Types in, types out: extraction with FunctAI¶

import functai
functai.configure(lm="gpt-4.1-mini", temperature=0)

from functai import ai, _ai

@ai
def fruits(text: str) -> list[str]:
    """The fruits mentioned, in order."""
    ...

fruits("I'll go shopping for 2 apples, one orange and a dozen bananas.")

['apples', 'orange', 'bananas']

@ai
def quantities(text: str) -> dict[str, int]:
    """How many of each fruit, by fruit name."""
    ...

quantities("I'll go shopping for 2 apples, one orange and a dozen bananas.")

{'apples': 2, 'orange': 1, 'bananas': 12}

@ai
def total_items(text: str) -> int:
    """The total number of items to buy."""
    counts: dict[str, int] = _ai["How many of each item."]
    return sum(counts.values())

total_items("I'll go shopping for 2 apples, one orange and a dozen bananas.")

15

total_items.predict("2 apples, one orange and a dozen bananas")

Prediction(counts={'apples': 2, 'oranges': 1, 'bananas': 12})

@ai
def critique_and_improve(text: str) -> tuple[str, str]:
    """Criticize the text constructively, then improve it."""
    critique: str = _ai["What is unclear or impolite, in one sentence."]
    improved: str = _ai["The improved text."]
    return critique, improved

critique, improved = critique_and_improve("U should fix this asap, it's broken.")
print(critique)
print(improved)

The text is too informal and lacks politeness, which may come across as rude or demanding.
Could you please fix this as soon as possible? It appears to be broken. Thank you!

from enum import Enum
from typing import Literal

class Priority(Enum):
    LOW = "low"
    MEDIUM = "medium"
    HIGH = "high"

@ai
def priority(issue: str) -> Priority:
    """How urgent the issue is."""
    ...

@ai
def sentiment(review: str) -> Literal["positive", "negative", "mixed"]:
    """The review's overall sentiment."""
    ...

priority("The production database is down."), sentiment("Great food, rude waiter.")

(<Priority.HIGH: 'high'>, 'mixed')

from dataclasses import dataclass

@dataclass
class Address:
    street: str
    city: str
    country: Literal["US", "CA"]

@dataclass
class Patient:
    name: str               # full name
    age: int
    address: Address | None # None when the note gives no address

@ai
def extract_patient(clinical_note: str) -> Patient:
    """Extract the patient's details from the clinical note."""
    ...

extract_patient("John Doe, 45, lives at 123 Main St, Anytown. US resident.")

Patient(name='John Doe', age=45, address=Address(street='123 Main St', city='Anytown', country='US'))

extract_patient("Seen today: Marie Tremblay, 62 years old. No address on file.")

Patient(name='Marie Tremblay', age=62, address=None)

from pydantic import BaseModel, Field

class LineItem(BaseModel):
    description: str
    quantity: int
    unit_price: float

class Invoice(BaseModel):
    number: str
    vendor: str
    items: list[LineItem]
    currency: str = Field(description="ISO 4217 code, e.g. USD")

@ai
def extract_invoice(document: str) -> Invoice:
    """Extract the invoice."""
    ...

invoice = extract_invoice("""
    INVOICE INV-2025-101 from TechCorp Inc.
    5 x Laptop @ 1000.00
    2 x Monitor @ 300.00
    All amounts in US dollars.
""")
invoice

Invoice(number='INV-2025-101', vendor='TechCorp Inc.', items=[LineItem(description='Laptop', quantity=5, unit_price=1000.0), LineItem(description='Monitor', quantity=2, unit_price=300.0)], currency='USD')

@ai
def invoice_total(invoice: Invoice) -> float:
    """The invoice's total amount."""
    ...

invoice_total(invoice)

5600.0

p = extract_patient.predict("Jane Roe, 30, 5 Queen St, Toronto, Canada.")
p.result, p.repairs, p.attempts

(Patient(name='Jane Roe', age=30, address=Address(street='5 Queen St', city='Toronto', country='CA')),
 [],
 1)