anti
3ee55ec341
Second orchestrator worker (decnet emailgen) that drips persona-driven, threaded, multi-language fake emails into running mail deckies. Personas live on Topology.email_personas; topology-wide language_default falls through to any persona that doesn't pin its own. Em-dashes are suppressed at the prompt layer by default and only lifted for personas explicitly marked uses_llms_heavily — em-dashes are an LLM tell and a flat corpus of em-dashed mail is a giveaway. EML delivery writes into /var/spool/decnet-emails/<thread>/<msg>.eml on the mail decky via docker exec; wiring the IMAP/POP3 templates to read from that spool (replacing the hardcoded _BAIT_EMAILS) is the next step.
120 lines
4.3 KiB
Python
120 lines
4.3 KiB
Python
"""Persona schema for the emailgen worker.
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Stored as a JSON list on :attr:`Topology.email_personas`. Each persona
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describes one fictional employee whose mailbox lives on the topology's
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IMAP/POP3 decky. The schema deliberately stays narrow: the LLM gets
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*enough* differentiation to write distinct voices, no more.
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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 emailgen 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
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from decnet.logging import get_logger
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logger = get_logger("orchestrator.emailgen")
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Tone = Literal["formal", "direct", "casual", "technical"]
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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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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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@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("emailgen 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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"emailgen 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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"emailgen 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 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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