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red person
83 lines
3.6 KiB
Python
83 lines
3.6 KiB
Python
"""Adaptive input-token budget for the agent loop (#1170).
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The agent soft-trims its input context to ``agent_input_token_budget`` (default
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6000). The old computation was ``min(context_length or budget, budget)``, which
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made the 6000 default a hard ceiling for *every* model — so a 128K or 1M context
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model was silently capped at 6000 input tokens even though it can hold far more.
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This derives the effective budget from the model's discovered context window when
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the user has NOT set an explicit budget, while still honouring an explicit setting
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exactly (clamped to the window). Pure and side-effect free so it is unit-testable.
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"""
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# Generous ceiling so long-context models are unblocked without sending a
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# pathologically large prompt every agent turn. Tunable; chosen to fully cover
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# 128K models and give 1M models a large but bounded budget.
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DEFAULT_HARD_MAX = 200_000
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DEFAULT_BUDGET = 6000
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DEFAULT_HEADROOM = 0.85
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def _int_or_zero(value) -> int:
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try:
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return int(value or 0)
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except (TypeError, ValueError):
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return 0
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def compute_input_token_budget(
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configured: int,
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context_length: int,
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explicit: bool,
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*,
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default: int = DEFAULT_BUDGET,
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headroom: float = DEFAULT_HEADROOM,
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hard_max: int = DEFAULT_HARD_MAX,
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) -> int:
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"""Return the effective soft input-token budget.
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Args:
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configured: the value read from settings (may be the default).
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context_length: the model's discovered context window. Pass 0 when the
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window is unknown / only a bare fallback — auto-scaling then stays
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conservative instead of trusting an unproven window (review on #4122).
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explicit: True if the user set a NON-default budget. The default value is
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the "auto" sentinel (scale to the window); any other value is an
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explicit cap. (A deliberately-chosen default can't be distinguished
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from a materialized default by value, so the default reads as auto.)
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Rules:
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- Explicit user budget is honoured exactly, only clamped to the model's
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window when that window is known (the user's deliberate choice wins;
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``hard_max`` is an auto-budget ceiling only — see #1230).
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- Otherwise (auto), scale to ``headroom`` of the context window, capped at
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``hard_max`` — so long-context models use their capacity.
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- When the window is unknown (context_length <= 0), use the conservative
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``default`` budget and do NOT scale off the fallback.
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"""
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configured = _int_or_zero(configured)
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context_length = _int_or_zero(context_length)
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if explicit and configured > 0:
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return min(configured, context_length) if context_length > 0 else configured
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if context_length > 0:
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scaled = int(context_length * headroom)
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return max(1, min(scaled, hard_max))
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return configured if configured > 0 else default
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def budget_is_explicit(configured: int, *, default: int = DEFAULT_BUDGET) -> bool:
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"""Whether a configured agent_input_token_budget is a deliberate explicit cap.
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The default value is the "auto" sentinel (scale to the model's window), so only
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a NON-default positive value counts as explicit. This keys off the VALUE, not
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settings *presence* — the settings-save path materializes every default into
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settings.json, so a persisted default must still read as auto (the regression
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#4121 / #1230 are about). Centralised here so the materialized-default contract
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is unit-testable and can't silently regress to a presence check.
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"""
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configured = int(configured or 0)
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return configured > 0 and configured != default
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