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Comment by juxtaposicion

3 days ago

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?

4 comments

juxtaposicion

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themanmaran  3 days ago

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.

ngrislain  3 days ago

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.

  • juxtaposicion  3 days ago

    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.

    • lyu07282  3 days ago

      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?