agent-company
f33447eef8
Add APScheduler-based background task that periodically re-analyzes tracked companies and alerts on significant patent count changes. - Add tracked_companies and alerts tables to database schema - Add SPARC/scheduler.py with configurable interval and threshold - Add admin endpoints: GET/POST/DELETE /admin/tracked, GET /admin/alerts - Scheduler starts at app startup; interval via SCHEDULE_INTERVAL_HOURS - Change threshold configurable via CHANGE_THRESHOLD_PERCENT env var - apscheduler is optional; graceful fallback if not installed Closes leeworks-agents/SPARC#22 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
110 lines
3.7 KiB
Python
110 lines
3.7 KiB
Python
"""Scheduled patent analysis for tracked companies.
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Uses APScheduler to periodically re-analyze tracked companies and
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detect significant changes in patent counts.
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"""
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import logging
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import os
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from SPARC import config
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from SPARC.analyzer import CompanyAnalyzer
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from SPARC.database import DatabaseClient
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logger = logging.getLogger(__name__)
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# Configurable via environment variable (in hours, default 24)
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SCHEDULE_INTERVAL_HOURS = int(os.getenv("SCHEDULE_INTERVAL_HOURS", "24"))
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# Patent count change threshold (percentage) to trigger an alert
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CHANGE_THRESHOLD_PERCENT = int(os.getenv("CHANGE_THRESHOLD_PERCENT", "20"))
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def run_scheduled_analysis() -> None:
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"""Re-analyze all tracked companies and check for significant changes."""
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db = DatabaseClient(config.database_url)
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db.connect()
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db.initialize_schema()
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tracked = db.list_tracked_companies()
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if not tracked:
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logger.info("No tracked companies configured; skipping scheduled analysis")
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return
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logger.info("Running scheduled analysis for %d tracked companies", len(tracked))
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analyzer = CompanyAnalyzer(db_client=db)
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for company_row in tracked:
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name = company_row["company_name"]
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old_count = company_row.get("last_patent_count", 0) or 0
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try:
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result = analyzer._analyze_company_safe(name)
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if result.success:
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new_count = result.patent_count
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# Update tracking record
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db.update_tracked_company(name, new_count)
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# Check for significant change
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if old_count > 0:
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delta_pct = abs(new_count - old_count) / old_count * 100
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if delta_pct >= CHANGE_THRESHOLD_PERCENT:
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direction = "increased" if new_count > old_count else "decreased"
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message = (
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f"Patent count for {name} {direction} by {delta_pct:.0f}% "
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f"({old_count} -> {new_count})"
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)
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logger.warning("ALERT: %s", message)
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db.store_alert(
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company_name=name,
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alert_type="patent_count_change",
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message=message,
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old_value=old_count,
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new_value=new_count,
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)
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elif new_count > 0:
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# First analysis -- record baseline
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logger.info("Baseline for %s: %d patents", name, new_count)
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else:
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logger.warning("Scheduled analysis failed for %s: %s", name, result.error)
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except Exception as e:
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logger.error("Error analyzing tracked company %s: %s", name, e)
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db.close()
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logger.info("Scheduled analysis complete")
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def start_scheduler() -> None:
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"""Start the APScheduler background scheduler.
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Safe to call at application startup. If apscheduler is not installed,
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the function logs a warning and returns without starting anything.
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"""
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try:
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from apscheduler.schedulers.background import BackgroundScheduler
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except ImportError:
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logger.warning(
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"apscheduler not installed; scheduled analysis disabled. "
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"Install with: pip install apscheduler"
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)
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return
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scheduler = BackgroundScheduler()
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scheduler.add_job(
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run_scheduled_analysis,
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"interval",
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hours=SCHEDULE_INTERVAL_HOURS,
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id="scheduled_patent_analysis",
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replace_existing=True,
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)
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scheduler.start()
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logger.info(
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"Scheduled patent analysis started (every %d hours, threshold %d%%)",
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SCHEDULE_INTERVAL_HOURS,
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CHANGE_THRESHOLD_PERCENT,
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)
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