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Jason Liu
100 lines
2.8 KiB
Markdown
100 lines
2.8 KiB
Markdown
# Handling Missing Data
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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.
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This pattern is particularly useful when making LLM calls, as providing language models with an escape hatch can effectively reduce hallucinations.
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## Defining the Model
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Using Pydantic, we'll first define the `UserDetail` and `MaybeUser` classes.
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```python
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from pydantic import BaseModel, Field
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from typing import Optional
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class UserDetail(BaseModel):
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age: int
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name: str
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role: Optional[str] = Field(default=None)
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class MaybeUser(BaseModel):
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result: Optional[UserDetail] = Field(default=None)
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error: bool = Field(default=False)
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message: Optional[str] = Field(default=None)
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def __bool__(self):
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return self.result is not None
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```
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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.
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## Defining the function
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Once we have the model defined, we can create a function that uses the `Maybe` pattern to extract the data.
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```python
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import instructor
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import openai
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from pydantic import BaseModel, Field
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from typing import Optional
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# This enables the `response_model` keyword
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client = instructor.patch(openai.OpenAI())
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class UserDetail(BaseModel):
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age: int
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name: str
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role: Optional[str] = Field(default=None)
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class MaybeUser(BaseModel):
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result: Optional[UserDetail] = Field(default=None)
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error: bool = Field(default=False)
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message: Optional[str] = Field(default=None)
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def __bool__(self):
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return self.result is not None
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def extract(content: str) -> MaybeUser:
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return client.chat.completions.create(
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model="gpt-3.5-turbo",
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response_model=MaybeUser,
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messages=[
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{"role": "user", "content": f"Extract `{content}`"},
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],
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)
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user1 = extract("Jason is a 25-year-old scientist")
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print(user1.model_dump_json(indent=2))
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"""
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{
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"result": {
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"age": 25,
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"name": "Jason",
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"role": "scientist"
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},
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"error": false,
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"message": null
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}
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"""
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user2 = extract("Unknown user")
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print(user2.model_dump_json(indent=2))
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"""
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{
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"result": null,
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"error": false,
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"message": "Unknown user"
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}
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"""
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```
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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.
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If you want to learn more about pattern matching, check out Pydantic's docs on [Structural Pattern Matching](https://docs.pydantic.dev/latest/concepts/models/#structural-pattern-matching)
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