pydantic_ai.models.bedrock

Setup

For details on how to set up authentication with this model, see model configuration for Bedrock.

LatestBedrockModelNames module-attribute

LatestBedrockModelNames = Literal[
    "amazon.titan-tg1-large",
    "amazon.titan-text-lite-v1",
    "amazon.titan-text-express-v1",
    "us.amazon.nova-2-lite-v1:0",
    "us.amazon.nova-pro-v1:0",
    "us.amazon.nova-lite-v1:0",
    "us.amazon.nova-micro-v1:0",
    "anthropic.claude-3-5-sonnet-20241022-v2:0",
    "us.anthropic.claude-3-5-sonnet-20241022-v2:0",
    "anthropic.claude-3-5-haiku-20241022-v1:0",
    "us.anthropic.claude-3-5-haiku-20241022-v1:0",
    "anthropic.claude-instant-v1",
    "anthropic.claude-v2:1",
    "anthropic.claude-v2",
    "anthropic.claude-3-sonnet-20240229-v1:0",
    "us.anthropic.claude-3-sonnet-20240229-v1:0",
    "anthropic.claude-3-haiku-20240307-v1:0",
    "us.anthropic.claude-3-haiku-20240307-v1:0",
    "anthropic.claude-3-opus-20240229-v1:0",
    "us.anthropic.claude-3-opus-20240229-v1:0",
    "anthropic.claude-3-5-sonnet-20240620-v1:0",
    "us.anthropic.claude-3-5-sonnet-20240620-v1:0",
    "anthropic.claude-3-7-sonnet-20250219-v1:0",
    "us.anthropic.claude-3-7-sonnet-20250219-v1:0",
    "anthropic.claude-opus-4-20250514-v1:0",
    "us.anthropic.claude-opus-4-20250514-v1:0",
    "global.anthropic.claude-opus-4-5-20251101-v1:0",
    "anthropic.claude-sonnet-4-20250514-v1:0",
    "us.anthropic.claude-sonnet-4-20250514-v1:0",
    "eu.anthropic.claude-sonnet-4-20250514-v1:0",
    "anthropic.claude-sonnet-4-5-20250929-v1:0",
    "us.anthropic.claude-sonnet-4-5-20250929-v1:0",
    "eu.anthropic.claude-sonnet-4-5-20250929-v1:0",
    "anthropic.claude-sonnet-4-6",
    "us.anthropic.claude-sonnet-4-6",
    "eu.anthropic.claude-sonnet-4-6",
    "anthropic.claude-haiku-4-5-20251001-v1:0",
    "us.anthropic.claude-haiku-4-5-20251001-v1:0",
    "eu.anthropic.claude-haiku-4-5-20251001-v1:0",
    "cohere.command-text-v14",
    "cohere.command-r-v1:0",
    "cohere.command-r-plus-v1:0",
    "cohere.command-light-text-v14",
    "meta.llama3-8b-instruct-v1:0",
    "meta.llama3-70b-instruct-v1:0",
    "meta.llama3-1-8b-instruct-v1:0",
    "us.meta.llama3-1-8b-instruct-v1:0",
    "meta.llama3-1-70b-instruct-v1:0",
    "us.meta.llama3-1-70b-instruct-v1:0",
    "meta.llama3-1-405b-instruct-v1:0",
    "us.meta.llama3-2-11b-instruct-v1:0",
    "us.meta.llama3-2-90b-instruct-v1:0",
    "us.meta.llama3-2-1b-instruct-v1:0",
    "us.meta.llama3-2-3b-instruct-v1:0",
    "us.meta.llama3-3-70b-instruct-v1:0",
    "mistral.mistral-7b-instruct-v0:2",
    "mistral.mixtral-8x7b-instruct-v0:1",
    "mistral.mistral-large-2402-v1:0",
    "mistral.mistral-large-2407-v1:0",
]

Latest Bedrock models.

BedrockModelName module-attribute

BedrockModelName = str | LatestBedrockModelNames

Possible Bedrock model names.

Since Bedrock supports a variety of date-stamped models, we explicitly list the latest models but allow any name in the type hints. See the Bedrock docs for a full list.

BedrockModelSettings

Bases: ModelSettings

Settings for Bedrock models.

See the Bedrock Converse API docs for a full list. See the boto3 implementation of the Bedrock Converse API.

Source code in pydantic_ai_slim/pydantic_ai/models/bedrock.py

class BedrockModelSettings(ModelSettings, total=False):
    """Settings for Bedrock models.

    See [the Bedrock Converse API docs](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Converse.html#API_runtime_Converse_RequestSyntax) for a full list.
    See [the boto3 implementation](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/bedrock-runtime/client/converse.html) of the Bedrock Converse API.
    """

    # ALL FIELDS MUST BE `bedrock_` PREFIXED SO YOU CAN MERGE THEM WITH OTHER MODELS.

    bedrock_guardrail_config: GuardrailConfigurationTypeDef
    """Content moderation and safety settings for Bedrock API requests.

    See more about it on <https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_GuardrailConfiguration.html>.
    """

    bedrock_performance_configuration: PerformanceConfigurationTypeDef
    """Performance optimization settings for model inference.

    See more about it on <https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_PerformanceConfiguration.html>.
    """

    bedrock_request_metadata: dict[str, str]
    """Additional metadata to attach to Bedrock API requests.

    See more about it on <https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Converse.html#API_runtime_Converse_RequestSyntax>.
    """

    bedrock_additional_model_response_fields_paths: list[str]
    """JSON paths to extract additional fields from model responses.

    See more about it on <https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html>.
    """

    bedrock_prompt_variables: Mapping[str, PromptVariableValuesTypeDef]
    """Variables for substitution into prompt templates.

    See more about it on <https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_PromptVariableValues.html>.
    """

    bedrock_additional_model_requests_fields: Mapping[str, Any]
    """Additional model-specific parameters to include in requests.

    See more about it on <https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html>.
    """

    bedrock_cache_tool_definitions: bool
    """Whether to add a cache point after the last tool definition.

    When enabled, the last tool in the `tools` array will include a `cachePoint`, allowing Bedrock to cache tool
    definitions and reduce costs for compatible models.
    See https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html for more information.
    """

    bedrock_cache_instructions: bool
    """Whether to add a cache point after the system prompt blocks.

    When enabled, an extra `cachePoint` is appended to the system prompt so Bedrock can cache system instructions.
    See https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html for more information.
    """

    bedrock_cache_messages: bool
    """Convenience setting to enable caching for the last user message.

    When enabled, this automatically adds a cache point to the last content block
    in the final user message, which is useful for caching conversation history
    or context in multi-turn conversations.

    Note: Uses 1 of Bedrock's 4 available cache points per request. Any additional CachePoint
    markers in messages will be automatically limited to respect the 4-cache-point maximum.
    See https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html for more information.
    """

    bedrock_service_tier: ServiceTierTypeDef
    """Setting for optimizing performance and cost

    See more about it on <https://docs.aws.amazon.com/bedrock/latest/userguide/service-tiers-inference.html>.
    """

    bedrock_inference_profile: str
    """An [inference profile](https://docs.aws.amazon.com/bedrock/latest/userguide/inference-profiles.html) ARN to use as the `modelId` in API requests.

    When set, this value is used as the `modelId` in `converse` and `converse_stream` API calls instead of the
    base `model_name`. This allows you to pass the base model name (e.g. `'anthropic.claude-sonnet-4-5-20250929-v1:0'`)
    as `model_name` for detecting model capabilities and token counting, while routing requests through an inference profile
    for cost tracking or cross-region inference.
    """

bedrock_guardrail_config instance-attribute

bedrock_guardrail_config: GuardrailConfigurationTypeDef

Content moderation and safety settings for Bedrock API requests.

See more about it on https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_GuardrailConfiguration.html.

bedrock_performance_configuration instance-attribute

bedrock_performance_configuration: (
    PerformanceConfigurationTypeDef
)

Performance optimization settings for model inference.

See more about it on https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_PerformanceConfiguration.html.

bedrock_request_metadata instance-attribute

bedrock_request_metadata: dict[str, str]

Additional metadata to attach to Bedrock API requests.

See more about it on https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Converse.html#API_runtime_Converse_RequestSyntax.

bedrock_additional_model_response_fields_paths instance-attribute

bedrock_additional_model_response_fields_paths: list[str]

JSON paths to extract additional fields from model responses.

See more about it on https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html.

bedrock_prompt_variables instance-attribute

bedrock_prompt_variables: Mapping[
    str, PromptVariableValuesTypeDef
]

Variables for substitution into prompt templates.

See more about it on https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_PromptVariableValues.html.

bedrock_additional_model_requests_fields instance-attribute

bedrock_additional_model_requests_fields: Mapping[str, Any]

Additional model-specific parameters to include in requests.

See more about it on https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html.

bedrock_cache_tool_definitions instance-attribute

bedrock_cache_tool_definitions: bool

Whether to add a cache point after the last tool definition.

When enabled, the last tool in the tools array will include a cachePoint, allowing Bedrock to cache tool definitions and reduce costs for compatible models. See https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html for more information.

bedrock_cache_instructions instance-attribute

bedrock_cache_instructions: bool

Whether to add a cache point after the system prompt blocks.

When enabled, an extra cachePoint is appended to the system prompt so Bedrock can cache system instructions. See https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html for more information.

bedrock_cache_messages instance-attribute

bedrock_cache_messages: bool

Convenience setting to enable caching for the last user message.

When enabled, this automatically adds a cache point to the last content block in the final user message, which is useful for caching conversation history or context in multi-turn conversations.

Note: Uses 1 of Bedrock's 4 available cache points per request. Any additional CachePoint markers in messages will be automatically limited to respect the 4-cache-point maximum. See https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html for more information.

bedrock_service_tier instance-attribute

bedrock_service_tier: ServiceTierTypeDef

Setting for optimizing performance and cost

See more about it on https://docs.aws.amazon.com/bedrock/latest/userguide/service-tiers-inference.html.

bedrock_inference_profile instance-attribute

bedrock_inference_profile: str

An inference profile ARN to use as the modelId in API requests.

When set, this value is used as the modelId in converse and converse_stream API calls instead of the base model_name. This allows you to pass the base model name (e.g. 'anthropic.claude-sonnet-4-5-20250929-v1:0') as model_name for detecting model capabilities and token counting, while routing requests through an inference profile for cost tracking or cross-region inference.

BedrockConverseModel dataclass

Bases: Model

A model that uses the Bedrock Converse API.

Source code in pydantic_ai_slim/pydantic_ai/models/bedrock.py

@dataclass(init=False)
class BedrockConverseModel(Model):
    """A model that uses the Bedrock Converse API."""

    client: BedrockRuntimeClient

    _model_name: BedrockModelName = field(repr=False)
    _provider: Provider[BaseClient] = field(repr=False)

    def __init__(
        self,
        model_name: BedrockModelName,
        *,
        provider: Literal['bedrock', 'gateway'] | Provider[BaseClient] = 'bedrock',
        profile: ModelProfileSpec | None = None,
        settings: ModelSettings | None = None,
    ):
        """Initialize a Bedrock model.

        Args:
            model_name: The name of the model to use.
            model_name: The name of the Bedrock model to use. List of model names available
                [here](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html).
            provider: The provider to use for authentication and API access. Can be either the string
                'bedrock' or an instance of `Provider[BaseClient]`. If not provided, a new provider will be
                created using the other parameters.
            profile: The model profile to use. Defaults to a profile picked by the provider based on the model name.
            settings: Model-specific settings that will be used as defaults for this model.
        """
        self._model_name = model_name

        if isinstance(provider, str):
            provider = infer_provider('gateway/bedrock' if provider == 'gateway' else provider)
        self._provider = provider
        self.client = cast('BedrockRuntimeClient', provider.client)

        super().__init__(settings=settings, profile=profile or provider.model_profile)

    @property
    def base_url(self) -> str:
        return str(self.client.meta.endpoint_url)

    @property
    def model_name(self) -> str:
        """The model name."""
        return self._model_name

    @property
    def system(self) -> str:
        """The model provider."""
        return self._provider.name

    @classmethod
    def supported_builtin_tools(cls) -> frozenset[type[AbstractBuiltinTool]]:
        """The set of builtin tool types this model can handle."""
        return frozenset({CodeExecutionTool})

    def _get_tools(self, model_request_parameters: ModelRequestParameters) -> list[ToolTypeDef]:
        return [self._map_tool_definition(r) for r in model_request_parameters.tool_defs.values()]

    @staticmethod
    def _map_tool_definition(f: ToolDefinition) -> ToolTypeDef:
        tool_spec: ToolSpecificationTypeDef = {'name': f.name, 'inputSchema': {'json': f.parameters_json_schema}}

        if f.description:  # pragma: no branch
            tool_spec['description'] = f.description

        return {'toolSpec': tool_spec}

    async def request(
        self,
        messages: list[ModelMessage],
        model_settings: ModelSettings | None,
        model_request_parameters: ModelRequestParameters,
    ) -> ModelResponse:
        model_settings, model_request_parameters = self.prepare_request(
            model_settings,
            model_request_parameters,
        )
        settings = cast(BedrockModelSettings, model_settings or {})
        response = await self._messages_create(messages, False, settings, model_request_parameters)
        model_response = await self._process_response(response)
        return model_response

    async def count_tokens(
        self,
        messages: list[ModelMessage],
        model_settings: ModelSettings | None,
        model_request_parameters: ModelRequestParameters,
    ) -> usage.RequestUsage:
        """Count the number of tokens, works with limited models.

        Check the actual supported models on <https://docs.aws.amazon.com/bedrock/latest/userguide/count-tokens.html>
        """
        model_settings, model_request_parameters = self.prepare_request(model_settings, model_request_parameters)
        settings = cast(BedrockModelSettings, model_settings or {})
        system_prompt, bedrock_messages = await self._map_messages(messages, model_request_parameters, settings)
        params: CountTokensRequestTypeDef = {
            'modelId': remove_bedrock_geo_prefix(self.model_name),
            'input': {
                'converse': {
                    'messages': bedrock_messages,
                    'system': system_prompt,
                },
            },
        }
        try:
            response = await anyio.to_thread.run_sync(functools.partial(self.client.count_tokens, **params))
        except ClientError as e:
            status_code = e.response.get('ResponseMetadata', {}).get('HTTPStatusCode')
            if isinstance(status_code, int):
                raise ModelHTTPError(status_code=status_code, model_name=self.model_name, body=e.response) from e
            raise ModelAPIError(model_name=self.model_name, message=str(e)) from e
        return usage.RequestUsage(input_tokens=response['inputTokens'])

    @asynccontextmanager
    async def request_stream(
        self,
        messages: list[ModelMessage],
        model_settings: ModelSettings | None,
        model_request_parameters: ModelRequestParameters,
        run_context: RunContext[Any] | None = None,
    ) -> AsyncIterator[StreamedResponse]:
        model_settings, model_request_parameters = self.prepare_request(
            model_settings,
            model_request_parameters,
        )
        settings = cast(BedrockModelSettings, model_settings or {})
        response = await self._messages_create(messages, True, settings, model_request_parameters)
        yield BedrockStreamedResponse(
            model_request_parameters=model_request_parameters,
            _model_name=self.model_name,
            _event_stream=response['stream'],
            _provider_name=self._provider.name,
            _provider_url=self.base_url,
            _provider_response_id=response.get('ResponseMetadata', {}).get('RequestId', None),
        )

    async def _process_response(self, response: ConverseResponseTypeDef) -> ModelResponse:
        items: list[ModelResponsePart] = []
        if message := response['output'].get('message'):  # pragma: no branch
            for item in message['content']:
                if reasoning_content := item.get('reasoningContent'):
                    if redacted_content := reasoning_content.get('redactedContent'):
                        items.append(
                            ThinkingPart(
                                id='redacted_content',
                                content='',
                                signature=redacted_content.decode('utf-8'),
                                provider_name=self.system,
                            )
                        )
                    elif reasoning_text := reasoning_content.get('reasoningText'):  # pragma: no branch
                        signature = reasoning_text.get('signature')
                        items.append(
                            ThinkingPart(
                                content=reasoning_text['text'],
                                signature=signature,
                                provider_name=self.system if signature else None,
                            )
                        )
                if text := item.get('text'):
                    items.append(TextPart(content=text))
                elif tool_use := item.get('toolUse'):
                    if tool_use.get('type') == 'server_tool_use':
                        if tool_use['name'] == 'nova_code_interpreter':  # pragma: no branch
                            items.append(
                                BuiltinToolCallPart(
                                    provider_name=self.system,
                                    tool_name=CodeExecutionTool.kind,
                                    args=tool_use['input'],
                                    tool_call_id=tool_use['toolUseId'],
                                )
                            )
                    else:
                        items.append(
                            ToolCallPart(
                                tool_name=tool_use['name'],
                                args=tool_use['input'],
                                tool_call_id=tool_use['toolUseId'],
                            ),
                        )
                elif tool_result := item.get('toolResult'):
                    if tool_result.get('type') == 'nova_code_interpreter_result':  # pragma: no branch
                        items.append(
                            BuiltinToolReturnPart(
                                provider_name=self.system,
                                tool_name=CodeExecutionTool.kind,
                                content=tool_result['content'][0].get('json') if tool_result['content'] else None,
                                tool_call_id=tool_result.get('toolUseId'),
                                provider_details={'status': tool_result['status']} if 'status' in tool_result else {},
                            )
                        )

        input_tokens = response['usage']['inputTokens']
        output_tokens = response['usage']['outputTokens']
        cache_read_tokens = response['usage'].get('cacheReadInputTokens', 0)
        cache_write_tokens = response['usage'].get('cacheWriteInputTokens', 0)
        u = usage.RequestUsage(
            input_tokens=input_tokens + cache_write_tokens + cache_read_tokens,
            output_tokens=output_tokens,
            cache_read_tokens=cache_read_tokens,
            cache_write_tokens=cache_write_tokens,
        )
        response_id = response.get('ResponseMetadata', {}).get('RequestId', None)
        raw_finish_reason = response['stopReason']
        provider_details = {'finish_reason': raw_finish_reason}
        finish_reason = _FINISH_REASON_MAP.get(raw_finish_reason)

        return ModelResponse(
            parts=items,
            usage=u,
            model_name=self.model_name,
            provider_response_id=response_id,
            provider_name=self._provider.name,
            provider_url=self.base_url,
            finish_reason=finish_reason,
            provider_details=provider_details,
        )

    def _get_thinking_fields(
        self,
        model_settings: BedrockModelSettings,
        model_request_parameters: ModelRequestParameters,
    ) -> dict[str, Any] | None:
        """Build thinking-related additionalModelRequestFields, using unified thinking as fallback."""
        existing = dict(model_settings.get('bedrock_additional_model_requests_fields') or {})
        thinking = model_request_parameters.thinking
        if thinking is None:
            return existing or None

        profile = BedrockModelProfile.from_profile(self.profile)
        variant = profile.bedrock_thinking_variant

        if variant == 'anthropic' and 'thinking' not in existing:
            if thinking is False:
                existing['thinking'] = {'type': 'disabled'}
            else:
                existing['thinking'] = {'type': 'enabled', 'budget_tokens': ANTHROPIC_THINKING_BUDGET_MAP[thinking]}
        elif variant == 'openai' and 'reasoning_effort' not in existing:
            if thinking is not False:  # Bedrock doesn't accept reasoning_effort='none'
                existing['reasoning_effort'] = OPENAI_REASONING_EFFORT_MAP[thinking]
        elif variant == 'qwen' and 'reasoning_config' not in existing:
            if thinking is not False:
                # Qwen only supports low/high; map others to closest
                level_map: dict[ThinkingLevel, str] = {
                    True: 'high',
                    'minimal': 'low',
                    'low': 'low',
                    'medium': 'high',
                    'high': 'high',
                    'xhigh': 'high',
                }
                existing['reasoning_config'] = level_map[thinking]

        return existing or None

    @overload
    async def _messages_create(
        self,
        messages: list[ModelMessage],
        stream: Literal[True],
        model_settings: BedrockModelSettings | None,
        model_request_parameters: ModelRequestParameters,
    ) -> ConverseStreamResponseTypeDef:
        pass

    @overload
    async def _messages_create(
        self,
        messages: list[ModelMessage],
        stream: Literal[False],
        model_settings: BedrockModelSettings | None,
        model_request_parameters: ModelRequestParameters,
    ) -> ConverseResponseTypeDef:
        pass

    async def _messages_create(
        self,
        messages: list[ModelMessage],
        stream: bool,
        model_settings: BedrockModelSettings | None,
        model_request_parameters: ModelRequestParameters,
    ) -> ConverseResponseTypeDef | ConverseStreamResponseTypeDef:
        settings = model_settings or BedrockModelSettings()
        system_prompt, bedrock_messages = await self._map_messages(messages, model_request_parameters, settings)
        inference_config = self._map_inference_config(settings)

        params: ConverseRequestTypeDef = {
            'modelId': settings.get('bedrock_inference_profile') or self.model_name,
            'messages': bedrock_messages,
            'system': system_prompt,
            'inferenceConfig': inference_config,
        }

        tool_config = self._map_tool_config(model_request_parameters, settings)
        if tool_config:
            params['toolConfig'] = tool_config

        tools: list[ToolTypeDef] = list(tool_config['tools']) if tool_config else []
        self._limit_cache_points(system_prompt, bedrock_messages, tools)

        # Bedrock supports a set of specific extra parameters
        if model_settings:
            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 := self._get_thinking_fields(model_settings, model_request_parameters):
                params['additionalModelRequestFields'] = additional_model_requests_fields
            if prompt_variables := model_settings.get('bedrock_prompt_variables', None):
                params['promptVariables'] = prompt_variables
            if service_tier := model_settings.get('bedrock_service_tier', None):
                params['serviceTier'] = service_tier

        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')
            if isinstance(status_code, int):
                raise ModelHTTPError(status_code=status_code, model_name=self.model_name, body=e.response) from e
            raise ModelAPIError(model_name=self.model_name, message=str(e)) 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,
        model_settings: BedrockModelSettings | None,
    ) -> ToolConfigurationTypeDef | None:
        tools = self._get_tools(model_request_parameters)
        for tool in model_request_parameters.builtin_tools:
            if tool.kind == CodeExecutionTool.kind:
                tools.append({'systemTool': {'name': 'nova_code_interpreter'}})
            else:
                raise NotImplementedError(
                    f"Builtin tool '{tool.kind}' is not supported yet. If it should be, please file an issue."
                )

        if not tools:
            return None

        profile = BedrockModelProfile.from_profile(self.profile)
        if (
            model_settings
            and model_settings.get('bedrock_cache_tool_definitions')
            and profile.bedrock_supports_tool_caching
        ):
            tools.append({'cachePoint': {'type': 'default'}})

        tool_choice: ToolChoiceTypeDef
        if not model_request_parameters.allow_text_output:
            tool_choice = {'any': {}}
        else:
            tool_choice = {'auto': {}}

        tool_config: ToolConfigurationTypeDef = {'tools': tools}
        if tool_choice and BedrockModelProfile.from_profile(self.profile).bedrock_supports_tool_choice:
            tool_config['toolChoice'] = tool_choice

        return tool_config

    async def _map_messages(  # noqa: C901
        self,
        messages: Sequence[ModelMessage],
        model_request_parameters: ModelRequestParameters,
        model_settings: BedrockModelSettings | None,
    ) -> tuple[list[SystemContentBlockTypeDef], list[MessageUnionTypeDef]]:
        """Maps a `pydantic_ai.Message` to the Bedrock `MessageUnionTypeDef`.

        Groups consecutive ToolReturnPart objects into a single user message as required by Bedrock Claude/Nova models.
        """
        settings = model_settings or BedrockModelSettings()
        profile = BedrockModelProfile.from_profile(self.profile)
        system_prompt: list[SystemContentBlockTypeDef] = []
        bedrock_messages: list[MessageUnionTypeDef] = []
        document_count: Iterator[int] = count(1)
        for message in messages:
            if isinstance(message, ModelRequest):
                for part in message.parts:
                    if isinstance(part, SystemPromptPart):
                        if part.content:  # pragma: no branch
                            system_prompt.append({'text': part.content})
                    elif isinstance(part, UserPromptPart):
                        bedrock_messages.extend(
                            await self._map_user_prompt(part, document_count, profile.bedrock_supports_prompt_caching)
                        )
                    elif isinstance(part, ToolReturnPart):
                        assert part.tool_call_id is not None
                        tool_result_content: list[Any] = []
                        sibling_content: list[ContentBlockUnionTypeDef] = []

                        content_mode: Literal['str', 'jsonable'] = (
                            'str' if profile.bedrock_tool_result_format == 'text' else 'jsonable'
                        )
                        for item in part.content_items(mode=content_mode):
                            if isinstance(item, UploadedFile):
                                if item.provider_name != self.system:
                                    raise UserError(
                                        f'UploadedFile with `provider_name={item.provider_name!r}` cannot be used with BedrockConverseModel. '
                                        f'Expected `provider_name` to be `{self.system!r}`.'
                                    )
                                if not item.file_id.startswith('s3://'):
                                    raise UserError(
                                        f'UploadedFile for Bedrock must use an S3 URL (s3://bucket/key), got: {item.file_id}'
                                    )
                                uf_source = _parse_s3_source(item.file_id)
                                try:
                                    uf_format = item.format
                                except ValueError as e:
                                    raise UserError(
                                        f'Unsupported media type for Bedrock UploadedFile: {item.media_type}'
                                    ) from e
                                if item.media_type.startswith('image/'):
                                    tool_result_content.append(_make_image_block(uf_format, uf_source))
                                elif item.media_type.startswith('video/'):
                                    tool_result_content.append(_make_video_block(uf_format, uf_source))
                                elif item.media_type.startswith('audio/'):
                                    raise UserError('Audio files are not supported for Bedrock UploadedFile')
                                else:
                                    tool_result_content.append(
                                        _make_document_block(f'Document {next(document_count)}', uf_format, uf_source)
                                    )
                            elif is_multi_modal_content(item):
                                if isinstance(item, AudioUrl):
                                    raise NotImplementedError('AudioUrl is not supported in Bedrock tool returns')
                                file_block = await self._map_file_to_content_block(item, document_count)  # pyright: ignore[reportArgumentType]
                                kind = next((k for k in ('image', 'document', 'video') if k in file_block), None)
                                if kind in profile.bedrock_supported_media_kinds_in_tool_returns:
                                    tool_result_content.append(file_block)
                                else:
                                    tool_result_content.append({'text': f'See file {item.identifier}.'})
                                    sibling_content.append({'text': f'This is file {item.identifier}:'})
                                    sibling_content.append(file_block)
                            elif isinstance(item, str):
                                tool_result_content.append({'text': item})
                            else:
                                tool_result_content.append({'json': item})

                        user_content: list[ContentBlockUnionTypeDef] = [
                            {
                                'toolResult': {
                                    'toolUseId': part.tool_call_id,
                                    'content': tool_result_content,
                                    'status': 'success',
                                }
                            }
                        ]
                        user_content.extend(sibling_content)
                        bedrock_messages.append({'role': 'user', 'content': user_content})
                    elif isinstance(part, RetryPromptPart):
                        if part.tool_name is None:
                            bedrock_messages.append({'role': 'user', 'content': [{'text': part.model_response()}]})
                        else:
                            assert part.tool_call_id is not None
                            bedrock_messages.append(
                                {
                                    'role': 'user',
                                    'content': [
                                        {
                                            'toolResult': {
                                                'toolUseId': part.tool_call_id,
                                                'content': [{'text': part.model_response()}],
                                                'status': 'error',
                                            }
                                        }
                                    ],
                                }
                            )
                    else:
                        assert_never(part)
            elif isinstance(message, ModelResponse):
                content: list[ContentBlockOutputTypeDef] = []
                for item in message.parts:
                    if isinstance(item, TextPart):
                        content.append({'text': item.content})
                    elif isinstance(item, ThinkingPart):
                        if (
                            item.provider_name == self.system
                            and item.signature
                            and BedrockModelProfile.from_profile(self.profile).bedrock_send_back_thinking_parts
                        ):
                            reasoning_content: ReasoningContentBlockOutputTypeDef
                            if item.id == 'redacted_content':
                                reasoning_content = {
                                    'redactedContent': item.signature.encode('utf-8'),
                                }
                            else:
                                reasoning_content = {
                                    'reasoningText': {
                                        'text': item.content,
                                        'signature': item.signature,
                                    }
                                }
                            content.append({'reasoningContent': reasoning_content})
                        else:
                            start_tag, end_tag = self.profile.thinking_tags
                            content.append({'text': '\n'.join([start_tag, item.content, end_tag])})
                    elif isinstance(item, BuiltinToolCallPart):
                        if item.provider_name == self.system:
                            if item.tool_name == CodeExecutionTool.kind:
                                server_tool_use_block_param: ToolUseBlockOutputTypeDef = {
                                    'toolUseId': _utils.guard_tool_call_id(t=item),
                                    'name': 'nova_code_interpreter',
                                    'input': item.args_as_dict(),
                                    'type': 'server_tool_use',
                                }
                                content.append({'toolUse': server_tool_use_block_param})
                    elif isinstance(item, BuiltinToolReturnPart):
                        if item.provider_name == self.system:
                            if item.tool_name == CodeExecutionTool.kind:
                                result_content: list[ToolResultContentBlockOutputTypeDef] = [
                                    {'json': cast(dict[str, Any], item.content)}
                                ]
                                tool_result: ToolResultBlockOutputTypeDef = {
                                    'toolUseId': _utils.guard_tool_call_id(t=item),
                                    'content': result_content,
                                    'type': 'nova_code_interpreter_result',
                                }
                                if item.provider_details and 'status' in item.provider_details:
                                    tool_result['status'] = item.provider_details['status']
                                content.append({'toolResult': tool_result})
                    else:
                        assert isinstance(item, ToolCallPart)
                        content.append(self._map_tool_call(item))
                if content:
                    bedrock_messages.append({'role': 'assistant', 'content': content})
            else:
                assert_never(message)

        # Merge together sequential user messages.
        processed_messages: list[MessageUnionTypeDef] = []
        last_message: dict[str, Any] | None = None
        for current_message in bedrock_messages:
            if (
                last_message is not None
                and current_message['role'] == last_message['role']
                and current_message['role'] == 'user'
            ):
                # Add the new user content onto the existing user message.
                last_content = list(last_message['content'])
                last_content.extend(current_message['content'])
                last_message['content'] = last_content
                continue

            # Add the entire message to the list of messages.
            processed_messages.append(current_message)
            last_message = cast(dict[str, Any], current_message)

        if instructions := self._get_instructions(messages, model_request_parameters):
            system_prompt.append({'text': instructions})

        if system_prompt and settings.get('bedrock_cache_instructions') and profile.bedrock_supports_prompt_caching:
            system_prompt.append({'cachePoint': {'type': 'default'}})

        if processed_messages and settings.get('bedrock_cache_messages') and profile.bedrock_supports_prompt_caching:
            last_user_content = self._get_last_user_message_content(processed_messages)
            if last_user_content is not None:
                # Note: _get_last_user_message_content ensures content doesn't already end with a cachePoint.
                _insert_cache_point_before_trailing_documents(last_user_content)

        # Bedrock requires conversations to start with a user message.
        # This can happen when there are no messages at all (only system prompt/instructions),
        # or when message_history starts with an assistant response (e.g. from a previous
        # system-prompt-only run). Prepend a synthetic user message in either case.
        # Note: Anthropic models on Bedrock reject whitespace-only text, so we use a period.
        if not processed_messages or processed_messages[0]['role'] != 'user':
            processed_messages.insert(0, {'role': 'user', 'content': [{'text': '.'}]})

        return system_prompt, processed_messages

    @staticmethod
    def _get_last_user_message_content(messages: list[MessageUnionTypeDef]) -> list[Any] | None:
        """Get the content list from the last user message that can receive a cache point.

        Returns the content list if:
        - A user message exists
        - It has a non-empty content list
        - The last content block doesn't already have a cache point

        Returns None otherwise.
        """
        user_messages = [msg for msg in messages if msg.get('role') == 'user']
        if not user_messages:
            return None

        content = user_messages[-1].get('content')  # Last user message
        if not content or not isinstance(content, list) or len(content) == 0:
            return None

        last_block = content[-1]
        if not isinstance(last_block, dict):
            return None
        if 'cachePoint' in last_block:  # Skip if already has a cache point
            return None
        return content

    @staticmethod
    async def _map_file_to_content_block(
        file: ImageUrl | DocumentUrl | VideoUrl | BinaryContent,
        document_count: Iterator[int],
    ) -> ContentBlockUnionTypeDef:
        """Map a multimodal file directly to a Bedrock content block."""
        source: DocumentSourceTypeDef

        if isinstance(file, BinaryContent):
            source = {'bytes': file.data}
            if file.is_image:
                return _make_image_block(file.format, source)
            elif file.is_document:
                return _make_document_block(f'Document {next(document_count)}', file.format, source)
            elif file.is_video:
                return _make_video_block(file.format, source)
            else:
                raise NotImplementedError(f'Unsupported binary content type for Bedrock: {file.media_type}')
        else:
            if file.url.startswith('s3://'):
                source = _parse_s3_source(file.url)
            else:
                downloaded = await download_item(file, data_format='bytes', type_format='extension')
                source = {'bytes': downloaded['data']}

            try:
                format = file.format
            except (KeyError, ValueError):
                format = file.media_type.split('/', 1)[1]

            if isinstance(file, ImageUrl):
                return _make_image_block(format, source)
            elif isinstance(file, DocumentUrl):
                return _make_document_block(f'Document {next(document_count)}', format, source)
            else:
                return _make_video_block(format, source)

    async def _map_user_prompt(  # noqa: C901
        self,
        part: UserPromptPart,
        document_count: Iterator[int],
        supports_prompt_caching: bool,
    ) -> list[MessageUnionTypeDef]:
        content: list[ContentBlockUnionTypeDef] = []
        if isinstance(part.content, str):
            content.append({'text': part.content})
        else:
            for item in part.content:
                if isinstance(item, str):
                    content.append({'text': item})
                elif isinstance(item, (BinaryContent, ImageUrl, DocumentUrl, VideoUrl)):
                    content.append(await BedrockConverseModel._map_file_to_content_block(item, document_count))
                elif isinstance(item, AudioUrl):
                    raise NotImplementedError('AudioUrl is not supported in Bedrock user prompts')
                elif isinstance(item, UploadedFile):
                    if item.provider_name != self.system:
                        raise UserError(
                            f'UploadedFile with `provider_name={item.provider_name!r}` cannot be used with BedrockConverseModel. '
                            f'Expected `provider_name` to be `{self.system!r}`.'
                        )
                    if not item.file_id.startswith('s3://'):
                        raise UserError(
                            f'UploadedFile for Bedrock must use an S3 URL (s3://bucket/key), got: {item.file_id}'
                        )
                    source: DocumentSourceTypeDef = _parse_s3_source(item.file_id)

                    try:
                        format = item.format
                    except ValueError as e:
                        raise UserError(f'Unsupported media type for Bedrock UploadedFile: {item.media_type}') from e

                    if item.media_type.startswith('image/'):
                        content.append(_make_image_block(format, source))
                    elif item.media_type.startswith('video/'):
                        content.append(_make_video_block(format, source))
                    elif item.media_type.startswith('audio/'):
                        raise UserError('Audio files are not supported for Bedrock UploadedFile')
                    else:
                        content.append(_make_document_block(f'Document {next(document_count)}', format, source))
                elif isinstance(item, CachePoint):
                    if not supports_prompt_caching:
                        # Silently skip CachePoint for models that don't support prompt caching
                        continue
                    if not content or 'cachePoint' in content[-1]:
                        raise UserError(
                            'CachePoint cannot be the first content in a user message - there must be previous content to cache when using Bedrock. '
                            'To cache system instructions or tool definitions, use the `bedrock_cache_instructions` or `bedrock_cache_tool_definitions` settings instead.'
                        )
                    _insert_cache_point_before_trailing_documents(content, raise_if_cannot_insert=True)
                else:
                    assert_never(item)
        # https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Message.html
        # "If you include a ContentBlock with a document field, you must also include a ContentBlock with a text field."
        has_document = any('document' in block for block in content)
        has_text = any('text' in block for block in content)
        if has_document and not has_text:
            content.insert(0, {'text': 'See attached document(s).'})
        return [{'role': 'user', 'content': content}]

    @staticmethod
    def _map_tool_call(t: ToolCallPart) -> ContentBlockOutputTypeDef:
        return {
            'toolUse': {
                'toolUseId': _utils.guard_tool_call_id(t=t),
                'name': _utils.sanitize_tool_name(t.tool_name),
                'input': t.args_as_dict(),
            }
        }

    @staticmethod
    def _limit_cache_points(
        system_prompt: list[SystemContentBlockTypeDef],
        bedrock_messages: list[MessageUnionTypeDef],
        tools: list[ToolTypeDef],
    ) -> None:
        """Limit the number of cache points in the request to Bedrock's maximum.

        Bedrock enforces a maximum of 4 cache points per request. This method ensures
        compliance by counting existing cache points and removing excess ones from messages.

        Strategy:
        1. Count cache points in system_prompt
        2. Count cache points in tools
        3. Raise UserError if system + tools already exceed MAX_CACHE_POINTS
        4. Calculate remaining budget for message cache points
        5. Traverse messages from newest to oldest, keeping the most recent cache points
           within the remaining budget
        6. Remove excess cache points from older messages to stay within limit

        Cache point priority (always preserved):
        - System prompt cache points
        - Tool definition cache points
        - Message cache points (newest first, oldest removed if needed)

        Raises:
            UserError: If system_prompt and tools combined already exceed MAX_CACHE_POINTS (4).
                      This indicates a configuration error that cannot be auto-fixed.
        """
        MAX_CACHE_POINTS = 4

        # Count existing cache points in system prompt
        used_cache_points = sum(1 for block in system_prompt if 'cachePoint' in block)

        # Count existing cache points in tools
        for tool in tools:
            if 'cachePoint' in tool:
                used_cache_points += 1

        # Calculate remaining cache points budget for messages
        remaining_budget = MAX_CACHE_POINTS - used_cache_points
        if remaining_budget < 0:  # pragma: no cover
            raise UserError(
                f'Too many cache points for Bedrock request. '
                f'System prompt and tool definitions already use {used_cache_points} cache points, '
                f'which exceeds the maximum of {MAX_CACHE_POINTS}.'
            )

        # Remove excess cache points from messages (newest to oldest)
        for message in reversed(bedrock_messages):
            content = message.get('content')
            if not content or not isinstance(content, list):  # pragma: no cover
                continue

            # Build a new content list, keeping only cache points within budget
            new_content: list[Any] = []
            for block in reversed(content):  # Process newest first
                is_cache_point = isinstance(block, dict) and 'cachePoint' in block
                if is_cache_point:
                    if remaining_budget > 0:
                        remaining_budget -= 1
                        new_content.append(block)
                else:
                    new_content.append(block)
            message['content'] = list(reversed(new_content))  # Restore original order

__init__

__init__(
    model_name: BedrockModelName,
    *,
    provider: (
        Literal["bedrock", "gateway"] | Provider[BaseClient]
    ) = "bedrock",
    profile: ModelProfileSpec | None = None,
    settings: ModelSettings | None = None
)

Initialize a Bedrock model.

Parameters:

Name	Type	Description	Default
model_name	BedrockModelName	The name of the model to use.	required
model_name	BedrockModelName	The name of the Bedrock model to use. List of model names available here.	required
provider	Literal['bedrock', 'gateway'] | Provider[BaseClient]	The provider to use for authentication and API access. Can be either the string 'bedrock' or an instance of Provider[BaseClient]. If not provided, a new provider will be created using the other parameters.	'bedrock'
profile	ModelProfileSpec | None	The model profile to use. Defaults to a profile picked by the provider based on the model name.	None
settings	ModelSettings | None	Model-specific settings that will be used as defaults for this model.	None

Source code in pydantic_ai_slim/pydantic_ai/models/bedrock.py

def __init__(
    self,
    model_name: BedrockModelName,
    *,
    provider: Literal['bedrock', 'gateway'] | Provider[BaseClient] = 'bedrock',
    profile: ModelProfileSpec | None = None,
    settings: ModelSettings | None = None,
):
    """Initialize a Bedrock model.

    Args:
        model_name: The name of the model to use.
        model_name: The name of the Bedrock model to use. List of model names available
            [here](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html).
        provider: The provider to use for authentication and API access. Can be either the string
            'bedrock' or an instance of `Provider[BaseClient]`. If not provided, a new provider will be
            created using the other parameters.
        profile: The model profile to use. Defaults to a profile picked by the provider based on the model name.
        settings: Model-specific settings that will be used as defaults for this model.
    """
    self._model_name = model_name

    if isinstance(provider, str):
        provider = infer_provider('gateway/bedrock' if provider == 'gateway' else provider)
    self._provider = provider
    self.client = cast('BedrockRuntimeClient', provider.client)

    super().__init__(settings=settings, profile=profile or provider.model_profile)

model_name property

model_name: str

The model name.

system property

system: str

The model provider.

supported_builtin_tools classmethod

supported_builtin_tools() -> (
    frozenset[type[AbstractBuiltinTool]]
)

The set of builtin tool types this model can handle.

Source code in pydantic_ai_slim/pydantic_ai/models/bedrock.py

@classmethod
def supported_builtin_tools(cls) -> frozenset[type[AbstractBuiltinTool]]:
    """The set of builtin tool types this model can handle."""
    return frozenset({CodeExecutionTool})

count_tokens async

count_tokens(
    messages: list[ModelMessage],
    model_settings: ModelSettings | None,
    model_request_parameters: ModelRequestParameters,
) -> RequestUsage

Count the number of tokens, works with limited models.

Check the actual supported models on https://docs.aws.amazon.com/bedrock/latest/userguide/count-tokens.html

Source code in pydantic_ai_slim/pydantic_ai/models/bedrock.py

async def count_tokens(
    self,
    messages: list[ModelMessage],
    model_settings: ModelSettings | None,
    model_request_parameters: ModelRequestParameters,
) -> usage.RequestUsage:
    """Count the number of tokens, works with limited models.

    Check the actual supported models on <https://docs.aws.amazon.com/bedrock/latest/userguide/count-tokens.html>
    """
    model_settings, model_request_parameters = self.prepare_request(model_settings, model_request_parameters)
    settings = cast(BedrockModelSettings, model_settings or {})
    system_prompt, bedrock_messages = await self._map_messages(messages, model_request_parameters, settings)
    params: CountTokensRequestTypeDef = {
        'modelId': remove_bedrock_geo_prefix(self.model_name),
        'input': {
            'converse': {
                'messages': bedrock_messages,
                'system': system_prompt,
            },
        },
    }
    try:
        response = await anyio.to_thread.run_sync(functools.partial(self.client.count_tokens, **params))
    except ClientError as e:
        status_code = e.response.get('ResponseMetadata', {}).get('HTTPStatusCode')
        if isinstance(status_code, int):
            raise ModelHTTPError(status_code=status_code, model_name=self.model_name, body=e.response) from e
        raise ModelAPIError(model_name=self.model_name, message=str(e)) from e
    return usage.RequestUsage(input_tokens=response['inputTokens'])

BedrockStreamedResponse dataclass

Bases: StreamedResponse

Implementation of StreamedResponse for Bedrock models.

Source code in pydantic_ai_slim/pydantic_ai/models/bedrock.py

@dataclass
class BedrockStreamedResponse(StreamedResponse):
    """Implementation of `StreamedResponse` for Bedrock models."""

    _model_name: BedrockModelName
    _event_stream: EventStream[ConverseStreamOutputTypeDef]
    _provider_name: str
    _provider_url: str
    _timestamp: datetime = field(default_factory=_utils.now_utc)
    _provider_response_id: str | None = None

    async def _get_event_iterator(self) -> AsyncIterator[ModelResponseStreamEvent]:  # noqa: C901
        """Return an async iterator of [`ModelResponseStreamEvent`][pydantic_ai.messages.ModelResponseStreamEvent]s.

        This method should be implemented by subclasses to translate the vendor-specific stream of events into
        pydantic_ai-format events.
        """
        if self._provider_response_id is not None:  # pragma: no cover
            self.provider_response_id = self._provider_response_id

        chunk: ConverseStreamOutputTypeDef
        tool_ids: dict[int, str] = {}

        # Bedrock has deltas for built-in tool returns, which aren't supported by parts manager.
        # We accumulate the deltas here and yield the complete return part once the content block ends
        builtin_tool_returns: dict[int, BuiltinToolReturnPart] = {}

        async for chunk in _AsyncIteratorWrapper(self._event_stream):
            match chunk:
                case {'messageStart': _}:
                    continue
                case {'messageStop': message_stop}:
                    raw_finish_reason = message_stop['stopReason']
                    self.provider_details = {'finish_reason': raw_finish_reason}
                    self.finish_reason = _FINISH_REASON_MAP.get(raw_finish_reason)
                case {'metadata': metadata}:
                    if 'usage' in metadata:  # pragma: no branch
                        self._usage += self._map_usage(metadata)
                case {'contentBlockStart': content_block_start}:
                    index = content_block_start['contentBlockIndex']
                    start = content_block_start['start']
                    if 'toolUse' in start:
                        tool_use_start = start['toolUse']
                        tool_id = tool_use_start['toolUseId']
                        tool_ids[index] = tool_id
                        tool_name = tool_use_start['name']
                        if tool_use_start.get('type') == 'server_tool_use':
                            if tool_name == 'nova_code_interpreter':  # pragma: no branch
                                part = BuiltinToolCallPart(
                                    tool_name=CodeExecutionTool.kind,
                                    tool_call_id=tool_id,
                                    provider_name=self.provider_name,
                                )
                                yield self._parts_manager.handle_part(vendor_part_id=index, part=part)
                        elif maybe_event := self._parts_manager.handle_tool_call_delta(
                            vendor_part_id=index,
                            tool_name=tool_name,
                            args=None,
                            tool_call_id=tool_id,
                        ):  # pragma: no branch
                            yield maybe_event
                    elif 'toolResult' in start:  # pragma: no branch
                        tool_result_start = start['toolResult']
                        tool_id = tool_result_start['toolUseId']

                        if tool_result_start.get('type') == 'nova_code_interpreter_result':  # pragma: no branch
                            return_part = BuiltinToolReturnPart(
                                provider_name=self.provider_name,
                                tool_name=CodeExecutionTool.kind,
                                content=None,
                                tool_call_id=tool_id,
                                provider_details={'status': tool_result_start['status']}
                                if 'status' in tool_result_start
                                else {},
                            )
                            builtin_tool_returns[index] = return_part
                            # Don't yield anything yet - we wait for content block end

                case {'contentBlockDelta': content_block_delta}:
                    index = content_block_delta['contentBlockIndex']
                    delta = content_block_delta['delta']
                    if 'reasoningContent' in delta:
                        if redacted_content := delta['reasoningContent'].get('redactedContent'):
                            for event in self._parts_manager.handle_thinking_delta(
                                vendor_part_id=index,
                                id='redacted_content',
                                signature=redacted_content.decode('utf-8'),
                                provider_name=self.provider_name,
                            ):
                                yield event
                        else:
                            signature = delta['reasoningContent'].get('signature')
                            for event in self._parts_manager.handle_thinking_delta(
                                vendor_part_id=index,
                                content=delta['reasoningContent'].get('text'),
                                signature=signature,
                                provider_name=self.provider_name if signature else None,
                            ):
                                yield event
                    if text := delta.get('text'):
                        for event in self._parts_manager.handle_text_delta(vendor_part_id=index, content=text):
                            yield event
                    if 'toolUse' in delta:
                        tool_use = delta['toolUse']
                        maybe_event = self._parts_manager.handle_tool_call_delta(
                            vendor_part_id=index,
                            tool_name=tool_use.get('name'),
                            args=tool_use.get('input'),
                            tool_call_id=tool_ids[index],
                        )
                        if maybe_event:  # pragma: no branch
                            yield maybe_event
                    if 'toolResult' in delta:  # pragma: no branch
                        if (
                            return_part := builtin_tool_returns.get(index)
                        ) and return_part.tool_name == CodeExecutionTool.kind:  # pragma: no branch
                            # For now, only process `contentBlockDelta.toolResult` for Code Exe tool.

                            if tr_content := delta['toolResult']:  # pragma: no branch
                                # Goal here is to convert to object form.
                                # This assumes the first item is the relevant one.
                                return_part.content = tr_content[0].get('json')

                            # Don't yield anything yet - we wait for content block end

                case {'contentBlockStop': content_block_stop}:
                    index = content_block_stop['contentBlockIndex']
                    if return_part := builtin_tool_returns.get(index):
                        # Emit the complete built-in tool return only once when the block closes.
                        yield self._parts_manager.handle_part(vendor_part_id=index, part=return_part)
                    tool_ids.pop(index, None)
                    builtin_tool_returns.pop(index, None)

                case _:  # pragma: no cover
                    pass  # pyright wants match statements to be exhaustive

    @property
    def model_name(self) -> str:
        """Get the model name of the response."""
        return self._model_name

    @property
    def provider_name(self) -> str:
        """Get the provider name."""
        return self._provider_name

    @property
    def provider_url(self) -> str:
        """Get the provider base URL."""
        return self._provider_url

    @property
    def timestamp(self) -> datetime:
        return self._timestamp

    def _map_usage(self, metadata: ConverseStreamMetadataEventTypeDef) -> usage.RequestUsage:
        input_tokens = metadata['usage']['inputTokens']
        output_tokens = metadata['usage']['outputTokens']
        cache_read_tokens = metadata['usage'].get('cacheReadInputTokens', 0)
        cache_write_tokens = metadata['usage'].get('cacheWriteInputTokens', 0)
        return usage.RequestUsage(
            input_tokens=input_tokens + cache_write_tokens + cache_read_tokens,
            output_tokens=output_tokens,
            cache_read_tokens=cache_read_tokens,
            cache_write_tokens=cache_write_tokens,
        )

model_name property

model_name: str

Get the model name of the response.

provider_name property

provider_name: str

Get the provider name.

provider_url property

provider_url: str

Get the provider base URL.