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
• Define signatures: dspy-signature-designer
• Evaluate performance: dspy-evaluation-suite

Inputs

Outputs

Workflow

Phase 1: Ensemble Voting

Combine multiple programs using the Ensemble optimizer:

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 programs)
# For simple demonstration, we'll use different predictors
program1 = dspy.Predict(BasicQA)
program2 = dspy.ChainOfThought(BasicQA)
program3 = dspy.Predict(BasicQA)

# Ensemble is an optimizer that compiles programs together
ensemble = Ensemble(reduce_fn=dspy.majority)
ensembled_program = ensemble.compile([program1, program2, program3])

# Use the ensembled program
result = ensembled_program(question="What is 2 + 2?")
print(result.answer)  # Voted answer

Phase 2: MultiChainComparison

Compare multiple reasoning attempts:

import dspy

class BasicQA(dspy.Signature):
    """Answer questions with short factoid answers."""
    question = dspy.InputField()
    answer = dspy.OutputField(desc="often between 1 and 5 words")

class ComparisonPipeline(dspy.Module):
    def __init__(self):
        # Generate multiple reasoning attempts
        self.cot = dspy.ChainOfThought(BasicQA)

        # Compare M attempts and select best
        # Must pass a Signature class, not a string
        self.compare = dspy.MultiChainComparison(
            BasicQA,
            M=3,  # Number of attempts to compare
            temperature=0.7
        )

    def forward(self, question):
        # Generate multiple completions to compare
        # Each completion must have rationale/​reasoning field
        completions = [
            self.cot(question=question)
            for _ in range(3)
        ]

        # MultiChainComparison synthesizes them into best answer
        # Pass completions as positional arg, not keyword arg
        return self.compare(completions, question=question)

# Usage
dspy.configure(lm=dspy.LM("openai/​gpt-4o-mini"))
pipeline = ComparisonPipeline()
result = pipeline(question="Explain quantum computing")
print(f"Best answer: {result.answer}")
print(f"Rationale: {result.rationale}")

Phase 3: Sequential Composition

Chain modules for multi-step workflows:

import dspy

# Define signatures for each step
class QueryRewrite(dspy.Signature):
    """Rewrite a question for better retrieval."""
    question = dspy.InputField()
    refined_query: str = dspy.OutputField()

class GenerateAnswer(dspy.Signature):
    """Generate answer from context and question."""
    context = dspy.InputField()
    question = dspy.InputField()
    answer = dspy.OutputField()

class ValidateAnswer(dspy.Signature):
    """Validate answer quality."""
    answer = dspy.InputField()
    question = dspy.InputField()
    is_valid: bool = dspy.OutputField()
    confidence: float = dspy.OutputField()

class SequentialRAG(dspy.Module):
    """Multi-step RAG pipeline."""

    def __init__(self):
        # Step 1: Query rewriting
        self.rewrite = dspy.Predict(QueryRewrite)

        # Step 2: Retrieval
        self.retrieve = dspy.Retrieve(k=5)

        # Step 3: Answer generation
        self.generate = dspy.ChainOfThought(GenerateAnswer)

        # Step 4: Validation
        self.validate = dspy.Predict(ValidateAnswer)

    def forward(self, question):
        # Sequential execution
        refined = self.rewrite(question=question)
        passages = self.retrieve(refined.refined_query).passages

        answer_pred = self.generate(
            context=passages,
            question=question
        )

        validation = self.validate(
            answer=answer_pred.answer,
            question=question
        )

        return dspy.Prediction(
            answer=answer_pred.answer,
            is_valid=validation.is_valid,
            confidence=validation.confidence
        )

# Usage
dspy.configure(lm=dspy.LM("openai/​gpt-4o-mini"))
rag = SequentialRAG()
result = rag(question="What causes lightning?")
print(f"Answer: {result.answer} (valid: {result.is_valid})")

Phase 4: Fallback Strategies

Handle failures with fallback modules:

import dspy
import logging

logger = logging.getLogger(__name__)

class BasicQA(dspy.Signature):
    """Answer questions with short factoid answers."""
    question = dspy.InputField()
    answer = dspy.OutputField()

class RobustQA(dspy.Module):
    """Fallback strategy for errors."""

    def __init__(self):
        self.primary = dspy.ChainOfThought(BasicQA)
        self.fallback = dspy.Predict(BasicQA)

    def forward(self, question):
        try:
            result = self.primary(question=question)
            if result.answer and len(result.answer) > 10:
                return result
        except Exception as e:
            logger.error(f"Primary failed: {e}")

        return self.fallback(question=question)

Production Example

import dspy
from dspy.teleprompt import BootstrapFewShot, Ensemble

class GenerateAnswer(dspy.Signature):
    """Generate answer from context and question."""
    context = dspy.InputField()
    question = dspy.InputField()
    answer = dspy.OutputField()

class MultiStrategyQA(dspy.Module):
    """Production QA with retrieval."""

    def __init__(self):
        self.retrieve = dspy.Retrieve(k=3)
        self.generate = dspy.ChainOfThought(GenerateAnswer)

    def forward(self, question: str):
        context = self.retrieve(question).passages
        return self.generate(context=context, question=question)

# Usage with optimization
dspy.configure(lm=dspy.LM("openai/​gpt-4o-mini"))
qa = MultiStrategyQA()

# First, optimize the base program
optimizer = BootstrapFewShot(
    metric=lambda ex, pred, trace: ex.answer in pred.answer,
    max_bootstrapped_demos=3
)

compiled_qa = optimizer.compile(qa, trainset=trainset)

# Then create ensemble from multiple optimized programs
# (train with different seeds or optimizers to get diversity)
program1 = optimizer.compile(qa, trainset=trainset)
program2 = optimizer.compile(qa, trainset=trainset)
program3 = optimizer.compile(qa, trainset=trainset)

ensemble = Ensemble(reduce_fn=dspy.majority)
final_program = ensemble.compile([program1, program2, program3])

Best Practices

1. Test modules independently - Validate each module before composition
2. Handle failures gracefully - Use try/​except in parallel composition
3. Balance cost vs accuracy - Ensembles are expensive (N × cost)
4. Optimize composed programs - Use BootstrapFewShot or MIPROv2 on final composition
5. Module reusability - Design modules to work in multiple compositions

Limitations

• Ensemble increases cost linearly with module count
• Voting strategies may not work for all output types
• Sequential composition amplifies latency
• Error propagation in chains can be hard to debug
• Parallel composition requires careful state management

Official Documentation

• DSPy Documentation: https://dspy.ai/
• DSPy GitHub: https://github.com/stanfordnlp/dspy
• Modules Guide: https://dspy.ai/learn/programming/modules/