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* feat(kb): apply-worker to auto-land approved proposals (stage 2 automation) Event-driven worker that turns a HUMAN-approved kb_stage proposal into canonical state, so an approval in Telegram surfaces in Leo's identity without Leo applying its own work. - Fires only on status='approved' (never pending_review) -> proposer != applier holds; the human approval stays the trigger. No auto-approve anywhere. - Reuses scripts/apply_proposal.py verbatim as the sole apply path (same txn, rowcount=1 guard, FK stamp). Connects as the narrow kb_apply role, never superuser, never inside the hermes harness. - Render hook (--render-cmd / KB_APPLY_RENDER_CMD) is inert until the SOUL renderer (PR2) is deployed; applying still works, rendered SOUL just lags. - Ships INERT: report-only unless --enable / KB_APPLY_WORKER_ENABLED=1. systemd oneshot service + 5min timer, both shipped disabled. - 10 unit tests; candidate query validated read-only vs prod (0 applyable today). * fix(kb): apply-worker --max-per-tick cap + poison-pill retry ceiling Fixer draft-exit items: - --max-per-tick=1 (default): an enabled worker lands applies one-at-a-time and observably instead of draining the whole approved queue in one tick. - --max-attempts=3 ceiling with a persisted failure-count state file: a deterministically-failing approved proposal is treated as a poison pill and skipped after N consecutive failures, instead of retrying every tick forever. State persists on disk because the worker runs oneshot per timer tick. Both are inert until the worker is enabled; it still ships disabled.
303 lines
13 KiB
Python
303 lines
13 KiB
Python
#!/usr/bin/env python3
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"""Event-driven worker that lands human-approved proposals into canonical state.
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Stage 2-of-the-loop automation: Leo proposes -> HUMAN approves -> WORKER applies
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-> renderer runs -> Leo reads the new self.
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This is the "natural evolution" engine: it makes an approval in Telegram surface
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in Leo's identity without Leo ever applying its own work. It does NOT think, does
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NOT approve, and does NOT create proposals -- it only acts on proposals a human
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has already moved to ``status='approved'``.
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Governance boundary (why this is safe)
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--------------------------------------
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* Proposer != applier. The worker fires ONLY on ``status='approved'``; it never
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touches ``pending_review`` and never auto-approves. The human approval stays the
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trigger, so "Leo proposes but does not self-apply" holds.
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* It connects as the narrow ``kb_apply`` role (never superuser, never Leo's creds)
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and reuses ``apply_proposal.py`` verbatim as the apply path -- same transaction,
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same ``rowcount=1`` concurrency guard, same FK stamp. No new write logic here.
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* It runs as an operator-side systemd unit, NOT inside the hermes harness.
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Safety posture
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--------------
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* Gated OFF by default. Without ``--enable`` (or ``KB_APPLY_WORKER_ENABLED=1``) the
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worker only REPORTS what it would apply -- it performs no writes. Flip it on only
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after the first apply has been proven by hand.
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* The render step is a configurable hook (``--render-cmd`` / ``KB_APPLY_RENDER_CMD``).
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Until the SOUL renderer (PR2) is deployed the hook is simply absent and the worker
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logs that render was skipped -- applying still works, Leo's canonical is correct,
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and the rendered SOUL.md just lags until the renderer exists.
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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 subprocess
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import sys
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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HERE = Path(__file__).resolve().parent
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sys.path.insert(0, str(HERE))
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import apply_proposal as ap # noqa: E402 (sibling module, reused verbatim)
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# Types the worker is willing to auto-apply. Deliberately the same set the engine
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# supports; a proposal type outside this list is ignored (never applied).
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WORKER_TYPES = ("revise_strategy", "add_edge", "attach_evidence")
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# Only a revise_strategy changes an agent's identity spine, so only it triggers a
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# SOUL re-render. Evidence/edge applies change the graph but not the rendered self.
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RENDER_TYPES = ("revise_strategy",)
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# --------------------------------------------------------------------------- #
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# Pure helpers (unit-tested without a DB) #
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# --------------------------------------------------------------------------- #
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def build_candidate_query(types: tuple = WORKER_TYPES, limit: int = 20) -> str:
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"""SELECT approved, applyable proposals that carry an apply_payload.
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Filters on status='approved' at the DB level -- this is the structural
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guarantee that the worker cannot act on anything a human has not approved.
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"""
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type_list = ", ".join(ap.sql_literal(t) for t in types)
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return f"""select jsonb_build_object(
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'id', id::text,
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'proposal_type', proposal_type,
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'agent_id', payload->'apply_payload'->>'agent_id')::text
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from kb_stage.kb_proposals
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where status = 'approved'
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and proposal_type in ({type_list})
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and payload ? 'apply_payload'
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order by created_at asc
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limit {int(limit)};"""
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def parse_candidates(psql_output: str) -> List[Dict[str, Any]]:
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"""Parse newline-delimited JSON rows from psql -At output."""
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rows: List[Dict[str, Any]] = []
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for line in psql_output.splitlines():
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line = line.strip()
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if line:
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rows.append(json.loads(line))
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return rows
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def build_render_command(render_cmd: Optional[str], agent_id: Optional[str]) -> Optional[List[str]]:
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"""Expand the render-hook template for one agent, or None if no hook configured.
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``render_cmd`` is a shell-word template, e.g. ``python3 render_soul.py --agent-id {agent_id}``.
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Returns a token list ready for subprocess, or None when unset (render skipped).
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"""
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if not render_cmd:
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return None
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if not agent_id:
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return None
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import shlex
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return [tok.format(agent_id=agent_id) for tok in shlex.split(render_cmd)]
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def load_failure_state(path: Optional[str]) -> Dict[str, int]:
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"""Load the persisted {proposal_id: consecutive_failure_count} map.
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The worker runs oneshot per timer tick, so in-memory failure counts do not
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survive between ticks. A poison-pill proposal (deterministically failing but
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stuck at 'approved') would otherwise be re-selected and re-attempted every
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tick forever. Persisting the count on disk lets the ceiling actually bite.
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Missing/corrupt file -> empty map (fail open to "no known failures").
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"""
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if not path:
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return {}
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p = Path(path)
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if not p.is_file():
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return {}
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try:
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data = json.loads(p.read_text(encoding="utf-8"))
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return {str(k): int(v) for k, v in data.items()} if isinstance(data, dict) else {}
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except Exception: # noqa: BLE001 -- a bad state file must never wedge the worker
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return {}
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def save_failure_state(path: Optional[str], state: Dict[str, int]) -> None:
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if not path:
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return
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p = Path(path)
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p.parent.mkdir(parents=True, exist_ok=True)
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p.write_text(json.dumps(state, sort_keys=True), encoding="utf-8")
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def partition_candidates(
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candidates: List[Dict[str, Any]],
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failure_counts: Dict[str, int],
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max_attempts: int,
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max_per_tick: int,
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) -> Dict[str, List[Dict[str, Any]]]:
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"""Split fetched candidates into what to apply now vs. skip.
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- ``poisoned``: already at/over the failure ceiling -> never re-attempted,
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surfaced loudly for the operator (needs a human fix, not another retry).
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- ``to_apply``: the first ``max_per_tick`` non-poisoned candidates -> capped
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so an enabled worker lands applies one-at-a-time and observably, rather
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than draining the whole approved queue in a single unobserved tick.
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"""
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poisoned = [c for c in candidates if failure_counts.get(c["id"], 0) >= max_attempts]
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eligible = [c for c in candidates if failure_counts.get(c["id"], 0) < max_attempts]
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return {"poisoned": poisoned, "to_apply": eligible[: max(0, max_per_tick)]}
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# --------------------------------------------------------------------------- #
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# Side-effecting steps #
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# --------------------------------------------------------------------------- #
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def _psql_args(args: argparse.Namespace) -> argparse.Namespace:
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"""Namespace shaped for ap.run_psql (reuses the kb_apply connection path)."""
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return argparse.Namespace(
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container=args.container, db=args.db, host=args.host, role=args.role
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)
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def fetch_candidates(args: argparse.Namespace, password: str) -> List[Dict[str, Any]]:
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sql = build_candidate_query(limit=args.limit)
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out = ap.run_psql(_psql_args(args), sql, password)
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return parse_candidates(out)
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def apply_one(args: argparse.Namespace, proposal_id: str) -> None:
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"""Apply via the audited apply_proposal.py CLI -- same txn + rowcount guard."""
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cmd = [
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sys.executable, str(args.apply_script), proposal_id,
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"--applied-by", args.applied_by,
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"--secrets-file", args.secrets_file,
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"--container", args.container, "--db", args.db,
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"--host", args.host, "--role", args.role,
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]
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result = subprocess.run(cmd, text=True, capture_output=True, check=False)
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if result.returncode != 0:
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raise RuntimeError(
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f"apply failed for {proposal_id}: {result.stdout.strip()} {result.stderr.strip()}"
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)
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def render_one(args: argparse.Namespace, agent_id: Optional[str]) -> str:
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cmd = build_render_command(args.render_cmd, agent_id)
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if cmd is None:
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return "render skipped (no render-cmd configured; PR2 renderer not deployed)"
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result = subprocess.run(cmd, text=True, capture_output=True, check=False)
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if result.returncode != 0:
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raise RuntimeError(
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f"render failed for agent {agent_id}: {result.stdout.strip()} {result.stderr.strip()}"
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)
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return f"rendered agent {agent_id}"
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# --------------------------------------------------------------------------- #
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# Main #
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# --------------------------------------------------------------------------- #
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def run(args: argparse.Namespace) -> int:
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password = ap.load_password(args.secrets_file)
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candidates = fetch_candidates(args, password)
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if not candidates:
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print("no approved+applyable proposals; nothing to do")
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return 0
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failure_counts = load_failure_state(args.failure_state_file)
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split = partition_candidates(candidates, failure_counts, args.max_attempts, args.max_per_tick)
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for c in split["poisoned"]:
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print(
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f"SKIP poison-pill {c['id']} ({c['proposal_type']}): failed "
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f"{failure_counts.get(c['id'], 0)}x >= max-attempts {args.max_attempts}; "
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f"needs a human fix, not another retry",
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file=sys.stderr,
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)
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enabled = args.enable or os.environ.get("KB_APPLY_WORKER_ENABLED") == "1"
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if not enabled:
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print(
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f"[report-only] worker disabled; {len(split['to_apply'])} proposal(s) would apply "
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f"this tick (cap {args.max_per_tick}):"
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)
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for c in split["to_apply"]:
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print(f" would apply {c['id']} ({c['proposal_type']}) agent={c.get('agent_id')}")
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print("enable with --enable or KB_APPLY_WORKER_ENABLED=1 after the first manual apply is proven")
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return 0
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failures = 0
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for c in split["to_apply"]:
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pid, ptype, agent_id = c["id"], c["proposal_type"], c.get("agent_id")
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try:
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apply_one(args, pid)
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failure_counts.pop(pid, None) # success clears any prior failure count
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print(f"applied {pid} ({ptype})")
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if ptype in RENDER_TYPES:
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print(" " + render_one(args, agent_id))
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except Exception as exc: # noqa: BLE001 -- one bad proposal must not stop the batch
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failures += 1
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# Leave the proposal at 'approved' so a fixed one reapplies next tick
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# (the rowcount=1 guard makes reapply safe), but bump its failure count
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# so a deterministically-failing proposal hits the ceiling instead of
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# retrying forever. Surface loudly for the operator.
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failure_counts[pid] = failure_counts.get(pid, 0) + 1
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print(f"ERROR applying {pid} ({ptype}) [attempt {failure_counts[pid]}/"
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f"{args.max_attempts}]: {exc}", file=sys.stderr)
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save_failure_state(args.failure_state_file, failure_counts)
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return 1 if failures else 0
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def parse_args(argv: List[str]) -> argparse.Namespace:
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p = argparse.ArgumentParser(
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description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
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)
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p.add_argument(
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"--enable", action="store_true",
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help="actually apply (default: report-only). Also honored via KB_APPLY_WORKER_ENABLED=1.",
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)
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p.add_argument("--limit", type=int, default=20, help="max candidates fetched per tick")
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p.add_argument(
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"--max-per-tick", type=int, default=1,
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help="max proposals actually APPLIED per tick (default 1: applies land "
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"one-at-a-time and observably, not a whole-queue drain)",
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)
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p.add_argument(
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"--max-attempts", type=int, default=3,
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help="consecutive apply failures before a proposal is treated as a "
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"poison pill and skipped (needs a human fix, not endless retries)",
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)
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p.add_argument(
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"--failure-state-file",
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default=os.environ.get("KB_APPLY_WORKER_STATE", "/opt/teleo-eval/logs/kb-apply-worker-failures.json"),
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help="persisted per-proposal failure counts (survives oneshot ticks so "
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"the poison-pill ceiling actually bites)",
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)
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p.add_argument(
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"--applied-by", default=ap.SERVICE_AGENT_HANDLE,
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help="handle recorded as applied_by (default: the kb-apply service agent)",
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)
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p.add_argument(
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"--apply-script", default=str(HERE / "apply_proposal.py"),
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help="path to the apply_proposal.py engine (the sole apply path)",
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)
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p.add_argument(
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"--render-cmd", default=os.environ.get("KB_APPLY_RENDER_CMD", ""),
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help="render hook template, e.g. 'python3 render_soul.py --agent-id {agent_id}'. "
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"Empty (default) skips render until the SOUL renderer is deployed.",
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)
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p.add_argument("--secrets-file", default=ap.DEFAULT_SECRETS_FILE)
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p.add_argument("--container", default=ap.DEFAULT_CONTAINER)
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p.add_argument("--db", default=ap.DEFAULT_DB)
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p.add_argument("--host", default=ap.DEFAULT_HOST)
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p.add_argument("--role", default=ap.DEFAULT_ROLE)
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return p.parse_args(argv)
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def main(argv: Optional[List[str]] = None) -> int:
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return run(parse_args(sys.argv[1:] if argv is None else argv))
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if __name__ == "__main__":
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raise SystemExit(main())
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