

  • Browser: available for this demo.

How to proceed

  1. Choose the run mode that fits the class: Browser.
  2. Review the default assumptions before changing anything.
  3. Change one or two inputs, then use `Run the main action`.
  4. Read the output first, then compare any supporting metrics, charts, or AI text.
  5. Capture one insight, one limitation, and one action recommendation.