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Day 14 · ~12 min●

summarize_and_classify from yesterday returns a single string. A reviewer comment needs more than a scope label — it needs a category (major/minor revision, clarification), an urgency level, and a one-line task summary for your task list. How do you extract all three in one call?

Define a Pydantic model with three fields — category, urgency, and one_line_summary — and pass it as result_type. The model fills in each field exactly, like a structured extraction form.

Exactly. One Pydantic model, three fields, one agent call:

python

from pydantic import BaseModel
class Ticket(BaseModel):
    category: str
    urgency: str
    one_line_summary: str
result = Agent(model, result_type=Ticket).run_sync(text)
return result.output.model_dump()

Can I constrain urgency to high/medium/low inside the Pydantic model, or does that require a separate Literal agent?

Pydantic supports Literal as a field type: urgency: Literal["high", "medium", "low"]. The model must fill in that field with one of those three values — the same guarantee as result_type=Literal[...], but scoped to one field of a larger schema. That's the production pattern for multi-field extraction with constrained values.

So I could have category: Literal["major","minor","clarification"] and urgency: Literal["high","medium","low"] in the same model. Three constrained fields, one call, zero manual triage.

That's the full codebook-compliant extraction form. Every reviewer comment becomes a structured task in your management system.

Forty-page revision request → structured task list in seconds. This is the Reviewer 2 nightmare solved with a Pydantic model.

Each field is immediately useful:

python

ticket = triage_ticket(review_text)
print(ticket["category"], ticket["urgency"])
print(ticket["one_line_summary"])

The model extracts. Your research judgment determines priority and response.

Multi-Field Pydantic Model

python

from pydantic import BaseModel
class Ticket(BaseModel):
    category: str
    urgency: str
    one_line_summary: str

result = Agent(model, result_type=Ticket).run_sync(text)
return result.output.model_dump()

Constrained field values

Add Literal as a field type for guaranteed values:

python

from typing import Literal
class Ticket(BaseModel):
    category: Literal["major", "minor", "clarification"]
    urgency: Literal["high", "medium", "low"]
    one_line_summary: str

Constrained field values

For production triage, use Literal field types in the Pydantic model:

python

from typing import Literal
class Ticket(BaseModel):
    category: Literal["major", "minor", "clarification"]
    urgency: Literal["high", "medium", "low"]
    one_line_summary: str

This gives per-field guarantees without separate Literal agents.

Sign up to practice

Create a free account to get started. Paid plans unlock all tracks.

or

Day 14 · ~12 min●

summarize_and_classify from yesterday returns a single string. A reviewer comment needs more than a scope label — it needs a category (major/minor revision, clarification), an urgency level, and a one-line task summary for your task list. How do you extract all three in one call?

Define a Pydantic model with three fields — category, urgency, and one_line_summary — and pass it as result_type. The model fills in each field exactly, like a structured extraction form.

Exactly. One Pydantic model, three fields, one agent call:

python

from pydantic import BaseModel
class Ticket(BaseModel):
    category: str
    urgency: str
    one_line_summary: str
result = Agent(model, result_type=Ticket).run_sync(text)
return result.output.model_dump()

Can I constrain urgency to high/medium/low inside the Pydantic model, or does that require a separate Literal agent?

Pydantic supports Literal as a field type: urgency: Literal["high", "medium", "low"]. The model must fill in that field with one of those three values — the same guarantee as result_type=Literal[...], but scoped to one field of a larger schema. That's the production pattern for multi-field extraction with constrained values.

So I could have category: Literal["major","minor","clarification"] and urgency: Literal["high","medium","low"] in the same model. Three constrained fields, one call, zero manual triage.

That's the full codebook-compliant extraction form. Every reviewer comment becomes a structured task in your management system.

Forty-page revision request → structured task list in seconds. This is the Reviewer 2 nightmare solved with a Pydantic model.

Each field is immediately useful:

python

ticket = triage_ticket(review_text)
print(ticket["category"], ticket["urgency"])
print(ticket["one_line_summary"])

The model extracts. Your research judgment determines priority and response.

Multi-Field Pydantic Model

python

from pydantic import BaseModel
class Ticket(BaseModel):
    category: str
    urgency: str
    one_line_summary: str

result = Agent(model, result_type=Ticket).run_sync(text)
return result.output.model_dump()

Constrained field values

Add Literal as a field type for guaranteed values:

python

from typing import Literal
class Ticket(BaseModel):
    category: Literal["major", "minor", "clarification"]
    urgency: Literal["high", "medium", "low"]
    one_line_summary: str

Constrained field values

For production triage, use Literal field types in the Pydantic model:

python

from typing import Literal
class Ticket(BaseModel):
    category: Literal["major", "minor", "clarification"]
    urgency: Literal["high", "medium", "low"]
    one_line_summary: str

This gives per-field guarantees without separate Literal agents.

Sign up to practice

Create a free account to get started. Paid plans unlock all tracks.

or