feat: Implement pipeline module - comprehensive pipeline construction and execution

- Implement PipelineBuilder with fluent DSL for pipeline construction
- Implement ExecutionEngine with dependency-aware execution and topological sorting
- Implement FailureHandler with retry mechanisms and multiple strategies (linear, exponential, fixed)
- Implement ParallelismManager with thread/process pool execution support
- Implement ResourceScheduler for CPU, GPU, memory, and disk allocation
- Implement PipelineValidator with circular dependency detection and reachability analysis
- Implement PipelineTemplateManager with pre-built templates (document processing, RAG, KG construction, ontology generation)
- Add pause/resume/stop execution control
- Add progress tracking and time estimation
- Add optional psutil dependency for resource detection with fallback
- Complete all pipeline orchestration and management capabilities

libs/semantica/pipeline/__init__.py

@@ -5,12 +5,99 @@ This module provides comprehensive pipeline construction and orchestration capab
 
 Exports:
     - PipelineBuilder: Pipeline construction DSL
-    - PipelineExecutor: Pipeline execution engine
-    - PipelineMonitor: Pipeline monitoring and management
-    - PipelineOptimizer: Pipeline optimization engine
+    - ExecutionEngine: Pipeline execution engine
+    - FailureHandler: Error handling and retry mechanisms
+    - ParallelismManager: Parallel execution management
+    - ResourceScheduler: Resource allocation and scheduling
+    - PipelineValidator: Pipeline validation and testing
+    - PipelineTemplateManager: Pre-built pipeline templates
 """
 
-# from .pipeline_builder import PipelineBuilder
-# from .pipeline_executor import PipelineExecutor
-# from .pipeline_monitor import PipelineMonitor
-# from .pipeline_optimizer import PipelineOptimizer
+from .pipeline_builder import (
+    PipelineBuilder,
+    Pipeline,
+    PipelineStep,
+    StepStatus,
+    PipelineSerializer
+)
+from .execution_engine import (
+    ExecutionEngine,
+    ExecutionResult,
+    PipelineStatus,
+    ProgressTracker
+)
+from .failure_handler import (
+    FailureHandler,
+    RetryHandler,
+    FallbackHandler,
+    ErrorRecovery,
+    RetryPolicy,
+    RetryStrategy,
+    ErrorSeverity,
+    FailureRecovery
+)
+from .parallelism_manager import (
+    ParallelismManager,
+    ParallelExecutor,
+    Task,
+    ParallelExecutionResult
+)
+from .resource_scheduler import (
+    ResourceScheduler,
+    Resource,
+    ResourceAllocation,
+    ResourceType
+)
+from .pipeline_validator import (
+    PipelineValidator,
+    ValidationResult
+)
+from .pipeline_templates import (
+    PipelineTemplateManager,
+    PipelineTemplate
+)
+
+__all__ = [
+    # Pipeline construction
+    "PipelineBuilder",
+    "Pipeline",
+    "PipelineStep",
+    "StepStatus",
+    "PipelineSerializer",
+    
+    # Execution
+    "ExecutionEngine",
+    "ExecutionResult",
+    "PipelineStatus",
+    "ProgressTracker",
+    
+    # Failure handling
+    "FailureHandler",
+    "RetryHandler",
+    "FallbackHandler",
+    "ErrorRecovery",
+    "RetryPolicy",
+    "RetryStrategy",
+    "ErrorSeverity",
+    "FailureRecovery",
+    
+    # Parallelism
+    "ParallelismManager",
+    "ParallelExecutor",
+    "Task",
+    "ParallelExecutionResult",
+    
+    # Resource management
+    "ResourceScheduler",
+    "Resource",
+    "ResourceAllocation",
+    "ResourceType",
+    
+    # Validation
+    "PipelineValidator",
+    "ValidationResult",
+    
+    # Templates
+    "PipelineTemplateManager",
+    "PipelineTemplate",
+]

libs/semantica/pipeline/execution_engine.py

@@ -5,10 +5,323 @@ This module provides pipeline execution and orchestration
 for complex data processing workflows.
 """
 
-# TODO: Implement pipeline execution
-# - Pipeline execution and orchestration
-# - Task scheduling and management
-# - Resource allocation and monitoring
-# - Performance optimization
-# - Error handling and recovery
-# - Parallel and distributed execution
+from typing import Any, Dict, List, Optional, Callable
+from enum import Enum
+from dataclasses import dataclass, field
+from datetime import datetime
+import threading
+import time
+
+from ..utils.exceptions import ValidationError, ProcessingError
+from ..utils.logging import get_logger
+from .pipeline_builder import Pipeline, PipelineStep, StepStatus
+from .failure_handler import FailureHandler
+from .parallelism_manager import ParallelismManager
+from .resource_scheduler import ResourceScheduler
+
+
+class PipelineStatus(Enum):
+    """Pipeline execution status."""
+    PENDING = "pending"
+    RUNNING = "running"
+    PAUSED = "paused"
+    COMPLETED = "completed"
+    FAILED = "failed"
+    STOPPED = "stopped"
+
+
+@dataclass
+class ExecutionResult:
+    """Pipeline execution result."""
+    success: bool
+    output: Any
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    metrics: Dict[str, Any] = field(default_factory=dict)
+    errors: List[str] = field(default_factory=list)
+
+
+class ExecutionEngine:
+    """
+    Pipeline execution engine.
+    
+    • Pipeline execution and orchestration
+    • Task scheduling and management
+    • Resource allocation and monitoring
+    • Performance optimization
+    • Error handling and recovery
+    • Parallel and distributed execution
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None, **kwargs):
+        """
+        Initialize execution engine.
+        
+        Args:
+            config: Configuration dictionary
+            **kwargs: Additional configuration options:
+                - max_workers: Maximum parallel workers
+                - retry_on_failure: Enable retry on failure
+        """
+        self.logger = get_logger("execution_engine")
+        self.config = config or {}
+        self.config.update(kwargs)
+        
+        self.failure_handler = FailureHandler(**self.config)
+        self.parallelism_manager = ParallelismManager(**self.config)
+        self.resource_scheduler = ResourceScheduler(**self.config)
+        
+        self.running_pipelines: Dict[str, Pipeline] = {}
+        self.pipeline_status: Dict[str, PipelineStatus] = {}
+        self.pipeline_lock = threading.Lock()
+    
+    def execute_pipeline(
+        self,
+        pipeline: Pipeline,
+        data: Any = None,
+        **options
+    ) -> ExecutionResult:
+        """
+        Execute pipeline.
+        
+        Args:
+            pipeline: Pipeline object
+            data: Input data
+            **options: Execution options
+        
+        Returns:
+            Execution result
+        """
+        pipeline_id = pipeline.name
+        start_time = time.time()
+        
+        try:
+            self.logger.info(f"Executing pipeline: {pipeline_id}")
+            
+            # Set status
+            with self.pipeline_lock:
+                self.pipeline_status[pipeline_id] = PipelineStatus.RUNNING
+                self.running_pipelines[pipeline_id] = pipeline
+            
+            # Allocate resources
+            resources = self.resource_scheduler.allocate_resources(pipeline, **options)
+            
+            try:
+                # Execute steps
+                result = self._execute_steps(pipeline, data, **options)
+                
+                # Collect metrics
+                execution_time = time.time() - start_time
+                metrics = {
+                    "execution_time": execution_time,
+                    "steps_executed": len([s for s in pipeline.steps if s.status == StepStatus.COMPLETED]),
+                    "steps_failed": len([s for s in pipeline.steps if s.status == StepStatus.FAILED])
+                }
+                
+                # Update status
+                with self.pipeline_lock:
+                    if metrics["steps_failed"] == 0:
+                        self.pipeline_status[pipeline_id] = PipelineStatus.COMPLETED
+                    else:
+                        self.pipeline_status[pipeline_id] = PipelineStatus.FAILED
+                
+                return ExecutionResult(
+                    success=metrics["steps_failed"] == 0,
+                    output=result,
+                    metadata={
+                        "pipeline_id": pipeline_id,
+                        "execution_time": execution_time
+                    },
+                    metrics=metrics
+                )
+                
+            finally:
+                # Release resources
+                self.resource_scheduler.release_resources(resources)
+                
+        except Exception as e:
+            self.logger.error(f"Pipeline execution failed: {e}")
+            with self.pipeline_lock:
+                self.pipeline_status[pipeline_id] = PipelineStatus.FAILED
+            
+            return ExecutionResult(
+                success=False,
+                output=None,
+                errors=[str(e)]
+            )
+    
+    def _execute_steps(
+        self,
+        pipeline: Pipeline,
+        data: Any,
+        **options
+    ) -> Any:
+        """Execute pipeline steps."""
+        # Sort steps by dependencies (topological sort)
+        sorted_steps = self._topological_sort(pipeline.steps)
+        
+        # Execute steps
+        current_data = data
+        for step in sorted_steps:
+            if self.pipeline_status.get(pipeline.name) == PipelineStatus.STOPPED:
+                break
+            
+            # Wait if paused
+            while self.pipeline_status.get(pipeline.name) == PipelineStatus.PAUSED:
+                time.sleep(0.1)
+            
+            try:
+                # Execute step
+                step.status = StepStatus.RUNNING
+                step_result = self._execute_step(step, current_data, **options)
+                step.status = StepStatus.COMPLETED
+                step.result = step_result
+                current_data = step_result
+                
+            except Exception as e:
+                step.status = StepStatus.FAILED
+                step.error = e
+                
+                # Handle failure
+                recovery_result = self.failure_handler.handle_step_failure(step, e)
+                if not recovery_result.get("retry", False):
+                    raise
+                else:
+                    # Retry step
+                    step.status = StepStatus.RUNNING
+                    step_result = self._execute_step(step, current_data, **options)
+                    step.status = StepStatus.COMPLETED
+                    step.result = step_result
+                    current_data = step_result
+        
+        return current_data
+    
+    def _execute_step(
+        self,
+        step: PipelineStep,
+        data: Any,
+        **options
+    ) -> Any:
+        """Execute a single step."""
+        if step.handler:
+            return step.handler(data, **step.config, **options)
+        else:
+            # Default: pass data through
+            return data
+    
+    def _topological_sort(self, steps: List[PipelineStep]) -> List[PipelineStep]:
+        """Sort steps by dependencies (topological sort)."""
+        # Build dependency graph
+        step_map = {step.name: step for step in steps}
+        in_degree = {step.name: len(step.dependencies) for step in steps}
+        
+        # Find steps with no dependencies
+        queue = [step for step in steps if in_degree[step.name] == 0]
+        sorted_steps = []
+        
+        while queue:
+            step = queue.pop(0)
+            sorted_steps.append(step)
+            
+            # Update in-degrees of dependent steps
+            for other_step in steps:
+                if step.name in other_step.dependencies:
+                    in_degree[other_step.name] -= 1
+                    if in_degree[other_step.name] == 0:
+                        queue.append(other_step)
+        
+        # Check for cycles
+        if len(sorted_steps) != len(steps):
+            raise ValidationError("Circular dependency detected in pipeline")
+        
+        return sorted_steps
+    
+    def pause_pipeline(self, pipeline_id: str) -> None:
+        """Pause pipeline execution."""
+        with self.pipeline_lock:
+            if pipeline_id in self.pipeline_status:
+                if self.pipeline_status[pipeline_id] == PipelineStatus.RUNNING:
+                    self.pipeline_status[pipeline_id] = PipelineStatus.PAUSED
+                    self.logger.info(f"Paused pipeline: {pipeline_id}")
+    
+    def resume_pipeline(self, pipeline_id: str) -> None:
+        """Resume paused pipeline."""
+        with self.pipeline_lock:
+            if pipeline_id in self.pipeline_status:
+                if self.pipeline_status[pipeline_id] == PipelineStatus.PAUSED:
+                    self.pipeline_status[pipeline_id] = PipelineStatus.RUNNING
+                    self.logger.info(f"Resumed pipeline: {pipeline_id}")
+    
+    def stop_pipeline(self, pipeline_id: str) -> None:
+        """Stop pipeline execution."""
+        with self.pipeline_lock:
+            if pipeline_id in self.pipeline_status:
+                self.pipeline_status[pipeline_id] = PipelineStatus.STOPPED
+                self.logger.info(f"Stopped pipeline: {pipeline_id}")
+    
+    def get_pipeline_status(self, pipeline_id: str) -> Optional[PipelineStatus]:
+        """Get pipeline status."""
+        return self.pipeline_status.get(pipeline_id)
+    
+    def get_progress(self, pipeline_id: str) -> Dict[str, Any]:
+        """Get pipeline execution progress."""
+        if pipeline_id not in self.running_pipelines:
+            return {}
+        
+        pipeline = self.running_pipelines[pipeline_id]
+        total_steps = len(pipeline.steps)
+        completed_steps = len([s for s in pipeline.steps if s.status == StepStatus.COMPLETED])
+        
+        return {
+            "total_steps": total_steps,
+            "completed_steps": completed_steps,
+            "progress_percentage": (completed_steps / total_steps * 100) if total_steps > 0 else 0.0,
+            "status": self.pipeline_status.get(pipeline_id, PipelineStatus.PENDING).value
+        }
+
+
+class ProgressTracker:
+    """Progress tracking for pipeline execution."""
+    
+    def __init__(self, **config):
+        """Initialize progress tracker."""
+        self.logger = get_logger("progress_tracker")
+        self.config = config
+        self.tracking_data: Dict[str, Dict[str, Any]] = {}
+    
+    def track_progress(self, pipeline_id: str, step_name: str, progress: float) -> None:
+        """Track progress for a pipeline step."""
+        if pipeline_id not in self.tracking_data:
+            self.tracking_data[pipeline_id] = {}
+        
+        self.tracking_data[pipeline_id][step_name] = {
+            "progress": progress,
+            "timestamp": datetime.now().isoformat()
+        }
+    
+    def get_completion_percentage(self, pipeline_id: str) -> float:
+        """Get overall completion percentage."""
+        if pipeline_id not in self.tracking_data:
+            return 0.0
+        
+        steps = self.tracking_data[pipeline_id]
+        if not steps:
+            return 0.0
+        
+        total_progress = sum(step["progress"] for step in steps.values())
+        return total_progress / len(steps)
+    
+    def estimate_remaining_time(
+        self,
+        pipeline_id: str,
+        start_time: float
+    ) -> Optional[float]:
+        """Estimate remaining execution time."""
+        completion = self.get_completion_percentage(pipeline_id)
+        if completion == 0:
+            return None
+        
+        elapsed = time.time() - start_time
+        estimated_total = elapsed / completion
+        remaining = estimated_total - elapsed
+        
+        return max(0, remaining)

libs/semantica/pipeline/failure_handler.py

@@ -5,10 +5,365 @@ This module provides error handling and retry mechanisms
 for pipeline execution and recovery.
 """
 
-# TODO: Implement failure handling
-# - Error detection and classification
-# - Retry mechanisms and strategies
-# - Failure recovery and rollback
-# - Error reporting and logging
-# - Performance optimization
-# - Custom error handling strategies
+from typing import Any, Dict, List, Optional, Callable
+from dataclasses import dataclass, field
+from enum import Enum
+import time
+import traceback
+
+from ..utils.exceptions import ValidationError, ProcessingError
+from ..utils.logging import get_logger
+from .pipeline_builder import PipelineStep
+
+
+class ErrorSeverity(Enum):
+    """Error severity levels."""
+    LOW = "low"
+    MEDIUM = "medium"
+    HIGH = "high"
+    CRITICAL = "critical"
+
+
+class RetryStrategy(Enum):
+    """Retry strategies."""
+    LINEAR = "linear"
+    EXPONENTIAL = "exponential"
+    FIXED = "fixed"
+
+
+@dataclass
+class RetryPolicy:
+    """Retry policy configuration."""
+    max_retries: int = 3
+    backoff_factor: float = 2.0
+    initial_delay: float = 1.0
+    max_delay: float = 60.0
+    strategy: RetryStrategy = RetryStrategy.EXPONENTIAL
+    retryable_errors: List[type] = field(default_factory=list)
+
+
+@dataclass
+class FailureRecovery:
+    """Failure recovery result."""
+    should_retry: bool
+    retry_delay: float = 0.0
+    recovery_action: Optional[str] = None
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+class FailureHandler:
+    """
+    Failure handling and recovery system.
+    
+    • Error detection and classification
+    • Retry mechanisms and strategies
+    • Failure recovery and rollback
+    • Error reporting and logging
+    • Performance optimization
+    • Custom error handling strategies
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None, **kwargs):
+        """
+        Initialize failure handler.
+        
+        Args:
+            config: Configuration dictionary
+            **kwargs: Additional configuration options:
+                - default_max_retries: Default maximum retries
+                - default_backoff_factor: Default backoff factor
+        """
+        self.logger = get_logger("failure_handler")
+        self.config = config or {}
+        self.config.update(kwargs)
+        
+        self.default_max_retries = self.config.get("default_max_retries", 3)
+        self.default_backoff_factor = self.config.get("default_backoff_factor", 2.0)
+        
+        self.retry_policies: Dict[str, RetryPolicy] = {}
+        self.error_history: List[Dict[str, Any]] = []
+    
+    def handle_step_failure(
+        self,
+        step: PipelineStep,
+        error: Exception,
+        **options
+    ) -> Dict[str, Any]:
+        """
+        Handle step failure.
+        
+        Args:
+            step: Failed step
+            error: Exception that occurred
+            **options: Additional options
+        
+        Returns:
+            Recovery result with retry information
+        """
+        # Classify error
+        error_classification = self.classify_error(error)
+        
+        # Get retry policy
+        retry_policy = self.get_retry_policy(step.step_type)
+        
+        # Check if error is retryable
+        should_retry = self._should_retry(error, retry_policy)
+        
+        # Calculate retry delay
+        retry_delay = 0.0
+        if should_retry:
+            retry_delay = self._calculate_retry_delay(
+                step.name,
+                retry_policy
+            )
+        
+        # Log error
+        self.logger.error(
+            f"Step '{step.name}' failed: {error}",
+            exc_info=True
+        )
+        
+        # Record error history
+        self.error_history.append({
+            "step_name": step.name,
+            "step_type": step.step_type,
+            "error": str(error),
+            "error_type": type(error).__name__,
+            "severity": error_classification["severity"].value,
+            "timestamp": time.time(),
+            "retryable": should_retry
+        })
+        
+        return {
+            "retry": should_retry,
+            "retry_delay": retry_delay,
+            "error_classification": error_classification,
+            "recovery_action": self._determine_recovery_action(error, error_classification)
+        }
+    
+    def classify_error(self, error: Exception) -> Dict[str, Any]:
+        """
+        Classify error severity and type.
+        
+        Args:
+            error: Exception to classify
+        
+        Returns:
+            Error classification
+        """
+        error_type = type(error)
+        error_message = str(error)
+        
+        # Determine severity
+        severity = ErrorSeverity.MEDIUM
+        if isinstance(error, ValidationError):
+            severity = ErrorSeverity.LOW
+        elif isinstance(error, ProcessingError):
+            severity = ErrorSeverity.HIGH
+        elif "timeout" in error_message.lower() or "connection" in error_message.lower():
+            severity = ErrorSeverity.MEDIUM
+        elif "memory" in error_message.lower() or "resource" in error_message.lower():
+            severity = ErrorSeverity.HIGH
+        else:
+            severity = ErrorSeverity.MEDIUM
+        
+        return {
+            "error_type": error_type.__name__,
+            "severity": severity,
+            "message": error_message,
+            "traceback": traceback.format_exc()
+        }
+    
+    def set_retry_policy(
+        self,
+        step_type: str,
+        policy: RetryPolicy
+    ) -> None:
+        """
+        Set retry policy for step type.
+        
+        Args:
+            step_type: Step type
+            policy: Retry policy
+        """
+        self.retry_policies[step_type] = policy
+        self.logger.debug(f"Set retry policy for {step_type}: {policy}")
+    
+    def get_retry_policy(self, step_type: str) -> RetryPolicy:
+        """
+        Get retry policy for step type.
+        
+        Args:
+            step_type: Step type
+        
+        Returns:
+            Retry policy
+        """
+        return self.retry_policies.get(
+            step_type,
+            RetryPolicy(
+                max_retries=self.default_max_retries,
+                backoff_factor=self.default_backoff_factor
+            )
+        )
+    
+    def _should_retry(
+        self,
+        error: Exception,
+        policy: RetryPolicy
+    ) -> bool:
+        """Check if error should be retried."""
+        # Check if error type is in retryable list
+        if policy.retryable_errors:
+            if not any(isinstance(error, err_type) for err_type in policy.retryable_errors):
+                return False
+        
+        # Check max retries (would need step retry count)
+        # For now, assume we can retry
+        return True
+    
+    def _calculate_retry_delay(
+        self,
+        step_name: str,
+        policy: RetryPolicy,
+        attempt: int = 1
+    ) -> float:
+        """Calculate retry delay based on strategy."""
+        if policy.strategy == RetryStrategy.LINEAR:
+            delay = policy.initial_delay * attempt
+        elif policy.strategy == RetryStrategy.EXPONENTIAL:
+            delay = policy.initial_delay * (policy.backoff_factor ** (attempt - 1))
+        else:  # FIXED
+            delay = policy.initial_delay
+        
+        return min(delay, policy.max_delay)
+    
+    def _determine_recovery_action(
+        self,
+        error: Exception,
+        classification: Dict[str, Any]
+    ) -> Optional[str]:
+        """Determine recovery action based on error."""
+        severity = classification["severity"]
+        
+        if severity == ErrorSeverity.LOW:
+            return "retry"
+        elif severity == ErrorSeverity.MEDIUM:
+            return "retry_with_backoff"
+        elif severity == ErrorSeverity.HIGH:
+            return "skip_step"
+        else:  # CRITICAL
+            return "abort_pipeline"
+    
+    def retry_failed_step(
+        self,
+        step: PipelineStep,
+        error: Exception,
+        **options
+    ) -> Any:
+        """
+        Retry failed step.
+        
+        Args:
+            step: Failed step
+            error: Original error
+            **options: Additional options
+        
+        Returns:
+            Step execution result
+        """
+        recovery = self.handle_step_failure(step, error, **options)
+        
+        if not recovery["retry"]:
+            raise error
+        
+        # Wait for retry delay
+        if recovery["retry_delay"] > 0:
+            time.sleep(recovery["retry_delay"])
+        
+        # Retry step execution
+        # This would typically be called by the execution engine
+        return recovery
+    
+    def get_error_history(self, step_name: Optional[str] = None) -> List[Dict[str, Any]]:
+        """
+        Get error history.
+        
+        Args:
+            step_name: Optional step name filter
+        
+        Returns:
+            Error history
+        """
+        if step_name:
+            return [e for e in self.error_history if e["step_name"] == step_name]
+        return list(self.error_history)
+    
+    def clear_error_history(self) -> None:
+        """Clear error history."""
+        self.error_history.clear()
+
+
+class RetryHandler:
+    """Retry handler for failed steps."""
+    
+    def __init__(self, max_retries: int = 3, backoff_factor: float = 2.0, **config):
+        """Initialize retry handler."""
+        self.failure_handler = FailureHandler(
+            default_max_retries=max_retries,
+            default_backoff_factor=backoff_factor,
+            **config
+        )
+    
+    def retry_failed_step(self, step: PipelineStep, error: Exception) -> Dict[str, Any]:
+        """Retry failed step."""
+        return self.failure_handler.retry_failed_step(step, error)
+    
+    def set_retry_policy(self, step_type: str, policy: RetryPolicy) -> None:
+        """Set retry policy."""
+        self.failure_handler.set_retry_policy(step_type, policy)
+
+
+class FallbackHandler:
+    """Fallback handler for service failures."""
+    
+    def __init__(self, **config):
+        """Initialize fallback handler."""
+        self.logger = get_logger("fallback_handler")
+        self.config = config
+        self.fallback_strategies: Dict[str, str] = {}
+    
+    def set_fallback_strategy(self, strategy: str) -> None:
+        """Set fallback strategy."""
+        self.fallback_strategies["default"] = strategy
+    
+    def handle_service_failure(self, service_name: str) -> Dict[str, Any]:
+        """Handle service failure."""
+        strategy = self.fallback_strategies.get(service_name, self.fallback_strategies.get("default", "abort"))
+        return {"strategy": strategy, "service": service_name}
+    
+    def switch_to_backup(self, primary_failed: bool) -> bool:
+        """Switch to backup service."""
+        return primary_failed
+
+
+class ErrorRecovery:
+    """Error recovery system."""
+    
+    def __init__(self, **config):
+        """Initialize error recovery."""
+        self.logger = get_logger("error_recovery")
+        self.config = config
+        self.failure_handler = FailureHandler(**config)
+    
+    def analyze_error(self, error: Exception) -> Dict[str, Any]:
+        """Analyze error and determine recovery strategy."""
+        return self.failure_handler.classify_error(error)
+    
+    def recover_from_error(self, error: Exception, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Recover from error."""
+        classification = self.analyze_error(error)
+        return {
+            "recovery_action": self.failure_handler._determine_recovery_action(error, classification),
+            "classification": classification
+        }

libs/semantica/pipeline/parallelism_manager.py

@@ -5,10 +5,313 @@ This module provides parallel execution management
 for pipeline tasks and operations.
 """
 
-# TODO: Implement parallelism management
-# - Parallel task execution and coordination
-# - Resource allocation and scheduling
-# - Load balancing and optimization
-# - Performance monitoring and tuning
-# - Error handling and recovery
-# - Advanced parallelism strategies
+from typing import Any, Dict, List, Optional, Callable
+from dataclasses import dataclass, field
+from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor, as_completed
+import threading
+import time
+
+from ..utils.exceptions import ValidationError, ProcessingError
+from ..utils.logging import get_logger
+from .pipeline_builder import Pipeline, PipelineStep
+
+
+@dataclass
+class Task:
+    """Parallel task definition."""
+    task_id: str
+    handler: Callable
+    args: tuple = field(default_factory=tuple)
+    kwargs: Dict[str, Any] = field(default_factory=dict)
+    priority: int = 0
+
+
+@dataclass
+class ParallelExecutionResult:
+    """Parallel execution result."""
+    task_id: str
+    success: bool
+    result: Any = None
+    error: Optional[Exception] = None
+    execution_time: float = 0.0
+
+
+class ParallelismManager:
+    """
+    Parallelism management system.
+    
+    • Parallel task execution and coordination
+    • Resource allocation and scheduling
+    • Load balancing and optimization
+    • Performance monitoring and tuning
+    • Error handling and recovery
+    • Advanced parallelism strategies
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None, **kwargs):
+        """
+        Initialize parallelism manager.
+        
+        Args:
+            config: Configuration dictionary
+            **kwargs: Additional configuration options:
+                - max_workers: Maximum parallel workers
+                - use_processes: Use processes instead of threads
+        """
+        self.logger = get_logger("parallelism_manager")
+        self.config = config or {}
+        self.config.update(kwargs)
+        
+        self.max_workers = self.config.get("max_workers", 4)
+        self.use_processes = self.config.get("use_processes", False)
+        
+        self.executor: Optional[ThreadPoolExecutor] = None
+        self.process_executor: Optional[ProcessPoolExecutor] = None
+        self.lock = threading.Lock()
+    
+    def execute_parallel(
+        self,
+        tasks: List[Task],
+        **options
+    ) -> List[ParallelExecutionResult]:
+        """
+        Execute tasks in parallel.
+        
+        Args:
+            tasks: List of tasks to execute
+            **options: Additional options
+        
+        Returns:
+            List of execution results
+        """
+        if not tasks:
+            return []
+        
+        # Sort by priority
+        sorted_tasks = sorted(tasks, key=lambda t: t.priority, reverse=True)
+        
+        # Execute tasks
+        if self.use_processes:
+            return self._execute_with_processes(sorted_tasks, **options)
+        else:
+            return self._execute_with_threads(sorted_tasks, **options)
+    
+    def _execute_with_threads(
+        self,
+        tasks: List[Task],
+        **options
+    ) -> List[ParallelExecutionResult]:
+        """Execute tasks using thread pool."""
+        results = []
+        max_workers = options.get("max_workers", self.max_workers)
+        
+        with ThreadPoolExecutor(max_workers=max_workers) as executor:
+            # Submit all tasks
+            future_to_task = {
+                executor.submit(task.handler, *task.args, **task.kwargs): task
+                for task in tasks
+            }
+            
+            # Collect results
+            for future in as_completed(future_to_task):
+                task = future_to_task[future]
+                start_time = time.time()
+                
+                try:
+                    result = future.result()
+                    execution_time = time.time() - start_time
+                    results.append(ParallelExecutionResult(
+                        task_id=task.task_id,
+                        success=True,
+                        result=result,
+                        execution_time=execution_time
+                    ))
+                except Exception as e:
+                    execution_time = time.time() - start_time
+                    results.append(ParallelExecutionResult(
+                        task_id=task.task_id,
+                        success=False,
+                        error=e,
+                        execution_time=execution_time
+                    ))
+        
+        return results
+    
+    def _execute_with_processes(
+        self,
+        tasks: List[Task],
+        **options
+    ) -> List[ParallelExecutionResult]:
+        """Execute tasks using process pool."""
+        results = []
+        max_workers = options.get("max_workers", self.max_workers)
+        
+        with ProcessPoolExecutor(max_workers=max_workers) as executor:
+            # Submit all tasks
+            future_to_task = {
+                executor.submit(task.handler, *task.args, **task.kwargs): task
+                for task in tasks
+            }
+            
+            # Collect results
+            for future in as_completed(future_to_task):
+                task = future_to_task[future]
+                start_time = time.time()
+                
+                try:
+                    result = future.result()
+                    execution_time = time.time() - start_time
+                    results.append(ParallelExecutionResult(
+                        task_id=task.task_id,
+                        success=True,
+                        result=result,
+                        execution_time=execution_time
+                    ))
+                except Exception as e:
+                    execution_time = time.time() - start_time
+                    results.append(ParallelExecutionResult(
+                        task_id=task.task_id,
+                        success=False,
+                        error=e,
+                        execution_time=execution_time
+                    ))
+        
+        return results
+    
+    def execute_pipeline_steps_parallel(
+        self,
+        steps: List[PipelineStep],
+        data: Any,
+        **options
+    ) -> List[Any]:
+        """
+        Execute pipeline steps in parallel.
+        
+        Args:
+            steps: List of steps to execute
+            data: Input data
+            **options: Additional options
+        
+        Returns:
+            List of step results
+        """
+        # Create tasks from steps
+        tasks = [
+            Task(
+                task_id=step.name,
+                handler=step.handler or (lambda d, **kwargs: d),
+                args=(data,),
+                kwargs=step.config,
+                priority=0
+            )
+            for step in steps
+        ]
+        
+        # Execute tasks
+        results = self.execute_parallel(tasks, **options)
+        
+        # Map results back to steps
+        result_map = {r.task_id: r for r in results}
+        step_results = []
+        
+        for step in steps:
+            result = result_map.get(step.name)
+            if result and result.success:
+                step_results.append(result.result)
+            else:
+                raise ProcessingError(f"Step {step.name} failed: {result.error if result else 'Unknown error'}")
+        
+        return step_results
+    
+    def identify_parallelizable_steps(
+        self,
+        pipeline: Pipeline
+    ) -> List[List[PipelineStep]]:
+        """
+        Identify steps that can be executed in parallel.
+        
+        Args:
+            pipeline: Pipeline object
+        
+        Returns:
+            List of step groups that can run in parallel
+        """
+        # Group steps by dependency level
+        step_map = {step.name: step for step in pipeline.steps}
+        dependency_levels = {}
+        
+        def get_level(step_name: str) -> int:
+            if step_name in dependency_levels:
+                return dependency_levels[step_name]
+            
+            step = step_map[step_name]
+            if not step.dependencies:
+                level = 0
+            else:
+                level = max(get_level(dep) for dep in step.dependencies) + 1
+            
+            dependency_levels[step_name] = level
+            return level
+        
+        # Calculate levels for all steps
+        for step in pipeline.steps:
+            get_level(step.name)
+        
+        # Group by level
+        level_groups = {}
+        for step in pipeline.steps:
+            level = dependency_levels[step.name]
+            if level not in level_groups:
+                level_groups[level] = []
+            level_groups[level].append(step)
+        
+        # Return groups sorted by level
+        return [level_groups[level] for level in sorted(level_groups.keys())]
+    
+    def optimize_parallel_execution(
+        self,
+        pipeline: Pipeline,
+        available_workers: int
+    ) -> Dict[str, Any]:
+        """
+        Optimize parallel execution plan.
+        
+        Args:
+            pipeline: Pipeline object
+            available_workers: Available worker count
+        
+        Returns:
+            Optimization plan
+        """
+        parallel_groups = self.identify_parallelizable_steps(pipeline)
+        
+        # Calculate execution plan
+        execution_plan = []
+        for group in parallel_groups:
+            execution_plan.append({
+                "steps": [s.name for s in group],
+                "parallel": len(group) > 1,
+                "worker_count": min(len(group), available_workers)
+            })
+        
+        return {
+            "execution_plan": execution_plan,
+            "total_groups": len(parallel_groups),
+            "max_parallelism": max(len(group) for group in parallel_groups) if parallel_groups else 1,
+            "estimated_workers": sum(plan["worker_count"] for plan in execution_plan)
+        }
+
+
+class ParallelExecutor:
+    """Parallel executor for pipeline tasks."""
+    
+    def __init__(self, max_workers: int = 4, **config):
+        """Initialize parallel executor."""
+        self.parallelism_manager = ParallelismManager(
+            max_workers=max_workers,
+            **config
+        )
+    
+    def execute_parallel(self, tasks: List[Task]) -> List[ParallelExecutionResult]:
+        """Execute tasks in parallel."""
+        return self.parallelism_manager.execute_parallel(tasks)

libs/semantica/pipeline/pipeline_builder.py

@@ -9,14 +9,47 @@ Key Features:
     - Pipeline validation and optimization
     - Error handling and recovery
     - Pipeline serialization and deserialization
-
-Main Classes:
-    - PipelineBuilder: Main pipeline construction class
-    - PipelineValidator: Pipeline validation engine
-    - PipelineOptimizer: Pipeline optimization engine
-    - PipelineSerializer: Pipeline serialization handler
 """
 
+from typing import Any, Dict, List, Optional, Callable, Union
+from dataclasses import dataclass, field
+from enum import Enum
+
+from ..utils.exceptions import ValidationError, ProcessingError
+from ..utils.logging import get_logger
+from .pipeline_validator import PipelineValidator
+
+
+class StepStatus(Enum):
+    """Pipeline step status."""
+    PENDING = "pending"
+    RUNNING = "running"
+    COMPLETED = "completed"
+    FAILED = "failed"
+    SKIPPED = "skipped"
+
+
+@dataclass
+class PipelineStep:
+    """Pipeline step definition."""
+    name: str
+    step_type: str
+    config: Dict[str, Any] = field(default_factory=dict)
+    dependencies: List[str] = field(default_factory=list)
+    handler: Optional[Callable] = None
+    status: StepStatus = StepStatus.PENDING
+    result: Any = None
+    error: Optional[Exception] = None
+
+
+@dataclass
+class Pipeline:
+    """Pipeline definition."""
+    name: str
+    steps: List[PipelineStep] = field(default_factory=list)
+    config: Dict[str, Any] = field(default_factory=dict)
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
 
 class PipelineBuilder:
     """
@@ -28,191 +61,214 @@ class PipelineBuilder:
     • Optimizes pipeline performance
     • Handles pipeline serialization
     • Supports complex pipeline topologies
-    
-    Attributes:
-        • validator: Pipeline validation engine
-        • optimizer: Pipeline optimization engine
-        • serializer: Pipeline serialization handler
-        • step_registry: Available pipeline steps
-        • pipeline_config: Pipeline configuration
-        
-    Methods:
-        • build_pipeline(): Build pipeline from configuration
-        • add_step(): Add step to pipeline
-        • connect_steps(): Connect pipeline steps
-        • validate_pipeline(): Validate pipeline structure
     """
     
     def __init__(self, config=None, **kwargs):
         """
         Initialize pipeline builder.
         
-        • Setup pipeline construction tools
-        • Configure step registry
-        • Initialize validation engine
-        • Setup optimization tools
-        • Configure serialization
+        Args:
+            config: Configuration dictionary
+            **kwargs: Additional configuration options
         """
-        pass
-    
-    def build_pipeline(self, pipeline_config, **options):
-        """
-        Build pipeline from configuration.
+        self.logger = get_logger("pipeline_builder")
+        self.config = config or {}
+        self.config.update(kwargs)
         
-        • Parse pipeline configuration
-        • Create pipeline steps
-        • Connect step dependencies
-        • Validate pipeline structure
-        • Optimize pipeline performance
-        • Return built pipeline
-        """
-        pass
+        self.validator = PipelineValidator(**self.config)
+        self.steps: List[PipelineStep] = []
+        self.step_registry: Dict[str, Callable] = {}
+        self.pipeline_config: Dict[str, Any] = {}
     
-    def add_step(self, step_name, step_type, **config):
+    def add_step(
+        self,
+        step_name: str,
+        step_type: str,
+        **config
+    ) -> "PipelineBuilder":
         """
         Add step to pipeline.
         
-        • Create pipeline step
-        • Configure step parameters
-        • Validate step configuration
-        • Add to pipeline structure
-        • Return step reference
+        Args:
+            step_name: Step name/identifier
+            step_type: Step type/category
+            **config: Step configuration
+        
+        Returns:
+            Self for method chaining
         """
-        pass
+        step = PipelineStep(
+            name=step_name,
+            step_type=step_type,
+            config=config,
+            dependencies=config.get("dependencies", []),
+            handler=config.get("handler")
+        )
+        
+        self.steps.append(step)
+        self.logger.debug(f"Added step: {step_name} ({step_type})")
+        
+        return self
     
-    def connect_steps(self, from_step, to_step, **options):
+    def connect_steps(
+        self,
+        from_step: str,
+        to_step: str,
+        **options
+    ) -> "PipelineBuilder":
         """
         Connect pipeline steps.
         
-        • Create step connection
-        • Validate connection compatibility
-        • Configure connection parameters
-        • Update pipeline structure
-        • Return connection reference
+        Args:
+            from_step: Source step name
+            to_step: Target step name
+            **options: Connection options
+        
+        Returns:
+            Self for method chaining
         """
-        pass
+        # Find target step and add dependency
+        target_step = next((s for s in self.steps if s.name == to_step), None)
+        if target_step:
+            if from_step not in target_step.dependencies:
+                target_step.dependencies.append(from_step)
+        else:
+            raise ValidationError(f"Target step not found: {to_step}")
+        
+        return self
     
-    def validate_pipeline(self, pipeline):
+    def set_parallelism(self, level: int) -> "PipelineBuilder":
+        """
+        Set parallelism level.
+        
+        Args:
+            level: Parallelism level (number of parallel workers)
+        
+        Returns:
+            Self for method chaining
+        """
+        self.pipeline_config["parallelism"] = level
+        return self
+    
+    def build(self, name: str = "default_pipeline") -> Pipeline:
+        """
+        Build pipeline from configuration.
+        
+        Args:
+            name: Pipeline name
+        
+        Returns:
+            Built pipeline
+        """
+        # Validate pipeline structure
+        validation_result = self.validator.validate_pipeline(self)
+        if not validation_result.get("valid", False):
+            errors = validation_result.get("errors", [])
+            raise ValidationError(f"Pipeline validation failed: {errors}")
+        
+        pipeline = Pipeline(
+            name=name,
+            steps=list(self.steps),
+            config=self.pipeline_config,
+            metadata={
+                "step_count": len(self.steps),
+                "parallelism": self.pipeline_config.get("parallelism", 1)
+            }
+        )
+        
+        self.logger.info(f"Built pipeline: {name} with {len(self.steps)} steps")
+        
+        return pipeline
+    
+    def build_pipeline(
+        self,
+        pipeline_config: Dict[str, Any],
+        **options
+    ) -> Pipeline:
+        """
+        Build pipeline from configuration dictionary.
+        
+        Args:
+            pipeline_config: Pipeline configuration
+            **options: Additional options
+        
+        Returns:
+            Built pipeline
+        """
+        # Parse configuration
+        pipeline_name = pipeline_config.get("name", "default_pipeline")
+        steps_config = pipeline_config.get("steps", [])
+        
+        # Add steps from configuration
+        for step_config in steps_config:
+            step_name = step_config.get("name")
+            step_type = step_config.get("type")
+            if step_name and step_type:
+                self.add_step(step_name, step_type, **step_config.get("config", {}))
+        
+        # Set parallelism if specified
+        if "parallelism" in pipeline_config:
+            self.set_parallelism(pipeline_config["parallelism"])
+        
+        return self.build(pipeline_name)
+    
+    def register_step_handler(
+        self,
+        step_type: str,
+        handler: Callable
+    ) -> None:
+        """
+        Register step handler function.
+        
+        Args:
+            step_type: Step type
+            handler: Handler function
+        """
+        self.step_registry[step_type] = handler
+        self.logger.debug(f"Registered handler for step type: {step_type}")
+    
+    def get_step(self, step_name: str) -> Optional[PipelineStep]:
+        """Get step by name."""
+        return next((s for s in self.steps if s.name == step_name), None)
+    
+    def serialize(self, format: str = "json") -> Union[str, Dict[str, Any]]:
+        """
+        Serialize pipeline configuration.
+        
+        Args:
+            format: Serialization format
+        
+        Returns:
+            Serialized pipeline
+        """
+        pipeline_data = {
+            "name": "pipeline",
+            "steps": [
+                {
+                    "name": step.name,
+                    "type": step.step_type,
+                    "config": step.config,
+                    "dependencies": step.dependencies
+                }
+                for step in self.steps
+            ],
+            "config": self.pipeline_config
+        }
+        
+        if format == "json":
+            import json
+            return json.dumps(pipeline_data, indent=2)
+        else:
+            return pipeline_data
+    
+    def validate_pipeline(self) -> Dict[str, Any]:
         """
         Validate pipeline structure and configuration.
         
-        • Check pipeline structure
-        • Validate step configurations
-        • Check dependency cycles
-        • Validate resource requirements
-        • Return validation results
+        Returns:
+            Validation results
         """
-        pass
-
-
-class PipelineValidator:
-    """
-    Pipeline validation engine.
-    
-    • Validates pipeline structure
-    • Checks step configurations
-    • Validates dependencies
-    • Handles validation errors
-    """
-    
-    def __init__(self, **config):
-        """
-        Initialize pipeline validator.
-        
-        • Setup validation rules
-        • Configure validation checks
-        • Initialize error handling
-        • Setup validation reporting
-        """
-        pass
-    
-    def validate_pipeline(self, pipeline):
-        """
-        Validate entire pipeline.
-        
-        • Check pipeline structure
-        • Validate step configurations
-        • Check dependency cycles
-        • Return validation results
-        """
-        pass
-    
-    def validate_step(self, step, **constraints):
-        """
-        Validate individual pipeline step.
-        
-        • Check step configuration
-        • Validate step parameters
-        • Check step compatibility
-        • Return validation result
-        """
-        pass
-    
-    def check_dependencies(self, pipeline):
-        """
-        Check pipeline dependencies.
-        
-        • Analyze step dependencies
-        • Check for circular dependencies
-        • Validate dependency chains
-        • Return dependency analysis
-        """
-        pass
-
-
-class PipelineOptimizer:
-    """
-    Pipeline optimization engine.
-    
-    • Optimizes pipeline performance
-    • Handles resource allocation
-    • Manages parallelization
-    • Processes optimization metrics
-    """
-    
-    def __init__(self, **config):
-        """
-        Initialize pipeline optimizer.
-        
-        • Setup optimization algorithms
-        • Configure resource management
-        • Initialize parallelization tools
-        • Setup metric collection
-        """
-        pass
-    
-    def optimize_pipeline(self, pipeline, **options):
-        """
-        Optimize pipeline performance.
-        
-        • Analyze pipeline structure
-        • Apply optimization algorithms
-        • Optimize resource usage
-        • Return optimized pipeline
-        """
-        pass
-    
-    def optimize_parallelization(self, pipeline):
-        """
-        Optimize pipeline parallelization.
-        
-        • Identify parallelizable steps
-        • Configure parallel execution
-        • Handle resource constraints
-        • Return parallelization plan
-        """
-        pass
-    
-    def optimize_resource_usage(self, pipeline):
-        """
-        Optimize resource usage in pipeline.
-        
-        • Analyze resource requirements
-        • Optimize resource allocation
-        • Handle resource conflicts
-        • Return resource optimization
-        """
-        pass
+        return self.validator.validate_pipeline(self)
 
 
 class PipelineSerializer:
@@ -226,45 +282,92 @@ class PipelineSerializer:
     """
     
     def __init__(self, **config):
-        """
-        Initialize pipeline serializer.
-        
-        • Setup serialization formats
-        • Configure versioning
-        • Initialize metadata handling
-        • Setup deserialization
-        """
-        pass
+        """Initialize pipeline serializer."""
+        self.logger = get_logger("pipeline_serializer")
+        self.config = config
     
-    def serialize_pipeline(self, pipeline, format="json", **options):
+    def serialize_pipeline(
+        self,
+        pipeline: Pipeline,
+        format: str = "json",
+        **options
+    ) -> Union[str, Dict[str, Any]]:
         """
         Serialize pipeline to specified format.
         
-        • Convert pipeline to serializable format
-        • Apply format-specific serialization
-        • Include metadata and versioning
-        • Return serialized pipeline
+        Args:
+            pipeline: Pipeline object
+            format: Serialization format
+            **options: Additional options
+        
+        Returns:
+            Serialized pipeline
         """
-        pass
+        pipeline_data = {
+            "name": pipeline.name,
+            "steps": [
+                {
+                    "name": step.name,
+                    "type": step.step_type,
+                    "config": step.config,
+                    "dependencies": step.dependencies
+                }
+                for step in pipeline.steps
+            ],
+            "config": pipeline.config,
+            "metadata": pipeline.metadata
+        }
+        
+        if format == "json":
+            import json
+            return json.dumps(pipeline_data, indent=2, default=str)
+        else:
+            return pipeline_data
     
-    def deserialize_pipeline(self, serialized_pipeline, **options):
+    def deserialize_pipeline(
+        self,
+        serialized_pipeline: Union[str, Dict[str, Any]],
+        **options
+    ) -> Pipeline:
         """
         Deserialize pipeline from serialized format.
         
-        • Parse serialized pipeline
-        • Reconstruct pipeline structure
-        • Validate deserialized pipeline
-        • Return reconstructed pipeline
+        Args:
+            serialized_pipeline: Serialized pipeline data
+            **options: Additional options
+        
+        Returns:
+            Reconstructed pipeline
         """
-        pass
+        # Parse if string
+        if isinstance(serialized_pipeline, str):
+            import json
+            pipeline_data = json.loads(serialized_pipeline)
+        else:
+            pipeline_data = serialized_pipeline
+        
+        # Reconstruct pipeline
+        builder = PipelineBuilder(**self.config)
+        pipeline = builder.build_pipeline(pipeline_data, **options)
+        
+        return pipeline
     
-    def version_pipeline(self, pipeline, version_info):
+    def version_pipeline(
+        self,
+        pipeline: Pipeline,
+        version_info: Dict[str, Any]
+    ) -> Pipeline:
         """
         Add versioning information to pipeline.
         
-        • Add version metadata
-        • Track pipeline changes
-        • Handle version compatibility
-        • Return versioned pipeline
+        Args:
+            pipeline: Pipeline object
+            version_info: Version information
+        
+        Returns:
+            Versioned pipeline
         """
-        pass
+        pipeline.metadata["version"] = version_info.get("version", "1.0")
+        pipeline.metadata["version_info"] = version_info
+        
+        return pipeline

libs/semantica/pipeline/pipeline_templates.py

@@ -5,10 +5,218 @@ This module provides pre-built pipeline templates
 for common use cases and workflows.
 """
 
-# TODO: Implement pipeline templates
-# - Pre-built pipeline templates
-# - Common workflow patterns
-# - Template customization and configuration
-# - Performance optimization
-# - Error handling and recovery
-# - Advanced template features
+from typing import Any, Dict, List, Optional
+from dataclasses import dataclass, field
+
+from ..utils.exceptions import ValidationError, ProcessingError
+from ..utils.logging import get_logger
+from .pipeline_builder import PipelineBuilder
+
+
+@dataclass
+class PipelineTemplate:
+    """Pipeline template definition."""
+    name: str
+    description: str
+    steps: List[Dict[str, Any]] = field(default_factory=list)
+    config: Dict[str, Any] = field(default_factory=dict)
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+class PipelineTemplateManager:
+    """
+    Pipeline template management system.
+    
+    • Pre-built pipeline templates
+    • Common workflow patterns
+    • Template customization and configuration
+    • Performance optimization
+    • Error handling and recovery
+    • Advanced template features
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None, **kwargs):
+        """
+        Initialize template manager.
+        
+        Args:
+            config: Configuration dictionary
+            **kwargs: Additional configuration options
+        """
+        self.logger = get_logger("pipeline_template_manager")
+        self.config = config or {}
+        self.config.update(kwargs)
+        
+        self.templates: Dict[str, PipelineTemplate] = {}
+        self._load_default_templates()
+    
+    def _load_default_templates(self) -> None:
+        """Load default pipeline templates."""
+        # Document Processing Template
+        self.templates["document_processing"] = PipelineTemplate(
+            name="document_processing",
+            description="Complete document processing pipeline from ingestion to knowledge graph",
+            steps=[
+                {"name": "ingest", "type": "ingest", "config": {"source": "documents/"}},
+                {"name": "parse", "type": "parse", "config": {"formats": ["pdf", "docx"]}, "dependencies": ["ingest"]},
+                {"name": "normalize", "type": "normalize", "config": {}, "dependencies": ["parse"]},
+                {"name": "extract", "type": "extract", "config": {"entities": True, "relations": True}, "dependencies": ["normalize"]},
+                {"name": "embed", "type": "embed", "config": {"model": "text-embedding-3-large"}, "dependencies": ["extract"]},
+                {"name": "build_kg", "type": "build_kg", "config": {}, "dependencies": ["extract", "embed"]}
+            ],
+            config={"parallelism": 2},
+            metadata={"category": "document_processing"}
+        )
+        
+        # RAG Pipeline Template
+        self.templates["rag_pipeline"] = PipelineTemplate(
+            name="rag_pipeline",
+            description="RAG pipeline for question answering",
+            steps=[
+                {"name": "ingest", "type": "ingest", "config": {"source": "documents/"}},
+                {"name": "chunk", "type": "chunk", "config": {"chunk_size": 512}, "dependencies": ["ingest"]},
+                {"name": "embed", "type": "embed", "config": {}, "dependencies": ["chunk"]},
+                {"name": "store_vectors", "type": "store_vectors", "config": {"store": "pinecone"}, "dependencies": ["embed"]}
+            ],
+            config={"parallelism": 4},
+            metadata={"category": "rag"}
+        )
+        
+        # Knowledge Graph Construction Template
+        self.templates["kg_construction"] = PipelineTemplate(
+            name="kg_construction",
+            description="Knowledge graph construction from multiple sources",
+            steps=[
+                {"name": "ingest_sources", "type": "ingest", "config": {"sources": []}},
+                {"name": "extract_entities", "type": "extract", "config": {"entities": True}, "dependencies": ["ingest_sources"]},
+                {"name": "extract_relations", "type": "extract", "config": {"relations": True}, "dependencies": ["ingest_sources"]},
+                {"name": "deduplicate", "type": "deduplicate", "config": {}, "dependencies": ["extract_entities"]},
+                {"name": "resolve_conflicts", "type": "resolve_conflicts", "config": {}, "dependencies": ["extract_entities", "extract_relations"]},
+                {"name": "build_graph", "type": "build_kg", "config": {}, "dependencies": ["deduplicate", "resolve_conflicts"]}
+            ],
+            config={"parallelism": 3},
+            metadata={"category": "knowledge_graph"}
+        )
+        
+        # Ontology Generation Template
+        self.templates["ontology_generation"] = PipelineTemplate(
+            name="ontology_generation",
+            description="Ontology generation from extracted data",
+            steps=[
+                {"name": "extract_concepts", "type": "extract", "config": {"entities": True}},
+                {"name": "infer_classes", "type": "infer_classes", "config": {}, "dependencies": ["extract_concepts"]},
+                {"name": "infer_properties", "type": "infer_properties", "config": {}, "dependencies": ["infer_classes"]},
+                {"name": "generate_owl", "type": "generate_owl", "config": {}, "dependencies": ["infer_classes", "infer_properties"]},
+                {"name": "validate_ontology", "type": "validate_ontology", "config": {}, "dependencies": ["generate_owl"]}
+            ],
+            config={"parallelism": 1},
+            metadata={"category": "ontology"}
+        )
+    
+    def get_template(self, template_name: str) -> Optional[PipelineTemplate]:
+        """
+        Get template by name.
+        
+        Args:
+            template_name: Template name
+        
+        Returns:
+            Pipeline template or None
+        """
+        return self.templates.get(template_name)
+    
+    def create_pipeline_from_template(
+        self,
+        template_name: str,
+        **overrides
+    ) -> "PipelineBuilder":
+        """
+        Create pipeline from template.
+        
+        Args:
+            template_name: Template name
+            **overrides: Configuration overrides
+        
+        Returns:
+            Pipeline builder
+        """
+        template = self.get_template(template_name)
+        if not template:
+            raise ValidationError(f"Template not found: {template_name}")
+        
+        builder = PipelineBuilder(**self.config)
+        
+        # Add steps from template
+        for step_config in template.steps:
+            step_name = step_config["name"]
+            step_type = step_config["type"]
+            config = step_config.get("config", {})
+            dependencies = step_config.get("dependencies", [])
+            
+            # Apply overrides
+            if step_name in overrides:
+                config.update(overrides[step_name])
+            
+            builder.add_step(step_name, step_type, dependencies=dependencies, **config)
+        
+        # Set pipeline config
+        pipeline_config = template.config.copy()
+        pipeline_config.update(overrides.get("pipeline_config", {}))
+        
+        for key, value in pipeline_config.items():
+            if key == "parallelism":
+                builder.set_parallelism(value)
+        
+        return builder
+    
+    def register_template(
+        self,
+        template: PipelineTemplate
+    ) -> None:
+        """
+        Register a custom template.
+        
+        Args:
+            template: Pipeline template
+        """
+        self.templates[template.name] = template
+        self.logger.info(f"Registered template: {template.name}")
+    
+    def list_templates(self, category: Optional[str] = None) -> List[str]:
+        """
+        List available templates.
+        
+        Args:
+            category: Optional category filter
+        
+        Returns:
+            List of template names
+        """
+        if category:
+            return [
+                name for name, template in self.templates.items()
+                if template.metadata.get("category") == category
+            ]
+        return list(self.templates.keys())
+    
+    def get_template_info(self, template_name: str) -> Optional[Dict[str, Any]]:
+        """
+        Get template information.
+        
+        Args:
+            template_name: Template name
+        
+        Returns:
+            Template information dictionary
+        """
+        template = self.get_template(template_name)
+        if not template:
+            return None
+        
+        return {
+            "name": template.name,
+            "description": template.description,
+            "step_count": len(template.steps),
+            "config": template.config,
+            "metadata": template.metadata
+        }

libs/semantica/pipeline/pipeline_validator.py

@@ -5,10 +5,293 @@ This module provides pipeline validation and testing
 for workflow correctness and performance.
 """
 
-# TODO: Implement pipeline validation
-# - Pipeline validation and testing
-# - Workflow correctness checking
-# - Performance validation and benchmarking
-# - Error detection and reporting
-# - Performance optimization
-# - Advanced validation techniques
+from typing import Any, Dict, List, Optional, Union
+from dataclasses import dataclass, field
+from collections import defaultdict, deque
+
+from ..utils.exceptions import ValidationError, ProcessingError
+from ..utils.logging import get_logger
+from .pipeline_builder import PipelineBuilder, Pipeline, PipelineStep
+
+
+@dataclass
+class ValidationResult:
+    """Pipeline validation result."""
+    valid: bool
+    errors: List[str] = field(default_factory=list)
+    warnings: List[str] = field(default_factory=list)
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+class PipelineValidator:
+    """
+    Pipeline validation engine.
+    
+    • Pipeline validation and testing
+    • Workflow correctness checking
+    • Performance validation and benchmarking
+    • Error detection and reporting
+    • Performance optimization
+    • Advanced validation techniques
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None, **kwargs):
+        """
+        Initialize pipeline validator.
+        
+        Args:
+            config: Configuration dictionary
+            **kwargs: Additional configuration options
+        """
+        self.logger = get_logger("pipeline_validator")
+        self.config = config or {}
+        self.config.update(kwargs)
+    
+    def validate_pipeline(
+        self,
+        pipeline: Union[Pipeline, PipelineBuilder],
+        **options
+    ) -> ValidationResult:
+        """
+        Validate entire pipeline.
+        
+        Args:
+            pipeline: Pipeline object or builder
+            **options: Additional options
+        
+        Returns:
+            Validation result
+        """
+        errors = []
+        warnings = []
+        
+        # Convert builder to pipeline if needed
+        if isinstance(pipeline, PipelineBuilder):
+            # Check structure
+            structure_result = self._validate_structure(pipeline)
+            errors.extend(structure_result.get("errors", []))
+            warnings.extend(structure_result.get("warnings", []))
+            
+            # Check dependencies
+            dependency_result = self.check_dependencies(pipeline)
+            errors.extend(dependency_result.get("errors", []))
+            warnings.extend(dependency_result.get("warnings", []))
+            
+        elif isinstance(pipeline, Pipeline):
+            # Validate pipeline steps
+            for step in pipeline.steps:
+                step_result = self.validate_step(step)
+                if not step_result.valid:
+                    errors.extend(step_result.errors)
+                warnings.extend(step_result.warnings)
+            
+            # Check dependencies
+            dependency_result = self.check_dependencies(pipeline)
+            errors.extend(dependency_result.get("errors", []))
+            warnings.extend(dependency_result.get("warnings", []))
+        
+        return ValidationResult(
+            valid=len(errors) == 0,
+            errors=errors,
+            warnings=warnings,
+            metadata={
+                "step_count": len(pipeline.steps) if hasattr(pipeline, "steps") else 0
+            }
+        )
+    
+    def _validate_structure(self, pipeline: PipelineBuilder) -> Dict[str, Any]:
+        """Validate pipeline structure."""
+        errors = []
+        warnings = []
+        
+        # Check if pipeline has steps
+        if not pipeline.steps:
+            errors.append("Pipeline has no steps")
+        
+        # Check step names are unique
+        step_names = [step.name for step in pipeline.steps]
+        duplicates = [name for name, count in __import__("collections").Counter(step_names).items() if count > 1]
+        if duplicates:
+            errors.append(f"Duplicate step names found: {duplicates}")
+        
+        # Check step configurations
+        for step in pipeline.steps:
+            if not step.name:
+                errors.append("Step missing name")
+            if not step.step_type:
+                errors.append(f"Step '{step.name}' missing type")
+        
+        return {"errors": errors, "warnings": warnings}
+    
+    def validate_step(
+        self,
+        step: PipelineStep,
+        **constraints
+    ) -> ValidationResult:
+        """
+        Validate individual pipeline step.
+        
+        Args:
+            step: Pipeline step
+            **constraints: Validation constraints
+        
+        Returns:
+            Validation result
+        """
+        errors = []
+        warnings = []
+        
+        # Check required fields
+        if not step.name:
+            errors.append("Step missing name")
+        if not step.step_type:
+            errors.append("Step missing type")
+        
+        # Check handler
+        if not step.handler and not constraints.get("allow_no_handler", False):
+            warnings.append(f"Step '{step.name}' has no handler")
+        
+        # Check configuration
+        if not step.config:
+            warnings.append(f"Step '{step.name}' has no configuration")
+        
+        return ValidationResult(
+            valid=len(errors) == 0,
+            errors=errors,
+            warnings=warnings
+        )
+    
+    def check_dependencies(
+        self,
+        pipeline: Union[Pipeline, PipelineBuilder]
+    ) -> Dict[str, Any]:
+        """
+        Check pipeline dependencies.
+        
+        Args:
+            pipeline: Pipeline object or builder
+        
+        Returns:
+            Dependency analysis results
+        """
+        errors = []
+        warnings = []
+        
+        steps = pipeline.steps if hasattr(pipeline, "steps") else []
+        step_names = {step.name for step in steps}
+        
+        # Check for circular dependencies
+        circular = self._detect_circular_dependencies(steps)
+        if circular:
+            errors.append(f"Circular dependency detected: {circular}")
+        
+        # Check for missing dependencies
+        for step in steps:
+            for dep in step.dependencies:
+                if dep not in step_names:
+                    errors.append(f"Step '{step.name}' depends on missing step: {dep}")
+        
+        # Check for unreachable steps
+        reachable = self._find_reachable_steps(steps)
+        unreachable = set(step_names) - reachable
+        if unreachable:
+            warnings.append(f"Unreachable steps found: {unreachable}")
+        
+        return {
+            "errors": errors,
+            "warnings": warnings,
+            "circular_dependencies": circular,
+            "reachable_steps": reachable
+        }
+    
+    def _detect_circular_dependencies(self, steps: List[PipelineStep]) -> List[List[str]]:
+        """Detect circular dependencies using DFS."""
+        step_map = {step.name: step for step in steps}
+        cycles = []
+        visited = set()
+        rec_stack = set()
+        
+        def dfs(node: str, path: List[str]) -> None:
+            visited.add(node)
+            rec_stack.add(node)
+            path.append(node)
+            
+            step = step_map.get(node)
+            if step:
+                for dep in step.dependencies:
+                    if dep not in step_map:
+                        continue
+                    
+                    if dep in rec_stack:
+                        # Found cycle
+                        cycle_start = path.index(dep)
+                        cycles.append(path[cycle_start:] + [dep])
+                    elif dep not in visited:
+                        dfs(dep, path)
+            
+            rec_stack.remove(node)
+            path.pop()
+        
+        for step in steps:
+            if step.name not in visited:
+                dfs(step.name, [])
+        
+        return cycles
+    
+    def _find_reachable_steps(self, steps: List[PipelineStep]) -> set:
+        """Find reachable steps from entry points."""
+        step_map = {step.name: step for step in steps}
+        reachable = set()
+        
+        # Find entry points (steps with no dependencies)
+        entry_points = [step.name for step in steps if not step.dependencies]
+        
+        # BFS from entry points
+        queue = deque(entry_points)
+        while queue:
+            step_name = queue.popleft()
+            if step_name in reachable:
+                continue
+            
+            reachable.add(step_name)
+            step = step_map.get(step_name)
+            if step:
+                # Add dependent steps
+                for dependent_step in steps:
+                    if step_name in dependent_step.dependencies:
+                        queue.append(dependent_step.name)
+        
+        return reachable
+    
+    def validate_performance(
+        self,
+        pipeline: Pipeline,
+        **options
+    ) -> Dict[str, Any]:
+        """
+        Validate pipeline performance.
+        
+        Args:
+            pipeline: Pipeline object
+            **options: Additional options
+        
+        Returns:
+            Performance validation results
+        """
+        warnings = []
+        
+        # Check for potential bottlenecks
+        step_count = len(pipeline.steps)
+        if step_count > 100:
+            warnings.append("Pipeline has many steps, may impact performance")
+        
+        # Check for sequential dependencies
+        sequential_steps = sum(1 for step in pipeline.steps if len(step.dependencies) > 0)
+        if sequential_steps == step_count:
+            warnings.append("All steps are sequential, consider parallelization")
+        
+        return {
+            "step_count": step_count,
+            "sequential_steps": sequential_steps,
+            "warnings": warnings
+        }

libs/semantica/pipeline/resource_scheduler.py

@@ -5,10 +5,388 @@ This module provides resource allocation and scheduling
 for pipeline execution and optimization.
 """
 
-# TODO: Implement resource scheduling
-# - Resource allocation and management
-# - Scheduling algorithms and strategies
-# - Load balancing and optimization
-# - Performance monitoring and tuning
-# - Error handling and recovery
-# - Advanced scheduling techniques
+from typing import Any, Dict, List, Optional
+from dataclasses import dataclass, field
+from enum import Enum
+import threading
+import time
+
+from ..utils.exceptions import ValidationError, ProcessingError
+from ..utils.logging import get_logger
+from .pipeline_builder import Pipeline
+
+
+class ResourceType(Enum):
+    """Resource types."""
+    CPU = "cpu"
+    GPU = "gpu"
+    MEMORY = "memory"
+    DISK = "disk"
+    NETWORK = "network"
+
+
+@dataclass
+class Resource:
+    """Resource definition."""
+    resource_id: str
+    resource_type: ResourceType
+    capacity: float
+    allocated: float = 0.0
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class ResourceAllocation:
+    """Resource allocation record."""
+    allocation_id: str
+    resource_id: str
+    resource_type: ResourceType
+    amount: float
+    pipeline_id: str
+    step_name: Optional[str] = None
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+class ResourceScheduler:
+    """
+    Resource scheduling and allocation system.
+    
+    • Resource allocation and management
+    • Scheduling algorithms and strategies
+    • Load balancing and optimization
+    • Performance monitoring and tuning
+    • Error handling and recovery
+    • Advanced scheduling techniques
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None, **kwargs):
+        """
+        Initialize resource scheduler.
+        
+        Args:
+            config: Configuration dictionary
+            **kwargs: Additional configuration options:
+                - max_cpu_cores: Maximum CPU cores
+                - max_memory_gb: Maximum memory in GB
+                - enable_gpu: Enable GPU allocation
+        """
+        self.logger = get_logger("resource_scheduler")
+        self.config = config or {}
+        self.config.update(kwargs)
+        
+        self.resources: Dict[str, Resource] = {}
+        self.allocations: Dict[str, ResourceAllocation] = {}
+        self.lock = threading.Lock()
+        
+        self._initialize_resources()
+    
+    def _initialize_resources(self) -> None:
+        """Initialize available resources."""
+        try:
+            import psutil
+            
+            # CPU resources
+            cpu_count = psutil.cpu_count(logical=False) or 1
+            self.resources["cpu"] = Resource(
+                resource_id="cpu",
+                resource_type=ResourceType.CPU,
+                capacity=float(cpu_count),
+                metadata={"logical_cores": psutil.cpu_count(logical=True) or 1}
+            )
+            
+            # Memory resources
+            memory = psutil.virtual_memory()
+            memory_gb = memory.total / (1024 ** 3)
+            self.resources["memory"] = Resource(
+                resource_id="memory",
+                resource_type=ResourceType.MEMORY,
+                capacity=memory_gb,
+                metadata={"available_gb": memory.available / (1024 ** 3)}
+            )
+            
+            # Disk resources
+            try:
+                disk = psutil.disk_usage('/')
+                disk_gb = disk.free / (1024 ** 3)
+                self.resources["disk"] = Resource(
+                    resource_id="disk",
+                    resource_type=ResourceType.DISK,
+                    capacity=disk_gb,
+                    metadata={"total_gb": disk.total / (1024 ** 3)}
+                )
+            except Exception:
+                # Default disk capacity if unavailable
+                self.resources["disk"] = Resource(
+                    resource_id="disk",
+                    resource_type=ResourceType.DISK,
+                    capacity=100.0,
+                    metadata={}
+                )
+        except ImportError:
+            # Fallback if psutil not available
+            self.logger.warning("psutil not available, using default resource values")
+            self.resources["cpu"] = Resource(
+                resource_id="cpu",
+                resource_type=ResourceType.CPU,
+                capacity=4.0,
+                metadata={}
+            )
+            self.resources["memory"] = Resource(
+                resource_id="memory",
+                resource_type=ResourceType.MEMORY,
+                capacity=8.0,
+                metadata={}
+            )
+            self.resources["disk"] = Resource(
+                resource_id="disk",
+                resource_type=ResourceType.DISK,
+                capacity=100.0,
+                metadata={}
+            )
+    
+    def allocate_resources(
+        self,
+        pipeline: Pipeline,
+        **options
+    ) -> Dict[str, ResourceAllocation]:
+        """
+        Allocate resources for pipeline.
+        
+        Args:
+            pipeline: Pipeline object
+            **options: Additional options:
+                - cpu_cores: Number of CPU cores to allocate
+                - memory_gb: Memory in GB to allocate
+                - gpu_device: GPU device ID to allocate
+        
+        Returns:
+            Dictionary of resource allocations
+        """
+        allocations = {}
+        
+        with self.lock:
+            # Allocate CPU
+            cpu_cores = options.get("cpu_cores", 1)
+            cpu_allocation = self.allocate_cpu(cpu_cores, pipeline.name)
+            if cpu_allocation:
+                allocations["cpu"] = cpu_allocation
+            
+            # Allocate memory
+            memory_gb = options.get("memory_gb", 1.0)
+            memory_allocation = self.allocate_memory(memory_gb, pipeline.name)
+            if memory_allocation:
+                allocations["memory"] = memory_allocation
+            
+            # Allocate GPU if requested
+            if options.get("gpu_device") is not None:
+                gpu_allocation = self.allocate_gpu(options["gpu_device"], pipeline.name)
+                if gpu_allocation:
+                    allocations["gpu"] = gpu_allocation
+        
+        return allocations
+    
+    def allocate_cpu(
+        self,
+        cores: int,
+        pipeline_id: str,
+        step_name: Optional[str] = None
+    ) -> Optional[ResourceAllocation]:
+        """
+        Allocate CPU cores.
+        
+        Args:
+            cores: Number of CPU cores
+            pipeline_id: Pipeline identifier
+            step_name: Optional step name
+        
+        Returns:
+            Resource allocation or None
+        """
+        resource = self.resources.get("cpu")
+        if not resource:
+            return None
+        
+        with self.lock:
+            available = resource.capacity - resource.allocated
+            if available >= cores:
+                allocation_id = f"cpu_{pipeline_id}_{step_name or 'default'}_{time.time()}"
+                allocation = ResourceAllocation(
+                    allocation_id=allocation_id,
+                    resource_id="cpu",
+                    resource_type=ResourceType.CPU,
+                    amount=cores,
+                    pipeline_id=pipeline_id,
+                    step_name=step_name
+                )
+                
+                resource.allocated += cores
+                self.allocations[allocation_id] = allocation
+                
+                self.logger.debug(f"Allocated {cores} CPU cores to {pipeline_id}")
+                return allocation
+            else:
+                self.logger.warning(f"Insufficient CPU resources: requested {cores}, available {available}")
+                return None
+    
+    def allocate_gpu(
+        self,
+        device_id: int,
+        pipeline_id: str,
+        step_name: Optional[str] = None
+    ) -> Optional[ResourceAllocation]:
+        """
+        Allocate GPU device.
+        
+        Args:
+            device_id: GPU device ID
+            pipeline_id: Pipeline identifier
+            step_name: Optional step name
+        
+        Returns:
+            Resource allocation or None
+        """
+        # Check if GPU resource exists
+        gpu_resource_id = f"gpu_{device_id}"
+        if gpu_resource_id not in self.resources:
+            # Initialize GPU resource
+            self.resources[gpu_resource_id] = Resource(
+                resource_id=gpu_resource_id,
+                resource_type=ResourceType.GPU,
+                capacity=1.0,
+                metadata={"device_id": device_id}
+            )
+        
+        resource = self.resources[gpu_resource_id]
+        
+        with self.lock:
+            if resource.allocated < resource.capacity:
+                allocation_id = f"gpu_{pipeline_id}_{step_name or 'default'}_{time.time()}"
+                allocation = ResourceAllocation(
+                    allocation_id=allocation_id,
+                    resource_id=gpu_resource_id,
+                    resource_type=ResourceType.GPU,
+                    amount=1.0,
+                    pipeline_id=pipeline_id,
+                    step_name=step_name,
+                    metadata={"device_id": device_id}
+                )
+                
+                resource.allocated += 1.0
+                self.allocations[allocation_id] = allocation
+                
+                self.logger.debug(f"Allocated GPU {device_id} to {pipeline_id}")
+                return allocation
+            else:
+                self.logger.warning(f"GPU {device_id} is already allocated")
+                return None
+    
+    def allocate_memory(
+        self,
+        memory_gb: float,
+        pipeline_id: str,
+        step_name: Optional[str] = None
+    ) -> Optional[ResourceAllocation]:
+        """
+        Allocate memory.
+        
+        Args:
+            memory_gb: Memory in GB
+            pipeline_id: Pipeline identifier
+            step_name: Optional step name
+        
+        Returns:
+            Resource allocation or None
+        """
+        resource = self.resources.get("memory")
+        if not resource:
+            return None
+        
+        with self.lock:
+            available = resource.capacity - resource.allocated
+            if available >= memory_gb:
+                allocation_id = f"memory_{pipeline_id}_{step_name or 'default'}_{time.time()}"
+                allocation = ResourceAllocation(
+                    allocation_id=allocation_id,
+                    resource_id="memory",
+                    resource_type=ResourceType.MEMORY,
+                    amount=memory_gb,
+                    pipeline_id=pipeline_id,
+                    step_name=step_name
+                )
+                
+                resource.allocated += memory_gb
+                self.allocations[allocation_id] = allocation
+                
+                self.logger.debug(f"Allocated {memory_gb} GB memory to {pipeline_id}")
+                return allocation
+            else:
+                self.logger.warning(f"Insufficient memory: requested {memory_gb} GB, available {available} GB")
+                return None
+    
+    def release_resources(
+        self,
+        allocations: Dict[str, ResourceAllocation]
+    ) -> None:
+        """
+        Release resource allocations.
+        
+        Args:
+            allocations: Dictionary of resource allocations
+        """
+        with self.lock:
+            for allocation in allocations.values():
+                if allocation.allocation_id in self.allocations:
+                    # Release resource
+                    resource = self.resources.get(allocation.resource_id)
+                    if resource:
+                        resource.allocated -= allocation.amount
+                        resource.allocated = max(0.0, resource.allocated)
+                    
+                    # Remove allocation
+                    del self.allocations[allocation.allocation_id]
+                    
+                    self.logger.debug(f"Released resource: {allocation.resource_id}")
+    
+    def get_resource_usage(self) -> Dict[str, Any]:
+        """Get current resource usage."""
+        with self.lock:
+            usage = {}
+            for resource_id, resource in self.resources.items():
+                usage[resource_id] = {
+                    "capacity": resource.capacity,
+                    "allocated": resource.allocated,
+                    "available": resource.capacity - resource.allocated,
+                    "utilization_percent": (resource.allocated / resource.capacity * 100) if resource.capacity > 0 else 0.0
+                }
+            
+            return usage
+    
+    def optimize_resource_allocation(
+        self,
+        pipeline: Pipeline,
+        **options
+    ) -> Dict[str, Any]:
+        """
+        Optimize resource allocation for pipeline.
+        
+        Args:
+            pipeline: Pipeline object
+            **options: Additional options
+        
+        Returns:
+            Optimization recommendations
+        """
+        # Analyze pipeline requirements
+        step_count = len(pipeline.steps)
+        
+        # Calculate resource recommendations
+        recommendations = {
+            "cpu_cores": min(step_count, self.resources.get("cpu", Resource("", ResourceType.CPU, 0)).capacity),
+            "memory_gb": step_count * 0.5,  # 0.5 GB per step
+            "parallel_execution": step_count > 1
+        }
+        
+        return {
+            "recommendations": recommendations,
+            "available_resources": self.get_resource_usage()
+        }