Chapter 10: DSPy Production Environment Deployment

Key Concepts

1. Production Architecture Design

When deploying DSPy applications to production, proper architectural design is crucial.

Microservices Architecture

import dspy
from typing import List, Dict, Any, Optional
from dataclasses import dataclass, field
import logging
import asyncio
from concurrent.futures import ThreadPoolExecutor
import redis
import json
from datetime import datetime
import hashlib

@dataclass
class ServiceConfig:
    """Service configuration"""
    service_name: str
    host: str = "localhost"
    port: int = 8000
    workers: int = 4
    timeout: int = 30
    max_retries: int = 3
    cache_enabled: bool = True
    cache_ttl: int = 3600
    log_level: str = "INFO"
    metrics_enabled: bool = True

class BaseService:
    """Base service class"""

    def __init__(self, config: ServiceConfig):
        self.config = config
        self.logger = self._setup_logger()
        self.redis_client = None

        if config.cache_enabled:
            self._setup_cache()

        if config.metrics_enabled:
            self._setup_metrics()

    def _setup_logger(self) -> logging.Logger:
        """Configure logger"""
        logger = logging.getLogger(self.config.service_name)
        logger.setLevel(getattr(logging, self.config.log_level))

        handler = logging.StreamHandler()
        formatter = logging.Formatter(
            '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
        )
        handler.setFormatter(formatter)
        logger.addHandler(handler)

        return logger

    def _setup_cache(self):
        """Setup cache"""
        try:
            self.redis_client = redis.Redis(
                host='localhost',
                port=6379,
                decode_responses=False
            )
            self.logger.info("Redis cache initialized successfully")
        except Exception as e:
            self.logger.warning(f"Failed to initialize Redis cache: {e}")
            self.redis_client = None

    def _setup_metrics(self):
        """Setup metrics collection"""
        self.metrics = {
            'requests_total': 0,
            'requests_success': 0,
            'requests_failed': 0,
            'total_latency': 0.0,
            'cache_hits': 0,
            'cache_misses': 0
        }

    def get_cache_key(self, *args, **kwargs) -> str:
        """Generate cache key"""
        key_data = f"{self.config.service_name}:{args}:{kwargs}"
        return hashlib.md5(key_data.encode()).hexdigest()

    def cache_get(self, key: str) -> Optional[Any]:
        """Get from cache"""
        if not self.redis_client:
            return None

        try:
            data = self.redis_client.get(key)
            if data:
                self.metrics['cache_hits'] += 1
                return json.loads(data)
            else:
                self.metrics['cache_misses'] += 1
                return None
        except Exception as e:
            self.logger.error(f"Cache get error: {e}")
            return None

    def cache_set(self, key: str, value: Any, ttl: int = None):
        """Set cache"""
        if not self.redis_client:
            return

        ttl = ttl or self.config.cache_ttl

        try:
            self.redis_client.setex(
                key,
                ttl,
                json.dumps(value)
            )
        except Exception as e:
            self.logger.error(f"Cache set error: {e}")

class DSPyService(BaseService):
    """DSPy service"""

    def __init__(self, config: ServiceConfig):
        super().__init__(config)
        self.executor = ThreadPoolExecutor(max_workers=config.workers)
        self._setup_dspy()

    def _setup_dspy(self):
        """Initialize DSPy"""
        # Configure language model
        lm = dspy.OpenAI(model="gpt-3.5-turbo")
        dspy.settings.configure(lm=lm)

        self.logger.info("DSPy initialized successfully")

    async def process_request(self, request_data: Dict[str, Any]) -> Dict[str, Any]:
        """Process request"""
        import time
        start_time = time.time()

        self.metrics['requests_total'] += 1

        try:
            # Generate cache key
            cache_key = self.get_cache_key(**request_data)

            # Try to get from cache
            if self.config.cache_enabled:
                cached_result = self.cache_get(cache_key)
                if cached_result is not None:
                    self.logger.info(f"Cache hit for request: {cache_key}")
                    return cached_result

            # Execute actual processing
            result = await self._execute_dspy_program(request_data)

            # Cache result
            if self.config.cache_enabled and result.get('success'):
                self.cache_set(cache_key, result)

            self.metrics['requests_success'] += 1

            # Record latency
            latency = time.time() - start_time
            self.metrics['total_latency'] += latency

            result['latency'] = latency

            return result

        except Exception as e:
            self.metrics['requests_failed'] += 1
            self.logger.error(f"Request processing error: {e}")

            return {
                'success': False,
                'error': str(e),
                'latency': time.time() - start_time
            }

    async def _execute_dspy_program(self, request_data: Dict[str, Any]) -> Dict[str, Any]:
        """Execute DSPy program"""
        # Run DSPy program in thread pool
        loop = asyncio.get_event_loop()
        result = await loop.run_in_executor(
            self.executor,
            self._run_dspy_sync,
            request_data
        )

        return result

    def _run_dspy_sync(self, request_data: Dict[str, Any]) -> Dict[str, Any]:
        """Execute DSPy synchronously (placeholder)"""
        # Here you should implement actual DSPy program logic
        # This is just a placeholder
        return {
            'success': True,
            'result': 'Processed successfully',
            'timestamp': datetime.now().isoformat()
        }

    def get_metrics(self) -> Dict[str, Any]:
        """Get service metrics"""
        avg_latency = (
            self.metrics['total_latency'] / self.metrics['requests_total']
            if self.metrics['requests_total'] > 0 else 0.0
        )

        cache_hit_rate = (
            self.metrics['cache_hits'] / (self.metrics['cache_hits'] + self.metrics['cache_misses'])
            if (self.metrics['cache_hits'] + self.metrics['cache_misses']) > 0 else 0.0
        )

        return {
            'service_name': self.config.service_name,
            'requests_total': self.metrics['requests_total'],
            'requests_success': self.metrics['requests_success'],
            'requests_failed': self.metrics['requests_failed'],
            'success_rate': (
                self.metrics['requests_success'] / self.metrics['requests_total']
                if self.metrics['requests_total'] > 0 else 0.0
            ),
            'average_latency': avg_latency,
            'cache_hit_rate': cache_hit_rate,
            'timestamp': datetime.now().isoformat()
        }

class LoadBalancer:
    """Load balancer"""

    def __init__(self, services: List[DSPyService]):
        self.services = services
        self.current_index = 0
        self.logger = logging.getLogger("LoadBalancer")

    async def route_request(self, request_data: Dict[str, Any]) -> Dict[str, Any]:
        """Route request"""
        # Simple round-robin load balancing
        service = self._get_next_service()

        if not service:
            return {
                'success': False,
                'error': 'No available services'
            }

        self.logger.info(f"Routing request to {service.config.service_name}")

        return await service.process_request(request_data)

    def _get_next_service(self) -> Optional[DSPyService]:
        """Get next service"""
        if not self.services:
            return None

        service = self.services[self.current_index]
        self.current_index = (self.current_index + 1) % len(self.services)

        return service

    def get_all_metrics(self) -> List[Dict[str, Any]]:
        """Get metrics from all services"""
        return [service.get_metrics() for service in self.services]

# Usage example
async def demonstrate_production_architecture():
    """Demonstrate production architecture"""

    # Create multiple service instances
    services = []
    for i in range(3):
        config = ServiceConfig(
            service_name=f"dspy_service_{i}",
            port=8000 + i,
            workers=4,
            cache_enabled=True
        )
        service = DSPyService(config)
        services.append(service)

    # Create load balancer
    load_balancer = LoadBalancer(services)

    print("🚀 Production environment started")
    print(f"Services count: {len(services)}")

    # Simulate requests
    requests = [
        {'task': 'classification', 'text': 'This is a test'},
        {'task': 'summarization', 'text': 'This is another test'},
        {'task': 'question_answering', 'question': 'What is DSPy?'}
    ]

    for req in requests:
        result = await load_balancer.route_request(req)
        print(f"Request result: {result}")

    # Get metrics
    print("\n📊 Service metrics:")
    all_metrics = load_balancer.get_all_metrics()
    for metrics in all_metrics:
        print(f"{metrics['service_name']}:")
        print(f"  Total requests: {metrics['requests_total']}")
        print(f"  Success rate: {metrics['success_rate']:.2%}")
        print(f"  Average latency: {metrics['average_latency']:.3f}s")
        print(f"  Cache hit rate: {metrics['cache_hit_rate']:.2%}")

# asyncio.run(demonstrate_production_architecture())

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Through this chapter, you should have mastered all key technologies and best practices for deploying DSPy applications to production environments. In actual projects, select appropriate deployment strategies and optimization measures based on specific requirements, and continuously improve system performance and reliability through monitoring and iteration.