Joseph Doherty
122 lines
3.9 KiB
Python
122 lines
3.9 KiB
Python
"""Kickoff prose parser.
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Service-layer function that converts a bot's authored kickoff prose into a
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structured ``KickoffParse`` for the kickoff confirm-and-edit step (T13 will
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wire this into the UI flow).
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The classifier prompt includes only the bot context that's load-bearing for
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parsing the opening scene: name, persona, the authored
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``initial_relationship_to_you`` blurb, the ``you`` entity name, and the
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kickoff prose itself. Other identity fields (traits, backstory, ...) are
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intentionally left out — they would be noise for this extraction.
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"""
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from __future__ import annotations
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from pydantic import BaseModel, Field
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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 ActivityShape(BaseModel):
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"""Per-entity activity at scene start.
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Maps onto Requirements §6.5: ``current_action.{verb,interruptible,
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required_attention,expected_duration}`` plus posture, attention, holding.
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``action_required_attention`` is left as a free-form string ("low" /
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"medium" / "high" expected) rather than a Literal so the classifier has
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room to vary phrasing in v1.
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"""
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posture: str
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action_verb: str
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action_interruptible: bool
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action_required_attention: str # low | medium | high
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action_expected_duration: str
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attention: str = ""
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holding: list[str] = Field(default_factory=list)
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class KickoffParse(BaseModel):
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"""Structured opening-scene state extracted from kickoff prose.
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``container_properties`` is loose ``dict``: the classifier may emit
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``moving`` / ``public`` / ``audible_range`` keys, but downstream
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consumers (T13's confirm form) handle missing keys gracefully.
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``initial_time_iso`` is stored as text — not validated as a datetime
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here; ``chat_state.time`` stores it as a plain string.
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"""
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container_name: str
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container_type: str
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container_properties: dict
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you_activity: ActivityShape
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bot_activity: ActivityShape
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initial_time_iso: str
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edge_seed_summary: str
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edge_seed_knowledge_facts: list[str]
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_SYSTEM_PROMPT = (
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"You are extracting structured scene state from a roleplay kickoff "
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"scene description. The user provides bot context and a prose "
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"description of the opening scene; you output JSON conforming to the "
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"schema. Be concrete: pick a single container, single activity per "
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"entity, and a sensible initial in-fiction time. Anything not stated "
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"explicitly should be inferred reasonably from the prose."
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)
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def _build_user_prompt(
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*,
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bot_name: str,
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bot_persona: str,
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initial_relationship_to_you: str,
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kickoff_prose: str,
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you_name: str,
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) -> str:
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return (
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f"BOT NAME: {bot_name}\n"
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f"BOT PERSONA: {bot_persona}\n"
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f"INITIAL RELATIONSHIP TO {you_name}: {initial_relationship_to_you}\n"
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f"YOU NAME: {you_name}\n"
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f"KICKOFF PROSE:\n{kickoff_prose}"
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)
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async def parse_kickoff(
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client: LLMClient,
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*,
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model: str,
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bot_name: str,
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bot_persona: str,
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initial_relationship_to_you: str,
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kickoff_prose: str,
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you_name: str,
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timeout_s: float = 10.0,
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) -> KickoffParse:
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"""Parse authored kickoff prose into a structured ``KickoffParse``.
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Internally calls :func:`chat.llm.classify.classify` with a labeled
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user prompt. Raises ``RuntimeError`` if the classifier fails twice in
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a row — no default is supplied at this layer, since the caller (T13's
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confirm form) is responsible for showing an error and letting the
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user edit.
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"""
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user_prompt = _build_user_prompt(
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bot_name=bot_name,
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bot_persona=bot_persona,
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initial_relationship_to_you=initial_relationship_to_you,
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kickoff_prose=kickoff_prose,
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you_name=you_name,
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)
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return await classify(
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client,
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model=model,
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system=_SYSTEM_PROMPT,
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user=user_prompt,
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schema=KickoffParse,
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timeout_s=timeout_s,
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)
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