merge: testing → main (reconcile 2-week divergence)
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153
decnet/realism/personas.py
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153
decnet/realism/personas.py
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"""Persona schema for realism content generation.
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Stored as a JSON list on :attr:`Topology.email_personas`. Each persona
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describes one fictional employee — sender of email *and* author of
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files (notes, TODOs, drafts, scripts) on the deckies they're sampled
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onto. The schema deliberately stays narrow: the LLM gets *enough*
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differentiation to write distinct voices, no more.
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The class is still named :class:`EmailPersona` because every persona
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in the pool today carries a mandatory email address (used for IMAP/
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POP3 spool delivery). Future per-decky personas without mailboxes
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would justify a rename / superclass; not in scope for the realism
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migration.
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Invalid entries are dropped with a warning (returned alongside the
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parsed list) rather than raising — a single typo in one persona must
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not stall the entire realism tick.
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"""
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from __future__ import annotations
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import json
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from typing import Literal, Optional
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from pydantic import BaseModel, Field, ValidationError, field_validator, model_validator
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from decnet.logging import get_logger
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logger = get_logger("realism.personas")
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Tone = Literal["formal", "direct", "casual", "technical", "custom"]
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ReplyLatency = Literal["fast", "normal", "slow"]
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class EmailPersona(BaseModel):
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"""One fake mailbox owner.
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``language`` is ISO 639-1 (``en``, ``es``, ``pt``…); when unset on the
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persona it falls back to the topology's ``language_default``.
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``uses_llms_heavily`` lifts the prompt-layer em-dash suppression for
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that persona — em-dashes are an LLM tell, but a persona explicitly
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pegged as a heavy LLM user should *naturally* produce them.
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"""
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name: str = Field(min_length=1, max_length=128)
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email: str = Field(min_length=3, max_length=255)
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role: str = Field(min_length=1, max_length=128)
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tone: Tone = "formal"
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tone_custom: Optional[str] = Field(default=None, max_length=128)
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mannerisms: list[str] = Field(default_factory=list, max_length=12)
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language: Optional[str] = Field(default=None, max_length=8)
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signature: Optional[str] = Field(default=None, max_length=512)
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active_hours: str = Field(default="09:00-18:00", max_length=32)
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reply_latency: ReplyLatency = "normal"
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uses_llms_heavily: bool = False
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@model_validator(mode="after")
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def _custom_tone_requires_text(self) -> "EmailPersona":
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# ``tone="custom"`` lets operators describe a voice the four canned
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# tones don't capture (sarcastic, deadpan, terse, etc.). The free
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# text is interpolated into the prompt verbatim, so an empty
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# value would just leave the LLM with the literal word "custom" —
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# reject it loudly instead of silently producing a useless prompt.
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if self.tone == "custom" and not (self.tone_custom and self.tone_custom.strip()):
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raise ValueError("tone_custom is required when tone is 'custom'")
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return self
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@field_validator("email")
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@classmethod
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def _email_shape(cls, v: str) -> str:
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# Cheap structural check — full RFC 5322 isn't worth the
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# dependency. We only need ``user@domain`` with non-empty parts
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# for the prompt builder + Message-ID generator.
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if "@" not in v:
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raise ValueError("email must contain '@'")
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local, _, domain = v.rpartition("@")
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if not local or not domain or "." not in domain:
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raise ValueError("email must look like user@domain.tld")
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return v
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def parse_personas(
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raw: str | list | None,
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*,
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language_default: str = "en",
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) -> list[EmailPersona]:
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"""Parse the JSON-or-list ``email_personas`` value into models.
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Resolves ``language`` against *language_default* so downstream
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consumers (prompt builder, scheduler) never need to know about
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fallback semantics.
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"""
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if not raw:
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return []
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if isinstance(raw, str):
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try:
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raw = json.loads(raw)
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except json.JSONDecodeError as exc:
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logger.warning("realism personas: invalid JSON, skipping: %s", exc)
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return []
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if not isinstance(raw, list):
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logger.warning(
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"realism personas: expected list, got %s", type(raw).__name__
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)
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return []
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out: list[EmailPersona] = []
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for i, entry in enumerate(raw):
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try:
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persona = EmailPersona.model_validate(entry)
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except ValidationError as exc:
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logger.warning(
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"realism personas: dropping invalid entry index=%d: %s",
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i, exc.errors(include_url=False),
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)
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continue
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if persona.language is None:
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persona = persona.model_copy(update={"language": language_default})
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out.append(persona)
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return out
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def login_for(persona: str) -> str:
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"""Return the linux login derived from a persona's display name.
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Lowercase, strip spaces; if the result isn't a plausible POSIX
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login (alnum ASCII), fall back to ``user`` so the path doesn't
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leak the persona's display name onto the decky filesystem.
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Shared by realism path naming (``decnet/realism/naming.py``) and
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canary cultivation (``decnet/canary/cultivator.py``).
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"""
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candidate = persona.lower().replace(" ", "")
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if candidate.isalnum() and candidate.isascii() and candidate:
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return candidate
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return "user"
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def in_active_hours(persona: EmailPersona, now_hour: int) -> bool:
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"""Return True if *now_hour* (0–23) falls in the persona's window.
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Format: ``"HH:MM-HH:MM"``. Wrap-around windows (``"22:00-06:00"``)
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are supported. Invalid windows treat the persona as always-on so a
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config typo never silences the whole fleet.
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"""
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try:
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start_s, end_s = persona.active_hours.split("-")
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start_h = int(start_s.split(":")[0])
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end_h = int(end_s.split(":")[0])
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except (ValueError, IndexError):
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return True
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if start_h == end_h:
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return True
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if start_h < end_h:
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return start_h <= now_hour < end_h
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# Wrap-around (e.g. 22:00-06:00).
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return now_hour >= start_h or now_hour < end_h
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