ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter9/browser-use-rpa/browser-use/browser_use/llm/aws/chat_anthropic.py

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+import json
+from collections.abc import Mapping
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any, TypeVar, overload
+
+from anthropic import (
+	NOT_GIVEN,
+	APIConnectionError,
+	APIStatusError,
+	AsyncAnthropicBedrock,
+	RateLimitError,
+)
+from anthropic.types import CacheControlEphemeralParam, Message, ToolParam
+from anthropic.types.text_block import TextBlock
+from anthropic.types.tool_choice_tool_param import ToolChoiceToolParam
+from pydantic import BaseModel
+
+from browser_use.llm.anthropic.serializer import AnthropicMessageSerializer
+from browser_use.llm.aws.chat_bedrock import ChatAWSBedrock
+from browser_use.llm.exceptions import ModelProviderError, ModelRateLimitError
+from browser_use.llm.messages import BaseMessage
+from browser_use.llm.views import ChatInvokeCompletion, ChatInvokeUsage
+
+if TYPE_CHECKING:
+	from boto3.session import Session  # pyright: ignore
+
+
+T = TypeVar('T', bound=BaseModel)
+
+
+@dataclass
+class ChatAnthropicBedrock(ChatAWSBedrock):
+	"""
+	AWS Bedrock Anthropic Claude chat model.
+
+	This is a convenience class that provides Claude-specific defaults
+	for the AWS Bedrock service. It inherits all functionality from
+	ChatAWSBedrock but sets Anthropic Claude as the default model.
+	"""
+
+	# Anthropic Claude specific defaults
+	model: str = 'anthropic.claude-3-5-sonnet-20240620-v1:0'
+	max_tokens: int = 8192
+	temperature: float | None = None
+	top_p: float | None = None
+	top_k: int | None = None
+	stop_sequences: list[str] | None = None
+	seed: int | None = None
+
+	# AWS credentials and configuration
+	aws_access_key: str | None = None
+	aws_secret_key: str | None = None
+	aws_session_token: str | None = None
+	aws_region: str | None = None
+	session: 'Session | None' = None
+
+	# Client initialization parameters
+	max_retries: int = 10
+	default_headers: Mapping[str, str] | None = None
+	default_query: Mapping[str, object] | None = None
+
+	@property
+	def provider(self) -> str:
+		return 'anthropic_bedrock'
+
+	def _get_client_params(self) -> dict[str, Any]:
+		"""Prepare client parameters dictionary for Bedrock."""
+		client_params: dict[str, Any] = {}
+
+		if self.session:
+			credentials = self.session.get_credentials()
+			client_params.update(
+				{
+					'aws_access_key': credentials.access_key,
+					'aws_secret_key': credentials.secret_key,
+					'aws_session_token': credentials.token,
+					'aws_region': self.session.region_name,
+				}
+			)
+		else:
+			# Use individual credentials
+			if self.aws_access_key:
+				client_params['aws_access_key'] = self.aws_access_key
+			if self.aws_secret_key:
+				client_params['aws_secret_key'] = self.aws_secret_key
+			if self.aws_region:
+				client_params['aws_region'] = self.aws_region
+			if self.aws_session_token:
+				client_params['aws_session_token'] = self.aws_session_token
+
+		# Add optional parameters
+		if self.max_retries:
+			client_params['max_retries'] = self.max_retries
+		if self.default_headers:
+			client_params['default_headers'] = self.default_headers
+		if self.default_query:
+			client_params['default_query'] = self.default_query
+
+		return client_params
+
+	def _get_client_params_for_invoke(self) -> dict[str, Any]:
+		"""Prepare client parameters dictionary for invoke."""
+		client_params = {}
+
+		if self.temperature is not None:
+			client_params['temperature'] = self.temperature
+		if self.max_tokens is not None:
+			client_params['max_tokens'] = self.max_tokens
+		if self.top_p is not None:
+			client_params['top_p'] = self.top_p
+		if self.top_k is not None:
+			client_params['top_k'] = self.top_k
+		if self.seed is not None:
+			client_params['seed'] = self.seed
+		if self.stop_sequences is not None:
+			client_params['stop_sequences'] = self.stop_sequences
+
+		return client_params
+
+	def get_client(self) -> AsyncAnthropicBedrock:
+		"""
+		Returns an AsyncAnthropicBedrock client.
+
+		Returns:
+			AsyncAnthropicBedrock: An instance of the AsyncAnthropicBedrock client.
+		"""
+		client_params = self._get_client_params()
+		return AsyncAnthropicBedrock(**client_params)
+
+	@property
+	def name(self) -> str:
+		return str(self.model)
+
+	def _get_usage(self, response: Message) -> ChatInvokeUsage | None:
+		"""Extract usage information from the response."""
+		usage = ChatInvokeUsage(
+			prompt_tokens=response.usage.input_tokens
+			+ (
+				response.usage.cache_read_input_tokens or 0
+			),  # Total tokens in Anthropic are a bit fucked, you have to add cached tokens to the prompt tokens
+			completion_tokens=response.usage.output_tokens,
+			total_tokens=response.usage.input_tokens + response.usage.output_tokens,
+			prompt_cached_tokens=response.usage.cache_read_input_tokens,
+			prompt_cache_creation_tokens=response.usage.cache_creation_input_tokens,
+			prompt_image_tokens=None,
+		)
+		return usage
+
+	@overload
+	async def ainvoke(self, messages: list[BaseMessage], output_format: None = None) -> ChatInvokeCompletion[str]: ...
+
+	@overload
+	async def ainvoke(self, messages: list[BaseMessage], output_format: type[T]) -> ChatInvokeCompletion[T]: ...
+
+	async def ainvoke(
+		self, messages: list[BaseMessage], output_format: type[T] | None = None
+	) -> ChatInvokeCompletion[T] | ChatInvokeCompletion[str]:
+		anthropic_messages, system_prompt = AnthropicMessageSerializer.serialize_messages(messages)
+
+		try:
+			if output_format is None:
+				# Normal completion without structured output
+				response = await self.get_client().messages.create(
+					model=self.model,
+					messages=anthropic_messages,
+					system=system_prompt or NOT_GIVEN,
+					**self._get_client_params_for_invoke(),
+				)
+
+				usage = self._get_usage(response)
+
+				# Extract text from the first content block
+				first_content = response.content[0]
+				if isinstance(first_content, TextBlock):
+					response_text = first_content.text
+				else:
+					# If it's not a text block, convert to string
+					response_text = str(first_content)
+
+				return ChatInvokeCompletion(
+					completion=response_text,
+					usage=usage,
+				)
+
+			else:
+				# Use tool calling for structured output
+				# Create a tool that represents the output format
+				tool_name = output_format.__name__
+				schema = output_format.model_json_schema()
+
+				# Remove title from schema if present (Anthropic doesn't like it in parameters)
+				if 'title' in schema:
+					del schema['title']
+
+				tool = ToolParam(
+					name=tool_name,
+					description=f'Extract information in the format of {tool_name}',
+					input_schema=schema,
+					cache_control=CacheControlEphemeralParam(type='ephemeral'),
+				)
+
+				# Force the model to use this tool
+				tool_choice = ToolChoiceToolParam(type='tool', name=tool_name)
+
+				response = await self.get_client().messages.create(
+					model=self.model,
+					messages=anthropic_messages,
+					tools=[tool],
+					system=system_prompt or NOT_GIVEN,
+					tool_choice=tool_choice,
+					**self._get_client_params_for_invoke(),
+				)
+
+				usage = self._get_usage(response)
+
+				# Extract the tool use block
+				for content_block in response.content:
+					if hasattr(content_block, 'type') and content_block.type == 'tool_use':
+						# Parse the tool input as the structured output
+						try:
+							return ChatInvokeCompletion(completion=output_format.model_validate(content_block.input), usage=usage)
+						except Exception as e:
+							# If validation fails, try to parse it as JSON first
+							if isinstance(content_block.input, str):
+								data = json.loads(content_block.input)
+								return ChatInvokeCompletion(
+									completion=output_format.model_validate(data),
+									usage=usage,
+								)
+							raise e
+
+				# If no tool use block found, raise an error
+				raise ValueError('Expected tool use in response but none found')
+
+		except APIConnectionError as e:
+			raise ModelProviderError(message=e.message, model=self.name) from e
+		except RateLimitError as e:
+			raise ModelRateLimitError(message=e.message, model=self.name) from e
+		except APIStatusError as e:
+			raise ModelProviderError(message=e.message, status_code=e.status_code, model=self.name) from e
+		except Exception as e:
+			raise ModelProviderError(message=str(e), model=self.name) from e