Handling Missing Data¶

The Maybe pattern is a concept in functional programming used for error handling. Instead of raising exceptions or returning None, you can use a Maybe type to encapsulate both the result and potential errors.

This pattern is particularly useful when making LLM calls, as providing language models with an escape hatch can effectively reduce hallucinations.

Defining the Model¶

Using Pydantic, we'll first define the UserDetail and MaybeUser classes.

from pydantic import BaseModel, Field
from typing import Optional


class UserDetail(BaseModel):
    age: int
    name: str
    role: Optional[str] = Field(default=None)


class MaybeUser(BaseModel):
    result: Optional[UserDetail] = Field(default=None)
    error: bool = Field(default=False)
    message: Optional[str] = Field(default=None)

    def __bool__(self):
        return self.result is not None

Notice that MaybeUser has a result field that is an optional UserDetail instance where the extracted data will be stored. The error field is a boolean that indicates whether an error occurred, and the message field is an optional string that contains the error message.

Defining the function¶

Once we have the model defined, we can create a function that uses the Maybe pattern to extract the data.

import instructor
import openai
from pydantic import BaseModel, Field
from typing import Optional

# This enables the `response_model` keyword
client = instructor.patch(openai.OpenAI())


class UserDetail(BaseModel):
    age: int
    name: str
    role: Optional[str] = Field(default=None)


class MaybeUser(BaseModel):
    result: Optional[UserDetail] = Field(default=None)
    error: bool = Field(default=False)
    message: Optional[str] = Field(default=None)

    def __bool__(self):
        return self.result is not None


def extract(content: str) -> MaybeUser:
    return client.chat.completions.create(
        model="gpt-3.5-turbo",
        response_model=MaybeUser,
        messages=[
            {"role": "user", "content": f"Extract `{content}`"},
        ],
    )


user1 = extract("Jason is a 25-year-old scientist")
print(user1.model_dump_json(indent=2))
"""
{
  "result": {
    "age": 25,
    "name": "Jason",
    "role": "scientist"
  },
  "error": false,
  "message": null
}
"""

user2 = extract("Unknown user")
print(user2.model_dump_json(indent=2))
"""
{
  "result": null,
  "error": false,
  "message": "Unknown user"
}
"""

As you can see, when the data is extracted successfully, the result field contains the UserDetail instance. When an error occurs, the error field is set to True, and the message field contains the error message.

If you want to learn more about pattern matching, check out Pydantic's docs on Structural Pattern Matching