DSPy Custom Module Design

Goal

Design production-quality custom DSPy modules with proper architecture, state management, serialization, and testing patterns.

When to Use

• Building reusable DSPy components
• Complex logic beyond built-in modules
• Need custom state management
• Sharing modules across projects
• Production deployment requirements

Related Skills

• Module composition: dspy-advanced-module-composition
• Signature design: dspy-signature-designer
• Optimization: dspy-miprov2-optimizer

Inputs

task_description

str

components

list

state

dict

Outputs

custom_module

dspy.Module

Workflow

Phase 1: Basic Module Structure

All custom modules inherit from

dspy.Module

import dspy

class BasicQA(dspy.Module):
    """Simple question answering module."""

    def __init__(self):
        super().__init__()
        self.predictor = dspy.Predict("question -> answer")

    def forward(self, question):
        """Entry point for module execution."""
        return self.predictor(question=question)

# Usage
dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))
qa = BasicQA()
result = qa(question="What is Python?")
print(result.answer)

Phase 2: Stateful Modules

Modules can maintain state across calls:

import dspy
import logging

logger = logging.getLogger(__name__)

class StatefulRAG(dspy.Module):
    """RAG with query caching."""

    def __init__(self, cache_size=100):
        super().__init__()
        self.retrieve = dspy.Retrieve(k=3)
        self.generate = dspy.ChainOfThought("context, question -> answer")
        self.cache = {}
        self.cache_size = cache_size

    def forward(self, question):
        # Check cache
        if question in self.cache:
            return self.cache[question]

        # Retrieve and generate
        passages = self.retrieve(question).passages
        result = self.generate(context=passages, question=question)

        # Update cache with size limit
        if len(self.cache) >= self.cache_size:
            self.cache.pop(next(iter(self.cache)))
        self.cache[question] = result

        return result

Phase 3: Error Handling and Validation

Production modules need robust error handling:

import dspy
from typing import Optional
import logging

logger = logging.getLogger(__name__)

class RobustClassifier(dspy.Module):
    """Classifier with validation."""

    def __init__(self, valid_labels: list[str]):
        super().__init__()
        self.valid_labels = set(valid_labels)
        self.classify = dspy.Predict("text -> label: str, confidence: float")

    def forward(self, text: str) -> dspy.Prediction:
        if not text or not text.strip():
            return dspy.Prediction(label="unknown", confidence=0.0, error="Empty input")

        try:
            result = self.classify(text=text)

            # Validate label
            if result.label not in self.valid_labels:
                result.label = "unknown"
                result.confidence = 0.0

            return result

        except Exception as e:
            logger.error(f"Classification failed: {e}")
            return dspy.Prediction(label="unknown", confidence=0.0, error=str(e))

Phase 4: Serialization

Modules support save/load:

import dspy

# Save module state
module = MyCustomModule()
module.save("my_module.json")

# Load requires creating instance first, then loading state
loaded = MyCustomModule()
loaded.load("my_module.json")

# For loading entire programs (dspy>=2.6.0)
module.save("./my_module/", save_program=True)
loaded = dspy.load("./my_module/")

Production Example

import dspy
from typing import List, Optional
import logging

logger = logging.getLogger(__name__)

class ProductionRAG(dspy.Module):
    """Production-ready RAG with all best practices."""

    def __init__(
        self,
        retriever_k: int = 5,
        cache_enabled: bool = True,
        cache_size: int = 1000
    ):
        super().__init__()

        # Configuration
        self.retriever_k = retriever_k
        self.cache_enabled = cache_enabled
        self.cache_size = cache_size

        # Components
        self.retrieve = dspy.Retrieve(k=retriever_k)
        self.generate = dspy.ChainOfThought("context, question -> answer")

        # State
        self.cache = {} if cache_enabled else None
        self.call_count = 0

    def forward(self, question: str) -> dspy.Prediction:
        """Execute RAG pipeline with caching."""
        self.call_count += 1

        # Validation
        if not question or not question.strip():
            return dspy.Prediction(
                answer="Please provide a valid question.",
                error="Invalid input"
            )

        # Cache check
        if self.cache_enabled and question in self.cache:
            logger.info(f"Cache hit (call #{self.call_count})")
            return self.cache[question]

        # Execute pipeline
        try:
            passages = self.retrieve(question).passages

            if not passages:
                logger.warning("No passages retrieved")
                return dspy.Prediction(
                    answer="No relevant information found.",
                    passages=[]
                )

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

            # Update cache
            if self.cache_enabled:
                self._update_cache(question, result)

            return result

        except Exception as e:
            logger.error(f"RAG execution failed: {e}")
            return dspy.Prediction(
                answer="An error occurred while processing your question.",
                error=str(e)
            )

    def _update_cache(self, key: str, value: dspy.Prediction):
        """Manage cache with size limit."""
        if len(self.cache) >= self.cache_size:
            self.cache.pop(next(iter(self.cache)))
        self.cache[key] = value

    def clear_cache(self):
        """Clear cache."""
        if self.cache_enabled:
            self.cache.clear()

Best Practices

1. Single responsibility - Each module does one thing well
2. Validate inputs - Check for None, empty strings, invalid types
3. Handle errors - Return Predictions with error fields, never raise
4. Log important events - Cache hits, errors, validation failures
5. Test independently - Unit test modules before composition

Limitations

• State increases memory usage (careful with large caches)
• Serialization doesn't automatically save custom state
• Module testing requires mocking LM calls
• Deep module hierarchies can be hard to debug
• Performance overhead from validation in hot paths

Official Documentation

• DSPy Documentation: https://dspy.ai/
• DSPy GitHub: https://github.com/stanfordnlp/dspy
• Custom Modules Guide: https://dspy.ai/tutorials/custom_module/
• Module API: https://dspy.ai/api/modules/