Joseph Doherty
90 lines
2.9 KiB
Python
90 lines
2.9 KiB
Python
"""Thread-detection service (T55).
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On scene close, classify the transcript into thread open/update/close
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candidates. Returns ThreadCandidate list; caller (T58 scene compression)
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emits one thread_opened/thread_updated/thread_closed event per candidate.
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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 ThreadCandidate(BaseModel):
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action: str # "open" | "update" | "close"
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title: str = "" # required for "open"; ignored otherwise
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summary: str = ""
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existing_thread_id: str | None = None # required for "update" / "close"
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class ThreadDetectionResult(BaseModel):
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candidates: list[ThreadCandidate] = Field(default_factory=list)
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_SYSTEM = (
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"You analyze a closed scene's transcript to identify narrative "
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"threads (unresolved arcs, dangling questions, promises made, "
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"open obligations). Choose actions:\n"
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"- 'open': a NEW thread the scene introduced. Provide title (short "
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"noun phrase) + summary (one sentence).\n"
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"- 'update': an EXISTING open thread that the scene developed. "
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"Provide existing_thread_id + new summary.\n"
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"- 'close': an EXISTING open thread that the scene resolved. "
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"Provide existing_thread_id; summary may capture the resolution.\n"
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"Conservative bias: most scenes do NOT open new threads. Only "
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"produce candidates when the transcript clearly justifies them. "
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"Output strict JSON matching the schema."
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)
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async def detect_threads(
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client: LLMClient,
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*,
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classifier_model: str,
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scene_transcript: list[dict], # [{speaker, text}, ...]
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open_threads: list[dict], # [{thread_id, title, summary}, ...]
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timeout_s: float = 30.0,
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) -> ThreadDetectionResult:
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"""Classify scene close into thread open/update/close candidates."""
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if not scene_transcript:
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return ThreadDetectionResult()
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transcript_lines = [
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f"{turn.get('speaker', 'unknown')}: {turn.get('text', '')}"
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for turn in scene_transcript
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]
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threads_lines = []
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if open_threads:
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threads_lines.append("Currently open threads:")
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for t in open_threads:
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threads_lines.append(
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f"- thread_id={t['thread_id']} "
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f"title={t.get('title', '')} "
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f"summary={t.get('summary', '')}"
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)
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else:
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threads_lines.append("No currently open threads.")
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user = (
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"Scene transcript:\n"
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+ "\n".join(transcript_lines)
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+ "\n\n"
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+ "\n".join(threads_lines)
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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=ThreadDetectionResult,
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default=ThreadDetectionResult(),
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timeout_s=timeout_s,
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)
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__all__ = ["ThreadCandidate", "ThreadDetectionResult", "detect_threads"]
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