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https://github.com/usestrix/strix.git
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Ahmed Allam
2163a66b78
The chars/4 fallback under-counts dense text (code, base64, CJK), which could let a summary request be packed past the real context window and get rejected. Use a conservative ~3-chars/token estimate instead so budget checks never under-count.
49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
"""Tests for model-aware token budgets."""
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from __future__ import annotations
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from typing import TYPE_CHECKING
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from strix.config import load_settings
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from strix.llm import context_budget
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if TYPE_CHECKING:
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import pytest
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def test_context_window_known_model() -> None:
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# gpt-4o is mapped by LiteLLM at 128k input tokens.
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assert context_budget.context_window("gpt-4o") == 128_000
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def test_context_window_strips_provider_prefix() -> None:
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assert context_budget.context_window("openai/gpt-4o") == 128_000
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def test_context_window_unmapped_uses_fallback(monkeypatch: pytest.MonkeyPatch) -> None:
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context_budget._model_info.cache_clear()
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def _raise(_model: str) -> dict[str, int]:
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raise ValueError("This model isn't mapped yet.")
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monkeypatch.setattr("strix.llm.context_budget.litellm.get_model_info", _raise)
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expected = load_settings().context.fallback_context_tokens
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assert context_budget.context_window("totally-made-up-model") == expected
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context_budget._model_info.cache_clear()
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def test_count_tokens_fallback_on_error(monkeypatch: pytest.MonkeyPatch) -> None:
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def _raise(**_kwargs: object) -> int:
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raise RuntimeError("no tokenizer")
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monkeypatch.setattr("strix.llm.context_budget.litellm.token_counter", _raise)
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# Conservative ~3-chars/token estimate, rounded up, so budgets never
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# under-count when no tokenizer is available.
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assert context_budget.count_tokens("weird-model", "x" * 400) == 134
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assert context_budget.count_tokens("weird-model", "x") == 1
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def test_count_tokens_empty_is_zero() -> None:
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assert context_budget.count_tokens("gpt-4o", "") == 0
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