feather-structured-output 0.1.1

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feather-structured-output

feather-structured-output is a lightweight Python library for turning LLM text responses into validated Python dictionaries.

It does not call an LLM, retry requests, or manage conversation history. It only handles schema definition, short JSON prompt generation, JSON extraction, field validation, normalization, and retry-friendly error messages.

Installation

This repository uses uv.

uv run pytest

Basic Usage

from feather_structured_output import IntField, Schema, StringField

schema = Schema(
    instruction="Extract the following JSON.",
    fields=[
        StringField(
            source_label="Company name",
            output_name="name",
            description="Use the formal company name.",
            example="Acme Corp",
        ),
        IntField(
            source_label="Quantity",
            output_name="quantity",
            gt=0,
        ),
    ],
)

prompt = schema.dump_prompt()

prompt becomes a compact JSON example with comments:

Extract the following JSON.

{
  "name": "Acme Corp", // Company name。String. Use the formal company name. Required.
  "quantity": 1 // Quantity。Integer. Greater than 0. Required.
}

Pass that prompt to any LLM client yourself, then validate the response:

result = schema.extract('{"name": "Acme Corp", "quantity": 3}')

if result.ok:
    data = result.data
else:
    retry_prompt = result.error_message

Fields

Available field types:

• StringField
• IntField
• FloatField
• BooleanField
• ObjectField
• UnionField

Common field options:

• source_label: label from the source document or UI; optional
• output_name: JSON key; if omitted, source_label is used
• description: extra instruction for the LLM
• example: sample value in the generated prompt
• allowed_values: fixed allowed values for the field
• presence: missing value policy
• repeatable, min_items, max_items: array support

At least one of output_name or source_label is required when a field is used in a Schema.

Presence Policy

from feather_structured_output import PresencePolicy, StringField

StringField(
    output_name="note",
    presence=PresencePolicy.NULLABLE,
)

Policies:

• REQUIRED: value is required
• OMITTABLE: missing value is omitted from normalized data
• NULLABLE: missing value is normalized to None
• BLANKABLE: missing value is normalized to ""

"" is treated as a missing value during validation.

Arrays and Objects

from feather_structured_output import IntField, ObjectField, StringField

items = ObjectField(
    output_name="items",
    repeatable=True,
    min_items=1,
    fields=[
        StringField(output_name="name"),
        IntField(output_name="quantity", gt=0),
    ],
)

Fixed Values and Unions

Use allowed_values for fixed values. Use UnionField when a value may match more than one field shape.

from feather_structured_output import IntField, StringField, UnionField

dog_age = UnionField(
    output_name="dog_age",
    variants=[
        StringField(allowed_values=["N/A"]),
        StringField(allowed_values=["-"]),
        IntField(gt=0),
    ],
)

JSON Extraction

Schema.extract() accepts raw JSON or a single fenced json code block. Text outside one JSON code block is ignored.

result = schema.extract(
    """Here is the result:
```json
{"name": "Acme Corp", "quantity": 3}
```"""
)

Multiple JSON code blocks, non-JSON code blocks, invalid JSON, and non-object JSON values return ExtractResult with validation errors.

Custom Validators

Use validators for cross-field rules.

from feather_structured_output import IntField, Schema, StringField, ValidationError


def validate_octopus_legs(data: dict[str, object]) -> list[ValidationError]:
    if data.get("pet") == "octopus" and data.get("legs") != 8:
        return [
            ValidationError(
                path="legs",
                message={
                    "en": "legs must be 8 when pet is octopus.",
                    "ja": "pet が octopus の場合、legs は 8 である必要があります。",
                },
                actual=data.get("legs"),
            )
        ]
    return []


schema = Schema(
    fields=[
        StringField(output_name="pet"),
        IntField(output_name="legs", ge=0),
    ],
    validators=[validate_octopus_legs],
)

Validators run only after field validation succeeds. Exceptions raised inside a validator are not converted to LLM-facing validation errors.

Error Messages and Locale

English is the default. Japanese retry messages are available with locale="ja".

schema = Schema(
    locale="ja",
    fields=[IntField(output_name="quantity", gt=0)],
)

result = schema.extract('{"quantity": 0}')
print(result.error_message)

Internal message templates are split by locale under src/feather_structured_output/locales/.