Joseph Doherty
63 lines
2.3 KiB
Python
63 lines
2.3 KiB
Python
"""Multi-entity state-update coordinator (T40).
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Wraps single-pair compute_state_update to run state updates for ALL
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directed pairs of present entities. With 3 present entities (you, host,
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guest) that's 6 directed pairs. With 2 present (you, host) it's 2 pairs.
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Calls run sequentially to respect Featherless's 2-connection cap (the
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client-level semaphore would serialize them anyway, but doing it here
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keeps the failure surface clean — a hung pair doesn't queue behind
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itself).
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"""
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from __future__ import annotations
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from chat.llm.client import LLMClient
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from chat.services.state_update import StateUpdate, compute_state_update
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async def compute_state_updates_for_present(
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client: LLMClient,
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*,
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classifier_model: str,
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present_ids: list[str],
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present_names: dict[str, str],
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personas: dict[str, str],
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prior_edges: dict[tuple[str, str], dict],
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recent_dialogue: list[dict],
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timeout_s: float = 30.0,
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) -> list[tuple[str, str, StateUpdate]]:
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"""Run compute_state_update for every directed pair (src != tgt) over
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``present_ids``. Returns list of ``(source_id, target_id, update)``
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tuples in the natural iteration order over ``present_ids x present_ids``.
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A single failing pair falls back to the schema-default StateUpdate
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(zero deltas, empty facts) inside ``compute_state_update``; the batch
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keeps going.
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"""
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out: list[tuple[str, str, StateUpdate]] = []
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for src in present_ids:
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for tgt in present_ids:
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if src == tgt:
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continue
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edge = prior_edges.get((src, tgt), {})
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update = await compute_state_update(
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client,
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model=classifier_model,
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source_id=src,
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target_id=tgt,
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source_name=present_names.get(src, src),
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source_persona=personas.get(src, "") or "",
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target_name=present_names.get(tgt, tgt),
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prior_affinity=int(edge.get("affinity", 50)),
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prior_trust=int(edge.get("trust", 50)),
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prior_summary=edge.get("summary", "") or "",
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recent_dialogue=recent_dialogue,
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timeout_s=timeout_s,
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)
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out.append((src, tgt, update))
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return out
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__all__ = ["compute_state_updates_for_present"]
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