Project Track 8: Tabletop RPG / Board Game Master

The Problem / Concept An assistant for complex games (like Dungeons & Dragons) that manages custom rules, lore, and complex board states.

Project Overview & Objectives

Running a tabletop RPG like Dungeons & Dragons requires tracking immense amounts of custom lore, NPC relationships, and homebrew rules. This application serves as the ultimate Game Master’s screen, instantly retrieving obscure campaign facts.

If you select this track, your goal is to build a functional Minimum Viable Prototype (MVP) that seamlessly integrates a Node.js/Express backend with Google’s Gemini API, utilizing Retrieval-Augmented Generation (RAG), multimodal vision, and autonomous tool calling.

Detailed Requirements Document (PRD)

1. RAG (Obsidian) Core Requirement

To prevent hallucination, the AI must be grounded in a specific, personal knowledge base. You will build this using Markdown files in Obsidian.

  • Knowledge Base Content: The Obsidian vault is the Campaign Wiki. It contains session summaries, NPC dialogue notes, custom ‘house rules’, and player character sheets. The AI can answer questions like ‘What was the name of the tavern keeper we met 6 months ago?’
  • Implementation Expectation: Your Express server must parse these Markdown files, generate vector embeddings using gemini-embedding-2, and perform a cosine similarity search against the user’s query. The retrieved text must be injected into the system prompt.

2. Multimodal (Vision) Stretch Goal

AI is not just text. Modern products must perceive the world.

  • Vision Use Case: The user takes a picture of a complex battle map grid with miniatures, or a cluster of physical dice rolled on a table. The AI can quickly sum up complex dice pools or assess line-of-sight and positioning on the grid.
  • Implementation Expectation: Your frontend HTML must include a file upload input. The image must be converted to base64, sent to the /query endpoint, and passed to gemini-2.5-flash alongside the text prompt and RAG context.

3. Tool Calling Stretch Goal

Agents need to take actions in the real world or fetch real-time data that isn’t in their RAG database.

  • Tool Definition: Implement a roll_dice(notation) tool (e.g., ‘4d6+2’) that returns mathematically random results, or a generate_npc(location) tool that the AI can call to instantly flesh out a new character with stats and motivations when players go off-script.
  • Implementation Expectation: You must define a strict JSON schema for this tool and register it in your Gemini API call. When the model decides to invoke the tool, your Node.js server must intercept the request, execute a mock function, and return the result to the model for the final response.

Step-by-Step Implementation Guide

If you are using this document to prompt an AI coding assistant (like GitHub Copilot or Cursor), use the following phasing:

  1. Phase 1: UI & Mock Backend: Ask the AI to generate a vanilla HTML/JS interface with a text input, file upload, and a submit button. Connect it to an Express POST /query route that returns mock JSON.
  2. Phase 2: Vanilla Gemini Integration: Connect the Express route to the actual @google/genai SDK. Send a simple text prompt and display the result.
  3. Phase 3: The RAG Pipeline: Ask the AI to write a script to read your Markdown files, split them into chunks, and get embeddings. Update your /query route to calculate cosine similarity and inject the top matches.
  4. Phase 4: Multimodal & Tools: Finally, add the base64 image parsing to the API call, and define your function declaration for the tool-calling stretch goal.

Note: In the future, a specific branch in the course repository will be provided containing starter scaffolding tailored to this exact project track.