# SAFLA Configuration Guide

## Overview

This guide provides comprehensive information about configuring SAFLA (Self-Aware Feedback Loop Algorithm) for optimal performance in your specific environment. SAFLA supports multiple configuration methods and provides extensive customization options for all core components.

## Configuration Methods

### 1. Environment Variables

SAFLA supports configuration through environment variables with the `SAFLA_` prefix:

```bash
# Core Meta-Cognitive Engine Settings
export SAFLA_ADAPTATION_THRESHOLD=0.1
export SAFLA_CONFIDENCE_THRESHOLD=0.8
export SAFLA_MAX_STRATEGIES=5
export SAFLA_STRATEGY_TIMEOUT=300

# Delta Evaluation Weights
export SAFLA_ALPHA_1=0.25  # Performance weight
export SAFLA_ALPHA_2=0.25  # Efficiency weight
export SAFLA_ALPHA_3=0.25  # Stability weight
export SAFLA_ALPHA_4=0.25  # Capability weight

# Memory Configuration
export SAFLA_VECTOR_DIM=512
export SAFLA_MAX_EPISODIC=10000
export SAFLA_MAX_SEMANTIC=5000
export SAFLA_WORKING_MEMORY_SIZE=100
export SAFLA_CONSOLIDATION_THRESHOLD=0.8

# Safety Settings
export SAFLA_RISK_TOLERANCE=medium
export SAFLA_VALIDATION_STRICTNESS=high
export SAFLA_CHECKPOINT_FREQUENCY=300
export SAFLA_MAX_ROLLBACK_DEPTH=10

# MCP Orchestration
export SAFLA_MCP_DISCOVERY_ENABLED=true
export SAFLA_MCP_HEALTH_CHECK_INTERVAL=30
export SAFLA_MCP_MAX_AGENTS=50
export SAFLA_MCP_LOAD_BALANCING=round_robin

# Logging and Monitoring
export SAFLA_LOG_LEVEL=INFO
export SAFLA_METRICS_ENABLED=true
export SAFLA_METRICS_INTERVAL=60
```

### 2. Configuration Files

#### YAML Configuration

Create a `safla_config.yaml` file:

```yaml
# SAFLA Configuration File
version: "1.0"

meta_cognitive:
  adaptation_threshold: 0.1
  confidence_threshold: 0.8
  max_strategies: 5
  strategy_timeout: 300
  self_awareness:
    introspection_interval: 60
    metacognition_depth: 3
    awareness_threshold: 0.7
  goal_management:
    max_active_goals: 10
    goal_priority_decay: 0.95
    goal_timeout: 3600
  strategy_selection:
    selection_algorithm: "weighted_random"
    exploration_rate: 0.1
    exploitation_bias: 0.8
  performance_monitoring:
    metrics_window: 300
    anomaly_detection: true
    trend_analysis: true
  adaptation_engine:
    learning_rate: 0.01
    adaptation_momentum: 0.9
    convergence_threshold: 0.001

delta_evaluation:
  weights:
    alpha_1: 0.25  # Performance
    alpha_2: 0.25  # Efficiency
    alpha_3: 0.25  # Stability
    alpha_4: 0.25  # Capability
  adaptive_weights:
    enabled: true
    adaptation_rate: 0.1
    min_weight: 0.05
    max_weight: 0.7
  calculation:
    normalization_method: "z_score"
    smoothing_factor: 0.3
    outlier_detection: true
  thresholds:
    significant_change: 0.05
    major_improvement: 0.2
    critical_degradation: -0.3

memory:
  vector_memory:
    dimension: 512
    similarity_metric: "cosine"
    index_type: "faiss"
    max_vectors: 100000
    clustering_enabled: true
    compression_enabled: false
  episodic_memory:
    max_episodes: 10000
    retention_policy: "importance_based"
    consolidation_interval: 3600
    importance_threshold: 0.6
  semantic_memory:
    max_concepts: 5000
    relationship_threshold: 0.7
    concept_decay_rate: 0.99
    auto_categorization: true
  working_memory:
    capacity: 100
    refresh_rate: 10
    priority_based_eviction: true
    context_window: 50
  consolidation:
    enabled: true
    frequency: 3600
    batch_size: 1000
    compression_ratio: 0.8

safety:
  validation:
    strictness: "high"  # low, medium, high, critical
    parallel_validation: true
    timeout: 30
    retry_attempts: 3
  risk_assessment:
    tolerance: "medium"  # low, medium, high
    categories:
      - "data_integrity"
      - "system_stability"
      - "performance_impact"
      - "security_risk"
    scoring_method: "weighted_average"
  constraints:
    performance_bounds:
      min_accuracy: 0.8
      max_latency: 1000  # milliseconds
      max_memory_usage: 0.9  # 90% of available
    operational_limits:
      max_concurrent_adaptations: 3
      max_strategy_changes_per_hour: 10
      min_stability_period: 300  # seconds
  checkpoints:
    frequency: 300  # seconds
    max_checkpoints: 20
    compression_enabled: true
    verification_enabled: true
  rollback:
    enabled: true
    max_depth: 10
    auto_rollback_conditions:
      - "critical_error"
      - "safety_violation"
      - "performance_degradation"

mcp_orchestration:
  server_management:
    discovery_enabled: true
    discovery_interval: 60
    health_check_interval: 30
    max_servers: 20
    load_balancing_strategy: "round_robin"
  agent_coordination:
    max_agents: 50
    task_queue_size: 1000
    conflict_resolution: "priority"
    capability_matching: "exact"
  context_sharing:
    embedding_dimension: 512
    similarity_threshold: 0.8
    ttl_default: 3600
    cleanup_interval: 300
  resource_management:
    pools:
      - pool_id: "cpu_pool"
        resource_type: "cpu"
        total_capacity: 8.0
      - pool_id: "memory_pool"
        resource_type: "memory"
        total_capacity: 16.0  # GB
      - pool_id: "gpu_pool"
        resource_type: "gpu"
        total_capacity: 4.0
  error_handling:
    max_retry_attempts: 3
    circuit_breaker_threshold: 5
    recovery_strategies:
      - "retry"
      - "failover"
      - "circuit_breaker"
      - "graceful_degradation"

logging:
  level: "INFO"  # DEBUG, INFO, WARNING, ERROR, CRITICAL
  format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
  handlers:
    - type: "console"
      level: "INFO"
    - type: "file"
      level: "DEBUG"
      filename: "safla.log"
      max_bytes: 10485760  # 10MB
      backup_count: 5
    - type: "rotating_file"
      level: "WARNING"
      filename: "safla_errors.log"
      max_bytes: 5242880  # 5MB
      backup_count: 3
  loggers:
    safla.core.meta_cognitive_engine:
      level: "DEBUG"
      propagate: false
    safla.core.safety_validation:
      level: "INFO"
      propagate: true

monitoring:
  metrics:
    enabled: true
    collection_interval: 60
    retention_period: 86400  # 24 hours
    export_format: "prometheus"
  performance:
    cpu_monitoring: true
    memory_monitoring: true
    disk_monitoring: true
    network_monitoring: false
  alerts:
    enabled: true
    channels:
      - type: "email"
        recipients: ["admin@example.com"]
      - type: "webhook"
        url: "https://hooks.slack.com/services/..."
    thresholds:
      high_cpu_usage: 0.8
      high_memory_usage: 0.9
      low_accuracy: 0.7
      high_error_rate: 0.1

development:
  debug_mode: false
  profiling_enabled: false
  test_mode: false
  mock_external_services: false
  seed_random_state: 42

production:
  optimization_level: "high"
  cache_enabled: true
  compression_enabled: true
  async_processing: true
  batch_processing: true
  connection_pooling: true
```

#### JSON Configuration

Create a `safla_config.json` file:

```json
{
  "version": "1.0",
  "meta_cognitive": {
    "adaptation_threshold": 0.1,
    "confidence_threshold": 0.8,
    "max_strategies": 5,
    "strategy_timeout": 300
  },
  "delta_evaluation": {
    "weights": {
      "alpha_1": 0.25,
      "alpha_2": 0.25,
      "alpha_3": 0.25,
      "alpha_4": 0.25
    },
    "adaptive_weights": {
      "enabled": true,
      "adaptation_rate": 0.1
    }
  },
  "memory": {
    "vector_memory": {
      "dimension": 512,
      "max_vectors": 100000
    },
    "episodic_memory": {
      "max_episodes": 10000
    }
  },
  "safety": {
    "validation": {
      "strictness": "high"
    },
    "risk_assessment": {
      "tolerance": "medium"
    }
  }
}
```

### 3. Programmatic Configuration

```python
from safla.core.meta_cognitive_engine import MetaCognitiveEngine
from safla.core.delta_evaluation import DeltaEvaluator, AdaptiveWeights
from safla.core.hybrid_memory import HybridMemoryArchitecture
from safla.core.safety_validation import SafetyValidationFramework

# Create configuration dictionary
config = {
    'meta_cognitive': {
        'adaptation_threshold': 0.15,
        'confidence_threshold': 0.85,
        'max_strategies': 8,
        'self_awareness': {
            'introspection_interval': 30,
            'metacognition_depth': 4
        }
    },
    'delta_evaluation': {
        'weights': {
            'alpha_1': 0.3,
            'alpha_2': 0.25,
            'alpha_3': 0.25,
            'alpha_4': 0.2
        }
    },
    'memory': {
        'vector_dimension': 768,
        'max_episodic_memories': 15000,
        'consolidation_threshold': 0.85
    },
    'safety': {
        'risk_tolerance': 'low',
        'validation_strictness': 'critical',
        'checkpoint_frequency': 180
    }
}

# Initialize components with configuration
engine = MetaCognitiveEngine(config['meta_cognitive'])
evaluator = DeltaEvaluator(AdaptiveWeights(**config['delta_evaluation']['weights']))
memory = HybridMemoryArchitecture(config['memory'])
safety = SafetyValidationFramework(config['safety'])
```

## Configuration Sections

### Meta-Cognitive Engine Configuration

```yaml
meta_cognitive:
  # Core adaptation settings
  adaptation_threshold: 0.1          # Minimum delta for triggering adaptation
  confidence_threshold: 0.8          # Minimum confidence for strategy execution
  max_strategies: 5                  # Maximum concurrent strategies
  strategy_timeout: 300              # Strategy execution timeout (seconds)
  
  # Self-awareness module
  self_awareness:
    introspection_interval: 60       # How often to perform introspection
    metacognition_depth: 3           # Depth of metacognitive analysis
    awareness_threshold: 0.7         # Threshold for self-awareness triggers
    state_tracking_window: 1800      # Window for state change tracking
    
  # Goal management
  goal_management:
    max_active_goals: 10             # Maximum concurrent active goals
    goal_priority_decay: 0.95        # Priority decay rate over time
    goal_timeout: 3600               # Goal timeout (seconds)
    success_threshold: 0.9           # Threshold for goal success
    
  # Strategy selection
  strategy_selection:
    selection_algorithm: "weighted_random"  # Algorithm for strategy selection
    exploration_rate: 0.1            # Rate of exploration vs exploitation
    exploitation_bias: 0.8           # Bias towards known good strategies
    diversity_factor: 0.3            # Factor for strategy diversity
    
  # Performance monitoring
  performance_monitoring:
    metrics_window: 300              # Window for metrics aggregation
    anomaly_detection: true          # Enable anomaly detection
    trend_analysis: true             # Enable trend analysis
    baseline_update_frequency: 3600  # How often to update baselines
    
  # Adaptation engine
  adaptation_engine:
    learning_rate: 0.01              # Learning rate for adaptations
    adaptation_momentum: 0.9         # Momentum for adaptation updates
    convergence_threshold: 0.001     # Threshold for convergence detection
    max_adaptation_steps: 100        # Maximum adaptation iterations
```

### Delta Evaluation Configuration

```yaml
delta_evaluation:
  # Base weights for delta calculation
  weights:
    alpha_1: 0.25    # Performance component weight
    alpha_2: 0.25    # Efficiency component weight
    alpha_3: 0.25    # Stability component weight
    alpha_4: 0.25    # Capability component weight
    
  # Adaptive weight adjustment
  adaptive_weights:
    enabled: true              # Enable adaptive weight adjustment
    adaptation_rate: 0.1       # Rate of weight adaptation
    min_weight: 0.05          # Minimum allowed weight
    max_weight: 0.7           # Maximum allowed weight
    adaptation_window: 1000    # Window for weight adaptation
    
  # Calculation parameters
  calculation:
    normalization_method: "z_score"  # Normalization method
    smoothing_factor: 0.3            # Smoothing factor for calculations
    outlier_detection: true          # Enable outlier detection
    outlier_threshold: 3.0           # Standard deviations for outlier detection
    
  # Delta thresholds
  thresholds:
    significant_change: 0.05         # Threshold for significant change
    major_improvement: 0.2           # Threshold for major improvement
    critical_degradation: -0.3       # Threshold for critical degradation
    noise_threshold: 0.01            # Threshold for filtering noise
```

### Memory Configuration

```yaml
memory:
  # Vector memory settings
  vector_memory:
    dimension: 512                   # Embedding dimension
    similarity_metric: "cosine"      # Similarity metric (cosine, euclidean, dot)
    index_type: "faiss"             # Index type (faiss, annoy, nmslib)
    max_vectors: 100000             # Maximum number of vectors
    clustering_enabled: true         # Enable vector clustering
    cluster_count: 100              # Number of clusters
    compression_enabled: false      # Enable vector compression
    
  # Episodic memory settings
  episodic_memory:
    max_episodes: 10000             # Maximum number of episodes
    retention_policy: "importance_based"  # Retention policy
    consolidation_interval: 3600    # Consolidation interval (seconds)
    importance_threshold: 0.6       # Importance threshold for retention
    temporal_decay: 0.99            # Temporal decay factor
    
  # Semantic memory settings
  semantic_memory:
    max_concepts: 5000              # Maximum number of concepts
    relationship_threshold: 0.7     # Threshold for concept relationships
    concept_decay_rate: 0.99        # Concept importance decay rate
    auto_categorization: true       # Enable automatic categorization
    hierarchy_depth: 5              # Maximum hierarchy depth
    
  # Working memory settings
  working_memory:
    capacity: 100                   # Working memory capacity
    refresh_rate: 10                # Refresh rate (seconds)
    priority_based_eviction: true   # Use priority-based eviction
    context_window: 50              # Context window size
    attention_mechanism: "weighted" # Attention mechanism
    
  # Memory consolidation
  consolidation:
    enabled: true                   # Enable memory consolidation
    frequency: 3600                 # Consolidation frequency (seconds)
    batch_size: 1000               # Batch size for consolidation
    compression_ratio: 0.8          # Target compression ratio
    quality_threshold: 0.7          # Quality threshold for consolidation
```

### Safety Configuration

```yaml
safety:
  # Validation settings
  validation:
    strictness: "high"              # Validation strictness level
    parallel_validation: true       # Enable parallel validation
    timeout: 30                     # Validation timeout (seconds)
    retry_attempts: 3               # Number of retry attempts
    cache_results: true             # Cache validation results
    
  # Risk assessment
  risk_assessment:
    tolerance: "medium"             # Risk tolerance level
    categories:                     # Risk categories to assess
      - "data_integrity"
      - "system_stability"
      - "performance_impact"
      - "security_risk"
      - "compliance_risk"
    scoring_method: "weighted_average"  # Risk scoring method
    confidence_weighting: true      # Weight by confidence scores
    
  # Safety constraints
  constraints:
    performance_bounds:
      min_accuracy: 0.8             # Minimum acceptable accuracy
      max_latency: 1000             # Maximum latency (milliseconds)
      max_memory_usage: 0.9         # Maximum memory usage (fraction)
      min_throughput: 100           # Minimum throughput (ops/sec)
    operational_limits:
      max_concurrent_adaptations: 3  # Maximum concurrent adaptations
      max_strategy_changes_per_hour: 10  # Maximum strategy changes per hour
      min_stability_period: 300     # Minimum stability period (seconds)
      max_error_rate: 0.05          # Maximum acceptable error rate
      
  # Checkpoint management
  checkpoints:
    frequency: 300                  # Checkpoint frequency (seconds)
    max_checkpoints: 20             # Maximum number of checkpoints
    compression_enabled: true       # Enable checkpoint compression
    verification_enabled: true     # Enable checkpoint verification
    incremental_checkpoints: true  # Use incremental checkpoints
    
  # Rollback configuration
  rollback:
    enabled: true                   # Enable rollback capability
    max_depth: 10                   # Maximum rollback depth
    auto_rollback_conditions:       # Conditions for automatic rollback
      - "critical_error"
      - "safety_violation"
      - "performance_degradation"
      - "resource_exhaustion"
    rollback_timeout: 60            # Rollback timeout (seconds)
```

### MCP Orchestration Configuration

```yaml
mcp_orchestration:
  # Server management
  server_management:
    discovery_enabled: true         # Enable server discovery
    discovery_interval: 60          # Discovery interval (seconds)
    health_check_interval: 30       # Health check interval (seconds)
    max_servers: 20                 # Maximum number of servers
    load_balancing_strategy: "round_robin"  # Load balancing strategy
    failover_enabled: true          # Enable automatic failover
    
  # Agent coordination
  agent_coordination:
    max_agents: 50                  # Maximum number of agents
    task_queue_size: 1000          # Task queue size
    conflict_resolution: "priority" # Conflict resolution strategy
    capability_matching: "exact"    # Capability matching strategy
    agent_timeout: 300              # Agent timeout (seconds)
    
  # Context sharing
  context_sharing:
    embedding_dimension: 512        # Context embedding dimension
    similarity_threshold: 0.8       # Similarity threshold
    ttl_default: 3600              # Default TTL for contexts (seconds)
    cleanup_interval: 300           # Cleanup interval (seconds)
    max_contexts: 10000            # Maximum number of contexts
    
  # Resource management
  resource_management:
    pools:                          # Resource pool definitions
      - pool_id: "cpu_pool"
        resource_type: "cpu"
        total_capacity: 8.0
        allocation_strategy: "fair"
      - pool_id: "memory_pool"
        resource_type: "memory"
        total_capacity: 16.0
        allocation_strategy: "priority"
      - pool_id: "gpu_pool"
        resource_type: "gpu"
        total_capacity: 4.0
        allocation_strategy: "exclusive"
        
  # Error handling
  error_handling:
    max_retry_attempts: 3           # Maximum retry attempts
    circuit_breaker_threshold: 5    # Circuit breaker threshold
    recovery_strategies:            # Available recovery strategies
      - "retry"
      - "failover"
      - "circuit_breaker"
      - "graceful_degradation"
    error_history_size: 1000       # Error history size
```

## Environment-Specific Configurations

### Development Environment

```yaml
# development.yaml
meta_cognitive:
  adaptation_threshold: 0.05  # Lower threshold for more frequent adaptations
  confidence_threshold: 0.6   # Lower confidence threshold for testing

delta_evaluation:
  weights:
    alpha_1: 0.4  # Higher weight on performance for development
    alpha_2: 0.2
    alpha_3: 0.2
    alpha_4: 0.2

memory:
  vector_memory:
    max_vectors: 10000  # Smaller limits for development

safety:
  validation:
    strictness: "medium"  # Less strict for development
  checkpoints:
    frequency: 60  # More frequent checkpoints

logging:
  level: "DEBUG"  # Verbose logging for development

development:
  debug_mode: true
  profiling_enabled: true
  test_mode: true
  mock_external_services: true
```

### Production Environment

```yaml
# production.yaml
meta_cognitive:
  adaptation_threshold: 0.15  # Higher threshold for stability
  confidence_threshold: 0.9   # Higher confidence requirement

delta_evaluation:
  weights:
    alpha_1: 0.25  # Balanced weights for production
    alpha_2: 0.25
    alpha_3: 0.3   # Higher weight on stability
    alpha_4: 0.2

memory:
  vector_memory:
    max_vectors: 1000000  # Larger limits for production
    compression_enabled: true

safety:
  validation:
    strictness: "critical"  # Maximum safety for production
  checkpoints:
    frequency: 300  # Less frequent but more comprehensive

logging:
  level: "WARNING"  # Less verbose logging

production:
  optimization_level: "high"
  cache_enabled: true
  compression_enabled: true
  async_processing: true
```

### Testing Environment

```yaml
# testing.yaml
meta_cognitive:
  adaptation_threshold: 0.01  # Very low threshold for testing
  max_strategies: 2           # Limited strategies for predictability

memory:
  vector_memory:
    max_vectors: 1000  # Small limits for fast tests

safety:
  validation:
    strictness: "low"  # Minimal safety for testing
    timeout: 5         # Short timeouts

development:
  test_mode: true
  mock_external_services: true
  seed_random_state: 42  # Deterministic behavior for tests
```

## Configuration Loading

### Python Configuration Loader

```python
import os
import yaml
import json
from typing import Dict, Any, Optional
from pathlib import Path

class SAFLAConfigLoader:
    """Configuration loader for SAFLA systems."""
    
    def __init__(self, config_dir: Optional[str] = None):
        self.config_dir = Path(config_dir) if config_dir else Path.cwd()
        self.config = {}
        
    def load_config(
        self, 
        config_file: Optional[str] = None,
        environment: Optional[str] = None
    ) -> Dict[str, Any]:
        """Load configuration from multiple sources."""
        
        # 1. Load default configuration
        self._load_defaults()
        
        # 2. Load from configuration file
        if config_file:
            self._load_from_file(config_file)
        else:
            # Try to find configuration files
            for filename in ['safla_config.yaml', 'safla_config.json', 'config.yaml']:
                config_path = self.config_dir / filename
                if config_path.exists():
                    self._load_from_file(str(config_path))
                    break
                    
        # 3. Load environment-specific configuration
        if environment:
            env_config_path = self.config_dir / f"{environment}.yaml"
            if env_config_path.exists():
                self._load_from_file(str(env_config_path))
                
        # 4. Override with environment variables
        self._load_from_environment()
        
        # 5. Validate configuration
        self._validate_config()
        
        return self.config
        
    def _load_defaults(self):
        """Load default configuration values."""
        self.config = {
            'meta_cognitive': {
                'adaptation_threshold': 0.1,
                'confidence_threshold': 0.8,
                'max_strategies': 5,
                'strategy_timeout': 300
            },
            'delta_evaluation': {
                'weights': {
                    'alpha_1': 0.25,
                    'alpha_2': 0.25,
                    'alpha_3': 0.25,
                    'alpha_4': 0.25
                }
            },
            'memory': {
                'vector_dimension': 512,
                'max_episodic_memories': 10000,
                'consolidation_threshold': 0.8
            },
            'safety': {
                'risk_tolerance': 'medium',
                'validation_strictness': 'high',
                'checkpoint_frequency': 300
            },
            'logging': {
                'level': 'INFO'
            }
        }
        
    def _load_from_file(self, config_file: str):
        """Load configuration from file."""
        config_path = Path(config_file)
        
        if not config_path.exists():
            raise FileNotFoundError(f"Configuration file not found: {config_file}")
            
        with open(config_path, 'r') as f:
            if config_path.suffix.lower() == '.yaml' or config_path.suffix.lower() == '.yml':
                file_config = yaml.safe_load(f)
            elif config_path.suffix.lower() == '.json':
                file_config = json.load(f)
            else:
                raise ValueError(f"Unsupported configuration file format: {config_path.suffix}")
                
        # Merge with existing configuration
        self._deep_merge(self.config, file_config)
        
    def _load_from_environment(self):
        """Load configuration from environment variables."""
        env_mappings = {
            'SAFLA_ADAPTATION_THRESHOLD': ('meta_cognitive', 'adaptation_threshold', float),
            'SAFLA_CONFIDENCE_THRESHOLD': ('meta_cognitive', 'confidence_threshold', float),
            'SAFLA_MAX_STRATEGIES': ('meta_cognitive', 'max_strategies', int),
            'SAFLA_ALPHA_1': ('delta_evaluation', 'weights', 'alpha_1', float),
            'SAFLA_ALPHA_2': ('delta_evaluation', 'weights', 'alpha_2', float),
            'SAFLA_ALPHA_3': ('delta_evaluation', 'weights', 'alpha_3', float),
            'SAFLA_ALPHA_4': ('delta_evaluation', 'weights', 'alpha_4', float),
            'SAFLA_VECTOR_DIM': ('memory', 'vector_dimension', int),
            'SAFLA_MAX_EPISODIC': ('memory', 'max_episodic_memories', int),
            'SAFLA_RISK_TOLERANCE': ('safety', 'risk_tolerance', str),
            'SAFLA_LOG_LEVEL': ('logging', 'level', str),
        }
        
        for env_var, config_path in env_mappings.items():
            value = os.getenv(env_var)
            if value is not None:
                # Convert value to appropriate type
                if len(config_path) == 3:
                    section, key, value_type = config_path
                    self.config[section][key] = value_type(value)
                elif len(config_path) == 4:
                    section, subsection, key, value_type = config_path
                    if section not in self.config:
                        self.config[section] = {}
                    if subsection not in self.config[section]:
                        self.config[section][subsection] = {}
                    self.config[section][subsection][key] = value_type(value)
                    
    def _deep_merge(self, base_dict: Dict, update_dict: Dict):
        """Deep merge two dictionaries."""
        for key, value in update_dict.items():
            if key in base_dict and isinstance(base_dict[key], dict) and isinstance(value, dict):
                self._deep_merge(base_dict[key], value)
            else:
                base_dict[key] = value
                
    def _validate_config(self):
        """Validate configuration values."""
        # Validate meta-cognitive settings
        meta_config = self.config.get('meta_cognitive', {})
        if meta_config.get('adaptation_threshold', 0) < 0:
            raise ValueError("adaptation_threshold must be non-negative")
        if not 0 <= meta_config.get('confidence_threshold', 0.8) <= 1:
            raise ValueError("confidence_threshold must be between 0 and 1")
            
        # Validate delta evaluation weights
        weights = self.config.get('delta_evaluation', {}).get('weights', {})
        weight_sum = sum(weights.values())
        if abs(weight_sum - 1.0) > 0.01:
            raise ValueError(f"Delta evaluation weights must sum to 1.0, got {weight_sum}")
            
        # Validate memory settings
        memory_config = self.config.get('memory', {})
        if memory_config.get('vector_dimension', 512) <= 0:
            raise ValueError("vector_dimension must be positive")
            
        # Validate safety settings
        safety_config = self.config.get('safety', {})
        valid_risk_levels = ['low', 'medium', 'high']
        if safety_config.get('risk_tolerance') not in valid_risk_levels:
            raise ValueError(f"risk_tolerance must be one of {valid_risk_levels}")

# Usage example
config_loader = SAFLAConfigLoader()
config = config_loader.load_config(environment='production')
```

## Configuration Best Practices

### 1. Environment Separation

- Use separate configuration files for different environments
- Override sensitive settings with environment variables
- Use configuration validation to catch errors early

### 2. Security Considerations

```yaml
# Never store secrets in configuration files
# Use environment variables or secret management systems
database:
  host: "localhost"
  port: 5432
  username: "${DB_USERNAME}"  # From environment
  password: "${DB_PASSWORD}"  # From environment

# Use secure defaults
safety:
  validation:
    strictness: "high"  # Default to high security
  encryption:
    enabled: true
    algorithm: "AES-256"
```

### 3. Performance Tuning

```yaml
# Tune for your specific workload
memory:
  vector_memory:
    dimension: 768  # Higher dimension for better accuracy
    max_vectors: 1000000  # Scale based on available memory
    
meta_cognitive:
  adaptation_threshold: 0.05  # Lower for more responsive systems
  max_strategies: 10  # Higher for complex environments
  
# Enable optimizations for production
production:
  optimization_level: "high"
  cache_enabled: true
  async_processing: true
  batch_processing: true
```

### 4. Monitoring and Observability

```yaml
monitoring:
  metrics:
    enabled: true
    collection_interval: 30  # More frequent for critical systems
    export_endpoints:
      - "http://prometheus:9090/metrics"
      - "http://grafana:3000/api/datasources"
      
logging:
  structured_logging: true
  correlation_ids: true
  sampling_rate: 0.1  # Sample 10% of logs for performance
```

## Troubleshooting Configuration Issues

### Common Configuration Problems

1. **Invalid Weight Configuration**
   ```
   Error: Delta evaluation weights must sum to 1.0
   Solution: Ensure alpha_1 + alpha_2 + alpha_3 + alpha_4 = 1.0
   ```

2. **Memory Dimension Mismatch**
   ```
   Error: Embedding dimension mismatch
   Solution: Ensure all memory components use the same vector dimension
   ```

3. **Safety Constraint Violations**
   ```
   Error: Performance bounds violation
   Solution: Adjust safety constraints or improve system performance
   ```

### Configuration Validation Tool

```python
def validate_safla_config(config: Dict[str, Any]) -> List[str]:
    """Validate SAFLA configuration and return list of issues."""
    issues = []
    
    # Check delta evaluation weights
    weights = config.get('delta_evaluation', {}).get('weights', {})
    if weights:
        weight_sum = sum(weights.values())
        if abs(weight_sum - 1.0) > 0.01:
            issues.append(f"Delta weights sum to {weight_sum}, should be 1.0")
            
    # Check memory dimensions
    vector_dim = config.get('memory', {}).get('vector_dimension', 512)
    context_dim = config.get('mcp_orchestration', {}).get('context_sharing', {}).get('embedding_dimension', 512)
    if vector_dim != context_dim:
        issues.append(f"Vector dimension mismatch: {vector_dim} vs {context_dim}")
        
    return issues
```

## Next Steps

- Review the [API Reference](09-api-reference.md) for implementation details
- Check the [SDK Guide](10-sdk-guide.md) for integration patterns
- Explore [Examples](12-examples.md) for practical configurations
- Study [Algorithms](13-algorithms.md) for parameter tuning guidance