This looks great; very useful for (example) ranking outputs by confidence so you can do human reviews of the not-confident ones.
Any chance we can get Pydantic support?
Fyi logprobs !== confidence.
If you run "bananas,fishbowl,phonebook," and get {"sponge": 0.76}
It doesn't mean that "placemat" was the 76% correct answer. Just that the word "sponge" was the next most likely word for the model to generate.
Actually, OpenAI provides Pydantic support for structured output (see client.beta.chat.completions.parse in https://platform.openai.com/docs/guides/structured-outputs).
The library is compatible with that but does not use Pydantic further than that.
Right the hope was to go further. E.g. if the input is:
```
class Classification(BaseModel):
color: Literal['red', 'blue', 'green']
then the output type would be:
```
class ClassificationWithLogProbs(BaseModel):
color: Dict[Literal['red', 'blue', 'green'], float]
Don't take this too literally; I'm not convinced that this is the right way to do it. But it would provide structure and scores without dealing with a mess of complex JSON.
but this ultimately just converts to json schema, or the openai function calling definition format.
One question I always had was what about the descriptions you can attach to the class and attributes? ( = Field(description=...) in pydantic) is the model made aware of those descriptions?