Extracting base types

Mirascope also makes it possible to extract base types without defining a pydantic.BaseModel with the same exact format for extraction:

from mirascope.openai import OpenAIExtractor


class BookRecommender(OpenAIExtractor[list[str]]):
    extract_schema: Type[list[str]] = list[str]
    prompt_template = "Please recommend some science fiction books."


books = BookRecommendation().extract()
print(books)
#> ['Dune', 'Neuromancer', "Ender's Game", "The Hitchhiker's Guide to the Galaxy", 'Foundation', 'Snow Crash']

We currently support: str, int, float, bool, list, set, tuple, and Enum.

We also support using Union, Literal, and Annotated

Note

If you’re using mypy you’ll need to add # type: ignore due to how these types are handled differently by Python.

Using Enum or Literal for classification

One nice feature of extracting base types is that we can easily use Enum or Literal to define a set of labels that the model should use to classify the prompt. For example, let’s classify whether or not some email text is spam:

from enum import Enum
# from typing import Literal

from mirascope.openai import OpenAIExtractor

# Label = Literal["is spam", "is not spam"]


class Label(Enum):
    NOT_SPAM = "not_spam"
    SPAM = "spam"


class NotSpam(OpenAIExtractor[Label]):
    extract_schema: Type[Label] = Label
    prompt_template = "Your car insurance payment has been processed. Thank you for your business."


class Spam(OpenAIExtractor[Label]):
    extract_schema: Type[Label] = Label
    prompt_template = "I can make you $1000 in just an hour. Interested?"


# assert NotSpam().extract() == "is not spam"
# assert Spam().extract() == "is spam"
assert NotSpam().extract() == Label.NOT_SPAM
assert Spam().extract() == Label.SPAM