LabeledFewShot

This page is outdated and may not be fully accurate in DSPy 2.5

Constructor

The constructor initializes the LabeledFewShot class and sets up its attributes, particularly defining k number of samples to be used by the predictor.

class LabeledFewShot(Teleprompter):
    def __init__(self, k=16):
        self.k = k

Parameters: - k (int): Number of samples to be used for each predictor. Defaults to 16.

Method

compile(self, student, *, trainset)

This method compiles the LabeledFewShot instance by configuring the student predictor. It assigns subsets of the trainset in each student's predictor's demos attribute. If the trainset is empty, the method returns the original student.

Parameters: - student (Teleprompter): Student predictor to be compiled. - trainset (list): Training dataset for compiling with student predictor.

Returns: - The compiled student predictor with assigned training samples for each predictor or the original student if the trainset is empty.

Example

import dspy

#Assume defined trainset
class RAG(dspy.Module):
    def __init__(self, num_passages=3):
        super().__init__()

        #declare retrieval and predictor modules
        self.retrieve = dspy.Retrieve(k=num_passages)
        self.generate_answer = dspy.ChainOfThought(GenerateAnswer)

    #flow for answering questions using predictor and retrieval modules
    def forward(self, question):
        context = self.retrieve(question).passages
        prediction = self.generate_answer(context=context, question=question)
        return dspy.Prediction(context=context, answer=prediction.answer)

#Define teleprompter
teleprompter = LabeledFewShot()

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