Shoe Store — ChromaDB Semantic Search Demo

A minimal demo of vector/semantic search using ChromaDB. Type a natural-language query and get the best-matching shoes from a small catalog, ranked by semantic similarity.

Companion article: ChromaDB Tutorial

What it does

• Loads 7 shoes (Nike, Adidas, Converse, Vans, etc.) into an in-memory ChromaDB collection
• Each shoe has a text description + metadata (brand, color, price, type)
• Queries the collection with your input and returns the top 3 matches with similarity distances

Setup

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Run

python shoe-store.py

Example:

What kind of shoe are you looking for? waterproof boot for hiking

Results for: "waterproof boot for hiking"

Top 3 matches:

  1. Timberland 6 Inch  (timberland-6-inch)
     timberland · wheat · $198 · boot
     durable, waterproof, leather hiking and work boot with padded collar...
     distance: 0.312  (lower = better match)
  ...

Stack

• ChromaDB — embedded vector database
• sentence-transformers — local embedding model (no API key needed)