anti
b321e29002
Stage 3b of the realism migration. A TODO.md planted on Monday gets a checkbox flipped on Tuesday; a notes file grows a follow-up line; a cron log gets a fresh entry tacked on. The synthetic_files row's edit_count, last_modified, and content_hash advance. New surface: - EditAction dataclass (peer of FileAction in scheduler.py): carries decky, path, persona, content_class, previous_body, mtime, and synthetic_file_uuid for the worker's update path. - realism.bodies.next_iteration(cls, persona, prev, rng): per-class deterministic mutators. TODO flips an unchecked box and/or appends; notes/drafts/scripts append; logs are append-only (mirroring real log behaviour). Canary, cache_tmp, email raise KeyError — unsupported. - realism.planner.pick gains an edit branch: 60% create, 30% edit (when an edit_candidate is supplied), 10% leave-alone. Returns None on leave-alone — quiet ticks are realism too. - scheduler.pick_file pre-fetches a single edit candidate via repo.pick_random_synthetic_file_for_edit ~50% of ticks; the planner decides whether to use it. - SSHDriver._run_edit: turns next_iteration output into a plant_file call (mtime-bumped, mode 0o644). Stashes new_body in result.payload so the worker can hash it for synthetic_files. - worker._bump_synthetic_file_after_edit: patches edit_count + 1, last_modified=now, content_hash, last_body for the row UUID. No-op when the row was pruned mid-flight. - events.to_row / topic_for / event_type_for now recognise EditAction (kind="file", action="file:edit").
191 lines
6.7 KiB
Python
191 lines
6.7 KiB
Python
"""Realism planner — picks the next ``(decky, persona, class, action)`` tuple.
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Stage 3: returns ``create``-only plans (the edit branch lands in
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stage 3b). Pure-function, deterministic given the same inputs:
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caller passes deckies (with personas pre-resolved on each row),
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``now``, and an RNG.
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The persona resolution split — topology-pool vs. global-pool — is
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the orchestrator's job, not the planner's. Each decky dict reaching
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:func:`pick` carries a ``_realism_personas`` key with the resolved
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:class:`~decnet.realism.personas.EmailPersona` list. Keeps the
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planner test-isolated and avoids forcing it to know about the
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:class:`~decnet.web.db.repository.BaseRepository` / topology pool /
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global pool.
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Diurnal gating uses :func:`decnet.realism.diurnal.in_work_hours` per
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persona; we filter the (decky, persona) pairs *before* picking, so a
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persona outside its window is never considered.
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"""
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from __future__ import annotations
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import secrets
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from datetime import datetime
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from typing import Any, Optional, Sequence
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from decnet.realism import bodies, naming
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from decnet.realism.diurnal import in_work_hours, sample_mtime
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from decnet.realism.personas import EmailPersona
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from decnet.realism.taxonomy import ContentClass, Plan, PlanAction # noqa: F401
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# Stage-3 weighted sampling:
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# * User content (notes/todo/draft/script) gets the bulk — those are
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# the realism win when a persona "looks busy."
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# * System content (cron/daemon/cache) is plausible filler.
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# * Email + canary are owned by other paths and not picked here.
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_USER_CLASS_WEIGHTS: tuple[tuple[ContentClass, int], ...] = (
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(ContentClass.NOTE, 30),
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(ContentClass.TODO, 20),
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(ContentClass.DRAFT, 15),
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(ContentClass.SCRIPT, 10),
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)
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_SYSTEM_CLASS_WEIGHTS: tuple[tuple[ContentClass, int], ...] = (
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(ContentClass.LOG_CRON, 12),
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(ContentClass.LOG_DAEMON, 8),
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(ContentClass.CACHE_TMP, 5),
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)
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def _weighted_pick(
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weights: tuple[tuple[ContentClass, int], ...],
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rng: secrets.SystemRandom,
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) -> ContentClass:
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total = sum(w for _, w in weights)
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target = rng.randint(1, total)
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running = 0
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for cls, w in weights:
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running += w
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if target <= running:
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return cls
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return weights[-1][0] # unreachable, satisfy mypy
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def _eligible_pairs(
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deckies: Sequence[dict[str, Any]],
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now: datetime,
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) -> list[tuple[dict[str, Any], EmailPersona]]:
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"""Cross-product of deckies × resolved personas, diurnal-filtered.
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A decky with no personas (empty ``_realism_personas``) is skipped
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entirely; same fail-quiet semantics as the emailgen scheduler.
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"""
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out: list[tuple[dict[str, Any], EmailPersona]] = []
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for decky in deckies:
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personas: list[EmailPersona] = decky.get("_realism_personas") or []
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for persona in personas:
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if in_work_hours(persona.active_hours, now):
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out.append((decky, persona))
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return out
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def pick(
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deckies: Sequence[dict[str, Any]],
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now: datetime,
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*,
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edit_candidate: Optional[dict[str, Any]] = None,
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rand: Optional[secrets.SystemRandom] = None,
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) -> Optional[Plan]:
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"""Return a single :class:`Plan` for the orchestrator's tick.
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Stage-3b policy: weighted action roll — 60% create, 30% edit, 10%
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"leave alone" (planner returns ``None`` to skip). When the roll
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is "edit" and *edit_candidate* is set (a row from
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:meth:`BaseRepository.pick_random_synthetic_file_for_edit`), we
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return an edit Plan; otherwise we fall through to create.
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The orchestrator scheduler is responsible for fetching the edit
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candidate before calling — keeps this function pure-of-DB and
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test-friendly.
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Returns ``None`` when no eligible (decky, persona) pair exists or
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when the action roll lands on "leave alone."
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"""
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rng = rand or secrets.SystemRandom()
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eligible = _eligible_pairs(deckies, now)
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if not eligible:
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return None
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# Action roll. Edit only fires when there's a candidate from the
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# repo — otherwise we either re-roll to create or skip.
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roll = rng.random()
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if roll < 0.10:
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return None # "leave alone" — quiet tick is realism too
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if roll < 0.40 and edit_candidate is not None:
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return _edit_plan(edit_candidate, now, rng)
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decky, persona = rng.choice(eligible)
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# User vs system content — biased toward user (realism wins are
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# bigger there).
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if rng.random() < 0.7:
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content_class = _weighted_pick(_USER_CLASS_WEIGHTS, rng)
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else:
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content_class = _weighted_pick(_SYSTEM_CLASS_WEIGHTS, rng)
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target_path = naming.make_path(content_class, persona.name, rand=rng)
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body_hint = bodies.make_body(content_class, persona.name, rand=rng)
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mtime = sample_mtime(persona.active_hours, now, rand=rng)
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return Plan(
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decky_uuid=decky["uuid"],
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decky_name=decky["name"],
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persona=persona.name,
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content_class=content_class,
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action="create",
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target_path=target_path,
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mtime=mtime,
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body_hint=body_hint,
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notes=(
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f"persona={persona.name}",
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f"class={content_class.value}",
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f"window={persona.active_hours}",
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),
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)
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def _edit_plan(
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candidate: dict[str, Any],
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now: datetime,
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rng: secrets.SystemRandom,
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) -> Optional[Plan]:
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"""Build an edit-action :class:`Plan` from a synthetic_files row.
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The candidate dict is the shape :meth:`BaseRepository.list_synthetic_files`
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returns — we only need ``decky_uuid``, ``path``, ``persona``,
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``content_class``, ``last_body``, ``uuid``. Returns ``None`` if
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the candidate's content_class is somehow not editable (defensive
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— the repo query already filters those out).
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"""
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try:
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cls = ContentClass(candidate["content_class"])
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except (KeyError, ValueError):
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return None
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if cls.is_canary() or cls == ContentClass.CACHE_TMP:
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return None
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# mtime: edits bump forward by ~hours-to-days, but never past now.
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# We model as "the file was edited some time after creation but
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# before now" — sample_mtime with a tighter cap keeps it recent.
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edit_mtime = sample_mtime(
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"00:00-00:00", now, rand=rng,
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backdate_min_hours=1.0, backdate_max_days=2.0,
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)
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return Plan(
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decky_uuid=candidate["decky_uuid"],
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decky_name=candidate.get("decky_name", ""),
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persona=candidate.get("persona", ""),
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content_class=cls,
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action="edit",
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target_path=candidate["path"],
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mtime=edit_mtime,
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body_hint=None, # edit uses previous_body, not a fresh hint
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previous_body=candidate.get("last_body", ""),
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notes=(
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f"persona={candidate.get('persona', '')}",
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f"class={cls.value}",
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"action=edit",
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f"synthetic_file_uuid={candidate.get('uuid', '')}",
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),
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)
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