Caching & Performance

Introduction

Language model calls are expensive. Optimizing your DSPy pipeline is essential for production viability. This guide covers caching strategies, batch processing, and performance monitoring.

Caching Strategies

1. Result Caching

The simplest form: cache inputs to outputs.

class ResultCache:
    def get(self, module, args):
        key = self._generate_key(module, args)
        return self.backend.get(key)

2. Semantic Caching

Using embeddings to find similar queries and return cached results, increasing hit rates.

class SemanticCache:
    def get(self, query):
        embedding = self.model.encode(query)
        # Find cosine similarity > threshold...
        pass

Batching & Bulk Processing

Processing requests in parallel drastically improves throughput.

class BatchProcessor:
    def process_batch(self, items, func):
        with ThreadPoolExecutor() as executor:
            return list(executor.map(func, items))

Performance Monitoring

You can't optimize what you don't measure. Use a dashboard to track latency and costs.

class PerformanceDashboard:
    def record_metrics(self, latency, tokens):
        self.metrics['latency'].append(latency)
        self.metrics['cost'].append(calculate_cost(tokens))