Repository: pydantic/pydantic-ai
File: pydantic_ai_slim/pydantic_ai/models/bedrock.py (Python)

Code before:
            if guardrail_config := model_settings.get('bedrock_guardrail_config', None):
                params['guardrailConfig'] = guardrail_config
            if performance_configuration := model_settings.get('bedrock_performance_configuration', None):
                params['performanceConfig'] = performance_configuration
            if request_metadata := model_settings.get('bedrock_request_metadata', None):
                params['requestMetadata'] = request_metadata
            if additional_model_response_fields_paths := model_settings.get(
                'bedrock_additional_model_response_fields_paths', None
            ):
                params['additionalModelResponseFieldPaths'] = additional_model_response_fields_paths
            if additional_model_requests_fields := model_settings.get('bedrock_additional_model_requests_fields', None):
                params['additionalModelRequestFields'] = additional_model_requests_fields
            if prompt_variables := model_settings.get('bedrock_prompt_variables', None):
                params['promptVariables'] = prompt_variables

        try:
            if stream:
                model_response = await anyio.to_thread.run_sync(
                    functools.partial(self.client.converse_stream, **params)
                )
            else:
                model_response = await anyio.to_thread.run_sync(functools.partial(self.client.converse, **params))
        except ClientError as e:
            status_code = e.response.get('ResponseMetadata', {}).get('HTTPStatusCode', 500)
            raise ModelHTTPError(
                status_code=status_code, model_name=self.model_name, body=e.response.get('Error')
            ) from e
        return model_response

    @staticmethod
    def _map_inference_config(
        model_settings: ModelSettings | None,
    ) -> InferenceConfigurationTypeDef:
        model_settings = model_settings or {}
        inference_config: InferenceConfigurationTypeDef = {}

        if max_tokens := model_settings.get('max_tokens'):
            inference_config['maxTokens'] = max_tokens
        if (temperature := model_settings.get('temperature')) is not None:
            inference_config['temperature'] = temperature
        if top_p := model_settings.get('top_p'):
            inference_config['topP'] = top_p
        if stop_sequences := model_settings.get('stop_sequences'):
            inference_config['stopSequences'] = stop_sequences

        return inference_config

    def _map_tool_config(self, model_request_parameters: ModelRequestParameters) -> ToolConfigurationTypeDef | None:
        tools = self._get_tools(model_request_parameters)
        if not tools:
            return None

Review comment (suggestion):
I'm not seeing the `Error` key on the `ConverseResponseTypeDef`, so let's use the entire `e.response` as `body`, that also matches the intent of that field better.

Code after:
            if guardrail_config := model_settings.get('bedrock_guardrail_config', None):
                params['guardrailConfig'] = guardrail_config
            if performance_configuration := model_settings.get('bedrock_performance_configuration', None):
                params['performanceConfig'] = performance_configuration
            if request_metadata := model_settings.get('bedrock_request_metadata', None):
                params['requestMetadata'] = request_metadata
            if additional_model_response_fields_paths := model_settings.get(
                'bedrock_additional_model_response_fields_paths', None
            ):
                params['additionalModelResponseFieldPaths'] = additional_model_response_fields_paths
            if additional_model_requests_fields := model_settings.get('bedrock_additional_model_requests_fields', None):
                params['additionalModelRequestFields'] = additional_model_requests_fields
            if prompt_variables := model_settings.get('bedrock_prompt_variables', None):
                params['promptVariables'] = prompt_variables

        try:
            if stream:
                model_response = await anyio.to_thread.run_sync(
                    functools.partial(self.client.converse_stream, **params)
                )
            else:
                model_response = await anyio.to_thread.run_sync(functools.partial(self.client.converse, **params))
        except ClientError as e:
            status_code = e.response.get('ResponseMetadata', {}).get('HTTPStatusCode', 500)
            raise ModelHTTPError(status_code=status_code, model_name=self.model_name, body=e.response) from e
        return model_response

    @staticmethod
    def _map_inference_config(
        model_settings: ModelSettings | None,
    ) -> InferenceConfigurationTypeDef:
        model_settings = model_settings or {}
        inference_config: InferenceConfigurationTypeDef = {}

        if max_tokens := model_settings.get('max_tokens'):
            inference_config['maxTokens'] = max_tokens
        if (temperature := model_settings.get('temperature')) is not None:
            inference_config['temperature'] = temperature
        if top_p := model_settings.get('top_p'):
            inference_config['topP'] = top_p
        if stop_sequences := model_settings.get('stop_sequences'):
            inference_config['stopSequences'] = stop_sequences

        return inference_config

    def _map_tool_config(self, model_request_parameters: ModelRequestParameters) -> ToolConfigurationTypeDef | None:
        tools = self._get_tools(model_request_parameters)
        if not tools:
            return None

        tool_choice: ToolChoiceTypeDef