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/deepseek/chat.py

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+from __future__ import annotations
+
+import json
+from dataclasses import dataclass
+from typing import Any, TypeVar, overload
+
+import httpx
+from openai import (
+	APIConnectionError,
+	APIError,
+	APIStatusError,
+	APITimeoutError,
+	AsyncOpenAI,
+	RateLimitError,
+)
+from pydantic import BaseModel
+
+from browser_use.llm.base import BaseChatModel
+from browser_use.llm.deepseek.serializer import DeepSeekMessageSerializer
+from browser_use.llm.exceptions import ModelProviderError, ModelRateLimitError
+from browser_use.llm.messages import BaseMessage
+from browser_use.llm.schema import SchemaOptimizer
+from browser_use.llm.views import ChatInvokeCompletion
+
+T = TypeVar('T', bound=BaseModel)
+
+
+@dataclass
+class ChatDeepSeek(BaseChatModel):
+	"""DeepSeek /chat/completions wrapper (OpenAI-compatible)."""
+
+	model: str = 'deepseek-chat'
+
+	# Generation parameters
+	max_tokens: int | None = None
+	temperature: float | None = None
+	top_p: float | None = None
+	seed: int | None = None
+
+	# Connection parameters
+	api_key: str | None = None
+	base_url: str | httpx.URL | None = 'https://api.deepseek.com/v1'
+	timeout: float | httpx.Timeout | None = None
+	client_params: dict[str, Any] | None = None
+
+	@property
+	def provider(self) -> str:
+		return 'deepseek'
+
+	def _client(self) -> AsyncOpenAI:
+		return AsyncOpenAI(
+			api_key=self.api_key,
+			base_url=self.base_url,
+			timeout=self.timeout,
+			**(self.client_params or {}),
+		)
+
+	@property
+	def name(self) -> str:
+		return self.model
+
+	@overload
+	async def ainvoke(
+		self,
+		messages: list[BaseMessage],
+		output_format: None = None,
+		tools: list[dict[str, Any]] | None = None,
+		stop: list[str] | None = None,
+	) -> ChatInvokeCompletion[str]: ...
+
+	@overload
+	async def ainvoke(
+		self,
+		messages: list[BaseMessage],
+		output_format: type[T],
+		tools: list[dict[str, Any]] | None = None,
+		stop: list[str] | None = None,
+	) -> ChatInvokeCompletion[T]: ...
+
+	async def ainvoke(
+		self,
+		messages: list[BaseMessage],
+		output_format: type[T] | None = None,
+		tools: list[dict[str, Any]] | None = None,
+		stop: list[str] | None = None,
+	) -> ChatInvokeCompletion[T] | ChatInvokeCompletion[str]:
+		"""
+		DeepSeek ainvoke supports:
+		1. Regular text/multi-turn conversation
+		2. Function Calling
+		3. JSON Output (response_format)
+		4. Conversation prefix continuation (beta, prefix, stop)
+		"""
+		client = self._client()
+		ds_messages = DeepSeekMessageSerializer.serialize_messages(messages)
+		common: dict[str, Any] = {}
+
+		if self.temperature is not None:
+			common['temperature'] = self.temperature
+		if self.max_tokens is not None:
+			common['max_tokens'] = self.max_tokens
+		if self.top_p is not None:
+			common['top_p'] = self.top_p
+		if self.seed is not None:
+			common['seed'] = self.seed
+
+		# Beta conversation prefix continuation (see official documentation)
+		if self.base_url and str(self.base_url).endswith('/beta'):
+			# The last assistant message must have prefix
+			if ds_messages and isinstance(ds_messages[-1], dict) and ds_messages[-1].get('role') == 'assistant':
+				ds_messages[-1]['prefix'] = True
+			if stop:
+				common['stop'] = stop
+
+		# ① Regular multi-turn conversation/text output
+		if output_format is None and not tools:
+			try:
+				resp = await client.chat.completions.create(  # type: ignore
+					model=self.model,
+					messages=ds_messages,  # type: ignore
+					**common,
+				)
+				return ChatInvokeCompletion(
+					completion=resp.choices[0].message.content or '',
+					usage=None,
+				)
+			except RateLimitError as e:
+				raise ModelRateLimitError(str(e), model=self.name) from e
+			except (APIError, APIConnectionError, APITimeoutError, APIStatusError) as e:
+				raise ModelProviderError(str(e), model=self.name) from e
+			except Exception as e:
+				raise ModelProviderError(str(e), model=self.name) from e
+
+		# ② Function Calling path (with tools or output_format)
+		if tools or (output_format is not None and hasattr(output_format, 'model_json_schema')):
+			try:
+				call_tools = tools
+				tool_choice = None
+				if output_format is not None and hasattr(output_format, 'model_json_schema'):
+					tool_name = output_format.__name__
+					schema = SchemaOptimizer.create_optimized_json_schema(output_format)
+					schema.pop('title', None)
+					call_tools = [
+						{
+							'type': 'function',
+							'function': {
+								'name': tool_name,
+								'description': f'Return a JSON object of type {tool_name}',
+								'parameters': schema,
+							},
+						}
+					]
+					tool_choice = {'type': 'function', 'function': {'name': tool_name}}
+				resp = await client.chat.completions.create(  # type: ignore
+					model=self.model,
+					messages=ds_messages,  # type: ignore
+					tools=call_tools,  # type: ignore
+					tool_choice=tool_choice,  # type: ignore
+					**common,
+				)
+				msg = resp.choices[0].message
+				if not msg.tool_calls:
+					raise ValueError('Expected tool_calls in response but got none')
+				raw_args = msg.tool_calls[0].function.arguments
+				if isinstance(raw_args, str):
+					parsed = json.loads(raw_args)
+				else:
+					parsed = raw_args
+				# --------- Fix: only use model_validate when output_format is not None ----------
+				if output_format is not None:
+					return ChatInvokeCompletion(
+						completion=output_format.model_validate(parsed),
+						usage=None,
+					)
+				else:
+					# If no output_format, return dict directly
+					return ChatInvokeCompletion(
+						completion=parsed,
+						usage=None,
+					)
+			except RateLimitError as e:
+				raise ModelRateLimitError(str(e), model=self.name) from e
+			except (APIError, APIConnectionError, APITimeoutError, APIStatusError) as e:
+				raise ModelProviderError(str(e), model=self.name) from e
+			except Exception as e:
+				raise ModelProviderError(str(e), model=self.name) from e
+
+		# ③ JSON Output path (official response_format)
+		if output_format is not None and hasattr(output_format, 'model_json_schema'):
+			try:
+				resp = await client.chat.completions.create(  # type: ignore
+					model=self.model,
+					messages=ds_messages,  # type: ignore
+					response_format={'type': 'json_object'},
+					**common,
+				)
+				content = resp.choices[0].message.content
+				if not content:
+					raise ModelProviderError('Empty JSON content in DeepSeek response', model=self.name)
+				parsed = output_format.model_validate_json(content)
+				return ChatInvokeCompletion(
+					completion=parsed,
+					usage=None,
+				)
+			except RateLimitError as e:
+				raise ModelRateLimitError(str(e), model=self.name) from e
+			except (APIError, APIConnectionError, APITimeoutError, APIStatusError) as e:
+				raise ModelProviderError(str(e), model=self.name) from e
+			except Exception as e:
+				raise ModelProviderError(str(e), model=self.name) from e
+
+		raise ModelProviderError('No valid ainvoke execution path for DeepSeek LLM', model=self.name)

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

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+from __future__ import annotations
+
+import json
+from typing import Any, overload
+
+from browser_use.llm.messages import (
+	AssistantMessage,
+	BaseMessage,
+	ContentPartImageParam,
+	ContentPartTextParam,
+	SystemMessage,
+	ToolCall,
+	UserMessage,
+)
+
+MessageDict = dict[str, Any]
+
+
+class DeepSeekMessageSerializer:
+	"""Serializer for converting browser-use messages to DeepSeek messages."""
+
+	# -------- content 处理 --------------------------------------------------
+	@staticmethod
+	def _serialize_text_part(part: ContentPartTextParam) -> str:
+		return part.text
+
+	@staticmethod
+	def _serialize_image_part(part: ContentPartImageParam) -> dict[str, Any]:
+		url = part.image_url.url
+		if url.startswith('data:'):
+			return {'type': 'image_url', 'image_url': {'url': url}}
+		return {'type': 'image_url', 'image_url': {'url': url}}
+
+	@staticmethod
+	def _serialize_content(content: Any) -> str | list[dict[str, Any]]:
+		if content is None:
+			return ''
+		if isinstance(content, str):
+			return content
+		serialized: list[dict[str, Any]] = []
+		for part in content:
+			if part.type == 'text':
+				serialized.append({'type': 'text', 'text': DeepSeekMessageSerializer._serialize_text_part(part)})
+			elif part.type == 'image_url':
+				serialized.append(DeepSeekMessageSerializer._serialize_image_part(part))
+			elif part.type == 'refusal':
+				serialized.append({'type': 'text', 'text': f'[Refusal] {part.refusal}'})
+		return serialized
+
+	# -------- Tool-call 处理 -------------------------------------------------
+	@staticmethod
+	def _serialize_tool_calls(tool_calls: list[ToolCall]) -> list[dict[str, Any]]:
+		deepseek_tool_calls: list[dict[str, Any]] = []
+		for tc in tool_calls:
+			try:
+				arguments = json.loads(tc.function.arguments)
+			except json.JSONDecodeError:
+				arguments = {'arguments': tc.function.arguments}
+			deepseek_tool_calls.append(
+				{
+					'id': tc.id,
+					'type': 'function',
+					'function': {
+						'name': tc.function.name,
+						'arguments': arguments,
+					},
+				}
+			)
+		return deepseek_tool_calls
+
+	# -------- 单条消息序列化 -------------------------------------------------
+	@overload
+	@staticmethod
+	def serialize(message: UserMessage) -> MessageDict: ...
+
+	@overload
+	@staticmethod
+	def serialize(message: SystemMessage) -> MessageDict: ...
+
+	@overload
+	@staticmethod
+	def serialize(message: AssistantMessage) -> MessageDict: ...
+
+	@staticmethod
+	def serialize(message: BaseMessage) -> MessageDict:
+		if isinstance(message, UserMessage):
+			return {
+				'role': 'user',
+				'content': DeepSeekMessageSerializer._serialize_content(message.content),
+			}
+		if isinstance(message, SystemMessage):
+			return {
+				'role': 'system',
+				'content': DeepSeekMessageSerializer._serialize_content(message.content),
+			}
+		if isinstance(message, AssistantMessage):
+			msg: MessageDict = {
+				'role': 'assistant',
+				'content': DeepSeekMessageSerializer._serialize_content(message.content),
+			}
+			if message.tool_calls:
+				msg['tool_calls'] = DeepSeekMessageSerializer._serialize_tool_calls(message.tool_calls)
+			return msg
+		raise ValueError(f'Unknown message type: {type(message)}')
+
+	# -------- 列表序列化 -----------------------------------------------------
+	@staticmethod
+	def serialize_messages(messages: list[BaseMessage]) -> list[MessageDict]:
+		return [DeepSeekMessageSerializer.serialize(m) for m in messages]