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105 lines
3.5 KiB
Python
105 lines
3.5 KiB
Python
"""Immutable execution settings for one LLM turn."""
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from __future__ import annotations
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from dataclasses import dataclass, replace
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from typing import TYPE_CHECKING
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from nanobot.providers.base import GenerationSettings, LLMProvider
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if TYPE_CHECKING:
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from nanobot.providers.factory import ProviderSnapshot
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@dataclass(frozen=True, slots=True)
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class LLMRuntime:
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"""One captured provider/model configuration used for an entire execution.
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The provider itself is stateful, but all mutable selection and generation
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values are copied into this frozen value. Consumers must use these fields
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instead of consulting ``provider.generation`` after admission.
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"""
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provider: LLMProvider
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model: str
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generation: GenerationSettings
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context_window_tokens: int
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model_preset: str | None = None
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snapshot_signature: tuple[object, ...] | None = None
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@classmethod
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def capture(
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cls,
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provider: LLMProvider,
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model: str,
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*,
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context_window_tokens: int,
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model_preset: str | None = None,
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snapshot_signature: tuple[object, ...] | None = None,
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) -> LLMRuntime:
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"""Capture provider defaults without retaining mutable generation state."""
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defaults = GenerationSettings()
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generation = getattr(provider, "generation", defaults)
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return cls(
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provider=provider,
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model=model,
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generation=GenerationSettings(
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temperature=getattr(generation, "temperature", defaults.temperature),
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max_tokens=getattr(generation, "max_tokens", defaults.max_tokens),
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reasoning_effort=getattr(
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generation,
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"reasoning_effort",
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defaults.reasoning_effort,
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),
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),
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context_window_tokens=context_window_tokens,
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model_preset=model_preset,
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snapshot_signature=snapshot_signature,
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)
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def with_generation_overrides(
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self,
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*,
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temperature: float | None = None,
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max_tokens: int | None = None,
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reasoning_effort: str | None = None,
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) -> LLMRuntime:
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"""Return a derived runtime for explicit per-run generation overrides."""
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generation = self.generation
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return replace(
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self,
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generation=GenerationSettings(
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temperature=(
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generation.temperature if temperature is None else temperature
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),
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max_tokens=generation.max_tokens if max_tokens is None else max_tokens,
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reasoning_effort=(
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generation.reasoning_effort
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if reasoning_effort is None
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else reasoning_effort
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),
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),
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)
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def runtime_from_provider_snapshot(
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snapshot: ProviderSnapshot,
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) -> LLMRuntime:
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"""Convert a provider factory snapshot into the canonical runtime value."""
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if snapshot.generation is not None:
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return LLMRuntime(
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provider=snapshot.provider,
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model=snapshot.model,
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generation=snapshot.generation,
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context_window_tokens=snapshot.context_window_tokens,
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model_preset=snapshot.model_preset,
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snapshot_signature=snapshot.signature,
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)
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return LLMRuntime.capture(
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snapshot.provider,
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snapshot.model,
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context_window_tokens=snapshot.context_window_tokens,
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model_preset=snapshot.model_preset,
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snapshot_signature=snapshot.signature,
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)
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