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chunguscodesandAhmed Allam
76e97e6a59
LiteLLM treats provider-qualified metadata lookups as an auth path. Use the underlying model slug so context sizing cannot block the scan loop in a device-code poll.
66 lines
2.2 KiB
Python
66 lines
2.2 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_chatgpt_prefix_skips_provider_auth(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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context_budget._model_info.cache_clear()
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calls: list[str] = []
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def _model_info(model: str) -> dict[str, int]:
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calls.append(model)
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return {"max_input_tokens": 1_050_000, "max_output_tokens": 128_000}
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monkeypatch.setattr("strix.llm.context_budget.litellm.get_model_info", _model_info)
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try:
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assert context_budget.context_window("chatgpt/gpt-5.6-luna") == 1_050_000
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assert calls == ["gpt-5.6-luna"]
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finally:
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context_budget._model_info.cache_clear()
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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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# Falls back to UTF-8 byte length (upper bound on tokens).
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assert context_budget.count_tokens("weird-model", "x" * 400) == 400
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assert context_budget.count_tokens("weird-model", "😀" * 10) == 40
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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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