dspy-advanced-module-composition

# DSPy Advanced Module Composition ## Goal Compose complex DSPy programs using the Ensemble optimizer, MultiChainComparison for reasoning synthesis, and sequential module patterns. ## When to Use - Need consensus from multiple approaches - Comparing different reasoning strategies - Building robust pipelines with fallbacks - Complex multi-step workflows with branching - Ensemble methods for improved accuracy ## Related Skills - Design modules: [dspy-custom-module-design](../dspy-custom-module-design/SKILL.md) - Define signatures: [dspy-signature-designer](../dspy-signature-designer/SKILL.md) - Evaluate performance: [dspy-evaluation-suite](../dspy-evaluation-suite/SKILL.md) ## Inputs | Input | Type | Description | |-------|------|-------------| | `modules` | `list[dspy.Module]` | Modules to compose | | `composition_type` | `str` | "ensemble", "sequential", "comparison" | ## Outputs | Output | Type | Description | |--------|------|-------------| | `composed_program` | `dspy.Module` | Composed multi-module program | ## Workflow ### Phase 1: Ensemble Voting Combine multiple programs using the Ensemble optimizer: ```python import dspy from dspy.teleprompt import Ensemble dspy.configure(lm=dspy.LM("openai/gpt-4o-mini")) # Define a signature for the task class BasicQA(dspy.Signature): """Answer questions with short factoid answers.""" question = dspy.InputField() answer = dspy.OutputField() # Create multiple program instances (should be optimized/compiled...