completion-fn-protocol.md

The Completion Function Protocol

Here are the interfaces needed to implement the completion function protocol. Any implementation of this interface can be used inside oaieval.

Reference implementations:

• OpenAICompletionFn
• LangChainLLMCompletionFn

CompletionFn

Completion functions should implement the CompletionFn interface:

class CompletionFn(Protocol):
    def __call__(
        self,
        prompt: Union[str, list[dict[str, str]]],
        **kwargs,
    ) -> CompletionResult:

We take a prompt representing a single sample from an eval. These prompts can be represented as either a text string or a list of messages in OpenAI Chat format. To work with the existing evals, Completion Function implementations would need to handle both types of inputs, but we provide helper functionality to convert Chat formatted messages into a text string if that is the preferred input for your program:

from evals.prompt.base import CompletionPrompt

# chat_prompt: list[dict[str, str]] -> text_prompt: str
text_prompt = CompletionPrompt(chat_prompt).to_formatted_prompt()

CompletionResult

The completion function should return an object implementing the CompletionResult interface:

class CompletionResult(ABC):
    @abstractmethod
    def get_completions(self) -> list[str]:
        pass

The get_completions method returns a list of string completions. Each element should be considered a unique completion (in most cases this will be a list of length 1).

Using your CompletionFn

This is all that's needed to implement a Completion function that works with our existing Evals, allowing you to more easily evaluate your end-to-end logic on tasks.

See completion-fns.md to see how to register and use your completion function with oaieval.