Completes the reviewer requirement from PR #1190 review that was carried over but not implemented in #1230: > "The hard max is a function-local constant. For this setting, the ceiling > should be configurable or at least represented as a named setting/default > with tests." — review on #1190 #1230 shipped the adaptive auto-derivation but left `DEFAULT_HARD_MAX = 200_000` as a hardcoded module constant in src/context_budget.py. Admins on premium APIs with large context windows (kimi-k2 / minimax-m3 at 1M, etc.) can use their full window today only by setting `agent_input_token_budget` explicitly — which then takes them off the adaptive auto-path entirely. ## What this PR changes - src/settings.py: register `agent_input_token_hard_max` in DEFAULT_SETTINGS, default 200_000 (matches `DEFAULT_HARD_MAX`). Inline comment documents the no-op semantics in the explicit branch. - src/agent_loop.py: read the setting at the call site and pass it as the `hard_max` kwarg of `compute_input_token_budget`. Defensive parsing — missing / non-int / zero values fall back to `DEFAULT_HARD_MAX`, so a misconfig cannot silently zero the budget. - src/tool_implementations.py: three friendly aliases for `manage_settings`: - "hard max" -> agent_input_token_hard_max - "token budget cap" -> agent_input_token_hard_max - "input budget cap" -> agent_input_token_hard_max Plus the existing "token budget" -> agent_input_token_budget keeps a matching shorter alias "input budget". - tests/test_context_budget.py: 6 new tests on top of the existing 6: - hard_max raises the auto ceiling (1M ctx + raised cap -> 85% of ctx) - hard_max lowers the auto ceiling (128K ctx + 50K cap -> 50K) - hard_max has no effect on the explicit branch - DEFAULT_SETTINGS contains the new key - manage_settings aliases are registered - the live get_setting path returns the override value, and malformed values fall back per the agent_loop defensive parsing 12 passed in 0.04s. No changes to the pure helper signature or semantics; #1230's behavior is the default when the new setting is unset. ## How it lets users drop the explicit override Before this PR, on a 1M-context model: agent_input_token_budget = 900_000 (explicit) -> 900K [user override] agent_input_token_budget = <unset> (auto) -> 200K [HARD_MAX] After this PR, same model: agent_input_token_budget = <unset> agent_input_token_hard_max = 900_000 -> min(1M * 0.85, 900K) = 850K [auto, no override needed] The explicit-override path keeps working unchanged for users who prefer it.
119 lines
5.5 KiB
Python
119 lines
5.5 KiB
Python
"""Issue #1170 — the agent input-token budget adapts to the model context window.
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Pins the pure budget computation and the explicit-override detection.
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"""
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import json
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from src.context_budget import compute_input_token_budget, DEFAULT_HARD_MAX
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def test_default_scales_to_context_window():
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# Not explicit, big window -> ~85% of the window (the old code capped at 6000).
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assert compute_input_token_budget(6000, 128000, explicit=False) == int(128000 * 0.85)
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def test_default_capped_at_hard_max_for_huge_windows():
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assert compute_input_token_budget(6000, 1_000_000, explicit=False) == DEFAULT_HARD_MAX
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def test_explicit_budget_is_honoured():
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# User explicitly chose 6000 -> keep it even on a 128K model.
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assert compute_input_token_budget(6000, 128000, explicit=True) == 6000
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# A larger explicit budget is honoured too, clamped to the window.
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assert compute_input_token_budget(50000, 128000, explicit=True) == 50000
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def test_explicit_budget_clamped_to_window():
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assert compute_input_token_budget(200000, 32000, explicit=True) == 32000
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def test_unknown_window_falls_back_to_configured():
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assert compute_input_token_budget(6000, 0, explicit=False) == 6000
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assert compute_input_token_budget(0, 0, explicit=False) == 6000 # default
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def test_is_setting_overridden_reads_raw_saved_file(tmp_path, monkeypatch):
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import src.settings as settings
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f = tmp_path / "settings.json"
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f.write_text(json.dumps({"agent_input_token_budget": 12000}), encoding="utf-8")
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monkeypatch.setattr(settings, "SETTINGS_FILE", str(f))
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assert settings.is_setting_overridden("agent_input_token_budget") is True
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assert settings.is_setting_overridden("some_unset_key") is False
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f.write_text(json.dumps({}), encoding="utf-8")
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assert settings.is_setting_overridden("agent_input_token_budget") is False
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# ---------------------------------------------------------------------------
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# Configurable hard_max — completes the reviewer requirement from #1190 that
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# was carried over but not implemented in #1230: the ceiling on the auto-
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# derived path should be a setting, not a hidden constant. Without this,
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# admins on premium APIs with very large windows (1M+ context) can only
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# raise the ceiling by editing src/context_budget.py.
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# ---------------------------------------------------------------------------
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def test_custom_hard_max_overrides_default_in_auto_branch():
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"""A caller-supplied hard_max lifts the auto-derived ceiling."""
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# Without override: 1M ctx -> capped at DEFAULT_HARD_MAX (200K)
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assert compute_input_token_budget(6000, 1_000_000, explicit=False) == DEFAULT_HARD_MAX
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# With explicit raise: 1M ctx -> 850K (85% of 1M), under the raised ceiling
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assert compute_input_token_budget(6000, 1_000_000, explicit=False, hard_max=900_000) == int(1_000_000 * 0.85)
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def test_custom_hard_max_lowers_default_for_cost_paranoid_setups():
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"""A lower ceiling caps the auto-derived budget below the default."""
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# 128K ctx, default ceiling 200K -> 85% of 128K = 108800
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assert compute_input_token_budget(6000, 128_000, explicit=False) == int(128_000 * 0.85)
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# Same ctx, ceiling lowered to 50K -> capped at 50K instead
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assert compute_input_token_budget(6000, 128_000, explicit=False, hard_max=50_000) == 50_000
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def test_hard_max_has_no_effect_on_explicit_branch():
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"""When the user set an explicit budget, hard_max must not silently cap it."""
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# User chose 900K explicitly; ctx is 1M; ceiling is 100K — user's choice wins.
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assert compute_input_token_budget(900_000, 1_000_000, explicit=True, hard_max=100_000) == 900_000
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def test_default_settings_registers_hard_max_key():
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"""Required so /api/auth/settings and manage_settings can persist the key."""
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from src.settings import DEFAULT_SETTINGS
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assert "agent_input_token_hard_max" in DEFAULT_SETTINGS
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assert DEFAULT_SETTINGS["agent_input_token_hard_max"] == DEFAULT_HARD_MAX
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def test_alias_map_registers_friendly_names():
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"""`manage_settings` should accept 'hard max' and friends."""
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from pathlib import Path
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src = Path("src/tool_implementations.py").read_text()
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assert '"hard max": "agent_input_token_hard_max"' in src
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assert '"token budget cap": "agent_input_token_hard_max"' in src
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assert '"input budget cap": "agent_input_token_hard_max"' in src
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def test_agent_loop_reads_hard_max_setting(tmp_path, monkeypatch):
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"""End-to-end: a saved settings.json value for agent_input_token_hard_max
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must reach compute_input_token_budget on the real agent_loop call path."""
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import src.settings as settings
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# Point SETTINGS_FILE at a temp file with our override.
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f = tmp_path / "settings.json"
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f.write_text(json.dumps({"agent_input_token_hard_max": 750_000}), encoding="utf-8")
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monkeypatch.setattr(settings, "SETTINGS_FILE", str(f))
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monkeypatch.setattr(settings, "_settings_cache", None)
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# Read via the same import path the agent loop uses.
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assert settings.get_setting("agent_input_token_hard_max", DEFAULT_HARD_MAX) == 750_000
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# Malformed value falls back to DEFAULT_HARD_MAX (defensive, matches the
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# try/except in src/agent_loop.py).
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f.write_text(json.dumps({"agent_input_token_hard_max": "huge"}), encoding="utf-8")
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monkeypatch.setattr(settings, "_settings_cache", None)
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raw = settings.get_setting("agent_input_token_hard_max", DEFAULT_HARD_MAX)
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try:
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parsed = int(raw)
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except (TypeError, ValueError):
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parsed = DEFAULT_HARD_MAX
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if parsed <= 0:
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parsed = DEFAULT_HARD_MAX
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assert parsed == DEFAULT_HARD_MAX
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