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49 lines
2.0 KiB
Python
49 lines
2.0 KiB
Python
"""LM Studio reasoning-effort resolution shared by the chat-completions
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transport and run_agent's iteration-limit summary path.
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LM Studio publishes per-model ``capabilities.reasoning.allowed_options`` (e.g.
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``["off","on"]`` for toggle-style models, ``["off","minimal","low"]`` for
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graduated models). We map the user's ``reasoning_config`` onto LM Studio's
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OpenAI-compatible vocabulary, then clamp against the model's allowed set so
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the server doesn't 400 on an unsupported effort.
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"""
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from __future__ import annotations
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from typing import List, Optional
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# LM Studio accepts these top-level reasoning_effort values via its
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# OpenAI-compatible chat.completions endpoint.
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_LM_VALID_EFFORTS = {"none", "minimal", "low", "medium", "high", "xhigh"}
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# Toggle-style models publish allowed_options as ["off","on"] in /api/v1/models.
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# Map them onto the OpenAI-compatible request vocabulary.
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_LM_EFFORT_ALIASES = {"off": "none", "on": "medium"}
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def resolve_lmstudio_effort(
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reasoning_config: Optional[dict],
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allowed_options: Optional[List[str]],
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) -> Optional[str]:
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"""Return the ``reasoning_effort`` string to send to LM Studio, or ``None``.
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``None`` means "omit the field": the user picked a level the model can't
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honor, so let LM Studio fall back to the model's declared default rather
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than silently substituting a different effort. When ``allowed_options`` is
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falsy (probe failed), skip clamping and send the resolved effort anyway.
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"""
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effort = "medium"
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if reasoning_config and isinstance(reasoning_config, dict):
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if reasoning_config.get("enabled") is False:
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effort = "none"
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else:
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raw = (reasoning_config.get("effort") or "").strip().lower()
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raw = _LM_EFFORT_ALIASES.get(raw, raw)
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if raw in _LM_VALID_EFFORTS:
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effort = raw
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if allowed_options:
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allowed = {_LM_EFFORT_ALIASES.get(opt, opt) for opt in allowed_options}
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if effort not in allowed:
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return None
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return effort
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