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.
SAFLA supports configuration through environment variables with the SAFLA_ prefix:
# 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=60Create a safla_config.yaml file:
# 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: trueCreate a safla_config.json file:
{
"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"
}
}
}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'])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 iterationsdelta_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 noisememory:
# 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 consolidationsafety:
# 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:
# 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# 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.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.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 testsimport 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')- Use separate configuration files for different environments
- Override sensitive settings with environment variables
- Use configuration validation to catch errors early
# 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"# 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: truemonitoring:
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-
Invalid Weight Configuration
Error: Delta evaluation weights must sum to 1.0 Solution: Ensure alpha_1 + alpha_2 + alpha_3 + alpha_4 = 1.0 -
Memory Dimension Mismatch
Error: Embedding dimension mismatch Solution: Ensure all memory components use the same vector dimension -
Safety Constraint Violations
Error: Performance bounds violation Solution: Adjust safety constraints or improve system performance
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- Review the API Reference for implementation details
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