from typing import List from mimetypes import guess_type from anthropic import AsyncAnthropic from anthropic.types import Message from astrbot.core.utils.io import download_image_by_url from astrbot.core.db import BaseDatabase from astrbot.api.provider import Provider, Personality from astrbot import logger from astrbot.core.provider.func_tool_manager import FuncCall from ..register import register_provider_adapter from astrbot.core.message.message_event_result import MessageChain from astrbot.core.provider.entities import LLMResponse, ToolCallsResult from .openai_source import ProviderOpenAIOfficial @register_provider_adapter( "anthropic_chat_completion", "Anthropic Claude API 提供商适配器" ) class ProviderAnthropic(ProviderOpenAIOfficial): def __init__( self, provider_config: dict, provider_settings: dict, db_helper: BaseDatabase, persistant_history=True, default_persona: Personality = None, ) -> None: # Skip OpenAI's __init__ and call Provider's __init__ directly Provider.__init__( self, provider_config, provider_settings, persistant_history, db_helper, default_persona, ) self.chosen_api_key = None self.api_keys: List = provider_config.get("key", []) self.chosen_api_key = self.api_keys[0] if len(self.api_keys) > 0 else None self.base_url = provider_config.get("api_base", "https://api.anthropic.com") self.timeout = provider_config.get("timeout", 120) if isinstance(self.timeout, str): self.timeout = int(self.timeout) self.client = AsyncAnthropic( api_key=self.chosen_api_key, timeout=self.timeout, base_url=self.base_url ) self.set_model(provider_config["model_config"]["model"]) async def _query(self, payloads: dict, tools: FuncCall) -> LLMResponse: if tools: tool_list = tools.get_func_desc_anthropic_style() if tool_list: payloads["tools"] = tool_list completion = await self.client.messages.create(**payloads, stream=False) assert isinstance(completion, Message) logger.debug(f"completion: {completion}") if len(completion.content) == 0: raise Exception("API 返回的 completion 为空。") # TODO: 如果进行函数调用，思维链被截断，用户可能需要思维链的内容 # 选最后一条消息，如果要进行函数调用，anthropic会先返回文本消息的思维链，然后再返回函数调用请求 content = completion.content[-1] llm_response = LLMResponse("assistant") if content.type == "text": # text completion completion_text = str(content.text).strip() # llm_response.completion_text = completion_text llm_response.result_chain = MessageChain().message(completion_text) # Anthropic每次只返回一个函数调用 if completion.stop_reason == "tool_use": # tools call (function calling) args_ls = [] func_name_ls = [] tool_use_ids = [] func_name_ls.append(content.name) args_ls.append(content.input) tool_use_ids.append(content.id) llm_response.role = "tool" llm_response.tools_call_args = args_ls llm_response.tools_call_name = func_name_ls llm_response.tools_call_ids = tool_use_ids if not llm_response.completion_text and not llm_response.tools_call_args: logger.error(f"API 返回的 completion 无法解析：{completion}。") raise Exception(f"API 返回的 completion 无法解析：{completion}。") llm_response.raw_completion = completion return llm_response async def text_chat( self, prompt: str, session_id: str = None, image_urls: List[str] = [], func_tool: FuncCall = None, contexts=[], system_prompt=None, tool_calls_result: ToolCallsResult = None, **kwargs, ) -> LLMResponse: if not prompt: prompt = "" new_record = await self.assemble_context(prompt, image_urls) context_query = [*contexts, new_record] for part in context_query: if "_no_save" in part: del part["_no_save"] if tool_calls_result: # 暂时这样写。 prompt += f"Here are the related results via using tools: {str(tool_calls_result.tool_calls_result)}" model_config = self.provider_config.get("model_config", {}) payloads = {"messages": context_query, **model_config} # Anthropic has a different way of handling system prompts if system_prompt: payloads["system"] = system_prompt llm_response = None try: llm_response = await self._query(payloads, func_tool) except Exception as e: if "maximum context length" in str(e): retry_cnt = 20 while retry_cnt > 0: logger.warning( f"上下文长度超过限制。尝试弹出最早的记录然后重试。当前记录条数: {len(context_query)}" ) try: await self.pop_record(context_query) response = await self.client.messages.create( messages=context_query, **model_config ) llm_response = LLMResponse("assistant") llm_response.result_chain = MessageChain().message(response.content[0].text) llm_response.raw_completion = response return llm_response except Exception as e: if "maximum context length" in str(e): retry_cnt -= 1 else: raise e return LLMResponse("err", "err: 请尝试 /reset 清除会话记录。") else: logger.error(f"发生了错误。Provider 配置如下: {model_config}") raise e return llm_response async def text_chat_stream( self, prompt, session_id=None, image_urls=..., func_tool=None, contexts=..., system_prompt=None, tool_calls_result=None, **kwargs, ): # raise NotImplementedError("This method is not implemented yet.") # 调用 text_chat 模拟流式 llm_response = await self.text_chat( prompt=prompt, session_id=session_id, image_urls=image_urls, func_tool=func_tool, contexts=contexts, system_prompt=system_prompt, tool_calls_result=tool_calls_result, ) llm_response.is_chunk = True yield llm_response llm_response.is_chunk = False yield llm_response async def assemble_context(self, text: str, image_urls: List[str] = None): """组装上下文，支持文本和图片""" if not image_urls: return {"role": "user", "content": text} content = [] content.append({"type": "text", "text": text}) for image_url in image_urls: if image_url.startswith("http"): image_path = await download_image_by_url(image_url) image_data = await self.encode_image_bs64(image_path) elif image_url.startswith("file:///"): image_path = image_url.replace("file:///", "") image_data = await self.encode_image_bs64(image_path) else: image_data = await self.encode_image_bs64(image_url) if not image_data: logger.warning(f"图片 {image_url} 得到的结果为空，将忽略。") continue # Get mime type for the image mime_type, _ = guess_type(image_url) if not mime_type: mime_type = "image/jpeg" # Default to JPEG if can't determine content.append( { "type": "image", "source": { "type": "base64", "media_type": mime_type, "data": image_data.split("base64,")[1] if "base64," in image_data else image_data, }, } ) return {"role": "user", "content": content}