Magic Item Shop

Teaching example for IEOR 4576.

This app introduces Retrieval Augmented Generation (RAG) using a D&D magic item shop as the scenario.

A shopkeeper character (powered by Gemini via LiteLLM) decides when to search its inventory, 1,031 magic items embedded in ChromaDB, and responds only with what it finds.

The key ideas demonstrated:

• Offline indexing: embed a corpus once, persist it to disk (index_items.py)
• Tool-call-driven retrieval: the LLM invokes search_magic_items() when it decides to, not on every message
• Manual tool-calling loop: explicit TOOL_FN_MAP dispatch with no agent framework

What we skip:

• Chunking
• Anything fancy

Setup

uv sync                          # install dependencies
uv run python index_items.py     # build chroma_data/ (one-time)
uv run python app.py             # start server → http://localhost:8000

Docker

Indexing runs at build time. The image is fully self-contained:

docker build -t magic-item-shop .
docker run -p 8000:8000 magic-item-shop