The SAFLA SDK provides a comprehensive toolkit for building autonomous systems with self-aware feedback loops. This guide covers integration patterns, best practices, and practical usage scenarios for implementing SAFLA in your applications.
pip install saflaimport asyncio
from safla.core.meta_cognitive_engine import MetaCognitiveEngine
from safla.core.delta_evaluation import DeltaEvaluator
from safla.core.hybrid_memory import HybridMemoryArchitecture
from safla.core.safety_validation import SafetyValidationFramework
async def main():
# Initialize core components
engine = MetaCognitiveEngine()
delta_evaluator = DeltaEvaluator()
memory = HybridMemoryArchitecture()
safety = SafetyValidationFramework()
# Initialize the system
await engine.initialize()
# Your application logic here
if __name__ == "__main__":
asyncio.run(main())The meta-cognitive engine is the heart of SAFLA. Here's how to integrate it effectively:
from safla.core.meta_cognitive_engine import (
MetaCognitiveEngine,
SystemState,
Goal,
Strategy,
PerformanceMetrics
)
class MyAutonomousSystem:
def __init__(self):
self.engine = MetaCognitiveEngine({
'adaptation_threshold': 0.1,
'confidence_threshold': 0.8,
'max_strategies': 5
})
async def initialize(self):
"""Initialize the autonomous system."""
await self.engine.initialize()
# Define initial goals
goal = Goal(
goal_id="optimize_performance",
description="Optimize system performance metrics",
target_metrics={"accuracy": 0.95, "efficiency": 0.90},
priority=1,
success_criteria={"min_accuracy": 0.90}
)
await self.engine.add_goal(goal)
async def process_feedback_loop(self, external_feedback: dict):
"""Process feedback through the meta-cognitive loop."""
# Get current system state
current_state = self.engine.get_system_state()
# Process feedback
adaptation_result = await self.engine.process_feedback(external_feedback)
# Check if adaptation was successful
if adaptation_result.success:
print(f"Adaptation successful: {adaptation_result.changes_made}")
else:
print(f"Adaptation failed: {adaptation_result.metadata.get('error')}")
return adaptation_resultclass AdvancedStrategyManager:
def __init__(self, engine: MetaCognitiveEngine):
self.engine = engine
async def create_adaptive_strategy(self, context: dict) -> Strategy:
"""Create a strategy that adapts to current context."""
strategy = Strategy(
strategy_id=f"adaptive_{int(time.time())}",
name="Adaptive Performance Strategy",
description="Strategy that adapts based on current performance",
actions=[
{
"type": "parameter_adjustment",
"parameters": self._calculate_optimal_parameters(context)
},
{
"type": "resource_reallocation",
"allocation": self._optimize_resource_allocation(context)
}
],
expected_outcomes={"performance_gain": 0.15},
confidence_score=0.85,
resource_requirements={"cpu": 0.3, "memory": 0.2}
)
return strategy
def _calculate_optimal_parameters(self, context: dict) -> dict:
"""Calculate optimal parameters based on context."""
# Your parameter optimization logic here
return {"learning_rate": 0.01, "batch_size": 32}
def _optimize_resource_allocation(self, context: dict) -> dict:
"""Optimize resource allocation based on context."""
# Your resource optimization logic here
return {"worker_threads": 4, "memory_pool": "2GB"}The delta evaluation system quantifies improvements using the SAFLA formula:
from safla.core.delta_evaluation import DeltaEvaluator, AdaptiveWeights, PerformanceMetrics
class PerformanceTracker:
def __init__(self):
# Initialize with custom weights
weights = AdaptiveWeights(
alpha_1=0.3, # Performance weight
alpha_2=0.25, # Efficiency weight
alpha_3=0.25, # Stability weight
alpha_4=0.2 # Capability weight
)
self.delta_evaluator = DeltaEvaluator(weights)
self.previous_metrics = None
async def track_performance(self, current_data: dict) -> float:
"""Track performance and calculate delta improvements."""
# Convert current data to PerformanceMetrics
current_metrics = PerformanceMetrics(
timestamp=time.time(),
accuracy=current_data.get('accuracy', 0.0),
efficiency=current_data.get('efficiency', 0.0),
stability=current_data.get('stability', 0.0),
capability_score=current_data.get('capability', 0.0),
custom_metrics=current_data.get('custom', {})
)
if self.previous_metrics:
# Calculate delta improvement
delta_result = self.delta_evaluator.calculate_delta(
current_metrics,
self.previous_metrics
)
print(f"Total Delta: {delta_result.total_delta}")
print(f"Component Deltas: {delta_result.component_deltas}")
# Adapt weights based on results
if delta_result.total_delta > 0.1:
await self._adapt_weights(delta_result)
self.previous_metrics = current_metrics
return delta_result.total_delta
else:
self.previous_metrics = current_metrics
return 0.0
async def _adapt_weights(self, delta_result):
"""Adapt weights based on delta results."""
# Example adaptive weight adjustment
new_weights = AdaptiveWeights(
alpha_1=min(0.4, delta_result.weights_used.alpha_1 + 0.05),
alpha_2=delta_result.weights_used.alpha_2,
alpha_3=delta_result.weights_used.alpha_3,
alpha_4=delta_result.weights_used.alpha_4
)
self.delta_evaluator.update_weights(new_weights)The hybrid memory system provides sophisticated memory management:
from safla.core.hybrid_memory import HybridMemoryArchitecture, MemoryItem
class IntelligentMemoryManager:
def __init__(self):
self.memory = HybridMemoryArchitecture({
'vector_dimension': 768,
'max_episodic_memories': 10000,
'semantic_consolidation_threshold': 0.8,
'working_memory_capacity': 100
})
async def store_experience(self, experience: dict) -> str:
"""Store an experience in the appropriate memory system."""
# Determine memory type based on experience characteristics
if experience.get('type') == 'immediate':
memory_type = 'working'
elif experience.get('importance', 0.5) > 0.8:
memory_type = 'episodic'
elif experience.get('generalizable', False):
memory_type = 'semantic'
else:
memory_type = 'vector'
# Store the experience
memory_id = await self.memory.store_memory(
content=experience,
memory_type=memory_type,
metadata={
'importance': experience.get('importance', 0.5),
'context': experience.get('context', {}),
'tags': experience.get('tags', [])
}
)
return memory_id
async def retrieve_relevant_memories(self, query: dict, max_results: int = 5):
"""Retrieve memories relevant to a query."""
# Convert query to vector (you'll need your own embedding function)
query_vector = await self._embed_query(query)
# Search for similar memories
similar_memories = await self.memory.search_similar(
query_vector=query_vector,
memory_type='vector',
max_results=max_results,
similarity_threshold=0.7
)
return similar_memories
async def _embed_query(self, query: dict) -> list:
"""Convert query to embedding vector."""
# Implement your embedding logic here
# This could use transformers, sentence-transformers, etc.
return [0.1] * 768 # PlaceholderSafety is paramount in autonomous systems:
from safla.core.safety_validation import SafetyValidationFramework, ValidationResult
class SafeAutonomousSystem:
def __init__(self):
self.safety = SafetyValidationFramework({
'risk_tolerance': 'low',
'validation_strictness': 'high',
'checkpoint_frequency': 300 # 5 minutes
})
self.checkpoint_history = []
async def execute_safe_action(self, action: dict) -> bool:
"""Execute an action with safety validation."""
# Create checkpoint before action
checkpoint_id = await self.safety.create_checkpoint(
f"Before action: {action.get('type', 'unknown')}"
)
self.checkpoint_history.append(checkpoint_id)
try:
# Validate action safety
validation_result = await self.safety.validate_action(action)
if not validation_result.is_valid:
print(f"Action rejected: {validation_result.violations}")
return False
# Assess risk
risk_assessment = await self.safety.assess_risk(
context=await self._get_current_context(),
proposed_action=action
)
if risk_assessment.overall_risk_score > 0.7:
print(f"High risk detected: {risk_assessment.risk_categories}")
# Apply mitigation strategies
for strategy in risk_assessment.mitigation_strategies:
await self._apply_mitigation(strategy)
# Execute the action
result = await self._execute_action(action)
# Validate post-execution state
post_validation = await self._validate_post_execution()
if not post_validation:
# Rollback if post-execution validation fails
await self.safety.rollback_to_checkpoint(checkpoint_id)
return False
return result
except Exception as e:
# Rollback on any exception
print(f"Exception during action execution: {e}")
await self.safety.rollback_to_checkpoint(checkpoint_id)
return False
async def _get_current_context(self) -> dict:
"""Get current system context for risk assessment."""
return {
'system_load': 0.6,
'active_processes': 15,
'memory_usage': 0.7,
'network_status': 'stable'
}
async def _apply_mitigation(self, strategy: str):
"""Apply a risk mitigation strategy."""
if strategy == "reduce_concurrency":
# Reduce concurrent operations
pass
elif strategy == "increase_monitoring":
# Increase monitoring frequency
pass
async def _execute_action(self, action: dict) -> bool:
"""Execute the actual action."""
# Your action execution logic here
return True
async def _validate_post_execution(self) -> bool:
"""Validate system state after action execution."""
# Your post-execution validation logic here
return TrueFor distributed systems with multiple agents:
from safla.core.mcp_orchestration import MCPOrchestrator, MCPServer, Agent
class DistributedSAFLASystem:
def __init__(self):
self.orchestrator = MCPOrchestrator()
self.agents = {}
async def initialize_distributed_system(self):
"""Initialize the distributed SAFLA system."""
# Start the orchestrator
await self.orchestrator.start()
# Register MCP servers
servers = [
MCPServer(
server_id="analysis_server",
name="Data Analysis Server",
endpoint="http://localhost:8001",
capabilities=["data_analysis", "pattern_recognition"],
status="active"
),
MCPServer(
server_id="optimization_server",
name="Optimization Server",
endpoint="http://localhost:8002",
capabilities=["optimization", "parameter_tuning"],
status="active"
)
]
for server in servers:
self.orchestrator.server_manager.register_server(server)
# Register agents
agents = [
Agent(
agent_id="analyzer_agent",
name="Data Analyzer",
agent_type="analysis",
capabilities=["data_analysis"],
status="active",
server_id="analysis_server",
priority=2
),
Agent(
agent_id="optimizer_agent",
name="System Optimizer",
agent_type="optimization",
capabilities=["optimization"],
status="active",
server_id="optimization_server",
priority=1
)
]
for agent in agents:
self.orchestrator.agent_coordinator.register_agent(agent)
self.agents[agent.agent_id] = agent
async def process_distributed_request(self, request: dict):
"""Process a request through the distributed system."""
# Add request metadata
request.update({
"request_id": f"req_{int(time.time())}",
"timestamp": time.time()
})
# Process through orchestrator
response = await self.orchestrator.process_request(request)
return response
async def get_system_health(self):
"""Get comprehensive system health status."""
return self.orchestrator.get_system_status()import os
from typing import Dict, Any
class SAFLAConfig:
"""Centralized configuration management for SAFLA systems."""
@staticmethod
def get_default_config() -> Dict[str, Any]:
return {
'meta_cognitive': {
'adaptation_threshold': float(os.getenv('SAFLA_ADAPTATION_THRESHOLD', '0.1')),
'confidence_threshold': float(os.getenv('SAFLA_CONFIDENCE_THRESHOLD', '0.8')),
'max_strategies': int(os.getenv('SAFLA_MAX_STRATEGIES', '5'))
},
'delta_evaluation': {
'alpha_1': float(os.getenv('SAFLA_ALPHA_1', '0.25')),
'alpha_2': float(os.getenv('SAFLA_ALPHA_2', '0.25')),
'alpha_3': float(os.getenv('SAFLA_ALPHA_3', '0.25')),
'alpha_4': float(os.getenv('SAFLA_ALPHA_4', '0.25'))
},
'memory': {
'vector_dimension': int(os.getenv('SAFLA_VECTOR_DIM', '512')),
'max_episodic_memories': int(os.getenv('SAFLA_MAX_EPISODIC', '10000')),
'consolidation_threshold': float(os.getenv('SAFLA_CONSOLIDATION_THRESHOLD', '0.8'))
},
'safety': {
'risk_tolerance': os.getenv('SAFLA_RISK_TOLERANCE', 'medium'),
'validation_strictness': os.getenv('SAFLA_VALIDATION_STRICTNESS', 'high'),
'checkpoint_frequency': int(os.getenv('SAFLA_CHECKPOINT_FREQ', '300'))
}
}import logging
from typing import Optional
from functools import wraps
# Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
def safla_error_handler(fallback_value=None):
"""Decorator for consistent error handling in SAFLA operations."""
def decorator(func):
@wraps(func)
async def wrapper(*args, **kwargs):
try:
return await func(*args, **kwargs)
except Exception as e:
logger = logging.getLogger(func.__module__)
logger.error(f"Error in {func.__name__}: {e}", exc_info=True)
# Return fallback value or re-raise based on criticality
if fallback_value is not None:
return fallback_value
else:
raise
return wrapper
return decorator
class SAFLALogger:
"""Specialized logger for SAFLA operations."""
def __init__(self, component_name: str):
self.logger = logging.getLogger(f"safla.{component_name}")
def log_adaptation(self, adaptation_result):
"""Log adaptation results."""
self.logger.info(
f"Adaptation {adaptation_result.adaptation_id}: "
f"Success={adaptation_result.success}, "
f"Delta={adaptation_result.performance_impact}"
)
def log_safety_violation(self, violation):
"""Log safety violations."""
self.logger.warning(
f"Safety violation: {violation.constraint_type} - {violation.description}"
)
def log_memory_operation(self, operation: str, memory_id: str, memory_type: str):
"""Log memory operations."""
self.logger.debug(f"Memory {operation}: {memory_id} ({memory_type})")import unittest
from unittest.mock import AsyncMock, MagicMock
import asyncio
class TestSAFLAIntegration(unittest.TestCase):
"""Test patterns for SAFLA integration."""
def setUp(self):
"""Set up test environment."""
self.loop = asyncio.new_event_loop()
asyncio.set_event_loop(self.loop)
def tearDown(self):
"""Clean up test environment."""
self.loop.close()
def test_meta_cognitive_engine_initialization(self):
"""Test meta-cognitive engine initialization."""
async def run_test():
engine = MetaCognitiveEngine()
result = await engine.initialize()
self.assertTrue(result)
self.loop.run_until_complete(run_test())
def test_delta_evaluation_calculation(self):
"""Test delta evaluation calculation."""
evaluator = DeltaEvaluator()
current_metrics = PerformanceMetrics(
timestamp=time.time(),
accuracy=0.95,
efficiency=0.90,
stability=0.85,
capability_score=0.88
)
previous_metrics = PerformanceMetrics(
timestamp=time.time() - 100,
accuracy=0.90,
efficiency=0.85,
stability=0.80,
capability_score=0.83
)
result = evaluator.calculate_delta(current_metrics, previous_metrics)
self.assertGreater(result.total_delta, 0)
def test_safety_validation(self):
"""Test safety validation."""
async def run_test():
safety = SafetyValidationFramework()
action = {
"type": "parameter_update",
"parameters": {"learning_rate": 0.01}
}
result = await safety.validate_action(action)
self.assertIsInstance(result, ValidationResult)
self.loop.run_until_complete(run_test())import asyncio
from concurrent.futures import ThreadPoolExecutor
import time
class OptimizedSAFLASystem:
"""Performance-optimized SAFLA system implementation."""
def __init__(self):
self.executor = ThreadPoolExecutor(max_workers=4)
self.cache = {}
self.cache_ttl = 300 # 5 minutes
async def cached_operation(self, operation_key: str, operation_func, *args, **kwargs):
"""Perform operation with caching."""
current_time = time.time()
# Check cache
if operation_key in self.cache:
cached_result, timestamp = self.cache[operation_key]
if current_time - timestamp < self.cache_ttl:
return cached_result
# Perform operation
if asyncio.iscoroutinefunction(operation_func):
result = await operation_func(*args, **kwargs)
else:
# Run CPU-bound operations in thread pool
result = await asyncio.get_event_loop().run_in_executor(
self.executor, operation_func, *args, **kwargs
)
# Cache result
self.cache[operation_key] = (result, current_time)
return result
async def batch_process_feedback(self, feedback_batch: list):
"""Process multiple feedback items in parallel."""
tasks = []
for feedback in feedback_batch:
task = asyncio.create_task(self._process_single_feedback(feedback))
tasks.append(task)
results = await asyncio.gather(*tasks, return_exceptions=True)
return results
async def _process_single_feedback(self, feedback: dict):
"""Process a single feedback item."""
# Your feedback processing logic here
await asyncio.sleep(0.1) # Simulate processing time
return {"processed": True, "feedback_id": feedback.get("id")}from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import asyncio
app = FastAPI(title="SAFLA Web Service")
# Global SAFLA system instance
safla_system = None
class FeedbackRequest(BaseModel):
data: dict
context: dict = {}
@app.on_event("startup")
async def startup_event():
"""Initialize SAFLA system on startup."""
global safla_system
safla_system = MyAutonomousSystem()
await safla_system.initialize()
@app.post("/feedback")
async def process_feedback(request: FeedbackRequest):
"""Process feedback through SAFLA system."""
try:
result = await safla_system.process_feedback_loop(request.data)
return {
"success": result.success,
"adaptation_id": result.adaptation_id,
"changes": result.changes_made
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/status")
async def get_system_status():
"""Get current system status."""
state = safla_system.engine.get_system_state()
return {
"state_id": state.state_id,
"confidence": state.confidence_level,
"active_goals": state.active_goals,
"performance": state.performance_metrics
}import torch
import torch.nn as nn
from sklearn.metrics import accuracy_score
class SAFLAMLPipeline:
"""Machine learning pipeline with SAFLA integration."""
def __init__(self, model: nn.Module):
self.model = model
self.safla_system = MyAutonomousSystem()
self.performance_tracker = PerformanceTracker()
async def train_with_safla(self, train_loader, val_loader, epochs: int):
"""Train model with SAFLA feedback loop."""
await self.safla_system.initialize()
for epoch in range(epochs):
# Training phase
train_loss = self._train_epoch(train_loader)
# Validation phase
val_loss, val_accuracy = self._validate_epoch(val_loader)
# Calculate performance metrics
performance_data = {
'accuracy': val_accuracy,
'efficiency': 1.0 / (train_loss + 1e-8),
'stability': self._calculate_stability(),
'capability': self._calculate_capability()
}
# Track performance and get delta
delta = await self.performance_tracker.track_performance(performance_data)
# Process through SAFLA if significant change
if abs(delta) > 0.05:
feedback = {
'epoch': epoch,
'delta': delta,
'metrics': performance_data,
'model_state': self._get_model_state()
}
adaptation = await self.safla_system.process_feedback_loop(feedback)
if adaptation.success:
# Apply adaptations to model
await self._apply_model_adaptations(adaptation.changes_made)
def _train_epoch(self, train_loader):
"""Train for one epoch."""
# Your training logic here
return 0.1 # Mock loss
def _validate_epoch(self, val_loader):
"""Validate for one epoch."""
# Your validation logic here
return 0.05, 0.95 # Mock loss and accuracy
def _calculate_stability(self):
"""Calculate model stability metric."""
# Your stability calculation here
return 0.9
def _calculate_capability(self):
"""Calculate model capability metric."""
# Your capability calculation here
return 0.85
def _get_model_state(self):
"""Get current model state."""
return {
'parameters': len(list(self.model.parameters())),
'architecture': str(self.model)
}
async def _apply_model_adaptations(self, changes: list):
"""Apply adaptations to the model."""
for change in changes:
if change['type'] == 'learning_rate_adjustment':
# Adjust learning rate
pass
elif change['type'] == 'architecture_modification':
# Modify architecture
pass-
Memory Leaks in Long-Running Systems
# Implement periodic cleanup async def periodic_cleanup(): while True: await asyncio.sleep(3600) # Every hour memory.cleanup_expired_contexts() gc.collect()
-
Performance Degradation
# Monitor and optimize performance async def performance_monitor(): while True: metrics = await collect_performance_metrics() if metrics['response_time'] > threshold: await optimize_system_performance()
-
Safety Validation Failures
# Implement graceful degradation if not validation_result.is_valid: await enable_safe_mode() await notify_administrators(validation_result.violations)
- Review the Configuration Guide for detailed setup options
- Explore Examples for more practical implementations
- Study Algorithms for mathematical foundations
- Check the API Reference for complete method documentation
- GitHub Issues: Report bugs and request features
- Documentation: Comprehensive guides and tutorials
- Community Forum: Ask questions and share experiences
- Professional Support: Enterprise support options available