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/openrouter/chat.py

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+from collections.abc import Mapping
+from dataclasses import dataclass
+from typing import Any, TypeVar, overload
+
+import httpx
+from openai import APIConnectionError, APIStatusError, AsyncOpenAI, RateLimitError
+from openai.types.chat.chat_completion import ChatCompletion
+from openai.types.shared_params.response_format_json_schema import (
+	JSONSchema,
+	ResponseFormatJSONSchema,
+)
+from pydantic import BaseModel
+
+from browser_use.llm.base import BaseChatModel
+from browser_use.llm.exceptions import ModelProviderError, ModelRateLimitError
+from browser_use.llm.messages import BaseMessage
+from browser_use.llm.openrouter.serializer import OpenRouterMessageSerializer
+from browser_use.llm.schema import SchemaOptimizer
+from browser_use.llm.views import ChatInvokeCompletion, ChatInvokeUsage
+
+T = TypeVar('T', bound=BaseModel)
+
+
+@dataclass
+class ChatOpenRouter(BaseChatModel):
+	"""
+	A wrapper around OpenRouter's chat API, which provides access to various LLM models
+	through a unified OpenAI-compatible interface.
+
+	This class implements the BaseChatModel protocol for OpenRouter's API.
+	"""
+
+	# Model configuration
+	model: str
+
+	# Model params
+	temperature: float | None = None
+	top_p: float | None = None
+	seed: int | None = None
+
+	# Client initialization parameters
+	api_key: str | None = None
+	http_referer: str | None = None  # OpenRouter specific parameter for tracking
+	base_url: str | httpx.URL = 'https://openrouter.ai/api/v1'
+	timeout: float | httpx.Timeout | None = None
+	max_retries: int = 10
+	default_headers: Mapping[str, str] | None = None
+	default_query: Mapping[str, object] | None = None
+	http_client: httpx.AsyncClient | None = None
+	_strict_response_validation: bool = False
+
+	# Static
+	@property
+	def provider(self) -> str:
+		return 'openrouter'
+
+	def _get_client_params(self) -> dict[str, Any]:
+		"""Prepare client parameters dictionary."""
+		# Define base client params
+		base_params = {
+			'api_key': self.api_key,
+			'base_url': self.base_url,
+			'timeout': self.timeout,
+			'max_retries': self.max_retries,
+			'default_headers': self.default_headers,
+			'default_query': self.default_query,
+			'_strict_response_validation': self._strict_response_validation,
+			'top_p': self.top_p,
+			'seed': self.seed,
+		}
+
+		# Create client_params dict with non-None values
+		client_params = {k: v for k, v in base_params.items() if v is not None}
+
+		# Add http_client if provided
+		if self.http_client is not None:
+			client_params['http_client'] = self.http_client
+
+		return client_params
+
+	def get_client(self) -> AsyncOpenAI:
+		"""
+		Returns an AsyncOpenAI client configured for OpenRouter.
+
+		Returns:
+		    AsyncOpenAI: An instance of the AsyncOpenAI client with OpenRouter base URL.
+		"""
+		if not hasattr(self, '_client'):
+			client_params = self._get_client_params()
+			self._client = AsyncOpenAI(**client_params)
+		return self._client
+
+	@property
+	def name(self) -> str:
+		return str(self.model)
+
+	def _get_usage(self, response: ChatCompletion) -> ChatInvokeUsage | None:
+		"""Extract usage information from the OpenRouter response."""
+		if response.usage is None:
+			return None
+
+		prompt_details = getattr(response.usage, 'prompt_tokens_details', None)
+		cached_tokens = prompt_details.cached_tokens if prompt_details else None
+
+		return ChatInvokeUsage(
+			prompt_tokens=response.usage.prompt_tokens,
+			prompt_cached_tokens=cached_tokens,
+			prompt_cache_creation_tokens=None,
+			prompt_image_tokens=None,
+			# Completion
+			completion_tokens=response.usage.completion_tokens,
+			total_tokens=response.usage.total_tokens,
+		)
+
+	@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]:
+		"""
+		Invoke the model with the given messages through OpenRouter.
+
+		Args:
+		    messages: List of chat messages
+		    output_format: Optional Pydantic model class for structured output
+
+		Returns:
+		    Either a string response or an instance of output_format
+		"""
+		openrouter_messages = OpenRouterMessageSerializer.serialize_messages(messages)
+
+		# Set up extra headers for OpenRouter
+		extra_headers = {}
+		if self.http_referer:
+			extra_headers['HTTP-Referer'] = self.http_referer
+
+		try:
+			if output_format is None:
+				# Return string response
+				response = await self.get_client().chat.completions.create(
+					model=self.model,
+					messages=openrouter_messages,
+					temperature=self.temperature,
+					top_p=self.top_p,
+					seed=self.seed,
+					extra_headers=extra_headers,
+				)
+
+				usage = self._get_usage(response)
+				return ChatInvokeCompletion(
+					completion=response.choices[0].message.content or '',
+					usage=usage,
+				)
+
+			else:
+				# Create a JSON schema for structured output
+				schema = SchemaOptimizer.create_optimized_json_schema(output_format)
+
+				response_format_schema: JSONSchema = {
+					'name': 'agent_output',
+					'strict': True,
+					'schema': schema,
+				}
+
+				# Return structured response
+				response = await self.get_client().chat.completions.create(
+					model=self.model,
+					messages=openrouter_messages,
+					temperature=self.temperature,
+					top_p=self.top_p,
+					seed=self.seed,
+					response_format=ResponseFormatJSONSchema(
+						json_schema=response_format_schema,
+						type='json_schema',
+					),
+					extra_headers=extra_headers,
+				)
+
+				if response.choices[0].message.content is None:
+					raise ModelProviderError(
+						message='Failed to parse structured output from model response',
+						status_code=500,
+						model=self.name,
+					)
+				usage = self._get_usage(response)
+
+				parsed = output_format.model_validate_json(response.choices[0].message.content)
+
+				return ChatInvokeCompletion(
+					completion=parsed,
+					usage=usage,
+				)
+
+		except RateLimitError as e:
+			raise ModelRateLimitError(message=e.message, model=self.name) from e
+
+		except APIConnectionError as e:
+			raise ModelProviderError(message=str(e), 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

chapter9/browser-use-rpa/browser-use/browser_use/llm/openrouter/serializer.py

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+from openai.types.chat import ChatCompletionMessageParam
+
+from browser_use.llm.messages import BaseMessage
+from browser_use.llm.openai.serializer import OpenAIMessageSerializer
+
+
+class OpenRouterMessageSerializer:
+	"""
+	Serializer for converting between custom message types and OpenRouter message formats.
+
+	OpenRouter uses the OpenAI-compatible API, so we can reuse the OpenAI serializer.
+	"""
+
+	@staticmethod
+	def serialize_messages(messages: list[BaseMessage]) -> list[ChatCompletionMessageParam]:
+		"""
+		Serialize a list of browser_use messages to OpenRouter-compatible messages.
+
+		Args:
+		    messages: List of browser_use messages
+
+		Returns:
+		    List of OpenRouter-compatible messages (identical to OpenAI format)
+		"""
+		# OpenRouter uses the same message format as OpenAI
+		return OpenAIMessageSerializer.serialize_messages(messages)