Object - dottxt docs

Use "type": "object" for keyed JSON structures.

Core object keywords

Keyword	What it does
`properties`	Declares named fields and their schemas
`patternProperties`	Applies schemas to keys matching a regex
`required`	Lists fields that must be present
`additionalProperties`	Controls unknown keys
`minProperties`	Minimum number of properties
`maxProperties`	Maximum number of properties
`propertyNames`	Schema that every property name must satisfy

unevaluatedProperties is not supported right now. If you have a use case that requires it, reach out to us.

Example

{
  "type": "object",
  "properties": {
    "name": { "type": "string", "minLength": 1 },
    "age": { "type": "integer", "minimum": 0 },
    "email": { "type": "string", "format": "email" }
  },
  "required": ["name", "email"],
  "additionalProperties": false
}

from pydantic import BaseModel, ConfigDict, EmailStr, Field

class Payload(BaseModel):
    model_config = ConfigDict(extra="forbid")

    name: str = Field(..., min_length=1)
    age: int = Field(0, ge=0)
    email: EmailStr

import { z } from "zod";

const payloadSchema = z
  .object({
    name: z.string().min(1),
    age: z.number().int().min(0).optional(),
    email: z.string().email(),
  })
  .strict();

Set additionalProperties: false when you want strict, predictable output shape.

`patternProperties`

Use regex-based key validation for dynamic maps:

JSON Schema

{
  "type": "object",
  "patternProperties": {
    "^S_[A-Z0-9]+$": { "type": "string" },
    "^I_[A-Z0-9]+$": { "type": "integer", "minimum": 0 }
  },
  "additionalProperties": false
}

Pydantic and Zod examples are omitted here because there is no direct keyword mapping for patternProperties.

`title`, `description`, `default`, and `examples`

Use annotation keywords to document intent and provide generation hints:

{
  "type": "object",
  "title": "Customer Profile",
  "description": "Customer profile returned by the extraction pipeline.",
  "default": {
    "name": "Unknown Customer",
    "email": "unknown@example.com"
  },
  "examples": [
    {
      "name": "Alice Johnson",
      "email": "alice@example.com"
    }
  ],
  "properties": {
    "name": {
      "type": "string",
      "description": "Full legal name."
    },
    "email": {
      "type": "string",
      "format": "email",
      "examples": ["alice@example.com"]
    }
  },
  "required": ["name", "email"],
  "additionalProperties": false
}

from pydantic import BaseModel, ConfigDict, EmailStr, Field

class Payload(BaseModel):
    """Customer profile returned by the extraction pipeline."""
    model_config = ConfigDict(
        extra="forbid",
        json_schema_extra={
            "examples": [
                {
                    "name": "Alice Johnson",
                    "email": "alice@example.com",
                }
            ]
        },
    )

    name: str = Field(description="Full legal name.")
    email: EmailStr = Field(
        json_schema_extra={"examples": ["alice@example.com"]}
    )

import { z } from "zod";

const payloadSchema = z
  .object({
    name: z.string().describe("Full legal name."),
    email: z.string().email(),
  })
  .strict()
  .describe("Customer profile returned by the extraction pipeline.");

These annotation keywords are ignored by structured generation and do not constrain the output. However, they can influence generation if they are included in the prompt.

​Core object keywords

​Example

​patternProperties

​title, description, default, and examples

​Related

Core object keywords

Example

`patternProperties`

`title`, `description`, `default`, and `examples`

Related