Scaffold an Extraction Pipeline

Builds the full pipeline for a new extraction domain: model, fields, example, and tests.

Before Starting

Ask the user:

• Domain / use case — what kind of data to extract
• Fields — list with types, or infer from a sample text
• Provider/model — default ollama/gpt-oss:20b
• Method — one-shot or stepwise (see selection guide below)

Method Selection Guide

Deliverables

1. Pydantic Model

from pydantic import BaseModel, Field

class InvoiceData(BaseModel):
    vendor_name: str = Field(description="Company that issued the invoice")
    total_amount: float = Field(description="Total amount due")
    currency: str = Field(default="USD", description="ISO 4217 currency code")

Rules:

• Field(description=...) on every field — these become LLM instructions
• Type-appropriate defaults: "", 0, 0.0, []
• Optional[T] = None only when a field genuinely might not exist

2. Field Definitions (optional)

Register in prompture/field_definitions.py only if fields are general-purpose. Use the add-field skill.

3. Example Script

Create examples/{domain}extractionexample.py. Use the add-example skill.

4. Tests

Unit test for the Pydantic model + integration test for end-to-end extraction. Use the add-test skill.

Sample Call

from prompture import extract_with_model

result = extract_with_model(
    model_cls=InvoiceData,
    text=invoice_text,
    model_name="ollama/gpt-oss:20b",
    instruction_template="Extract all invoice details from the following document:",
)

invoice = result["model"]    # Pydantic instance
usage = result["usage"]      # Token counts and cost