Related Tables¶

Use this workflow when a folder contains one CSV file per related table. The file stem becomes the entity name.

Input Layout¶

example_dataset/
  customers.csv
  orders.csv

The checked-in fixture already has this shape.

Generate The Dataset¶

test-data-agent generate-from-example tests/fixtures/example_dataset \
  --count 25 \
  --seed 12345 \
  --format csv \
  --output out/example_dataset

Expected summary:

Generated synthetic dataset: out/example_dataset | rows: customers=25, orders=25 | seed: 12345 | validation: passed | source rows copied: no

The output contains:

out/example_dataset/
  customers.csv
  orders.csv
  profile.json
  dataset_spec.yaml
  generation_manifest.json
  validation_report.json

Review The Relationship¶

Open out/example_dataset/dataset_spec.yaml and locate relationships. The inferred customer/order relationship should name:

parent_entity: customers
parent_field: customer_id
child_entity: orders
child_field: customer_id

The generator creates new parent identifiers first and assigns child foreign keys from those generated values. Source identifiers are not reused.

Run The Stages Separately¶

Use separate commands when the inferred specification needs review or editing:

test-data-agent profile-example tests/fixtures/example_dataset \
  --output out/profile.json

test-data-agent infer-spec out/profile.json \
  --count 25 \
  --output out/dataset_spec.yaml

test-data-agent generate out/dataset_spec.yaml \
  --seed 12345 \
  --format csv \
  --output out/generated

test-data-agent validate out/dataset_spec.yaml out/generated \
  --output out/generated/validation_report.json

Do not generate until a reviewer accepts inferred relationships and constraints for a new domain.

See Profiles And Specs for the purpose of each stage.