mcp-run-python

from __future__ import annotations import functools import typing from collections.abc import AsyncIterator, Iterable, Iterator, Mapping 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, cast, overload import anyio.to_thread from botocore.exceptions import ClientError from typing_extensions import ParamSpec, 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.exceptions import ModelAPIError, ModelHTTPError, UserError from pydantic_ai.models import Model, ModelRequestParameters, StreamedResponse, download_item from pydantic_ai.providers import Provider, infer_provider from pydantic_ai.providers.bedrock import BEDROCK_GEO_PREFIXES, BedrockModelProfile 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, DocumentBlockTypeDef, GuardrailConfigurationTypeDef, ImageBlockTypeDef, InferenceConfigurationTypeDef, MessageUnionTypeDef, PerformanceConfigurationTypeDef, PromptVariableValuesTypeDef, ReasoningContentBlockOutputTypeDef, SystemContentBlockTypeDef, ToolChoiceTypeDef, ToolConfigurationTypeDef, ToolSpecificationTypeDef, ToolTypeDef, VideoBlockTypeDef, ) LatestBedrockModelNames = Literal[ 'amazon.titan-tg1-large', 'amazon.titan-text-lite-v1', 'amazon.titan-text-express-v1', '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-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', 'stop_sequence': 'stop', 'tool_use': 'tool_call', } 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. """ @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 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': self._remove_inference_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'): items.append( ToolCallPart( tool_name=tool_use['name'], args=tool_use['input'], tool_call_id=tool_use['toolUseId'], ), ) u = usage.RequestUsage( 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), ) 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) if model_request_parameters.builtin_tools: raise UserError('Bedrock does not support built-in 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 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) 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: list[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) and part.content: 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 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): # TODO(Marcelo): We need to add a test here. if part.tool_name is None: # pragma: no cover 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', } } ], } ) 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 | BuiltinToolReturnPart): pass 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.insert(0, {'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: # AWS currently rejects cache points that directly follow non-text content. # Insert a newline text block as a workaround. if 'text' not in last_user_content[-1]: last_user_content.append({'text': '\n'}) last_user_content.append({'cachePoint': {'type': 'default'}}) 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_user_prompt( # noqa: C901 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): format = item.format if item.is_document: name = f'Document {next(document_count)}' assert format in ('pdf', 'txt', 'csv', 'doc', 'docx', 'xls', 'xlsx', 'html', 'md') content.append({'document': {'name': name, 'format': format, 'source': {'bytes': item.data}}}) elif item.is_image: assert format in ('jpeg', 'png', 'gif', 'webp') content.append({'image': {'format': format, 'source': {'bytes': item.data}}}) elif item.is_video: assert format in ('mkv', 'mov', 'mp4', 'webm', 'flv', 'mpeg', 'mpg', 'wmv', 'three_gp') content.append({'video': {'format': format, 'source': {'bytes': item.data}}}) else: raise NotImplementedError('Binary content is not supported yet.') elif isinstance(item, ImageUrl | DocumentUrl | VideoUrl): downloaded_item = await download_item(item, data_format='bytes', type_format='extension') format = downloaded_item['data_type'] if item.kind == 'image-url': format = item.media_type.split('/')[1] assert format in ('jpeg', 'png', 'gif', 'webp'), f'Unsupported image format: {format}' image: ImageBlockTypeDef = {'format': format, 'source': {'bytes': downloaded_item['data']}} content.append({'image': image}) elif item.kind == 'document-url': name = f'Document {next(document_count)}' document: DocumentBlockTypeDef = { 'name': name, 'format': item.format, 'source': {'bytes': downloaded_item['data']}, } content.append({'document': document}) elif item.kind == 'video-url': # pragma: no branch format = item.media_type.split('/')[1] assert format in ( 'mkv', 'mov', 'mp4', 'webm', 'flv', 'mpeg', 'mpg', 'wmv', 'three_gp', ), f'Unsupported video format: {format}' video: VideoBlockTypeDef = {'format': format, 'source': {'bytes': downloaded_item['data']}} content.append({'video': video}) elif isinstance(item, AudioUrl): # pragma: no cover raise NotImplementedError('Audio is not supported yet.') 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.' ) if 'text' not in content[-1]: # AWS currently rejects cache points that directly follow non-text content. # Insert an empty text block as a workaround (see https://github.com/pydantic/pydantic-ai/issues/3418 # and https://github.com/pydantic/pydantic-ai/pull/2560#discussion_r2349209916). content.append({'text': '\n'}) content.append({'cachePoint': {'type': 'default'}}) 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 _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 @staticmethod def _remove_inference_geo_prefix(model_name: BedrockModelName) -> BedrockModelName: """Remove inference geographic prefix from model ID if present.""" for prefix in BEDROCK_GEO_PREFIXES: if model_name.startswith(f'{prefix}.'): return model_name.removeprefix(f'{prefix}.') return model_name @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_id: str | None = None 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: # pragma: no branch tool_use_start = start['toolUse'] tool_id = tool_use_start['toolUseId'] tool_name = tool_use_start['name'] maybe_event = self._parts_manager.handle_tool_call_delta( vendor_part_id=index, tool_name=tool_name, args=None, tool_call_id=tool_id, ) if maybe_event: # pragma: no branch yield maybe_event 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_id, ) if maybe_event: # pragma: no branch yield maybe_event case _: 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: return 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), ) 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