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57775e9e16
Add TestParseCharBasedOutputCap for the LM Studio / llama.cpp phrasing (context in tokens, prompt in characters): the reported error resolves to the available output budget, the retried cap plus the estimated input stays inside the window, and a prompt larger than the window falls through to None so the prompt-too-long/compression path still owns that case.
27 lines
1.3 KiB
Python
27 lines
1.3 KiB
Python
import pytest
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from agent.model_metadata import parse_available_output_tokens_from_error
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class TestParseOpenRouterOutputCap:
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"""OpenRouter/Nous phrase the output-cap error as a context breakdown."""
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def test_openrouter_breakdown_format(self):
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msg = ("This endpoint's maximum context length is 200000 tokens. "
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"However, you requested about 195000 tokens "
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"(150000 of text input, 40000 of tool input, 5000 in the output).")
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# available output = 200000 - 150000 - 40000 = 10000
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assert parse_available_output_tokens_from_error(msg) == 10000
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def test_anthropic_format_still_works(self):
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msg = ("max_tokens: 32768 > context_window: 200000 - "
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"input_tokens: 190000 = available_tokens: 10000")
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assert parse_available_output_tokens_from_error(msg) == 10000
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def test_non_output_cap_error_returns_none(self):
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assert parse_available_output_tokens_from_error("some unrelated 400 error") is None
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def test_breakdown_with_no_room_returns_none(self):
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# ctx - text - tool <= 0 -> None (don't return a non-positive cap)
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msg = ("maximum context length is 1000 tokens "
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"(900 of text input, 200 of tool input, 0 in the output)")
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assert parse_available_output_tokens_from_error(msg) is None
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