Joseph Doherty
101 lines
3.9 KiB
Python
101 lines
3.9 KiB
Python
"""Interjection classifier service (T39).
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Per Requirements §6.2, when a guest is present and the addressee bot has
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just spoken, the *non-addressee* bot may follow on with a brief
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interjection beat. This service decides whether that interjection
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fires. Conservative bias: most turns return ``should_interject=False``
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— the addressee has the floor and an interjection is the exception.
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Trigger ``True`` only when the silent witness's character, given their
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persona and edges, would plausibly speak up: jealousy, surprise, strong
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agreement worth voicing, correcting a factual falsehood, urgency.
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T44 (turn flow) calls this and, on ``True``, generates the brief
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follow-on response as the silent witness. Classifier failure falls back
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to ``should_interject=False`` with ``reason="fallback"`` so the chat
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keeps moving (§3.3 graceful-degradation rule); callers that care can
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distinguish a real "no" from a degraded "no" by the reason string.
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"""
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from __future__ import annotations
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from pydantic import BaseModel
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from chat.llm.classify import classify
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from chat.llm.client import LLMClient
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class InterjectionDecision(BaseModel):
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"""Whether the silent witness interjects, plus a short reason.
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Defaults are a deliberate no-op: ``should_interject=False`` with an
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empty reason. The classifier-failure fallback uses
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``reason="fallback"`` so it's distinguishable from a real "no".
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"""
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should_interject: bool = False
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reason: str = ""
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_SYSTEM = (
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"You decide whether a silent witness character interjects after the "
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"addressee character finishes speaking. STRONGLY default to false — "
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"the addressee has the floor and most turns should NOT have an "
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"interjection. Only return true when the silent witness's character, "
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"given their persona and edges, would plausibly speak up: jealousy, "
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"surprise, strong agreement worth voicing, correcting a factual "
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"falsehood, urgency. Output strict JSON matching the schema."
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)
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async def detect_interjection(
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client: LLMClient,
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*,
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classifier_model: str,
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addressee_name: str,
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addressee_just_said: str,
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silent_witness_name: str,
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silent_witness_persona: str,
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silent_witness_edge_to_addressee: dict, # {affinity, trust, summary}
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silent_witness_edge_to_you: dict,
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you_just_said: str,
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timeout_s: float = 30.0,
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) -> InterjectionDecision:
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"""Decide whether the silent witness bot interjects after the addressee
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finishes speaking.
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The two ``silent_witness_edge_*`` dicts carry the silent witness's
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directed edges toward the addressee and toward the user ("you"),
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each shaped ``{affinity: int, trust: int, summary: str}``. Missing
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keys fall back to a 50/50 baseline with an empty summary so this
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function tolerates partially-populated edge state without raising.
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"""
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user = (
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f"You said: {you_just_said}\n\n"
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f"{addressee_name} just said: {addressee_just_said}\n\n"
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f"Silent witness: {silent_witness_name}\n"
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f"Persona: {silent_witness_persona}\n"
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f"Edge {silent_witness_name} -> {addressee_name}: "
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f"affinity={silent_witness_edge_to_addressee.get('affinity', 50)}, "
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f"trust={silent_witness_edge_to_addressee.get('trust', 50)}, "
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f"summary={silent_witness_edge_to_addressee.get('summary', '')}\n"
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f"Edge {silent_witness_name} -> you: "
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f"affinity={silent_witness_edge_to_you.get('affinity', 50)}, "
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f"trust={silent_witness_edge_to_you.get('trust', 50)}, "
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f"summary={silent_witness_edge_to_you.get('summary', '')}\n\n"
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f"Should {silent_witness_name} interject?"
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)
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return await classify(
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client,
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model=classifier_model,
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system=_SYSTEM,
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user=user,
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schema=InterjectionDecision,
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default=InterjectionDecision(
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should_interject=False, reason="fallback"
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),
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timeout_s=timeout_s,
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)
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__all__ = ["InterjectionDecision", "detect_interjection"]
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