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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.
64 lines
3.1 KiB
Python
64 lines
3.1 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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class TestParseCharBasedOutputCap:
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"""LM Studio / llama.cpp report context in tokens but prompt in characters.
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These servers send a hard 400 even on a trivial prompt when the default
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output cap equals the context window (#42741): the request asks for the
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whole window as output, leaving zero room for input.
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"""
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def test_char_based_output_cap_format(self):
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msg = ("This model's maximum context length is 65536 tokens. However, "
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"you requested 65536 output tokens and your prompt contains "
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"77409 characters (more than 0 characters, which is the upper "
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"bound for 0 input tokens). Please reduce the length of the "
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"input prompt or the number of requested output tokens.")
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# est input = ceil(77409 / 3) = 25803; available = 65536 - 25803 = 39733
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assert parse_available_output_tokens_from_error(msg) == 39733
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def test_char_based_leaves_room_for_input(self):
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# The whole point: the retried output cap + the estimated input must
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# fit inside the reported context window.
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ctx = 65536
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chars = 77409
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available = parse_available_output_tokens_from_error(
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f"maximum context length is {ctx} tokens. However, you requested "
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f"{ctx} output tokens and your prompt contains {chars} characters."
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)
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assert available is not None
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assert available + (chars + 2) // 3 <= ctx
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def test_char_based_no_room_returns_none(self):
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# Prompt larger than the window (in tokens) -> not an output-cap fix;
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# let the prompt-too-long / compression path handle it.
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msg = ("maximum context length is 1000 tokens. However, you requested "
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"1000 output tokens and your prompt contains 9000 characters.")
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assert parse_available_output_tokens_from_error(msg) is None
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