quickstart

Quickstart example

The smallest runnable Recotem example. Trains a TopPop recommender from a tiny synthetic CSV (60 users, 20 items, ~850 rows). No network, no extras.

Files

• recipe.yaml — minimal recipe (name, source, schema, training, output)
• interactions.csv — synthetic interaction log with two columns: user_id, item_id
• artifacts/ — created on first train; gitignored

Run

From the repository root:

# 1. Generate keys (once per machine). Copy the values into the exports below.
recotem keygen --type signing --kid dev
recotem keygen --type api     --kid dev

export RECOTEM_SIGNING_KEYS="dev:<signing-hex64>"       # signing: env_entry value (the 64-char hex from keygen)
export RECOTEM_API_KEYS="dev:sha256:<api-hash>"         # api:     env_entry value
export RECOTEM_API_PLAINTEXT="<api-plaintext>"          # api:     plaintext, for curl

# 2. Train
recotem train examples/quickstart/recipe.yaml
# → examples/quickstart/artifacts/top_picks.<sha>.recotem (signed)

# 3. Serve (foreground)
recotem serve --recipes examples/quickstart/

# 4. Predict (in another terminal)
curl -X POST http://localhost:8080/v1/recipes/top_picks:recommend \
  -H "X-API-Key: $RECOTEM_API_PLAINTEXT" \
  -H "Content-Type: application/json" \
  -d '{"user_id": "u01", "limit": 10}'

What's next

• Widen the algorithm search: training.algorithms: [IALS, CosineKNN, TopPop]
• Add a time_column and switch to split.scheme: time_user
• See docs/recipe-reference.md for every field.
• See examples/csv-local for a richer local-CSV setup and examples/tutorial-purchase-log for the end-to-end Docker walkthrough.