Schemas - Lasso

A schema defines the columns that Lasso extracts from your source data. Think of it as a blueprint for your table.

Column definitions

Each column has a key, label, and type:

{
  "key": "product_name",
  "label": "Product Name",
  "type": "text",
  "required": true
}

key — Machine-readable identifier (snake_case). Used to access data in rows.
label — Human-readable name shown in exports and the dashboard.
type — The data type Lasso should extract. See below.
required — Whether this column must have a value for every row.

Column types

Type	Description	Example value
`text`	Plain text	`"iPhone 15 Pro"`
`number`	Integer or decimal	`999.99`
`url`	Valid URL	`"https://example.com"`
`email`	Email address	`"info@example.com"`
`date`	Date or datetime	`"2025-03-10"`
`boolean`	True or false	`true`
`richtext`	HTML or markdown	`"<p>Description</p>"`
`enum`	One of predefined values	`"electronics"`
`tags`	Array of strings	`["wireless", "bluetooth"]`
`image`	Single image URL	`"https://cdn.example.com/img.jpg"`
`images`	Array of image URLs	`["https://...1.jpg", "https://...2.jpg"]`
`json`	Arbitrary JSON	`{"specs": {"weight": "187g"}}`

Auto-generated schemas

If you are not sure what columns to define, you can let the AI generate a schema from sample data:

const schema = await client.schemas.generate({
  sample_data: "Product: iPhone 15 Pro, Price: $999, Color: Titanium",
  name: "Smartphones",
});

console.log(schema.columns);

schema = client.schemas.generate(
    sample_data="Product: iPhone 15 Pro, Price: $999, Color: Titanium",
    name="Smartphones",
)

print(schema["columns"])

The AI analyzes your sample and infers appropriate column keys, labels, and types.

Reusing schemas

Schemas are reusable across multiple tables. Create a schema once, then reference it by ID when creating new tables. You can update a schema at any time — existing tables retain their original column definitions.