dspex

# Generated by SnakeBridge v0.13.0 - DO NOT EDIT MANUALLY
# Regenerate with: mix compile
# Library: dspy 3.1.2
# Python module: dspy
# Python class: KNNFewShot

defmodule Dspy.KNNFewShot do
  @moduledoc """
  Wrapper for Python class KNNFewShot.
  """
  def __snakebridge_python_name__, do: "dspy"
  def __snakebridge_python_class__, do: "KNNFewShot"
  def __snakebridge_library__, do: "dspy"
  @opaque t :: SnakeBridge.Ref.t()

  @doc """
  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

  - `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.

  ## Examples

      ```python
      import dspy
      from sentence_transformers import SentenceTransformer

      # Define a QA module with chain of thought
      qa = dspy.ChainOfThought("question -> answer")

      # Create a training dataset with examples
      trainset = [
      dspy.Example(question="What is the capital of France?", answer="Paris").with_inputs("question"),
      # ... more examples ...
      ]

      # Initialize KNNFewShot with a sentence transformer model
      knn_few_shot = KNNFewShot(
      k=3,
      trainset=trainset,
      vectorizer=dspy.Embedder(SentenceTransformer("all-MiniLM-L6-v2").encode)
      )

      # Compile the QA module with few-shot learning
      compiled_qa = knn_few_shot.compile(qa)

      # Use the compiled module
      result = compiled_qa("What is the capital of Belgium?")
      ```
  """
  @spec new(
          integer(),
          list(Dspy.Primitives.Example.t()),
          Dspy.Clients.Embedding.Embedder.t(),
          keyword()
        ) :: {:ok, SnakeBridge.Ref.t()} | {:error, Snakepit.Error.t()}
  def new(k, trainset, vectorizer, opts \\ []) do
    SnakeBridge.Runtime.call_class(__MODULE__, :__init__, [k, trainset, vectorizer], opts)
  end

  @doc """
  Optimize the student program.



  ## Parameters

  - `student` - The student program to optimize.
  - `trainset` - The training set to use for optimization.
  - `teacher` - The teacher program to use for optimization.
  - `valset` - The validation set to use for optimization.

  ## Returns

  - `term()`
  """
  @spec compile(SnakeBridge.Ref.t(), term(), keyword()) ::
          {:ok, term()} | {:error, Snakepit.Error.t()}
  def compile(ref, student, opts \\ []) do
    SnakeBridge.Runtime.call_method(ref, :compile, [student], opts)
  end

  @doc """
  Get the parameters of the teleprompter.

  ## Returns

  - `%{optional(String.t()) => term()}`
  """
  @spec get_params(SnakeBridge.Ref.t(), keyword()) ::
          {:ok, %{optional(String.t()) => term()}} | {:error, Snakepit.Error.t()}
  def get_params(ref, opts \\ []) do
    SnakeBridge.Runtime.call_method(ref, :get_params, [], opts)
  end
end