Anto01
775960c8c8
Pipeline observability: - Retrieval harness runs nightly (Step E in batch-extract.sh) - Pipeline summary persisted to project state after each run (pipeline_last_run, pipeline_summary, retrieval_harness_result) - Dashboard enhanced: interaction total + by_client, pipeline health (last_run, hours_since, harness results, triage stats), dynamic project list from registry Phase 10 — reinforcement-based auto-promotion: - auto_promote_reinforced(): candidates with reference_count >= 3 and confidence >= 0.7 auto-graduate to active - expire_stale_candidates(): candidates unreinforced for 14+ days auto-rejected to prevent unbounded queue growth - Both wired into nightly cron (Step B2) - Batch script: scripts/auto_promote_reinforced.py (--dry-run support) Knowledge seeding: - scripts/seed_project_state.py: 26 curated Trusted Project State entries across p04-gigabit, p05-interferometer, p06-polisher, atomizer-v2, abb-space, atocore (decisions, requirements, facts, contacts, milestones) Tests: 299 → 303 (4 new Phase 10 tests) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
80 lines
3.1 KiB
Python
80 lines
3.1 KiB
Python
#!/usr/bin/env python3
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"""Auto-promote reinforced candidates + expire stale ones.
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Phase 10: reinforcement-based auto-promotion. Candidates referenced
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by 3+ interactions with confidence >= 0.7 graduate to active.
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Candidates unreinforced for 14+ days are auto-rejected.
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Usage:
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python3 scripts/auto_promote_reinforced.py [--base-url URL] [--dry-run]
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"""
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from __future__ import annotations
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import argparse
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import json
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import os
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import sys
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# Allow importing from src/ when run from repo root
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src"))
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from atocore.memory.service import auto_promote_reinforced, expire_stale_candidates
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def main() -> None:
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parser = argparse.ArgumentParser(description="Auto-promote + expire candidates")
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parser.add_argument("--dry-run", action="store_true", help="Report only, don't change anything")
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parser.add_argument("--min-refs", type=int, default=3, help="Min reference_count for promotion")
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parser.add_argument("--min-confidence", type=float, default=0.7, help="Min confidence for promotion")
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parser.add_argument("--expire-days", type=int, default=14, help="Days before unreinforced candidates expire")
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args = parser.parse_args()
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if args.dry_run:
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print("DRY RUN — no changes will be made")
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# For dry-run, query directly and report
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from atocore.models.database import get_connection
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from datetime import datetime, timedelta, timezone
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cutoff_promote = (datetime.now(timezone.utc) - timedelta(days=args.expire_days)).strftime("%Y-%m-%d %H:%M:%S")
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cutoff_expire = cutoff_promote
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with get_connection() as conn:
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promotable = conn.execute(
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"SELECT id, content, memory_type, project, confidence, reference_count "
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"FROM memories WHERE status = 'candidate' "
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"AND COALESCE(reference_count, 0) >= ? AND confidence >= ? "
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"AND last_referenced_at >= ?",
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(args.min_refs, args.min_confidence, cutoff_promote),
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).fetchall()
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expirable = conn.execute(
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"SELECT id, content, memory_type, project "
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"FROM memories WHERE status = 'candidate' "
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"AND COALESCE(reference_count, 0) = 0 AND created_at < ?",
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(cutoff_expire,),
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).fetchall()
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print(f"\nWould promote {len(promotable)} candidates:")
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for r in promotable:
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print(f" [{r['memory_type']}] refs={r['reference_count']} conf={r['confidence']:.2f} | {r['content'][:80]}...")
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print(f"\nWould expire {len(expirable)} stale candidates:")
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for r in expirable:
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print(f" [{r['memory_type']}] {r['project'] or 'global'} | {r['content'][:80]}...")
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return
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promoted = auto_promote_reinforced(
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min_reference_count=args.min_refs,
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min_confidence=args.min_confidence,
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)
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expired = expire_stale_candidates(max_age_days=args.expire_days)
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print(f"promoted={len(promoted)} expired={len(expired)}")
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if promoted:
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print(f"Promoted IDs: {promoted}")
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if expired:
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print(f"Expired IDs: {expired}")
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if __name__ == "__main__":
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main()
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