Fully leveraging pydantic

Annotated and Field

Numbers in a valid range

Pydantic's Field lets us be very specific about what we want from the LLM.

from typing import Annotated
import marvin
from pydantic import Field
from typing_extensions import TypedDict

ActivationField = Field(
    description=(
        "A score between -1 (not descriptive) and 1"
        " (very descriptive) for the given emotion"
    ),
    ge=-1,
    le=1
)

SentimentActivation = Annotated[float, ActivationField]

class DetailedSentiment(TypedDict):
    happy: SentimentActivation
    sad: SentimentActivation
    angry: SentimentActivation
    surprised: SentimentActivation
    amused: SentimentActivation
    scared: SentimentActivation

@marvin.fn
def sentiment_analysis(text: str) -> DetailedSentiment:
    """Analyze the sentiment of a given text"""

sentiment_analysis(
    "dude i cannot believe how hard that"
    " kangaroo just punched that guy 🤣"
    " - he really had it coming, but glad he's ok"
)

Result

{
    'happy': 0.8,
    'sad': -0.1,
    'angry': -0.2,
    'surprised': 0.7,
    'amused': 1.0,
    'scared': -0.1
}

Complex types

Using BaseModel and Field

To parse and validate complex nested types, use BaseModel and Field:

import marvin
from pydantic import BaseModel, Field

class Location(BaseModel):
    city: str
    state: str | None = Field(description="Two-letter state code")
    country: str
    latitute: float | None = Field(
        description="Latitude in degrees",
        ge=-90,
        le=90
    )
    longitude: float | None = Field(
        description="Longitude in degrees",
        ge=-180,
        le=180
    )

class Traveler(BaseModel):
    name: str
    age: int | None = Field(description="Age in years")

class Trip(BaseModel):
    travelers: list[Traveler]
    origin: Location
    destination: Location

trip = marvin.model(Trip)(
    "Marvin and Ford are heading from Chi to SF for their 30th birthdays"
)

Result

Trip(
    travelers=[
        Traveler(name='Marvin', age=30),
        Traveler(name='Ford', age=30)
    ],
    origin=Location(
        city='Chicago',
        state='IL',
        country='USA',
        latitute=41.8781,
        longitude=-87.6298
    ),
    destination=Location(
        city='San Francisco',
        state='CA',
        country='USA',
        latitute=37.7749,
        longitude=-122.4194
    )
)