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dspy.KNNFewShot

dspy.KNNFewShot(k: int, trainset: list[Example], vectorizer: Embedder, **few_shot_bootstrap_args)

Bases: Teleprompter

KNNFewShot is an optimizer that uses an in-memory KNN retriever to find the k nearest neighbors in a trainset at test time. For each input example in a forward call, it identifies the k most similar examples from the trainset and attaches them as demonstrations to the student module.

Parameters:

Name Type Description Default
k int

The number of nearest neighbors to attach to the student model.

 required
trainset list[Example]

The training set to use for few-shot prompting.

 required
vectorizer Embedder

The Embedder to use for vectorization

 required
**few_shot_bootstrap_args

Additional arguments for the BootstrapFewShot optimizer.

 {}
Example

import dspy from sentence_transformers import SentenceTransformer

qa = dspy.ChainOfThought("question -> answer") trainset = [dspy.Example(question="What is the capital of France?", answer="Paris").with_inputs("question"), ...] knn_few_shot = KNNFewShot(k=3, trainset=trainset, vectorizer=dspy.Embedder(SentenceTransformer("all-MiniLM-L6-v2").encode)) compiled_qa = knn_few_shot.compile(qa) compiled_qa("What is the capital of Belgium?")

Source code in dspy/teleprompt/knn_fewshot.py 
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def __init__(self, k: int, trainset: list[Example], vectorizer: Embedder, **few_shot_bootstrap_args):
    """
    KNNFewShot is an optimizer that uses an in-memory KNN retriever to find the k nearest neighbors
    in a trainset at test time. For each input example in a forward call, it identifies the k most
    similar examples from the trainset and attaches them as demonstrations to the student module.

    Args:
        k: The number of nearest neighbors to attach to the student model.
        trainset: The training set to use for few-shot prompting.
        vectorizer: The `Embedder` to use for vectorization
        **few_shot_bootstrap_args: Additional arguments for the `BootstrapFewShot` optimizer.

    Example:
        >>> import dspy
        >>> from sentence_transformers import SentenceTransformer
        >>>
        >>> qa = dspy.ChainOfThought("question -> answer")
        >>> trainset = [dspy.Example(question="What is the capital of France?", answer="Paris").with_inputs("question"), ...]
        >>> knn_few_shot = KNNFewShot(k=3, trainset=trainset, vectorizer=dspy.Embedder(SentenceTransformer("all-MiniLM-L6-v2").encode))
        >>> compiled_qa = knn_few_shot.compile(qa)
        >>> compiled_qa("What is the capital of Belgium?")
    """
    self.KNN = KNN(k, trainset, vectorizer=vectorizer)
    self.few_shot_bootstrap_args = few_shot_bootstrap_args

Functions

compile(student, *, teacher=None)

Source code in dspy/teleprompt/knn_fewshot.py 
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def compile(self, student, *, teacher=None):
    student_copy = student.reset_copy()

    def forward_pass(_, **kwargs):
        knn_trainset = self.KNN(**kwargs)
        few_shot_bootstrap = BootstrapFewShot(**self.few_shot_bootstrap_args)
        compiled_program = few_shot_bootstrap.compile(
            student,
            teacher=teacher,
            trainset=knn_trainset,
        )
        return compiled_program(**kwargs)

    student_copy.forward = types.MethodType(forward_pass, student_copy)
    return student_copy