Gmail Smart Compose Architecture

class SmartComposeService:
    def __init__(self, config):
        # Core models
        self.completion_model = self.load_completion_model()
        self.context_encoder = ContextEncoder()
        self.personalization_engine = PersonalizationEngine()
        self.quality_filter = QualityFilter()
        
        # Infrastructure
        self.cache_manager = DistributedCache()
        self.feature_store = FeatureStore()
        self.metrics_collector = MetricsCollector()
        
        # Performance optimization
        self.batch_processor = BatchProcessor(max_latency_ms=50)
        self.model_cache = ModelCache()
        
    async def generate_suggestions(self, 
                                 email_context: EmailContext,
                                 user_context: UserContext,
                                 cursor_position: int) -> List[Suggestion]:
        """Generate real-time email completion suggestions"""
        
        request_start = time.time()
        
        try:
            # Step 1: Context processing (5-10ms)
            processed_context = await self.process_context(
                email_context, user_context, cursor_position
            )
            
            # Step 2: Feature extraction (5-10ms)
            features = await self.extract_features(processed_context)
            
            # Step 3: Model inference (30-50ms)
            raw_suggestions = await self.generate_completions(
                processed_context, features
            )
            
            # Step 4: Personalization (5-10ms)
            personalized_suggestions = await self.personalization_engine.adapt(
                raw_suggestions, user_context
            )
            
            # Step 5: Quality filtering (5-10ms)
            filtered_suggestions = await self.quality_filter.filter(
                personalized_suggestions, processed_context
            )
            
            # Step 6: Ranking and selection (5ms)
            final_suggestions = self.rank_suggestions(
                filtered_suggestions, features
            )
            
            # Record metrics
            total_latency = time.time() - request_start
            await self.record_request_metrics(total_latency, len(final_suggestions))
            
            return final_suggestions[:3]  # Return top 3 suggestions
            
        except Exception as e:
            # Graceful degradation
            await self.record_error(e)
            return []  # Return empty suggestions on error
    
    async def process_context(self, 
                            email_context: EmailContext,
                            user_context: UserContext,
                            cursor_position: int) -> ProcessedContext:
        """Process email and user context for completion"""
        
        # Extract email thread context
        thread_context = await self.extract_thread_context(email_context)
        
        # Parse current email state
        current_email = self.parse_current_email(
            email_context.current_draft,
            cursor_position
        )
        
        # Extract relevant user patterns
        user_patterns = await self.feature_store.get_user_features(
            user_context.user_id,
            features=['writing_style', 'common_phrases', 'language_preference']
        )
        
        return ProcessedContext(
            thread_context=thread_context,
            current_email=current_email,
            user_patterns=user_patterns,
            recipient_context=email_context.recipients,
            timestamp=email_context.timestamp
        )
    
    async def generate_completions(self, 
                                 context: ProcessedContext,
                                 features: Dict) -> List[RawSuggestion]:
        """Generate completion suggestions using transformer model"""
        
        # Prepare model input
        model_input = self.prepare_model_input(context, features)
        
        # Check cache first
        cache_key = self.create_cache_key(model_input)
        cached_result = await self.cache_manager.get(cache_key)
        
        if cached_result:
            return cached_result
        
        # Generate suggestions with batching for efficiency
        suggestions = await self.batch_processor.process_request(
            model_input,
            generation_params={
                'max_tokens': 20,
                'temperature': 0.3,
                'top_p': 0.9,
                'num_return_sequences': 5
            }
        )
        
        # Cache results
        await self.cache_manager.set(
            cache_key, 
            suggestions, 
            ttl_seconds=300  # 5 minute cache
        )
        
        return suggestions
    
    def prepare_model_input(self, 
                          context: ProcessedContext, 
                          features: Dict) -> ModelInput:
        """Prepare input for completion model"""
        
        # Create context template
        context_template = self.build_context_template(context)
        
        # Add special tokens
        input_text = f"""
        <thread_context>{context.thread_context}</thread_context>
        <current_email>{context.current_email.text_before_cursor}</current_email>
        <recipients>{', '.join(context.recipient_context)}</recipients>
        <user_style>{context.user_patterns.get('writing_style', 'formal')}</user_style>
        <continue>
        """
        
        # Tokenize and prepare for model
        tokens = self.completion_model.tokenizer.encode(
            input_text,
            max_length=512,  # Keep context manageable
            truncation=True,
            return_tensors='pt'
        )
        
        return ModelInput(
            input_ids=tokens,
            attention_mask=torch.ones_like(tokens),
            context_features=features
        )

class BatchProcessor:
    """Efficient batch processing for real-time inference"""
    
    def __init__(self, max_latency_ms: int = 50, max_batch_size: int = 32):
        self.max_latency_ms = max_latency_ms
        self.max_batch_size = max_batch_size
        self.pending_requests = []
        self.processing = False
        
        # Start processing loop
        asyncio.create_task(self.processing_loop())
    
    async def process_request(self, model_input: ModelInput, 
                            generation_params: Dict) -> List[RawSuggestion]:
        """Add request to batch processing queue"""
        
        result_future = asyncio.Future()
        
        self.pending_requests.append(BatchRequest(
            model_input=model_input,
            generation_params=generation_params,
            result_future=result_future,
            timestamp=time.time()
        ))
        
        return await result_future
    
    async def processing_loop(self):
        """Main batch processing loop"""
        
        while True:
            if not self.pending_requests:
                await asyncio.sleep(0.001)
                continue
            
            # Check if we should process now
            oldest_request_age = time.time() - self.pending_requests[0].timestamp
            should_process = (
                len(self.pending_requests) >= self.max_batch_size or
                oldest_request_age * 1000 > self.max_latency_ms
            )
            
            if should_process and not self.processing:
                await self.process_batch()
    
    async def process_batch(self):
        """Process current batch of requests"""
        
        if not self.pending_requests:
            return
        
        self.processing = True
        
        try:
            # Extract batch
            batch = self.pending_requests[:self.max_batch_size]
            self.pending_requests = self.pending_requests[self.max_batch_size:]
            
            # Process batch
            batch_results = await self.run_inference_batch(batch)
            
            # Return results
            for request, result in zip(batch, batch_results):
                request.result_future.set_result(result)
                
        except Exception as e:
            # Return errors to all requests
            for request in batch:
                request.result_future.set_exception(e)
        finally:
            self.processing = False