Elixir implementation of DSPy - a framework for algorithmically optimizing LM prompts and weights.

DSPy provides a unified interface for composing LM programs with automatic optimization.

Core Components

• Dspy.Signature - Define typed input/output interfaces for LM calls
• Dspy.Module - Composable building blocks for LM programs
• Dspy.Predict - Basic prediction modules
• Dspy.ChainOfThought - Step-by-step reasoning
• Dspy.LM - Language model client abstraction
• Dspy.Example - Training examples and data structures
• Dspy.Teleprompter - Prompt optimization algorithms

Quick Start

# Configure language model
Dspy.configure(lm: %Dspy.LM.OpenAI{model: "gpt-4.1"})

# Define signature
defmodule QA do
  use Dspy.Signature
  input_field :question, :string, "Question to answer"
  output_field :answer, :string, "Answer to the question"
end

# Create and use module
predict = Dspy.Predict.new(QA)
result = Dspy.Module.forward(predict, %{question: "What is 2+2?"})