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fix(bedrock): clamp maxTokens to the 16-token minimum for OpenAI GPT models on Converse
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
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2edda5aec3
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2 changed files with 34 additions and 1 deletions
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@ -107,6 +107,7 @@ BEDROCK_COMPUTER_USE_TOOLS: Final = [
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"bash_",
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"text_editor_",
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]
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BEDROCK_OPENAI_GPT_MIN_MAX_TOKENS: Final = 16
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# Beta header patterns that are not supported by Bedrock Converse API
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# These will be filtered out to prevent errors
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@ -377,6 +378,12 @@ class AmazonConverseConfig(BaseConfig):
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def _is_openai_gpt_reasoning_model(model: str) -> bool:
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return re.search(r"openai\.gpt-\d", model) is not None
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@staticmethod
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def _enforce_min_max_tokens(max_tokens: object) -> object:
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if isinstance(max_tokens, int) and max_tokens < BEDROCK_OPENAI_GPT_MIN_MAX_TOKENS:
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return BEDROCK_OPENAI_GPT_MIN_MAX_TOKENS
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return max_tokens
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def _is_nova_2_model(self, model: str) -> bool:
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"""
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Check if the model is a Nova 2 model that supports reasoningConfig.
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@ -999,7 +1006,11 @@ class AmazonConverseConfig(BaseConfig):
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is_thinking_enabled=is_thinking_enabled,
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)
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if param == "max_tokens" or param == "max_completion_tokens":
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optional_params["maxTokens"] = value
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optional_params["maxTokens"] = (
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self._enforce_min_max_tokens(cast(object, value))
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if self._is_openai_gpt_reasoning_model(model)
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else value
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)
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if param == "stream":
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optional_params["stream"] = value
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if param == "stop":
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@ -457,6 +457,28 @@ def test_reasoning_with_forced_tool_choice_switches_to_auto():
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assert optional_params["tool_choice"] == {"auto": {}}
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@pytest.mark.parametrize(
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"model, param, value, expected_max_tokens",
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[
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("us.openai.gpt-6-astra", "max_tokens", 1, 16),
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("us.openai.gpt-6-astra", "max_completion_tokens", 1, 16),
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("us.openai.gpt-6-astra", "max_tokens", 64, 64),
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("anthropic.claude-sonnet-4-5-20250929-v1:0", "max_tokens", 1, 1),
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],
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)
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def test_map_openai_params_enforces_minimum_max_tokens_for_openai_gpt_reasoning_models(
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model: str, param: str, value: int, expected_max_tokens: int
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):
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optional_params = AmazonConverseConfig().map_openai_params(
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non_default_params={param: value},
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optional_params={},
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model=model,
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drop_params=False,
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)
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assert optional_params["maxTokens"] == expected_max_tokens
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@pytest.mark.parametrize(
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"model",
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[
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