e1108b6112
Adds corporate memory v1 (verification flywheel + contradiction detection + confidence scoring) and v1.5 (audience-based distribution + per-item privacy + admin curation). Server: GET /api/memory/bundle returns mandatory + ranked-approved items within a token budget; POST /api/memory/admin/mandate accepts an audience field gated against user_group_members; /api/memory/stats uses SQL aggregation. CLI: da sync writes received items to .claude/rules/km_*.md. Verification detector extracts knowledge candidates from session JSONL files. Auto-tagging via Haiku when ai: is configured. Adapted from the v9-era branch onto v13/v14 RBAC: _is_privileged_viewer + _effective_groups now query user_group_members JOIN user_groups; require_role(Role.KM_ADMIN) replaced with require_admin (km_admin collapsed into admin). Schema v15: knowledge_items context-engineering columns + knowledge_contradictions + session_extraction_state. Schema v16: verification_evidence. Cuts release v0.15.0 (also bundles #116 /me/debug page).
95 lines
3.2 KiB
Python
95 lines
3.2 KiB
Python
"""CLI entry point for the verification detector service.
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Usage:
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python -m services.verification_detector [--dry-run] [--verbose] [--reset]
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TODO(scheduler-v2): Trigger is manual-only today (CLI) but detect_and_record is
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also called inline per new knowledge item submission. Wire into
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services/scheduler/__main__.py JOBS list (e.g. hourly) and expose an admin
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endpoint /api/admin/run-verification that calls detector.run() so the
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scheduler stays the single source of truth for cadence.
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TODO(notifications): When new pending items land in knowledge_items via
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detector.run(), there is no admin notification. Hook into services/telegram_bot
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or email so km_admins are pinged with a digest of pending items to triage.
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"""
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import argparse
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import logging
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import sys
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from src.db import get_system_db
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from . import detector
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logger = logging.getLogger(__name__)
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def main() -> None:
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parser = argparse.ArgumentParser(
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description="Extract verified organizational knowledge from analyst session transcripts."
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)
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parser.add_argument(
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"--dry-run",
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action="store_true",
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help="Analyze sessions but do not write results to the database.",
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)
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parser.add_argument(
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"--verbose",
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action="store_true",
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help="Enable debug-level logging.",
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)
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parser.add_argument(
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"--reset",
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action="store_true",
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help="Reset session processing state before running.",
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)
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args = parser.parse_args()
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logging.basicConfig(
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level=logging.DEBUG if args.verbose else logging.INFO,
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format="%(asctime)s %(levelname)s %(name)s: %(message)s",
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)
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# Load AI config lazily (same pattern as corporate memory collector)
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try:
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from config.loader import load_instance_config
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config = load_instance_config()
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ai_config = config.get("ai")
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if not ai_config:
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logger.error("No ai: section in instance.yaml, cannot run verification detector")
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sys.exit(1)
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except (ValueError, FileNotFoundError) as e:
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logger.error("Failed to load config: %s", e)
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sys.exit(1)
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from connectors.llm import create_extractor
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extractor = create_extractor(ai_config)
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conn = get_system_db()
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if args.reset:
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logger.info("Resetting session extraction state...")
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conn.execute("DELETE FROM session_extraction_state")
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logger.info("Session extraction state cleared.")
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stats = detector.run(conn, extractor, dry_run=args.dry_run)
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print("\n--- Verification Detector Summary ---")
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print(f"Sessions scanned: {stats['sessions_scanned']}")
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print(f"Sessions processed: {stats['sessions_processed']}")
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print(f"Sessions skipped: {stats['sessions_skipped']}")
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print(f"Verifications extracted: {stats['verifications_extracted']}")
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print(f"Items created: {stats['items_created']}")
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if stats["errors"]:
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print(f"Errors: {len(stats['errors'])}")
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for err in stats["errors"]:
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print(f" - {err}")
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if args.dry_run:
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print("\n(dry-run mode -- no changes were written)")
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if stats["errors"]:
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sys.exit(1)
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if __name__ == "__main__":
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main()
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