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teleprompt.BootstrapFewShot

Constructor​

The constructor initializes the BootstrapFewShot class and sets up parameters for bootstrapping.

class BootstrapFewShot(Teleprompter):
    def __init__(self, metric=None, teacher_settings={}, max_bootstrapped_demos=4, max_labeled_demos=16, max_rounds=1):
        self.metric = metric
        self.teacher_settings = teacher_settings

        self.max_bootstrapped_demos = max_bootstrapped_demos
        self.max_labeled_demos = max_labeled_demos
        self.max_rounds = max_rounds

Parameters:

  • metric (callable, optional): Metric function to evaluate examples during bootstrapping. Defaults to None.
  • teacher_settings (dict, optional): Settings for teacher predictor. Defaults to empty dictionary.
  • max_bootstrapped_demos (int, optional): Maximum number of bootstrapped demonstrations per predictor. Defaults to 4.
  • max_labeled_demos (int, optional): Maximum number of labeled demonstrations per predictor. Defaults to 16.
  • max_rounds (int, optional): Maximum number of bootstrapping rounds. Defaults to 1.

Method​

compile(self, student, *, teacher=None, trainset, valset=None)​

This method compiles the BootstrapFewShot instance by performing bootstrapping to refine the student predictor.

This process includes preparing the student and teacher predictors, which involves creating predictor copies, verifying the student predictor is uncompiled, and compiling the teacher predictor with labeled demonstrations via LabeledFewShot if the teacher predictor hasn't been compiled.

The next stage involves preparing predictor mappings by validating that both the student and teacher predictors have the same program structure and the same signatures but are different objects.

The final stage is performing the bootstrapping iterations.

Parameters:

  • student (Teleprompter): Student predictor to be compiled.
  • teacher (Teleprompter, optional): Teacher predictor used for bootstrapping. Defaults to None.
  • trainset (list): Training dataset used in bootstrapping.
  • valset (list, optional): Validation dataset used in compilation. Defaults to None.

Returns:

  • The compiled student predictor after bootstrapping with refined demonstrations.

Example​

#Assume defined trainset
#Assume defined RAG class
...

#Define teleprompter and include teacher
teacher = dspy.OpenAI(model='gpt-3.5-turbo', api_key = openai.api_key, api_provider = "openai", model_type = "chat")
teleprompter = BootstrapFewShot(teacher_settings=dict({'lm': teacher}))

# Compile!
compiled_rag = teleprompter.compile(student=RAG(), trainset=trainset)