bedrock.py

from __future__ import annotations

import functools
import typing
from collections.abc import AsyncIterator, Iterable, Iterator, Mapping, Sequence
from contextlib import asynccontextmanager
from dataclasses import dataclass, field
from datetime import datetime
from itertools import count
from typing import TYPE_CHECKING, Any, Generic, Literal, TypeAlias, cast, overload
from urllib.parse import parse_qs, urlparse

import anyio.to_thread
from botocore.exceptions import ClientError
from typing_extensions import NotRequired, ParamSpec, TypedDict, assert_never

from pydantic_ai import (
    AudioUrl,
    BinaryContent,
    BuiltinToolCallPart,
    BuiltinToolReturnPart,
    CachePoint,
    DocumentUrl,
    FinishReason,
    ImageUrl,
    ModelMessage,
    ModelProfileSpec,
    ModelRequest,
    ModelResponse,
    ModelResponsePart,
    ModelResponseStreamEvent,
    RetryPromptPart,
    SystemPromptPart,
    TextPart,
    ThinkingPart,
    ToolCallPart,
    ToolReturnPart,
    UserPromptPart,
    VideoUrl,
    _utils,
    usage,
)
from pydantic_ai._run_context import RunContext
from pydantic_ai.builtin_tools import AbstractBuiltinTool, CodeExecutionTool
from pydantic_ai.exceptions import ModelAPIError, ModelHTTPError, UserError
from pydantic_ai.messages import UploadedFile
from pydantic_ai.models import Model, ModelRequestParameters, StreamedResponse, download_item
from pydantic_ai.providers import Provider, infer_provider
from pydantic_ai.providers.bedrock import BedrockModelProfile, remove_bedrock_geo_prefix
from pydantic_ai.settings import ModelSettings
from pydantic_ai.tools import ToolDefinition

if TYPE_CHECKING:
    from botocore.client import BaseClient
    from botocore.eventstream import EventStream
    from mypy_boto3_bedrock_runtime import BedrockRuntimeClient
    from mypy_boto3_bedrock_runtime.literals import StopReasonType
    from mypy_boto3_bedrock_runtime.type_defs import (
        ContentBlockOutputTypeDef,
        ContentBlockUnionTypeDef,
        ConverseRequestTypeDef,
        ConverseResponseTypeDef,
        ConverseStreamMetadataEventTypeDef,
        ConverseStreamOutputTypeDef,
        ConverseStreamResponseTypeDef,
        CountTokensRequestTypeDef,
        DocumentSourceTypeDef,
        GuardrailConfigurationTypeDef,
        InferenceConfigurationTypeDef,
        MessageUnionTypeDef,
        PerformanceConfigurationTypeDef,
        PromptVariableValuesTypeDef,
        ReasoningContentBlockOutputTypeDef,
        S3LocationTypeDef,
        ServiceTierTypeDef,
        SystemContentBlockTypeDef,
        ToolChoiceTypeDef,
        ToolConfigurationTypeDef,
        ToolResultBlockOutputTypeDef,
        ToolSpecificationTypeDef,
        ToolTypeDef,
        ToolUseBlockOutputTypeDef,
    )


_SUPPORTED_IMAGE_FORMATS = ('jpeg', 'png', 'gif', 'webp')
_SUPPORTED_VIDEO_FORMATS = ('mkv', 'mov', 'mp4', 'webm', 'flv', 'mpeg', 'mpg', 'wmv', 'three_gp')
_SUPPORTED_DOCUMENT_FORMATS = ('pdf', 'txt', 'csv', 'doc', 'docx', 'xls', 'xlsx', 'html', 'md')

BedrockPromptCacheTTL = Literal['5m', '1h']
BedrockPromptCacheSetting: TypeAlias = bool | BedrockPromptCacheTTL


class _BedrockCachePointBlock(TypedDict):
    type: Literal['default']
    ttl: NotRequired[BedrockPromptCacheTTL]


class _BedrockCachePoint(TypedDict):
    cachePoint: _BedrockCachePointBlock


def _make_image_block(format: str, source: DocumentSourceTypeDef) -> ContentBlockUnionTypeDef:
    if format not in _SUPPORTED_IMAGE_FORMATS:
        raise UserError(f'Unsupported image format: {format}')
    return {'image': {'format': format, 'source': source}}


def _make_video_block(format: str, source: DocumentSourceTypeDef) -> ContentBlockUnionTypeDef:
    if format not in _SUPPORTED_VIDEO_FORMATS:
        raise UserError(f'Unsupported video format: {format}')
    return {'video': {'format': format, 'source': source}}


def _make_document_block(name: str, format: str, source: DocumentSourceTypeDef) -> ContentBlockUnionTypeDef:
    if format not in _SUPPORTED_DOCUMENT_FORMATS:
        raise UserError(f'Unsupported document format: {format}')
    return {'document': {'name': name, 'format': format, 'source': source}}


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 = 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](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) for a full list.
"""

P = ParamSpec('P')
T = typing.TypeVar('T')

_FINISH_REASON_MAP: dict[StopReasonType, FinishReason] = {
    'content_filtered': 'content_filter',
    'end_turn': 'stop',
    'guardrail_intervened': 'content_filter',
    'max_tokens': 'length',
    'model_context_window_exceeded': 'length',
    'stop_sequence': 'stop',
    'tool_use': 'tool_call',
}


def _parse_s3_source(url: str) -> DocumentSourceTypeDef:
    """Parse an S3 URL into a Bedrock DocumentSourceTypeDef."""
    parsed = urlparse(url)
    s3_location: S3LocationTypeDef = {'uri': f'{parsed.scheme}://{parsed.netloc}{parsed.path}'}
    if bucket_owner := parse_qs(parsed.query).get('bucketOwner', [None])[0]:
        s3_location['bucketOwner'] = bucket_owner
    return {'s3Location': s3_location}


def _insert_cache_point_before_trailing_documents(
    content: list[Any],
    cache_point: _BedrockCachePoint,
    *,
    raise_if_cannot_insert: bool = False,
) -> bool:
    """Insert a cache point before trailing document/video content.

    AWS rejects cache points that directly follow documents and videos (but not images).
    This function finds the start of the trailing contiguous group of documents/videos
    and inserts a cache point before it.

    Args:
        content: The content list to modify in place.
        cache_point: The cache point block to insert.
        raise_if_cannot_insert: If True, raises UserError when cache point cannot be inserted
            (e.g., when the message contains only documents/videos). If False, silently skips.

    Returns:
        True if a cache point was inserted, False otherwise.

    Raises:
        UserError: If raise_if_cannot_insert is True and the cache point cannot be placed.
    """
    multimodal_keys = ['document', 'video']
    # Find where the trailing contiguous group of documents/videos starts
    trailing_start: int | None = None
    for i in range(len(content) - 1, -1, -1):
        if any(key in content[i] for key in multimodal_keys):
            trailing_start = i
        else:
            break

    if trailing_start is not None and trailing_start > 0:
        # Skip if there's already a cache point at the insertion position
        prev_block = content[trailing_start - 1]
        if isinstance(prev_block, dict) and 'cachePoint' in prev_block:
            return False
        content.insert(trailing_start, cache_point)
        return True
    elif trailing_start is None:
        # No trailing document/video content, append cache point at the end
        content.append(cache_point)
        return True
    else:
        # trailing_start == 0, can't insert at start
        if raise_if_cannot_insert:
            raise UserError(
                'CachePoint cannot be placed when the user message contains only a document or video, '
                'due to Bedrock API restrictions. '
                'Add text content before or after your document or video to enable caching.'
            )
        return False


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: BedrockPromptCacheSetting
    """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: BedrockPromptCacheSetting
    """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: BedrockPromptCacheSetting
    """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>.
    """


@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,
        )

    @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': 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 := 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
            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 cache_tool_definitions := (model_settings or {}).get('bedrock_cache_tool_definitions'):
            if profile.bedrock_supports_tool_caching:
                tools.append(cast('ToolTypeDef', self._get_cache_point(cache_tool_definitions)))

        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))
                    elif isinstance(part, ToolReturnPart):
                        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_str()}
                                                if profile.bedrock_tool_result_format == 'text'
                                                else {'json': part.model_response_object()}
                                            ],
                                            'status': 'success',
                                        }
                                    }
                                ],
                            }
                        )
                    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
                        ):
                            if item.id == 'redacted_content':
                                reasoning_content: ReasoningContentBlockOutputTypeDef = {
                                    'redactedContent': item.signature.encode('utf-8'),
                                }
                            else:
                                reasoning_content: ReasoningContentBlockOutputTypeDef = {
                                    '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:
                                tool_result: ToolResultBlockOutputTypeDef = {
                                    'toolUseId': _utils.guard_tool_call_id(t=item),
                                    'content': [{'json': cast(Any, item.content)}] if item.content else [],
                                    '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 (cache_instructions := settings.get('bedrock_cache_instructions')):
            if profile.bedrock_supports_prompt_caching:
                system_prompt.append(cast('SystemContentBlockTypeDef', self._get_cache_point(cache_instructions)))

        if processed_messages and (cache_messages := settings.get('bedrock_cache_messages')):
            if 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, self._get_cache_point(cache_messages)
                    )

        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

    async def _map_user_prompt(  # noqa: C901
        self,
        part: UserPromptPart,
        document_count: Iterator[int],
        profile: BedrockModelProfile,
    ) -> 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):
                    format = item.format
                    source: DocumentSourceTypeDef = {'bytes': item.data}
                    if item.is_document:
                        content.append(_make_document_block(f'Document {next(document_count)}', format, source))
                    elif item.is_image:
                        content.append(_make_image_block(format, source))
                    elif item.is_video:
                        content.append(_make_video_block(format, source))
                    else:
                        raise NotImplementedError('Binary content is not supported yet.')
                elif isinstance(item, ImageUrl | DocumentUrl | VideoUrl):
                    if item.url.startswith('s3://'):
                        source = _parse_s3_source(item.url)
                    else:
                        downloaded_item = await download_item(item, data_format='bytes', type_format='extension')
                        source = {'bytes': downloaded_item['data']}

                    try:
                        format = item.format
                    except (KeyError, ValueError):
                        # Unknown media type — fall back to raw subtype so the
                        # validation inside _make_*_block produces a clear UserError.
                        format = item.media_type.split('/', 1)[1]

                    if item.kind == 'image-url':
                        content.append(_make_image_block(format, source))
                    elif item.kind == 'document-url':
                        content.append(_make_document_block(f'Document {next(document_count)}', format, source))
                    elif item.kind == 'video-url':  # pragma: no branch
                        content.append(_make_video_block(format, source))
                elif isinstance(item, AudioUrl):  # pragma: no cover
                    raise NotImplementedError('Audio is not supported yet.')
                elif isinstance(item, UploadedFile):
                    # Verify provider matches
                    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}`.'
                        )
                    # UploadedFile.file_id should be an S3 URL for Bedrock
                    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 = _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 profile.bedrock_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,
                        BedrockConverseModel._get_cache_point(item.ttl),
                        raise_if_cannot_insert=True,
                    )
                else:
                    assert_never(item)
        return [{'role': 'user', 'content': content}]

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

    @staticmethod
    def _get_cache_point(cache_setting: BedrockPromptCacheSetting) -> _BedrockCachePoint:
        cache_point: _BedrockCachePointBlock = {'type': 'default'}
        if isinstance(cache_setting, str):
            cache_point['ttl'] = cache_setting
        return {'cachePoint': cache_point}

    @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


@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,
        )


class _AsyncIteratorWrapper(Generic[T]):
    """Wrap a synchronous iterator in an async iterator."""

    def __init__(self, sync_iterator: Iterable[T]):
        self.sync_iterator = iter(sync_iterator)

    def __aiter__(self):
        return self

    async def __anext__(self) -> T:
        try:
            return await anyio.to_thread.run_sync(next, self.sync_iterator)
        except RuntimeError as e:
            if type(e.__cause__) is StopIteration:
                raise StopAsyncIteration
            else:
                raise e  # pragma: lax no cover