""" Anthropic Claude 提供商适配器 """ import logging from typing import AsyncGenerator, Dict, List, Any from .base import BaseLLMProvider, LLMConfig, LLMMessage, LLMResponse, LLMStreamChunk, ToolCall from .registry import ProviderRegistry logger = logging.getLogger(__name__) @ProviderRegistry.register class ClaudeProvider(BaseLLMProvider): """ Anthropic Claude 提供商适配器 支持 Claude 3 系列模型 """ provider_type = 'claude' provider_name = 'Anthropic Claude' supported_model_types = ['chat'] DEFAULT_API_BASE = 'https://api.anthropic.com' def __init__(self, api_key: str = '', api_base: str = '', **kwargs): super().__init__(api_key, api_base, **kwargs) self.api_base = api_base or self.DEFAULT_API_BASE self._client = None self._async_client = None def _get_client(self): """获取同步客户端""" if self._client is None: try: import anthropic self._client = anthropic.Anthropic( api_key=self.api_key, base_url=self.api_base if self.api_base != self.DEFAULT_API_BASE else None, ) except ImportError: raise ImportError('请安装 anthropic 库: pip install anthropic') return self._client def _get_async_client(self): """获取异步客户端""" if self._async_client is None: try: import anthropic self._async_client = anthropic.AsyncAnthropic( api_key=self.api_key, base_url=self.api_base if self.api_base != self.DEFAULT_API_BASE else None, ) except ImportError: raise ImportError('请安装 anthropic 库: pip install anthropic') return self._async_client def _convert_messages(self, messages: List[LLMMessage]) -> tuple: """ 转换消息格式 Claude API 需要将 system 消息单独传递 """ system_prompt = '' converted_messages = [] for msg in messages: if msg.role == 'system': system_prompt = msg.content elif msg.role == 'tool': # Claude 的工具结果消息格式 converted_messages.append({ 'role': 'user', 'content': [{ 'type': 'tool_result', 'tool_use_id': msg.tool_call_id, 'content': msg.content, }], }) elif msg.role == 'assistant' and msg.tool_calls: # 包含工具调用的助手消息 content = [] if msg.content: content.append({'type': 'text', 'text': msg.content}) for tc in msg.tool_calls: content.append({ 'type': 'tool_use', 'id': tc.get('id', ''), 'name': tc.get('function', {}).get('name', ''), 'input': tc.get('function', {}).get('arguments', {}), }) converted_messages.append({ 'role': 'assistant', 'content': content, }) else: converted_messages.append({ 'role': msg.role, 'content': msg.content, }) return system_prompt, converted_messages def chat( self, messages: List[LLMMessage], config: LLMConfig, ) -> LLMResponse: """同步对话""" client = self._get_client() system_prompt, converted_messages = self._convert_messages(messages) kwargs = { 'model': config.model, 'messages': converted_messages, 'max_tokens': config.max_tokens, 'temperature': config.temperature, 'top_p': config.top_p, } if system_prompt: kwargs['system'] = system_prompt if config.stop: kwargs['stop_sequences'] = config.stop # 添加 Function Calling 参数 if config.tools: kwargs['tools'] = [t.to_claude_format() for t in config.tools] if config.tool_choice == 'required': kwargs['tool_choice'] = {'type': 'any'} elif config.tool_choice == 'none': # Claude 不支持 none，不传递 tools 即可 del kwargs['tools'] elif config.tool_choice != 'auto': # 指定具体工具 kwargs['tool_choice'] = {'type': 'tool', 'name': config.tool_choice} else: kwargs['tool_choice'] = {'type': 'auto'} response = client.messages.create(**kwargs) # 解析响应内容和工具调用 content = '' tool_calls = [] for block in response.content: if block.type == 'text': content = block.text elif block.type == 'tool_use': tool_calls.append(ToolCall( id=block.id, name=block.name, arguments=block.input if isinstance(block.input, dict) else {}, )) return LLMResponse( content=content, model=response.model, prompt_tokens=response.usage.input_tokens, completion_tokens=response.usage.output_tokens, total_tokens=response.usage.input_tokens + response.usage.output_tokens, finish_reason=response.stop_reason or '', raw_response=response.model_dump(), tool_calls=tool_calls if tool_calls else None, ) async def chat_async( self, messages: List[LLMMessage], config: LLMConfig, ) -> LLMResponse: """异步对话""" client = self._get_async_client() system_prompt, converted_messages = self._convert_messages(messages) kwargs = { 'model': config.model, 'messages': converted_messages, 'max_tokens': config.max_tokens, 'temperature': config.temperature, 'top_p': config.top_p, } if system_prompt: kwargs['system'] = system_prompt if config.stop: kwargs['stop_sequences'] = config.stop # 添加 Function Calling 参数 if config.tools: kwargs['tools'] = [t.to_claude_format() for t in config.tools] if config.tool_choice == 'required': kwargs['tool_choice'] = {'type': 'any'} elif config.tool_choice == 'none': del kwargs['tools'] elif config.tool_choice != 'auto': kwargs['tool_choice'] = {'type': 'tool', 'name': config.tool_choice} else: kwargs['tool_choice'] = {'type': 'auto'} response = await client.messages.create(**kwargs) # 解析响应内容和工具调用 content = '' tool_calls = [] for block in response.content: if block.type == 'text': content = block.text elif block.type == 'tool_use': tool_calls.append(ToolCall( id=block.id, name=block.name, arguments=block.input if isinstance(block.input, dict) else {}, )) return LLMResponse( content=content, model=response.model, prompt_tokens=response.usage.input_tokens, completion_tokens=response.usage.output_tokens, total_tokens=response.usage.input_tokens + response.usage.output_tokens, finish_reason=response.stop_reason or '', raw_response=response.model_dump(), tool_calls=tool_calls if tool_calls else None, ) async def chat_stream( self, messages: List[LLMMessage], config: LLMConfig, ) -> AsyncGenerator[LLMStreamChunk, None]: """流式对话""" client = self._get_async_client() system_prompt, converted_messages = self._convert_messages(messages) kwargs = { 'model': config.model, 'messages': converted_messages, 'max_tokens': config.max_tokens, 'temperature': config.temperature, 'top_p': config.top_p, } if system_prompt: kwargs['system'] = system_prompt if config.stop: kwargs['stop_sequences'] = config.stop # 添加 Function Calling 参数 if config.tools: kwargs['tools'] = [t.to_claude_format() for t in config.tools] if config.tool_choice == 'required': kwargs['tool_choice'] = {'type': 'any'} elif config.tool_choice == 'none': del kwargs['tools'] elif config.tool_choice != 'auto': kwargs['tool_choice'] = {'type': 'tool', 'name': config.tool_choice} else: kwargs['tool_choice'] = {'type': 'auto'} # 用于累积工具调用 tool_calls_accumulator = [] # List of {id, name, input_json} current_tool_call = None async with client.messages.stream(**kwargs) as stream: async for event in stream: if hasattr(event, 'type'): if event.type == 'content_block_start': if hasattr(event, 'content_block') and event.content_block.type == 'tool_use': current_tool_call = { 'id': event.content_block.id, 'name': event.content_block.name, 'input_json': '', } elif event.type == 'content_block_delta': if hasattr(event, 'delta'): if event.delta.type == 'text_delta': yield LLMStreamChunk( content=event.delta.text, is_finished=False, ) elif event.delta.type == 'input_json_delta' and current_tool_call: current_tool_call['input_json'] += event.delta.partial_json elif event.type == 'content_block_stop': if current_tool_call: tool_calls_accumulator.append(current_tool_call) current_tool_call = None # 获取最终的 usage 信息 final_message = await stream.get_final_message() # 解析工具调用 tool_calls = None if tool_calls_accumulator: tool_calls = self._parse_accumulated_tool_calls(tool_calls_accumulator) yield LLMStreamChunk( content='', is_finished=True, finish_reason=final_message.stop_reason or '', prompt_tokens=final_message.usage.input_tokens, completion_tokens=final_message.usage.output_tokens, total_tokens=final_message.usage.input_tokens + final_message.usage.output_tokens, tool_calls=tool_calls, ) def _parse_accumulated_tool_calls(self, accumulator: List[Dict]) -> List[ToolCall]: """解析累积的工具调用""" import json result = [] for tc in accumulator: try: arguments = json.loads(tc['input_json']) if tc['input_json'] else {} except json.JSONDecodeError: arguments = {'raw': tc['input_json']} result.append(ToolCall( id=tc['id'], name=tc['name'], arguments=arguments, )) return result def chat_stream_sync( self, messages: List[LLMMessage], config: LLMConfig, ): """同步流式对话""" client = self._get_client() system_prompt, converted_messages = self._convert_messages(messages) kwargs = { 'model': config.model, 'messages': converted_messages, 'max_tokens': config.max_tokens, 'temperature': config.temperature, 'top_p': config.top_p, } if system_prompt: kwargs['system'] = system_prompt if config.stop: kwargs['stop_sequences'] = config.stop # 添加 Function Calling 参数 if config.tools: kwargs['tools'] = [t.to_claude_format() for t in config.tools] if config.tool_choice == 'required': kwargs['tool_choice'] = {'type': 'any'} elif config.tool_choice == 'none': del kwargs['tools'] elif config.tool_choice != 'auto': kwargs['tool_choice'] = {'type': 'tool', 'name': config.tool_choice} else: kwargs['tool_choice'] = {'type': 'auto'} # 用于累积工具调用 tool_calls_accumulator = [] current_tool_call = None with client.messages.stream(**kwargs) as stream: for event in stream: if hasattr(event, 'type'): if event.type == 'content_block_start': if hasattr(event, 'content_block') and event.content_block.type == 'tool_use': current_tool_call = { 'id': event.content_block.id, 'name': event.content_block.name, 'input_json': '', } elif event.type == 'content_block_delta': if hasattr(event, 'delta'): if event.delta.type == 'text_delta': yield LLMStreamChunk( content=event.delta.text, is_finished=False, ) elif event.delta.type == 'input_json_delta' and current_tool_call: current_tool_call['input_json'] += event.delta.partial_json elif event.type == 'content_block_stop': if current_tool_call: tool_calls_accumulator.append(current_tool_call) current_tool_call = None # 获取最终的 usage 信息 final_message = stream.get_final_message() # 解析工具调用 tool_calls = None if tool_calls_accumulator: tool_calls = self._parse_accumulated_tool_calls(tool_calls_accumulator) yield LLMStreamChunk( content='', is_finished=True, finish_reason=final_message.stop_reason or '', prompt_tokens=final_message.usage.input_tokens, completion_tokens=final_message.usage.output_tokens, total_tokens=final_message.usage.input_tokens + final_message.usage.output_tokens, tool_calls=tool_calls, ) async def fetch_models_from_api(self) -> List[Dict[str, Any]]: """通过 Anthropic /v1/models 端点在线拉取模型列表""" base_url = (self.api_base or self.DEFAULT_API_BASE).rstrip('/') try: return await self.fetch_models_from_api_strict() except Exception as e: logger.warning(f'在线拉取 Anthropic 模型列表失败 ({base_url}): {e}') return [] async def fetch_models_from_api_strict(self) -> List[Dict[str, Any]]: """通过 Anthropic 端点严格拉取模型列表，失败时保留上游异常""" import httpx base_url = (self.api_base or self.DEFAULT_API_BASE).rstrip('/') headers = { 'x-api-key': self.api_key, 'anthropic-version': '2023-06-01', } async with httpx.AsyncClient(timeout=15) as client: resp = await client.get(f'{base_url}/v1/models', headers=headers) resp.raise_for_status() data = resp.json() model_list = data.get('data', []) if not model_list: return [] results = [] for item in model_list: model_id = item.get('id', '') display = item.get('display_name', model_id) if not model_id: continue results.append({ 'model_name': model_id, 'display_name': display, 'model_type': 'chat', 'max_tokens': 4096, 'context_window': 200000, 'supports_vision': True, 'supports_function_call': True, 'input_price': 0, 'output_price': 0, }) results.sort(key=lambda m: m['model_name']) logger.info(f'从 Anthropic ({base_url}) 拉取到 {len(results)} 个模型') return results @classmethod def get_default_models(cls) -> List[Dict[str, Any]]: """获取默认模型列表""" return [ # ---- Chat 模型 ---- { 'model_name': 'claude-sonnet-4-20250514', 'display_name': 'Claude Sonnet 4', 'model_type': 'chat', 'max_tokens': 16384, 'context_window': 200000, 'supports_vision': True, 'supports_function_call': True, 'input_price': 0.003, 'output_price': 0.015, }, { 'model_name': 'claude-3-7-sonnet-20250219', 'display_name': 'Claude 3.7 Sonnet', 'model_type': 'chat', 'max_tokens': 16384, 'context_window': 200000, 'supports_vision': True, 'supports_function_call': True, 'input_price': 0.003, 'output_price': 0.015, }, { 'model_name': 'claude-3-5-sonnet-20241022', 'display_name': 'Claude 3.5 Sonnet', 'model_type': 'chat', 'max_tokens': 8192, 'context_window': 200000, 'supports_vision': True, 'supports_function_call': True, 'input_price': 0.003, 'output_price': 0.015, }, { 'model_name': 'claude-3-5-haiku-20241022', 'display_name': 'Claude 3.5 Haiku', 'model_type': 'chat', 'max_tokens': 8192, 'context_window': 200000, 'supports_vision': True, 'supports_function_call': True, 'input_price': 0.001, 'output_price': 0.005, }, { 'model_name': 'claude-3-opus-20240229', 'display_name': 'Claude 3 Opus', 'model_type': 'chat', 'max_tokens': 4096, 'context_window': 200000, 'supports_vision': True, 'supports_function_call': True, 'input_price': 0.015, 'output_price': 0.075, }, { 'model_name': 'claude-3-haiku-20240307', 'display_name': 'Claude 3 Haiku', 'model_type': 'chat', 'max_tokens': 4096, 'context_window': 200000, 'supports_vision': True, 'supports_function_call': True, 'input_price': 0.00025, 'output_price': 0.00125, }, ]