515 lines
20 KiB
Python
515 lines
20 KiB
Python
"""
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Data Source Evolution Manager for Calendar Generation Framework
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Manages the evolution of data sources without architectural changes,
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providing version management, enhancement planning, and evolution tracking.
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"""
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import logging
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from typing import Dict, Any, List, Optional
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from datetime import datetime
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from .registry import DataSourceRegistry
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logger = logging.getLogger(__name__)
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class DataSourceEvolutionManager:
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"""
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Manages the evolution of data sources without architectural changes.
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Provides comprehensive evolution management including version tracking,
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enhancement planning, implementation steps, and evolution monitoring.
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"""
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def __init__(self, registry: DataSourceRegistry):
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"""
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Initialize the data source evolution manager.
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Args:
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registry: Data source registry to manage
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"""
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self.registry = registry
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self.evolution_configs = self._load_evolution_configs()
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self.evolution_history = {}
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logger.info("Initialized DataSourceEvolutionManager")
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def _load_evolution_configs(self) -> Dict[str, Dict[str, Any]]:
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"""
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Load evolution configurations for data sources.
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Returns:
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Dictionary of evolution configurations
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"""
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return {
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"content_strategy": {
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"current_version": "2.0.0",
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"target_version": "2.5.0",
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"enhancement_plan": [
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"AI-powered strategy optimization",
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"Real-time strategy adaptation",
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"Advanced audience segmentation",
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"Predictive strategy recommendations"
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],
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"implementation_steps": [
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"Implement AI strategy optimization algorithms",
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"Add real-time strategy adaptation capabilities",
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"Enhance audience segmentation with ML",
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"Integrate predictive analytics for strategy recommendations"
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],
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"priority": "high",
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"estimated_effort": "medium"
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},
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"gap_analysis": {
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"current_version": "1.5.0",
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"target_version": "2.0.0",
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"enhancement_plan": [
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"AI-powered gap identification",
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"Competitor analysis integration",
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"Market trend analysis",
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"Content opportunity scoring"
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],
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"implementation_steps": [
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"Enhance data collection methods",
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"Add AI analysis capabilities",
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"Integrate competitor data sources",
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"Implement opportunity scoring algorithms"
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],
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"priority": "high",
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"estimated_effort": "medium"
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},
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"keywords": {
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"current_version": "1.5.0",
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"target_version": "2.0.0",
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"enhancement_plan": [
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"Dynamic keyword research",
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"Trending keywords integration",
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"Competitor keyword analysis",
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"Keyword difficulty scoring"
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],
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"implementation_steps": [
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"Add dynamic research capabilities",
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"Integrate trending data sources",
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"Implement competitor analysis",
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"Add difficulty scoring algorithms"
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],
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"priority": "medium",
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"estimated_effort": "medium"
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},
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"content_pillars": {
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"current_version": "1.5.0",
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"target_version": "2.0.0",
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"enhancement_plan": [
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"AI-generated dynamic pillars",
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"Market-based pillar optimization",
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"Performance-based pillar adjustment",
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"Audience preference integration"
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],
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"implementation_steps": [
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"Implement AI pillar generation",
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"Add market analysis integration",
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"Create performance tracking",
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"Integrate audience feedback"
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],
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"priority": "medium",
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"estimated_effort": "medium"
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},
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"performance_data": {
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"current_version": "1.0.0",
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"target_version": "1.5.0",
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"enhancement_plan": [
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"Real-time performance tracking",
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"Conversion rate analysis",
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"Engagement metrics integration",
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"ROI calculation and optimization"
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],
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"implementation_steps": [
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"Build performance tracking system",
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"Implement conversion tracking",
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"Add engagement analytics",
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"Create ROI optimization algorithms"
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],
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"priority": "high",
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"estimated_effort": "high"
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},
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"ai_analysis": {
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"current_version": "2.0.0",
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"target_version": "2.5.0",
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"enhancement_plan": [
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"Advanced predictive analytics",
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"Real-time market intelligence",
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"Automated competitive analysis",
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"Strategic recommendation engine"
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],
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"implementation_steps": [
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"Enhance predictive analytics capabilities",
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"Add real-time market data integration",
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"Implement automated competitive analysis",
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"Build strategic recommendation engine"
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],
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"priority": "high",
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"estimated_effort": "high"
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}
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}
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async def evolve_data_source(self, source_id: str, target_version: str) -> bool:
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"""
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Evolve a data source to a target version.
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Args:
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source_id: ID of the source to evolve
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target_version: Target version to evolve to
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Returns:
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True if evolution successful, False otherwise
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"""
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source = self.registry.get_source(source_id)
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if not source:
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logger.error(f"Data source not found for evolution: {source_id}")
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return False
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config = self.evolution_configs.get(source_id)
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if not config:
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logger.error(f"Evolution config not found for: {source_id}")
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return False
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try:
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logger.info(f"Starting evolution of {source_id} to version {target_version}")
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# Record evolution start
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evolution_record = {
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"source_id": source_id,
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"from_version": source.version,
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"to_version": target_version,
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"started_at": datetime.utcnow().isoformat(),
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"status": "in_progress",
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"steps_completed": [],
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"steps_failed": []
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}
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# Implement evolution steps
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implementation_steps = config.get("implementation_steps", [])
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for step in implementation_steps:
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try:
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await self._implement_evolution_step(source_id, step)
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evolution_record["steps_completed"].append(step)
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logger.info(f"Completed evolution step for {source_id}: {step}")
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except Exception as e:
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evolution_record["steps_failed"].append({"step": step, "error": str(e)})
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logger.error(f"Failed evolution step for {source_id}: {step} - {e}")
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# Update source version
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source.version = target_version
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# Record evolution completion
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evolution_record["completed_at"] = datetime.utcnow().isoformat()
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evolution_record["status"] = "completed" if not evolution_record["steps_failed"] else "partial"
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# Store evolution history
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if source_id not in self.evolution_history:
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self.evolution_history[source_id] = []
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self.evolution_history[source_id].append(evolution_record)
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logger.info(f"✅ Successfully evolved {source_id} to version {target_version}")
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return True
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except Exception as e:
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logger.error(f"Error evolving data source {source_id}: {e}")
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return False
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async def _implement_evolution_step(self, source_id: str, step: str):
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"""
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Implement a specific evolution step.
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Args:
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source_id: ID of the source
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step: Step to implement
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Raises:
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Exception: If step implementation fails
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"""
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# This is a simplified implementation
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# In a real implementation, this would contain actual evolution logic
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logger.info(f"Implementing evolution step for {source_id}: {step}")
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# Simulate step implementation
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# In reality, this would contain actual code to enhance the data source
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await self._simulate_evolution_step(source_id, step)
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async def _simulate_evolution_step(self, source_id: str, step: str):
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"""
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Simulate evolution step implementation.
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Args:
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source_id: ID of the source
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step: Step to simulate
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Raises:
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Exception: If simulation fails
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"""
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# Simulate processing time
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import asyncio
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await asyncio.sleep(0.1)
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# Simulate potential failure (10% chance)
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import random
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if random.random() < 0.1:
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raise Exception(f"Simulated failure in evolution step: {step}")
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def get_evolution_status(self) -> Dict[str, Dict[str, Any]]:
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"""
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Get evolution status for all data sources.
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Returns:
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Dictionary containing evolution status for all sources
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"""
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status = {}
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for source_id, config in self.evolution_configs.items():
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source = self.registry.get_source(source_id)
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evolution_history = self.evolution_history.get(source_id, [])
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status[source_id] = {
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"current_version": getattr(source, 'version', '1.0.0') if source else config["current_version"],
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"target_version": config["target_version"],
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"enhancement_plan": config["enhancement_plan"],
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"implementation_steps": config["implementation_steps"],
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"priority": config.get("priority", "medium"),
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"estimated_effort": config.get("estimated_effort", "medium"),
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"is_active": source.is_active if source else False,
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"evolution_history": evolution_history,
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"last_evolution": evolution_history[-1] if evolution_history else None,
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"evolution_status": self._get_evolution_status_for_source(source_id, config, source)
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}
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return status
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def _get_evolution_status_for_source(self, source_id: str, config: Dict[str, Any], source) -> str:
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"""
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Get evolution status for a specific source.
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Args:
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source_id: ID of the source
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config: Evolution configuration
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source: Data source object
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Returns:
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Evolution status string
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"""
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if not source:
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return "not_registered"
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current_version = getattr(source, 'version', config["current_version"])
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target_version = config["target_version"]
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if current_version == target_version:
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return "up_to_date"
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elif current_version < target_version:
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return "needs_evolution"
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else:
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return "ahead_of_target"
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def get_evolution_plan(self, source_id: str) -> Dict[str, Any]:
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"""
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Get evolution plan for a specific source.
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Args:
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source_id: ID of the source
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Returns:
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Evolution plan dictionary
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"""
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config = self.evolution_configs.get(source_id, {})
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source = self.registry.get_source(source_id)
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plan = {
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"source_id": source_id,
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"current_version": getattr(source, 'version', '1.0.0') if source else config.get("current_version", "1.0.0"),
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"target_version": config.get("target_version", "1.0.0"),
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"enhancement_plan": config.get("enhancement_plan", []),
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"implementation_steps": config.get("implementation_steps", []),
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"priority": config.get("priority", "medium"),
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"estimated_effort": config.get("estimated_effort", "medium"),
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"is_ready_for_evolution": self._is_ready_for_evolution(source_id),
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"dependencies": self._get_evolution_dependencies(source_id)
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}
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return plan
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def _is_ready_for_evolution(self, source_id: str) -> bool:
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"""
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Check if a source is ready for evolution.
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Args:
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source_id: ID of the source
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Returns:
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True if ready for evolution, False otherwise
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"""
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source = self.registry.get_source(source_id)
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if not source:
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return False
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# Check if source is active
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if not source.is_active:
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return False
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# Check if evolution is needed
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config = self.evolution_configs.get(source_id, {})
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current_version = getattr(source, 'version', config.get("current_version", "1.0.0"))
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target_version = config.get("target_version", "1.0.0")
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return current_version < target_version
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def _get_evolution_dependencies(self, source_id: str) -> List[str]:
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"""
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Get evolution dependencies for a source.
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Args:
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source_id: ID of the source
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Returns:
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List of dependency source IDs
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"""
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# Simplified dependency mapping
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# In a real implementation, this would be more sophisticated
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dependencies = {
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"gap_analysis": ["content_strategy"],
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"keywords": ["content_strategy", "gap_analysis"],
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"content_pillars": ["content_strategy", "gap_analysis"],
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"performance_data": ["content_strategy", "gap_analysis"],
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"ai_analysis": ["content_strategy", "gap_analysis", "keywords"]
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}
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return dependencies.get(source_id, [])
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def add_evolution_config(self, source_id: str, config: Dict[str, Any]) -> bool:
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"""
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Add evolution configuration for a data source.
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Args:
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source_id: ID of the source
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config: Evolution configuration
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Returns:
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True if added successfully, False otherwise
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"""
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try:
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if source_id in self.evolution_configs:
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logger.warning(f"Evolution config already exists for: {source_id}")
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return False
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# Validate required fields
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required_fields = ["current_version", "target_version", "enhancement_plan", "implementation_steps"]
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for field in required_fields:
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if field not in config:
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logger.error(f"Missing required field for evolution config {source_id}: {field}")
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return False
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self.evolution_configs[source_id] = config
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logger.info(f"Added evolution config for: {source_id}")
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return True
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except Exception as e:
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logger.error(f"Error adding evolution config for {source_id}: {e}")
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return False
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def update_evolution_config(self, source_id: str, config: Dict[str, Any]) -> bool:
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"""
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Update evolution configuration for a data source.
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Args:
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source_id: ID of the source
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config: Updated evolution configuration
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Returns:
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True if updated successfully, False otherwise
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"""
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try:
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if source_id not in self.evolution_configs:
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logger.error(f"Evolution config not found for: {source_id}")
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return False
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# Update configuration
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self.evolution_configs[source_id].update(config)
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logger.info(f"Updated evolution config for: {source_id}")
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return True
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except Exception as e:
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logger.error(f"Error updating evolution config for {source_id}: {e}")
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return False
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def get_evolution_summary(self) -> Dict[str, Any]:
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"""
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Get comprehensive evolution summary.
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Returns:
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Evolution summary dictionary
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"""
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summary = {
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"total_sources": len(self.evolution_configs),
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"sources_needing_evolution": 0,
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"sources_up_to_date": 0,
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"evolution_priority": {
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"high": 0,
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"medium": 0,
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"low": 0
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},
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"evolution_effort": {
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"high": 0,
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"medium": 0,
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"low": 0
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},
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"recent_evolutions": [],
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"evolution_recommendations": []
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}
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for source_id, config in self.evolution_configs.items():
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source = self.registry.get_source(source_id)
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if source:
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status = self._get_evolution_status_for_source(source_id, config, source)
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if status == "needs_evolution":
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summary["sources_needing_evolution"] += 1
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elif status == "up_to_date":
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summary["sources_up_to_date"] += 1
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# Count priorities and efforts
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priority = config.get("priority", "medium")
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effort = config.get("estimated_effort", "medium")
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summary["evolution_priority"][priority] += 1
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summary["evolution_effort"][effort] += 1
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# Get recent evolutions
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for source_id, history in self.evolution_history.items():
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if history:
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latest = history[-1]
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if latest.get("status") == "completed":
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summary["recent_evolutions"].append({
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"source_id": source_id,
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"from_version": latest.get("from_version"),
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"to_version": latest.get("to_version"),
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"completed_at": latest.get("completed_at")
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})
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# Generate recommendations
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for source_id, config in self.evolution_configs.items():
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if self._is_ready_for_evolution(source_id):
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summary["evolution_recommendations"].append({
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"source_id": source_id,
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"priority": config.get("priority", "medium"),
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"effort": config.get("estimated_effort", "medium"),
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"target_version": config.get("target_version")
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})
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return summary
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def __str__(self) -> str:
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"""String representation of the evolution manager."""
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return f"DataSourceEvolutionManager(sources={len(self.evolution_configs)}, registry={self.registry})"
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def __repr__(self) -> str:
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"""Detailed string representation of the evolution manager."""
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return f"DataSourceEvolutionManager(configs={list(self.evolution_configs.keys())}, history={list(self.evolution_history.keys())})"
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