struct-gpt 2.1.0

structured llm outputs

struct-gpt

Features

• Easy creation of custom models using the OpenAI API
• Integration with Pydantic for model validation and serialization
• Flexible configuration with retries and temperature settings

Usage

pip install struct-gpt

---

Template variables in the class' docstring are replaced with the keyword arguments passed to from_openai.

from struct_gpt import OpenAiBase
from pydantic import Field


class SentimentSchema(OpenAiBase):
    """
    Determine the sentiment of the given text:

    {content}
    """

    sentiment: str = Field(description="Either -1, 0, or 1.")


print(SentimentSchema.from_openai(content="I love pizza!").json())

outputs:

{
  "sentiment": "1"
}

Your classes can reference one another. You can also use the OpenAiMixin to add functionality to your own classes.

from struct_gpt import OpenAiBase, OpenAiMixin
from pydantic import Field, BaseModel
from typing import Mapping


class SentimentSchema(OpenAiBase):
    """
    Determine the sentiment of the given text:

    {content}
    """

    sentiment: str = Field(description="Either -1, 0, or 1.")


# you can use the OpenAiMixin to add functionality to your own classes
class SentimentAnalysis(BaseModel, OpenAiMixin):
    """
    Determine the sentiment of each word in the following: {text}

    Also determine the overall sentiment of the text and who the narrator is.
    """

    words_to_sentiment: Mapping[str, SentimentSchema]
    overall_sentiment: SentimentSchema
    narrator: str


print(
    SentimentAnalysis.from_openai(
        text="As president, I loved the beautiful scenery, but the long hike was exhausting."
    ).json(indent=2)
)

See Output

{
  "words_to_sentiment": {
    "As": {
      "sentiment": "0"
    },
    "president,": {
      "sentiment": "1"
    },
    "I": {
      "sentiment": "0"
    },
    "loved": {
      "sentiment": "1"
    },
    "the": {
      "sentiment": "0"
    },
    "beautiful": {
      "sentiment": "1"
    },
    "scenery,": {
      "sentiment": "1"
    },
    "but": {
      "sentiment": "-1"
    },
    "long": {
      "sentiment": "-1"
    },
    "hike": {
      "sentiment": "-1"
    },
    "was": {
      "sentiment": "0"
    },
    "exhausting.": {
      "sentiment": "-1"
    }
  },
  "overall_sentiment": {
    "sentiment": "0"
  },
  "narrator": "president"
}

Improving reliability with examples

create can accept a list of examples to guide the model and improve its accuracy.

Each example is a dictionary containing an input and output key.

The input is an example input and the output is its expected output, which should be an instance of the model or its json string representation.

In this example, we are providing the model with examples of positive and negative sentiments:

from struct_gpt import OpenAiBase
from pydantic import Field


class SentimentSchema(OpenAiBase):
    """
    Determine the sentiment of the given text:

    {content}
    """

    sentiment: str = Field(description="Either -1, 0, or 1.")


examples = [
    {
        "input": "don't touch that",
        "output": '{"sentiment": "-1"}',
    },
    {
        "input": "this library is neat!",
        # the output can be an instance of the model
        "output": SentimentSchema(sentiment="1"),
    },
]

print(SentimentSchema.from_openai(content="I love pizza!", examples=examples).json())

outputs:

{
  "sentiment": "1"
}