from __future__ import annotations as _annotations import io from collections.abc import AsyncGenerator, AsyncIterable, AsyncIterator from contextlib import asynccontextmanager from dataclasses import dataclass, field, replace from datetime import datetime from typing import Any, Literal, cast, overload from pydantic import TypeAdapter from typing_extensions import assert_never from .. import ModelHTTPError, UnexpectedModelBehavior, _utils, usage from .._run_context import RunContext from .._utils import guard_tool_call_id as _guard_tool_call_id from ..builtin_tools import CodeExecutionTool, MCPServerTool, MemoryTool, WebSearchTool from ..exceptions import UserError from ..messages import ( BinaryContent, BuiltinToolCallPart, BuiltinToolReturnPart, CachePoint, DocumentUrl, FilePart, FinishReason, ImageUrl, ModelMessage, ModelRequest, ModelResponse, ModelResponsePart, ModelResponseStreamEvent, RetryPromptPart, SystemPromptPart, TextPart, ThinkingPart, ToolCallPart, ToolReturnPart, UserPromptPart, ) from ..profiles import ModelProfileSpec from ..providers import Provider, infer_provider from ..providers.anthropic import AsyncAnthropicClient from ..settings import ModelSettings, merge_model_settings from ..tools import ToolDefinition from . import Model, ModelRequestParameters, StreamedResponse, check_allow_model_requests, download_item, get_user_agent _FINISH_REASON_MAP: dict[BetaStopReason, FinishReason] = { 'end_turn': 'stop', 'max_tokens': 'length', 'stop_sequence': 'stop', 'tool_use': 'tool_call', 'pause_turn': 'stop', 'refusal': 'content_filter', } try: from anthropic import NOT_GIVEN, APIStatusError, AsyncStream, omit as OMIT from anthropic.types.beta import ( BetaBase64PDFBlockParam, BetaBase64PDFSourceParam, BetaCacheControlEphemeralParam, BetaCitationsDelta, BetaCodeExecutionTool20250522Param, BetaCodeExecutionToolResultBlock, BetaCodeExecutionToolResultBlockContent, BetaCodeExecutionToolResultBlockParam, BetaCodeExecutionToolResultBlockParamContentParam, BetaContentBlock, BetaContentBlockParam, BetaImageBlockParam, BetaInputJSONDelta, BetaMCPToolResultBlock, BetaMCPToolUseBlock, BetaMCPToolUseBlockParam, BetaMemoryTool20250818Param, BetaMessage, BetaMessageParam, BetaMetadataParam, BetaPlainTextSourceParam, BetaRawContentBlockDeltaEvent, BetaRawContentBlockStartEvent, BetaRawContentBlockStopEvent, BetaRawMessageDeltaEvent, BetaRawMessageStartEvent, BetaRawMessageStopEvent, BetaRawMessageStreamEvent, BetaRedactedThinkingBlock, BetaRedactedThinkingBlockParam, BetaRequestMCPServerToolConfigurationParam, BetaRequestMCPServerURLDefinitionParam, BetaServerToolUseBlock, BetaServerToolUseBlockParam, BetaSignatureDelta, BetaStopReason, BetaTextBlock, BetaTextBlockParam, BetaTextDelta, BetaThinkingBlock, BetaThinkingBlockParam, BetaThinkingConfigParam, BetaThinkingDelta, BetaToolChoiceParam, BetaToolParam, BetaToolResultBlockParam, BetaToolUnionParam, BetaToolUseBlock, BetaToolUseBlockParam, BetaWebSearchTool20250305Param, BetaWebSearchToolResultBlock, BetaWebSearchToolResultBlockContent, BetaWebSearchToolResultBlockParam, BetaWebSearchToolResultBlockParamContentParam, ) from anthropic.types.beta.beta_web_search_tool_20250305_param import UserLocation from anthropic.types.model_param import ModelParam except ImportError as _import_error: raise ImportError( 'Please install `anthropic` to use the Anthropic model, ' 'you can use the `anthropic` optional group — `pip install "pydantic-ai-slim[anthropic]"`' ) from _import_error LatestAnthropicModelNames = ModelParam """Latest Anthropic models.""" AnthropicModelName = str | LatestAnthropicModelNames """Possible Anthropic model names. Since Anthropic supports a variety of date-stamped models, we explicitly list the latest models but allow any name in the type hints. See [the Anthropic docs](https://docs.anthropic.com/en/docs/about-claude/models) for a full list. """ class AnthropicModelSettings(ModelSettings, total=False): """Settings used for an Anthropic model request.""" # ALL FIELDS MUST BE `anthropic_` PREFIXED SO YOU CAN MERGE THEM WITH OTHER MODELS. anthropic_metadata: BetaMetadataParam """An object describing metadata about the request. Contains `user_id`, an external identifier for the user who is associated with the request. """ anthropic_thinking: BetaThinkingConfigParam """Determine whether the model should generate a thinking block. See [the Anthropic docs](https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking) for more information. """ anthropic_cache_tool_definitions: bool """Whether to add `cache_control` to the last tool definition. When enabled, the last tool in the `tools` array will have `cache_control` set, allowing Anthropic to cache tool definitions and reduce costs. See https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching for more information. """ anthropic_cache_instructions: bool """Whether to add `cache_control` to the last system prompt block. When enabled, the last system prompt will have `cache_control` set, allowing Anthropic to cache system instructions and reduce costs. See https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching for more information. """ @dataclass(init=False) class AnthropicModel(Model): """A model that uses the Anthropic API. Internally, this uses the [Anthropic Python client](https://github.com/anthropics/anthropic-sdk-python) to interact with the API. Apart from `__init__`, all methods are private or match those of the base class. """ client: AsyncAnthropicClient = field(repr=False) _model_name: AnthropicModelName = field(repr=False) _provider: Provider[AsyncAnthropicClient] = field(repr=False) def __init__( self, model_name: AnthropicModelName, *, provider: Literal['anthropic', 'gateway'] | Provider[AsyncAnthropicClient] = 'anthropic', profile: ModelProfileSpec | None = None, settings: ModelSettings | None = None, ): """Initialize an Anthropic model. Args: model_name: The name of the Anthropic model to use. List of model names available [here](https://docs.anthropic.com/en/docs/about-claude/models). provider: The provider to use for the Anthropic API. Can be either the string 'anthropic' or an instance of `Provider[AsyncAnthropicClient]`. If not provided, the other parameters will be used. profile: The model profile to use. Defaults to a profile picked by the provider based on the model name. settings: Default model settings for this model instance. """ self._model_name = model_name if isinstance(provider, str): provider = infer_provider('gateway/anthropic' if provider == 'gateway' else provider) self._provider = provider self.client = provider.client super().__init__(settings=settings, profile=profile or provider.model_profile) @property def base_url(self) -> str: return str(self.client.base_url) @property def model_name(self) -> AnthropicModelName: """The model name.""" return self._model_name @property def system(self) -> str: """The model provider.""" return self._provider.name async def request( self, messages: list[ModelMessage], model_settings: ModelSettings | None, model_request_parameters: ModelRequestParameters, ) -> ModelResponse: check_allow_model_requests() model_settings, model_request_parameters = self.prepare_request( model_settings, model_request_parameters, ) response = await self._messages_create( messages, False, cast(AnthropicModelSettings, model_settings or {}), model_request_parameters ) model_response = self._process_response(response) return model_response @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]: check_allow_model_requests() model_settings, model_request_parameters = self.prepare_request( model_settings, model_request_parameters, ) response = await self._messages_create( messages, True, cast(AnthropicModelSettings, model_settings or {}), model_request_parameters ) async with response: yield await self._process_streamed_response(response, model_request_parameters) def prepare_request( self, model_settings: ModelSettings | None, model_request_parameters: ModelRequestParameters ) -> tuple[ModelSettings | None, ModelRequestParameters]: settings = merge_model_settings(self.settings, model_settings) if ( model_request_parameters.output_tools and settings and (thinking := settings.get('anthropic_thinking')) and thinking.get('type') == 'enabled' ): if model_request_parameters.output_mode == 'auto': model_request_parameters = replace(model_request_parameters, output_mode='prompted') elif ( model_request_parameters.output_mode == 'tool' and not model_request_parameters.allow_text_output ): # pragma: no branch # This would result in `tool_choice=required`, which Anthropic does not support with thinking. raise UserError( 'Anthropic does not support thinking and output tools at the same time. Use `output_type=PromptedOutput(...)` instead.' ) return super().prepare_request(model_settings, model_request_parameters) @overload async def _messages_create( self, messages: list[ModelMessage], stream: Literal[True], model_settings: AnthropicModelSettings, model_request_parameters: ModelRequestParameters, ) -> AsyncStream[BetaRawMessageStreamEvent]: pass @overload async def _messages_create( self, messages: list[ModelMessage], stream: Literal[False], model_settings: AnthropicModelSettings, model_request_parameters: ModelRequestParameters, ) -> BetaMessage: pass async def _messages_create( self, messages: list[ModelMessage], stream: bool, model_settings: AnthropicModelSettings, model_request_parameters: ModelRequestParameters, ) -> BetaMessage | AsyncStream[BetaRawMessageStreamEvent]: # standalone function to make it easier to override tools = self._get_tools(model_request_parameters, model_settings) tools, mcp_servers, beta_features = self._add_builtin_tools(tools, model_request_parameters) tool_choice: BetaToolChoiceParam | None if not tools: tool_choice = None else: if not model_request_parameters.allow_text_output: tool_choice = {'type': 'any'} else: tool_choice = {'type': 'auto'} if (allow_parallel_tool_calls := model_settings.get('parallel_tool_calls')) is not None: tool_choice['disable_parallel_tool_use'] = not allow_parallel_tool_calls system_prompt, anthropic_messages = await self._map_message(messages, model_request_parameters, model_settings) try: extra_headers = model_settings.get('extra_headers', {}) extra_headers.setdefault('User-Agent', get_user_agent()) if beta_features: if 'anthropic-beta' in extra_headers: beta_features.insert(0, extra_headers['anthropic-beta']) extra_headers['anthropic-beta'] = ','.join(beta_features) return await self.client.beta.messages.create( max_tokens=model_settings.get('max_tokens', 4096), system=system_prompt or OMIT, messages=anthropic_messages, model=self._model_name, tools=tools or OMIT, tool_choice=tool_choice or OMIT, mcp_servers=mcp_servers or OMIT, stream=stream, thinking=model_settings.get('anthropic_thinking', OMIT), stop_sequences=model_settings.get('stop_sequences', OMIT), temperature=model_settings.get('temperature', OMIT), top_p=model_settings.get('top_p', OMIT), timeout=model_settings.get('timeout', NOT_GIVEN), metadata=model_settings.get('anthropic_metadata', OMIT), extra_headers=extra_headers, extra_body=model_settings.get('extra_body'), ) except APIStatusError as e: if (status_code := e.status_code) >= 400: raise ModelHTTPError(status_code=status_code, model_name=self.model_name, body=e.body) from e raise # pragma: lax no cover def _process_response(self, response: BetaMessage) -> ModelResponse: """Process a non-streamed response, and prepare a message to return.""" items: list[ModelResponsePart] = [] builtin_tool_calls: dict[str, BuiltinToolCallPart] = {} for item in response.content: if isinstance(item, BetaTextBlock): items.append(TextPart(content=item.text)) elif isinstance(item, BetaServerToolUseBlock): call_part = _map_server_tool_use_block(item, self.system) builtin_tool_calls[call_part.tool_call_id] = call_part items.append(call_part) elif isinstance(item, BetaWebSearchToolResultBlock): items.append(_map_web_search_tool_result_block(item, self.system)) elif isinstance(item, BetaCodeExecutionToolResultBlock): items.append(_map_code_execution_tool_result_block(item, self.system)) elif isinstance(item, BetaRedactedThinkingBlock): items.append( ThinkingPart(id='redacted_thinking', content='', signature=item.data, provider_name=self.system) ) elif isinstance(item, BetaThinkingBlock): items.append(ThinkingPart(content=item.thinking, signature=item.signature, provider_name=self.system)) elif isinstance(item, BetaMCPToolUseBlock): call_part = _map_mcp_server_use_block(item, self.system) builtin_tool_calls[call_part.tool_call_id] = call_part items.append(call_part) elif isinstance(item, BetaMCPToolResultBlock): call_part = builtin_tool_calls.get(item.tool_use_id) items.append(_map_mcp_server_result_block(item, call_part, self.system)) else: assert isinstance(item, BetaToolUseBlock), f'unexpected item type {type(item)}' items.append( ToolCallPart( tool_name=item.name, args=cast(dict[str, Any], item.input), tool_call_id=item.id, ) ) finish_reason: FinishReason | None = None provider_details: dict[str, Any] | None = None if raw_finish_reason := response.stop_reason: # pragma: no branch provider_details = {'finish_reason': raw_finish_reason} finish_reason = _FINISH_REASON_MAP.get(raw_finish_reason) return ModelResponse( parts=items, usage=_map_usage(response, self._provider.name, self._provider.base_url, self._model_name), model_name=response.model, provider_response_id=response.id, provider_name=self._provider.name, finish_reason=finish_reason, provider_details=provider_details, ) async def _process_streamed_response( self, response: AsyncStream[BetaRawMessageStreamEvent], model_request_parameters: ModelRequestParameters ) -> StreamedResponse: peekable_response = _utils.PeekableAsyncStream(response) first_chunk = await peekable_response.peek() if isinstance(first_chunk, _utils.Unset): raise UnexpectedModelBehavior('Streamed response ended without content or tool calls') # pragma: no cover assert isinstance(first_chunk, BetaRawMessageStartEvent) return AnthropicStreamedResponse( model_request_parameters=model_request_parameters, _model_name=first_chunk.message.model, _response=peekable_response, _timestamp=_utils.now_utc(), _provider_name=self._provider.name, _provider_url=self._provider.base_url, ) def _get_tools( self, model_request_parameters: ModelRequestParameters, model_settings: AnthropicModelSettings ) -> list[BetaToolUnionParam]: tools: list[BetaToolUnionParam] = [ self._map_tool_definition(r) for r in model_request_parameters.tool_defs.values() ] # Add cache_control to the last tool if enabled if tools and model_settings.get('anthropic_cache_tool_definitions'): last_tool = tools[-1] last_tool['cache_control'] = BetaCacheControlEphemeralParam(type='ephemeral') return tools def _add_builtin_tools( self, tools: list[BetaToolUnionParam], model_request_parameters: ModelRequestParameters ) -> tuple[list[BetaToolUnionParam], list[BetaRequestMCPServerURLDefinitionParam], list[str]]: beta_features: list[str] = [] mcp_servers: list[BetaRequestMCPServerURLDefinitionParam] = [] for tool in model_request_parameters.builtin_tools: if isinstance(tool, WebSearchTool): user_location = UserLocation(type='approximate', **tool.user_location) if tool.user_location else None tools.append( BetaWebSearchTool20250305Param( name='web_search', type='web_search_20250305', max_uses=tool.max_uses, allowed_domains=tool.allowed_domains, blocked_domains=tool.blocked_domains, user_location=user_location, ) ) elif isinstance(tool, CodeExecutionTool): # pragma: no branch tools.append(BetaCodeExecutionTool20250522Param(name='code_execution', type='code_execution_20250522')) beta_features.append('code-execution-2025-05-22') elif isinstance(tool, MemoryTool): # pragma: no branch if 'memory' not in model_request_parameters.tool_defs: raise UserError("Built-in `MemoryTool` requires a 'memory' tool to be defined.") # Replace the memory tool definition with the built-in memory tool tools = [tool for tool in tools if tool['name'] != 'memory'] tools.append(BetaMemoryTool20250818Param(name='memory', type='memory_20250818')) beta_features.append('context-management-2025-06-27') elif isinstance(tool, MCPServerTool) and tool.url: mcp_server_url_definition_param = BetaRequestMCPServerURLDefinitionParam( type='url', name=tool.id, url=tool.url, ) if tool.allowed_tools is not None: # pragma: no branch mcp_server_url_definition_param['tool_configuration'] = BetaRequestMCPServerToolConfigurationParam( enabled=bool(tool.allowed_tools), allowed_tools=tool.allowed_tools, ) if tool.authorization_token: # pragma: no cover mcp_server_url_definition_param['authorization_token'] = tool.authorization_token mcp_servers.append(mcp_server_url_definition_param) beta_features.append('mcp-client-2025-04-04') else: # pragma: no cover raise UserError( f'`{tool.__class__.__name__}` is not supported by `AnthropicModel`. If it should be, please file an issue.' ) return tools, mcp_servers, beta_features async def _map_message( # noqa: C901 self, messages: list[ModelMessage], model_request_parameters: ModelRequestParameters, model_settings: AnthropicModelSettings, ) -> tuple[str | list[BetaTextBlockParam], list[BetaMessageParam]]: """Just maps a `pydantic_ai.Message` to a `anthropic.types.MessageParam`.""" system_prompt_parts: list[str] = [] anthropic_messages: list[BetaMessageParam] = [] for m in messages: if isinstance(m, ModelRequest): user_content_params: list[BetaContentBlockParam] = [] for request_part in m.parts: if isinstance(request_part, SystemPromptPart): system_prompt_parts.append(request_part.content) elif isinstance(request_part, UserPromptPart): async for content in self._map_user_prompt(request_part): if isinstance(content, CachePoint): self._add_cache_control_to_last_param(user_content_params) else: user_content_params.append(content) elif isinstance(request_part, ToolReturnPart): tool_result_block_param = BetaToolResultBlockParam( tool_use_id=_guard_tool_call_id(t=request_part), type='tool_result', content=request_part.model_response_str(), is_error=False, ) user_content_params.append(tool_result_block_param) elif isinstance(request_part, RetryPromptPart): # pragma: no branch if request_part.tool_name is None: text = request_part.model_response() # pragma: no cover retry_param = BetaTextBlockParam(type='text', text=text) # pragma: no cover else: retry_param = BetaToolResultBlockParam( tool_use_id=_guard_tool_call_id(t=request_part), type='tool_result', content=request_part.model_response(), is_error=True, ) user_content_params.append(retry_param) if len(user_content_params) > 0: anthropic_messages.append(BetaMessageParam(role='user', content=user_content_params)) elif isinstance(m, ModelResponse): assistant_content_params: list[ BetaTextBlockParam | BetaToolUseBlockParam | BetaServerToolUseBlockParam | BetaWebSearchToolResultBlockParam | BetaCodeExecutionToolResultBlockParam | BetaThinkingBlockParam | BetaRedactedThinkingBlockParam | BetaMCPToolUseBlockParam | BetaMCPToolResultBlock ] = [] for response_part in m.parts: if isinstance(response_part, TextPart): if response_part.content: assistant_content_params.append(BetaTextBlockParam(text=response_part.content, type='text')) elif isinstance(response_part, ToolCallPart): tool_use_block_param = BetaToolUseBlockParam( id=_guard_tool_call_id(t=response_part), type='tool_use', name=response_part.tool_name, input=response_part.args_as_dict(), ) assistant_content_params.append(tool_use_block_param) elif isinstance(response_part, ThinkingPart): if ( response_part.provider_name == self.system and response_part.signature is not None ): # pragma: no branch if response_part.id == 'redacted_thinking': assistant_content_params.append( BetaRedactedThinkingBlockParam( data=response_part.signature, type='redacted_thinking', ) ) else: assistant_content_params.append( BetaThinkingBlockParam( thinking=response_part.content, signature=response_part.signature, type='thinking', ) ) elif response_part.content: # pragma: no branch start_tag, end_tag = self.profile.thinking_tags assistant_content_params.append( BetaTextBlockParam( text='\n'.join([start_tag, response_part.content, end_tag]), type='text' ) ) elif isinstance(response_part, BuiltinToolCallPart): if response_part.provider_name == self.system: tool_use_id = _guard_tool_call_id(t=response_part) if response_part.tool_name == WebSearchTool.kind: server_tool_use_block_param = BetaServerToolUseBlockParam( id=tool_use_id, type='server_tool_use', name='web_search', input=response_part.args_as_dict(), ) assistant_content_params.append(server_tool_use_block_param) elif response_part.tool_name == CodeExecutionTool.kind: server_tool_use_block_param = BetaServerToolUseBlockParam( id=tool_use_id, type='server_tool_use', name='code_execution', input=response_part.args_as_dict(), ) assistant_content_params.append(server_tool_use_block_param) elif ( response_part.tool_name.startswith(MCPServerTool.kind) and (server_id := response_part.tool_name.split(':', 1)[1]) and (args := response_part.args_as_dict()) and (tool_name := args.get('tool_name')) and (tool_args := args.get('tool_args')) ): # pragma: no branch mcp_tool_use_block_param = BetaMCPToolUseBlockParam( id=tool_use_id, type='mcp_tool_use', server_name=server_id, name=tool_name, input=tool_args, ) assistant_content_params.append(mcp_tool_use_block_param) elif isinstance(response_part, BuiltinToolReturnPart): if response_part.provider_name == self.system: tool_use_id = _guard_tool_call_id(t=response_part) if response_part.tool_name in ( WebSearchTool.kind, 'web_search_tool_result', # Backward compatibility ) and isinstance(response_part.content, dict | list): assistant_content_params.append( BetaWebSearchToolResultBlockParam( tool_use_id=tool_use_id, type='web_search_tool_result', content=cast( BetaWebSearchToolResultBlockParamContentParam, response_part.content, # pyright: ignore[reportUnknownMemberType] ), ) ) elif response_part.tool_name in ( # pragma: no branch CodeExecutionTool.kind, 'code_execution_tool_result', # Backward compatibility ) and isinstance(response_part.content, dict): assistant_content_params.append( BetaCodeExecutionToolResultBlockParam( tool_use_id=tool_use_id, type='code_execution_tool_result', content=cast( BetaCodeExecutionToolResultBlockParamContentParam, response_part.content, # pyright: ignore[reportUnknownMemberType] ), ) ) elif response_part.tool_name.startswith(MCPServerTool.kind) and isinstance( response_part.content, dict ): # pragma: no branch assistant_content_params.append( BetaMCPToolResultBlock( tool_use_id=tool_use_id, type='mcp_tool_result', **cast(dict[str, Any], response_part.content), # pyright: ignore[reportUnknownMemberType] ) ) elif isinstance(response_part, FilePart): # pragma: no cover # Files generated by models are not sent back to models that don't themselves generate files. pass else: assert_never(response_part) if len(assistant_content_params) > 0: anthropic_messages.append(BetaMessageParam(role='assistant', content=assistant_content_params)) else: assert_never(m) if instructions := self._get_instructions(messages, model_request_parameters): system_prompt_parts.insert(0, instructions) system_prompt = '\n\n'.join(system_prompt_parts) # If anthropic_cache_instructions is enabled, return system prompt as a list with cache_control if system_prompt and model_settings.get('anthropic_cache_instructions'): system_prompt_blocks = [ BetaTextBlockParam( type='text', text=system_prompt, cache_control=BetaCacheControlEphemeralParam(type='ephemeral') ) ] return system_prompt_blocks, anthropic_messages return system_prompt, anthropic_messages @staticmethod def _add_cache_control_to_last_param(params: list[BetaContentBlockParam]) -> None: """Add cache control to the last content block param. See https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching for more information. """ if not params: raise UserError( 'CachePoint cannot be the first content in a user message - there must be previous content to attach the CachePoint to. ' 'To cache system instructions or tool definitions, use the `anthropic_cache_instructions` or `anthropic_cache_tool_definitions` settings instead.' ) # Only certain types support cache_control # See https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching#what-can-be-cached cacheable_types = {'text', 'tool_use', 'server_tool_use', 'image', 'tool_result'} # Cast needed because BetaContentBlockParam is a union including response Block types (Pydantic models) # that don't support dict operations, even though at runtime we only have request Param types (TypedDicts). last_param = cast(dict[str, Any], params[-1]) if last_param['type'] not in cacheable_types: raise UserError(f'Cache control not supported for param type: {last_param["type"]}') # Add cache_control to the last param last_param['cache_control'] = BetaCacheControlEphemeralParam(type='ephemeral') @staticmethod async def _map_user_prompt( part: UserPromptPart, ) -> AsyncGenerator[BetaContentBlockParam | CachePoint]: if isinstance(part.content, str): if part.content: # Only yield non-empty text yield BetaTextBlockParam(text=part.content, type='text') else: for item in part.content: if isinstance(item, str): if item: # Only yield non-empty text yield BetaTextBlockParam(text=item, type='text') elif isinstance(item, CachePoint): yield item elif isinstance(item, BinaryContent): if item.is_image: yield BetaImageBlockParam( source={'data': io.BytesIO(item.data), 'media_type': item.media_type, 'type': 'base64'}, # type: ignore type='image', ) elif item.media_type == 'application/pdf': yield BetaBase64PDFBlockParam( source=BetaBase64PDFSourceParam( data=io.BytesIO(item.data), media_type='application/pdf', type='base64', ), type='document', ) else: raise RuntimeError('Only images and PDFs are supported for binary content') elif isinstance(item, ImageUrl): yield BetaImageBlockParam(source={'type': 'url', 'url': item.url}, type='image') elif isinstance(item, DocumentUrl): if item.media_type == 'application/pdf': yield BetaBase64PDFBlockParam(source={'url': item.url, 'type': 'url'}, type='document') elif item.media_type == 'text/plain': downloaded_item = await download_item(item, data_format='text') yield BetaBase64PDFBlockParam( source=BetaPlainTextSourceParam( data=downloaded_item['data'], media_type=item.media_type, type='text' ), type='document', ) else: # pragma: no cover raise RuntimeError(f'Unsupported media type: {item.media_type}') else: raise RuntimeError(f'Unsupported content type: {type(item)}') # pragma: no cover @staticmethod def _map_tool_definition(f: ToolDefinition) -> BetaToolParam: return { 'name': f.name, 'description': f.description or '', 'input_schema': f.parameters_json_schema, } def _map_usage( message: BetaMessage | BetaRawMessageStartEvent | BetaRawMessageDeltaEvent, provider: str, provider_url: str, model: str, existing_usage: usage.RequestUsage | None = None, ) -> usage.RequestUsage: if isinstance(message, BetaMessage): response_usage = message.usage elif isinstance(message, BetaRawMessageStartEvent): response_usage = message.message.usage elif isinstance(message, BetaRawMessageDeltaEvent): response_usage = message.usage else: assert_never(message) # In streaming, usage appears in different events. # The values are cumulative, meaning new values should replace existing ones entirely. details: dict[str, int] = (existing_usage.details if existing_usage else {}) | { key: value for key, value in response_usage.model_dump().items() if isinstance(value, int) } # Note: genai-prices already extracts cache_creation_input_tokens and cache_read_input_tokens # from the Anthropic response and maps them to cache_write_tokens and cache_read_tokens return usage.RequestUsage.extract( dict(model=model, usage=details), provider=provider, provider_url=provider_url, provider_fallback='anthropic', details=details, ) @dataclass class AnthropicStreamedResponse(StreamedResponse): """Implementation of `StreamedResponse` for Anthropic models.""" _model_name: AnthropicModelName _response: AsyncIterable[BetaRawMessageStreamEvent] _timestamp: datetime _provider_name: str _provider_url: str async def _get_event_iterator(self) -> AsyncIterator[ModelResponseStreamEvent]: # noqa: C901 current_block: BetaContentBlock | None = None builtin_tool_calls: dict[str, BuiltinToolCallPart] = {} async for event in self._response: if isinstance(event, BetaRawMessageStartEvent): self._usage = _map_usage(event, self._provider_name, self._provider_url, self._model_name) self.provider_response_id = event.message.id elif isinstance(event, BetaRawContentBlockStartEvent): current_block = event.content_block if isinstance(current_block, BetaTextBlock) and current_block.text: maybe_event = self._parts_manager.handle_text_delta( vendor_part_id=event.index, content=current_block.text ) if maybe_event is not None: # pragma: no branch yield maybe_event elif isinstance(current_block, BetaThinkingBlock): yield self._parts_manager.handle_thinking_delta( vendor_part_id=event.index, content=current_block.thinking, signature=current_block.signature, provider_name=self.provider_name, ) elif isinstance(current_block, BetaRedactedThinkingBlock): yield self._parts_manager.handle_thinking_delta( vendor_part_id=event.index, id='redacted_thinking', signature=current_block.data, provider_name=self.provider_name, ) elif isinstance(current_block, BetaToolUseBlock): maybe_event = self._parts_manager.handle_tool_call_delta( vendor_part_id=event.index, tool_name=current_block.name, args=cast(dict[str, Any], current_block.input) or None, tool_call_id=current_block.id, ) if maybe_event is not None: # pragma: no branch yield maybe_event elif isinstance(current_block, BetaServerToolUseBlock): call_part = _map_server_tool_use_block(current_block, self.provider_name) builtin_tool_calls[call_part.tool_call_id] = call_part yield self._parts_manager.handle_part( vendor_part_id=event.index, part=call_part, ) elif isinstance(current_block, BetaWebSearchToolResultBlock): yield self._parts_manager.handle_part( vendor_part_id=event.index, part=_map_web_search_tool_result_block(current_block, self.provider_name), ) elif isinstance(current_block, BetaCodeExecutionToolResultBlock): yield self._parts_manager.handle_part( vendor_part_id=event.index, part=_map_code_execution_tool_result_block(current_block, self.provider_name), ) elif isinstance(current_block, BetaMCPToolUseBlock): call_part = _map_mcp_server_use_block(current_block, self.provider_name) builtin_tool_calls[call_part.tool_call_id] = call_part args_json = call_part.args_as_json_str() # Drop the final `{}}` so that we can add tool args deltas args_json_delta = args_json[:-3] assert args_json_delta.endswith('"tool_args":'), ( f'Expected {args_json_delta!r} to end in `"tool_args":`' ) yield self._parts_manager.handle_part( vendor_part_id=event.index, part=replace(call_part, args=None) ) maybe_event = self._parts_manager.handle_tool_call_delta( vendor_part_id=event.index, args=args_json_delta, ) if maybe_event is not None: # pragma: no branch yield maybe_event elif isinstance(current_block, BetaMCPToolResultBlock): call_part = builtin_tool_calls.get(current_block.tool_use_id) yield self._parts_manager.handle_part( vendor_part_id=event.index, part=_map_mcp_server_result_block(current_block, call_part, self.provider_name), ) elif isinstance(event, BetaRawContentBlockDeltaEvent): if isinstance(event.delta, BetaTextDelta): maybe_event = self._parts_manager.handle_text_delta( vendor_part_id=event.index, content=event.delta.text ) if maybe_event is not None: # pragma: no branch yield maybe_event elif isinstance(event.delta, BetaThinkingDelta): yield self._parts_manager.handle_thinking_delta( vendor_part_id=event.index, content=event.delta.thinking, provider_name=self.provider_name, ) elif isinstance(event.delta, BetaSignatureDelta): yield self._parts_manager.handle_thinking_delta( vendor_part_id=event.index, signature=event.delta.signature, provider_name=self.provider_name, ) elif isinstance(event.delta, BetaInputJSONDelta): maybe_event = self._parts_manager.handle_tool_call_delta( vendor_part_id=event.index, args=event.delta.partial_json, ) if maybe_event is not None: # pragma: no branch yield maybe_event # TODO(Marcelo): We need to handle citations. elif isinstance(event.delta, BetaCitationsDelta): pass elif isinstance(event, BetaRawMessageDeltaEvent): self._usage = _map_usage(event, self._provider_name, self._provider_url, self._model_name, self._usage) if raw_finish_reason := event.delta.stop_reason: # pragma: no branch self.provider_details = {'finish_reason': raw_finish_reason} self.finish_reason = _FINISH_REASON_MAP.get(raw_finish_reason) elif isinstance(event, BetaRawContentBlockStopEvent): # pragma: no branch if isinstance(current_block, BetaMCPToolUseBlock): maybe_event = self._parts_manager.handle_tool_call_delta( vendor_part_id=event.index, args='}', ) if maybe_event is not None: # pragma: no branch yield maybe_event current_block = None elif isinstance(event, BetaRawMessageStopEvent): # pragma: no branch current_block = None @property def model_name(self) -> AnthropicModelName: """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 timestamp(self) -> datetime: """Get the timestamp of the response.""" return self._timestamp def _map_server_tool_use_block(item: BetaServerToolUseBlock, provider_name: str) -> BuiltinToolCallPart: if item.name == 'web_search': return BuiltinToolCallPart( provider_name=provider_name, tool_name=WebSearchTool.kind, args=cast(dict[str, Any], item.input) or None, tool_call_id=item.id, ) elif item.name == 'code_execution': return BuiltinToolCallPart( provider_name=provider_name, tool_name=CodeExecutionTool.kind, args=cast(dict[str, Any], item.input) or None, tool_call_id=item.id, ) elif item.name in ('web_fetch', 'bash_code_execution', 'text_editor_code_execution'): # pragma: no cover raise NotImplementedError(f'Anthropic built-in tool {item.name!r} is not currently supported.') else: assert_never(item.name) web_search_tool_result_content_ta: TypeAdapter[BetaWebSearchToolResultBlockContent] = TypeAdapter( BetaWebSearchToolResultBlockContent ) def _map_web_search_tool_result_block(item: BetaWebSearchToolResultBlock, provider_name: str) -> BuiltinToolReturnPart: return BuiltinToolReturnPart( provider_name=provider_name, tool_name=WebSearchTool.kind, content=web_search_tool_result_content_ta.dump_python(item.content, mode='json'), tool_call_id=item.tool_use_id, ) code_execution_tool_result_content_ta: TypeAdapter[BetaCodeExecutionToolResultBlockContent] = TypeAdapter( BetaCodeExecutionToolResultBlockContent ) def _map_code_execution_tool_result_block( item: BetaCodeExecutionToolResultBlock, provider_name: str ) -> BuiltinToolReturnPart: return BuiltinToolReturnPart( provider_name=provider_name, tool_name=CodeExecutionTool.kind, content=code_execution_tool_result_content_ta.dump_python(item.content, mode='json'), tool_call_id=item.tool_use_id, ) def _map_mcp_server_use_block(item: BetaMCPToolUseBlock, provider_name: str) -> BuiltinToolCallPart: return BuiltinToolCallPart( provider_name=provider_name, tool_name=':'.join([MCPServerTool.kind, item.server_name]), args={ 'action': 'call_tool', 'tool_name': item.name, 'tool_args': cast(dict[str, Any], item.input), }, tool_call_id=item.id, ) def _map_mcp_server_result_block( item: BetaMCPToolResultBlock, call_part: BuiltinToolCallPart | None, provider_name: str ) -> BuiltinToolReturnPart: return BuiltinToolReturnPart( provider_name=provider_name, tool_name=call_part.tool_name if call_part else MCPServerTool.kind, content=item.model_dump(mode='json', include={'content', 'is_error'}), tool_call_id=item.tool_use_id, )