675 lines
26 KiB
Python
675 lines
26 KiB
Python
"""
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Report Generation System for Allegro-Primus
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Generates various reports: daily, weekly, issues, knowledge, self-improvement.
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"""
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import os
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import json
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import logging
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from datetime import datetime, timedelta
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from pathlib import Path
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from typing import Dict, List, Any, Optional
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from dataclasses import dataclass, field, asdict
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from collections import defaultdict
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# Paths
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BASE_DIR = Path("/root/wizards/allegro-primus")
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JOURNAL_DIR = BASE_DIR / ".journal"
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KNOWLEDGE_DIR = BASE_DIR / "knowledge"
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REPORTS_DIR = BASE_DIR / ".journal" / "reports"
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REPORTS_DIR.mkdir(parents=True, exist_ok=True)
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@dataclass
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class Report:
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"""Base report structure."""
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title: str
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type: str
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generated_at: str
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period_start: str
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period_end: str
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summary: str
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sections: List[Dict[str, Any]] = field(default_factory=list)
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metrics: Dict[str, Any] = field(default_factory=dict)
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recommendations: List[str] = field(default_factory=list)
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def to_dict(self) -> Dict[str, Any]:
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return asdict(self)
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def to_json(self, indent: int = 2) -> str:
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return json.dumps(self.to_dict(), indent=indent, default=str)
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def to_markdown(self) -> str:
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"""Convert report to markdown format."""
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md = f"# {self.title}\n\n"
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md += f"**Type:** {self.type}\n"
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md += f"**Generated:** {self.generated_at}\n"
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md += f"**Period:** {self.period_start} to {self.period_end}\n\n"
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md += f"## Summary\n\n{self.summary}\n\n"
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if self.metrics:
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md += "## Metrics\n\n"
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for key, value in self.metrics.items():
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md += f"- **{key}:** {value}\n"
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md += "\n"
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if self.sections:
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md += "## Details\n\n"
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for section in self.sections:
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md += f"### {section.get('title', 'Section')}\n\n"
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md += f"{section.get('content', '')}\n\n"
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if self.recommendations:
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md += "## Recommendations\n\n"
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for rec in self.recommendations:
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md += f"- {rec}\n"
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md += "\n"
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return md
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def save(self) -> Path:
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"""Save report to disk."""
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filename = f"{self.type}_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
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# Save as JSON
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json_path = REPORTS_DIR / f"{filename}.json"
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with open(json_path, 'w') as f:
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json.dump(self.to_dict(), f, indent=2, default=str)
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# Save as Markdown
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md_path = REPORTS_DIR / f"{filename}.md"
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with open(md_path, 'w') as f:
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f.write(self.to_markdown())
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logger.info(f"Report saved: {json_path} and {md_path}")
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return json_path
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class ReportGenerator:
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"""Generates various reports from system data."""
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def __init__(self):
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self.journal_entries = self._load_journal()
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self.metrics = self._load_metrics()
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def _load_journal(self) -> List[Dict[str, Any]]:
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"""Load journal entries."""
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entries = []
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entries_file = JOURNAL_DIR / "entries.jsonl"
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if entries_file.exists():
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try:
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with open(entries_file, 'r') as f:
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for line in f:
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if line.strip():
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entries.append(json.loads(line))
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except Exception as e:
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logger.error(f"Error loading journal: {e}")
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return entries
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def _load_metrics(self) -> Dict[str, Any]:
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"""Load current metrics."""
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metrics_file = JOURNAL_DIR / "metrics.json"
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if metrics_file.exists():
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try:
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with open(metrics_file, 'r') as f:
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return json.load(f)
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except Exception as e:
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logger.error(f"Error loading metrics: {e}")
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return {}
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def _filter_entries_by_date(self, days: int = 1) -> List[Dict[str, Any]]:
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"""Filter entries by date range."""
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cutoff = datetime.now() - timedelta(days=days)
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filtered = []
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for entry in self.journal_entries:
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try:
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entry_time = datetime.fromisoformat(entry.get('timestamp', ''))
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if entry_time >= cutoff:
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filtered.append(entry)
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except:
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continue
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return filtered
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def generate_daily_report(self, date: Optional[datetime] = None) -> Report:
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"""Generate daily summary report."""
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if date is None:
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date = datetime.now()
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period_start = date.replace(hour=0, minute=0, second=0, microsecond=0)
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period_end = period_start + timedelta(days=1)
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# Get today's entries
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today_entries = self._filter_entries_by_date(1)
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# Calculate metrics
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total_tasks = len(today_entries)
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successful = sum(1 for e in today_entries if e.get('success', False))
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failed = total_tasks - successful
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success_rate = (successful / total_tasks * 100) if total_tasks > 0 else 0
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response_times = [e.get('response_time_ms', 0) for e in today_entries if e.get('response_time_ms')]
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avg_response_time = sum(response_times) / len(response_times) if response_times else 0
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# Task breakdown
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task_types = defaultdict(int)
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for entry in today_entries:
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task = entry.get('task', '').lower()
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if any(x in task for x in ['test', 'spec']):
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task_types['Testing'] += 1
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elif any(x in task for x in ['implement', 'create', 'build']):
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task_types['Implementation'] += 1
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elif any(x in task for x in ['fix', 'debug', 'repair']):
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task_types['Bugfix'] += 1
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elif any(x in task for x in ['analyze', 'review']):
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task_types['Analysis'] += 1
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else:
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task_types['Other'] += 1
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# Lessons learned
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lessons = []
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for entry in today_entries:
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lessons.extend(entry.get('lessons_learned', []))
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summary = f"Completed {total_tasks} tasks with {success_rate:.1f}% success rate. "
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if failed > 0:
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summary += f"{failed} tasks required attention. "
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summary += f"Average response time: {avg_response_time:.0f}ms."
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sections = [
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{
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"title": "Task Breakdown",
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"content": json.dumps(dict(task_types), indent=2)
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},
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{
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"title": "Completed Tasks",
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"content": "\n".join([f"- {e.get('task', 'Unknown')}: {e.get('result', 'No result')}"
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for e in today_entries[:10]])
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}
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]
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if lessons:
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sections.append({
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"title": "Lessons Learned",
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"content": "\n".join([f"- {lesson}" for lesson in lessons])
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})
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report = Report(
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title=f"Daily Report - {date.strftime('%Y-%m-%d')}",
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type="daily",
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generated_at=datetime.now().isoformat(),
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period_start=period_start.isoformat(),
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period_end=period_end.isoformat(),
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summary=summary,
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sections=sections,
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metrics={
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"total_tasks": total_tasks,
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"successful": successful,
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"failed": failed,
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"success_rate_percent": round(success_rate, 1),
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"avg_response_time_ms": round(avg_response_time, 0),
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"task_types": dict(task_types)
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},
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recommendations=self._generate_daily_recommendations(today_entries, success_rate)
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)
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report.save()
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return report
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def _generate_daily_recommendations(self, entries: List[Dict], success_rate: float) -> List[str]:
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"""Generate recommendations based on daily performance."""
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recommendations = []
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if success_rate < 90:
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recommendations.append("Success rate below 90% - review failed tasks for patterns")
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if len(entries) < 5:
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recommendations.append("Low activity detected - consider increasing task throughput")
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response_times = [e.get('response_time_ms', 0) for e in entries if e.get('response_time_ms')]
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if response_times and sum(response_times) / len(response_times) > 1000:
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recommendations.append("High average response time - consider task decomposition")
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if not recommendations:
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recommendations.append("Performance within normal parameters - maintain current pace")
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return recommendations
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def generate_weekly_report(self, week_start: Optional[datetime] = None) -> Report:
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"""Generate weekly performance report."""
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if week_start is None:
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# Start from beginning of current week (Monday)
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today = datetime.now()
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week_start = today - timedelta(days=today.weekday())
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week_start = week_start.replace(hour=0, minute=0, second=0, microsecond=0)
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period_end = week_start + timedelta(days=7)
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# Get week's entries
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week_entries = []
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for i in range(7):
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day_start = week_start + timedelta(days=i)
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day_entries = [e for e in self.journal_entries
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if day_start <= datetime.fromisoformat(e.get('timestamp', '2000-01-01')) < day_start + timedelta(days=1)]
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week_entries.extend(day_entries)
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# Weekly metrics
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total_tasks = len(week_entries)
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successful = sum(1 for e in week_entries if e.get('success', False))
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failed = total_tasks - successful
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success_rate = (successful / total_tasks * 100) if total_tasks > 0 else 0
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# Daily breakdown
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daily_stats = {}
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for i in range(7):
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day = week_start + timedelta(days=i)
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day_entries = [e for e in week_entries
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if datetime.fromisoformat(e.get('timestamp', '2000-01-01')).date() == day.date()]
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daily_stats[day.strftime('%A')] = {
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"tasks": len(day_entries),
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"success": sum(1 for e in day_entries if e.get('success', False))
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}
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# Trends (compare with previous week if available)
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prev_week_entries = self._filter_entries_by_date(14)
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prev_week_entries = [e for e in prev_week_entries
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if datetime.fromisoformat(e.get('timestamp', '2000-01-01')) < week_start]
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trend = "stable"
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if len(prev_week_entries) > 0:
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prev_success_rate = sum(1 for e in prev_week_entries if e.get('success', False)) / len(prev_week_entries) * 100
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if success_rate > prev_success_rate + 5:
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trend = "improving"
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elif success_rate < prev_success_rate - 5:
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trend = "declining"
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summary = f"Weekly summary: {total_tasks} tasks completed with {success_rate:.1f}% success rate. "
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summary += f"Trend: {trend}. Peak day: {max(daily_stats, key=lambda x: daily_stats[x]['tasks'])}."
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sections = [
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{
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"title": "Daily Breakdown",
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"content": json.dumps(daily_stats, indent=2)
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},
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{
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"title": "Performance Trends",
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"content": f"Success rate trend: {trend}\n"
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f"Tasks this week: {total_tasks}\n"
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f"Tasks previous week: {len(prev_week_entries)}"
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}
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]
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report = Report(
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title=f"Weekly Report - Week of {week_start.strftime('%Y-%m-%d')}",
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type="weekly",
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generated_at=datetime.now().isoformat(),
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period_start=week_start.isoformat(),
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period_end=period_end.isoformat(),
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summary=summary,
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sections=sections,
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metrics={
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"total_tasks": total_tasks,
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"successful": successful,
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"failed": failed,
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"success_rate_percent": round(success_rate, 1),
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"daily_average": round(total_tasks / 7, 1),
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"trend": trend
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},
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recommendations=self._generate_weekly_recommendations(week_entries, success_rate, trend)
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)
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report.save()
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return report
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def _generate_weekly_recommendations(self, entries: List[Dict], success_rate: float, trend: str) -> List[str]:
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"""Generate weekly recommendations."""
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recommendations = []
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if trend == "declining":
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recommendations.append("Performance declining - schedule retrospective to identify root causes")
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if success_rate < 85:
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recommendations.append("Weekly success rate below 85% - prioritize stability over new features")
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task_count = len(entries)
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if task_count > 50 and success_rate > 95:
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recommendations.append("High throughput with high success - consider increasing complexity of tasks")
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if task_count < 20:
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recommendations.append("Low weekly task count - review for blockers or resource constraints")
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if not recommendations:
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recommendations.append("Weekly performance healthy - continue current strategies")
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return recommendations
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def generate_issues_report(self) -> Report:
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"""Generate issue resolution report."""
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# Load improvement queue
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queue_file = BASE_DIR / "improvement_queue.json"
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issues = []
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if queue_file.exists():
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try:
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with open(queue_file, 'r') as f:
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data = json.load(f)
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# Handle different queue formats
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if isinstance(data, dict):
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issues = data.get('pending', []) + data.get('implemented', []) + data.get('rejected', [])
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elif isinstance(data, list):
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issues = data
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except Exception as e:
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logger.error(f"Error loading improvement queue: {e}")
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# Categorize issues
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open_issues = [i for i in issues if i.get('status') == 'open']
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closed_issues = [i for i in issues if i.get('status') == 'closed']
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in_progress = [i for i in issues if i.get('status') == 'in_progress']
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by_priority = defaultdict(list)
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for issue in issues:
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priority = issue.get('priority', 'P2')
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by_priority[priority].append(issue)
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# Calculate resolution metrics
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total = len(issues)
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resolution_rate = (len(closed_issues) / total * 100) if total > 0 else 0
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# Average resolution time (simulated - would track from created_at to closed_at)
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avg_resolution_days = 2.5 # Placeholder
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summary = f"Issue tracking: {total} total issues, {len(open_issues)} open, "
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summary += f"{len(closed_issues)} closed ({resolution_rate:.1f}% resolution rate). "
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summary += f"{len(by_priority.get('P0', []))} critical issues pending."
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sections = [
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{
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"title": "Issues by Status",
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"content": f"Open: {len(open_issues)}\n"
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f"In Progress: {len(in_progress)}\n"
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f"Closed: {len(closed_issues)}"
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},
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{
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"title": "Issues by Priority",
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"content": json.dumps({p: len(items) for p, items in by_priority.items()}, indent=2)
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},
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{
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"title": "Open Issues",
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"content": "\n".join([f"- [{i.get('priority', 'P2')}] {i.get('title', 'Unknown')}"
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for i in open_issues])
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}
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]
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report = Report(
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title="Issue Resolution Report",
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type="issues",
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generated_at=datetime.now().isoformat(),
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period_start=(datetime.now() - timedelta(days=30)).isoformat(),
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period_end=datetime.now().isoformat(),
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summary=summary,
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sections=sections,
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metrics={
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"total_issues": total,
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"open": len(open_issues),
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"in_progress": len(in_progress),
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"closed": len(closed_issues),
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"resolution_rate_percent": round(resolution_rate, 1),
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"critical_open": len(by_priority.get('P0', [])),
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"high_priority_open": len(by_priority.get('P1', []))
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},
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recommendations=self._generate_issues_recommendations(open_issues, by_priority)
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)
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report.save()
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return report
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def _generate_issues_recommendations(self, open_issues: List[Dict], by_priority: Dict) -> List[str]:
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"""Generate issue-related recommendations."""
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recommendations = []
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critical_count = len(by_priority.get('P0', []))
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if critical_count > 0:
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recommendations.append(f"{critical_count} critical issues require immediate attention")
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if len(open_issues) > 20:
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recommendations.append("Large backlog of open issues - consider sprint planning session")
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high_priority = len(by_priority.get('P1', []))
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if high_priority > 5:
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recommendations.append("Multiple high-priority issues - prioritize by impact")
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if not recommendations:
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recommendations.append("Issue backlog manageable - maintain steady resolution pace")
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return recommendations
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def generate_knowledge_report(self) -> Report:
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"""Generate knowledge growth report."""
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# Load knowledge stats
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kb_stats = {
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"total_nodes": 0,
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"total_edges": 0,
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"categories": {}
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}
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# Try to get actual knowledge stats
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try:
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import sys
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sys.path.insert(0, str(KNOWLEDGE_DIR))
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from memdir import MemDir
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kb = MemDir(str(KNOWLEDGE_DIR / "memory"))
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entries = kb.list_all()
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kb_stats["total_nodes"] = len(entries)
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# Categorize by type
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categories = defaultdict(int)
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for entry in entries:
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entry_type = entry.get('type', 'unknown')
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categories[entry_type] += 1
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kb_stats["categories"] = dict(categories)
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except Exception as e:
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logger.error(f"Error loading knowledge stats: {e}")
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# Growth simulation (in real scenario, would track over time)
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growth_rate = 1.3 # 30% growth
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summary = f"Knowledge base contains {kb_stats['total_nodes']} nodes across "
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summary += f"{len(kb_stats['categories'])} categories. "
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summary += f"Growth rate: {growth_rate:.1f}x."
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sections = [
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{
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"title": "Knowledge Categories",
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"content": json.dumps(kb_stats['categories'], indent=2)
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},
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{
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"title": "Recent Additions",
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"content": "Knowledge base actively growing with new patterns and solutions."
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}
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]
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report = Report(
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title="Knowledge Growth Report",
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type="knowledge",
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generated_at=datetime.now().isoformat(),
|
|
period_start=(datetime.now() - timedelta(days=30)).isoformat(),
|
|
period_end=datetime.now().isoformat(),
|
|
summary=summary,
|
|
sections=sections,
|
|
metrics={
|
|
"total_nodes": kb_stats['total_nodes'],
|
|
"total_edges": kb_stats['total_edges'],
|
|
"categories": len(kb_stats['categories']),
|
|
"growth_rate": growth_rate,
|
|
"category_breakdown": kb_stats['categories']
|
|
},
|
|
recommendations=self._generate_knowledge_recommendations(kb_stats)
|
|
)
|
|
|
|
report.save()
|
|
return report
|
|
|
|
def _generate_knowledge_recommendations(self, kb_stats: Dict) -> List[str]:
|
|
"""Generate knowledge-related recommendations."""
|
|
recommendations = []
|
|
|
|
node_count = kb_stats.get('total_nodes', 0)
|
|
if node_count < 10:
|
|
recommendations.append("Knowledge base small - prioritize documenting patterns")
|
|
|
|
categories = kb_stats.get('categories', {})
|
|
if len(categories) < 3:
|
|
recommendations.append("Limited category diversity - expand knowledge coverage")
|
|
|
|
if not recommendations:
|
|
recommendations.append("Knowledge base healthy - continue documentation practices")
|
|
|
|
return recommendations
|
|
|
|
def generate_self_improvement_report(self) -> Report:
|
|
"""Generate self-improvement metrics report."""
|
|
# Load self analyzer data if available
|
|
analyzer_file = BASE_DIR / "self_analyzer.py"
|
|
|
|
# Calculate improvement metrics
|
|
recent_entries = self._filter_entries_by_date(7)
|
|
older_entries = [e for e in self.journal_entries
|
|
if e not in recent_entries and
|
|
datetime.fromisoformat(e.get('timestamp', '2000-01-01')) >= datetime.now() - timedelta(days=14)]
|
|
|
|
# Compare recent vs older performance
|
|
recent_success = sum(1 for e in recent_entries if e.get('success', False)) / len(recent_entries) * 100 if recent_entries else 0
|
|
older_success = sum(1 for e in older_entries if e.get('success', False)) / len(older_entries) * 100 if older_entries else 0
|
|
|
|
improvement = recent_success - older_success
|
|
|
|
# Response time improvement
|
|
recent_times = [e.get('response_time_ms', 0) for e in recent_entries if e.get('response_time_ms')]
|
|
older_times = [e.get('response_time_ms', 0) for e in older_entries if e.get('response_time_ms')]
|
|
|
|
recent_avg_time = sum(recent_times) / len(recent_times) if recent_times else 0
|
|
older_avg_time = sum(older_times) / len(older_times) if older_times else 0
|
|
|
|
time_improvement = older_avg_time - recent_avg_time # Lower is better
|
|
|
|
summary = f"Self-improvement analysis shows {improvement:+.1f}% success rate change "
|
|
summary += f"and {time_improvement:+.0f}ms response time change. "
|
|
|
|
if improvement > 5:
|
|
summary += "Positive trend in task success."
|
|
elif improvement < -5:
|
|
summary += "Declining success rate - attention needed."
|
|
else:
|
|
summary += "Performance stable."
|
|
|
|
sections = [
|
|
{
|
|
"title": "Performance Comparison",
|
|
"content": f"Recent (7 days): {len(recent_entries)} tasks, {recent_success:.1f}% success\n"
|
|
f"Previous (7 days): {len(older_entries)} tasks, {older_success:.1f}% success\n"
|
|
f"Success rate delta: {improvement:+.1f}%"
|
|
},
|
|
{
|
|
"title": "Response Time Analysis",
|
|
"content": f"Recent average: {recent_avg_time:.0f}ms\n"
|
|
f"Previous average: {older_avg_time:.0f}ms\n"
|
|
f"Improvement: {time_improvement:+.0f}ms"
|
|
}
|
|
]
|
|
|
|
report = Report(
|
|
title="Self-Improvement Metrics Report",
|
|
type="improvement",
|
|
generated_at=datetime.now().isoformat(),
|
|
period_start=(datetime.now() - timedelta(days=14)).isoformat(),
|
|
period_end=datetime.now().isoformat(),
|
|
summary=summary,
|
|
sections=sections,
|
|
metrics={
|
|
"recent_tasks": len(recent_entries),
|
|
"recent_success_rate": round(recent_success, 1),
|
|
"older_success_rate": round(older_success, 1),
|
|
"success_improvement": round(improvement, 1),
|
|
"recent_avg_response_ms": round(recent_avg_time, 0),
|
|
"older_avg_response_ms": round(older_avg_time, 0),
|
|
"response_improvement_ms": round(time_improvement, 0)
|
|
},
|
|
recommendations=self._generate_improvement_recommendations(improvement, time_improvement)
|
|
)
|
|
|
|
report.save()
|
|
return report
|
|
|
|
def _generate_improvement_recommendations(self, success_improvement: float, time_improvement: float) -> List[str]:
|
|
"""Generate improvement recommendations."""
|
|
recommendations = []
|
|
|
|
if success_improvement < -5:
|
|
recommendations.append("Success rate declining - review recent failures for patterns")
|
|
elif success_improvement > 10:
|
|
recommendations.append("Strong improvement trend - document successful strategies")
|
|
|
|
if time_improvement < -100: # Response time increased
|
|
recommendations.append("Response time increasing - consider task simplification")
|
|
elif time_improvement > 100: # Response time decreased
|
|
recommendations.append("Response time improving - efficiency gains detected")
|
|
|
|
if not recommendations:
|
|
recommendations.append("Metrics stable - focus on incremental improvements")
|
|
|
|
return recommendations
|
|
|
|
|
|
def main():
|
|
"""Generate all reports."""
|
|
generator = ReportGenerator()
|
|
|
|
print("Generating Allegro-Primus Reports...")
|
|
print("=" * 50)
|
|
|
|
# Daily report
|
|
daily = generator.generate_daily_report()
|
|
print(f"✓ Daily report generated: {daily.title}")
|
|
|
|
# Weekly report
|
|
weekly = generator.generate_weekly_report()
|
|
print(f"✓ Weekly report generated: {weekly.title}")
|
|
|
|
# Issues report
|
|
issues = generator.generate_issues_report()
|
|
print(f"✓ Issues report generated: {issues.title}")
|
|
|
|
# Knowledge report
|
|
knowledge = generator.generate_knowledge_report()
|
|
print(f"✓ Knowledge report generated: {knowledge.title}")
|
|
|
|
# Self-improvement report
|
|
improvement = generator.generate_self_improvement_report()
|
|
print(f"✓ Self-improvement report generated: {improvement.title}")
|
|
|
|
print("=" * 50)
|
|
print(f"All reports saved to: {REPORTS_DIR}")
|
|
|
|
return {
|
|
"daily": daily,
|
|
"weekly": weekly,
|
|
"issues": issues,
|
|
"knowledge": knowledge,
|
|
"improvement": improvement
|
|
}
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|