Quickstart

​

Set up the base prompt

ape-common provides a Prompt class to help you manage your prompt templates. Prompt objects can be instantiated with a dictionary or loaded from a .prompt file. It is also a functor class, which means it can be called like a function to run the prompt with any inputs. (read more about it here).

​

Provide training data

The trainset will be used to evaluate the performance of prompts while Ape is writing new prompts. It will also be used to pick few-shot examples for the prompts.

​

Set up the evaluation metric

You can define your own evaluation metric by subclassing BaseMetric and implementing the compute method. You can also use predefined metrics: JsonMatchMetric, CosineSimilarityMetric, and SemanticF1Metric.

​

Run the optimization

from weavel import Weavel
from ape.common.metrics import JsonMatchMetric

wv = Weavel()

optimized_prompt = await wv.optimize(
    base_prompt=base_prompt,
    models=["claude-3-5-sonnet-20240620", "gpt-4o"],
    metric=JsonMatchMetric(),
    trainset=trainset,
)