6849af2bad
* refactor: LLM response handling with reasoning content - Added a `show_reasoning` parameter to `run_agent` to control the display of reasoning content. - Updated `LLMResponse` to include a `reasoning_content` field for storing reasoning text. - Modified `WebChatMessageEvent` to handle and send reasoning content in streaming responses. - Implemented reasoning extraction in various provider sources (e.g., OpenAI, Gemini). - Updated the chat interface to display reasoning content in a collapsible format. - Removed the deprecated `thinking_filter` package and its associated logic. - Updated localization files to include new reasoning-related strings. * feat: add Groq chat completion provider and associated configurations * Update astrbot/core/provider/sources/gemini_source.py Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com> --------- Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>
413 lines
15 KiB
Python
413 lines
15 KiB
Python
import base64
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import json
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from collections.abc import AsyncGenerator
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from mimetypes import guess_type
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import anthropic
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from anthropic import AsyncAnthropic
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from anthropic.types import Message
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from astrbot import logger
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from astrbot.api.provider import Provider
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from astrbot.core.provider.entities import LLMResponse
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from astrbot.core.provider.func_tool_manager import ToolSet
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from astrbot.core.utils.io import download_image_by_url
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from ..register import register_provider_adapter
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@register_provider_adapter(
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"anthropic_chat_completion",
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"Anthropic Claude API 提供商适配器",
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)
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class ProviderAnthropic(Provider):
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def __init__(
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self,
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provider_config,
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provider_settings,
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) -> None:
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super().__init__(
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provider_config,
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provider_settings,
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)
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self.chosen_api_key: str = ""
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self.api_keys: list = super().get_keys()
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self.chosen_api_key = self.api_keys[0] if len(self.api_keys) > 0 else ""
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self.base_url = provider_config.get("api_base", "https://api.anthropic.com")
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self.timeout = provider_config.get("timeout", 120)
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if isinstance(self.timeout, str):
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self.timeout = int(self.timeout)
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self.client = AsyncAnthropic(
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api_key=self.chosen_api_key,
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timeout=self.timeout,
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base_url=self.base_url,
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)
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self.set_model(provider_config["model_config"]["model"])
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def _prepare_payload(self, messages: list[dict]):
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"""准备 Anthropic API 的请求 payload
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Args:
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messages: OpenAI 格式的消息列表，包含用户输入和系统提示等信息
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Returns:
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system_prompt: 系统提示内容
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new_messages: 处理后的消息列表，去除系统提示
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"""
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system_prompt = ""
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new_messages = []
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for message in messages:
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if message["role"] == "system":
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system_prompt = message["content"]
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elif message["role"] == "assistant":
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blocks = []
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if isinstance(message["content"], str):
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blocks.append({"type": "text", "text": message["content"]})
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if "tool_calls" in message:
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for tool_call in message["tool_calls"]:
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blocks.append( # noqa: PERF401
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{
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"type": "tool_use",
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"name": tool_call["function"]["name"],
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"input": (
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json.loads(tool_call["function"]["arguments"])
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if isinstance(
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tool_call["function"]["arguments"],
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str,
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)
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else tool_call["function"]["arguments"]
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),
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"id": tool_call["id"],
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},
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)
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new_messages.append(
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{
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"role": "assistant",
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"content": blocks,
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},
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)
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elif message["role"] == "tool":
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new_messages.append(
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{
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"role": "user",
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"content": [
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{
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"type": "tool_result",
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"tool_use_id": message["tool_call_id"],
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"content": message["content"],
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},
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],
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},
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)
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else:
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new_messages.append(message)
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return system_prompt, new_messages
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async def _query(self, payloads: dict, tools: ToolSet | None) -> LLMResponse:
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if tools:
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if tool_list := tools.get_func_desc_anthropic_style():
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payloads["tools"] = tool_list
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completion = await self.client.messages.create(**payloads, stream=False)
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assert isinstance(completion, Message)
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logger.debug(f"completion: {completion}")
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if len(completion.content) == 0:
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raise Exception("API 返回的 completion 为空。")
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llm_response = LLMResponse(role="assistant")
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for content_block in completion.content:
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if content_block.type == "text":
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completion_text = str(content_block.text).strip()
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llm_response.completion_text = completion_text
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if content_block.type == "tool_use":
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llm_response.tools_call_args.append(content_block.input)
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llm_response.tools_call_name.append(content_block.name)
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llm_response.tools_call_ids.append(content_block.id)
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# TODO(Soulter): 处理 end_turn 情况
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if not llm_response.completion_text and not llm_response.tools_call_args:
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raise Exception(f"Anthropic API 返回的 completion 无法解析：{completion}。")
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return llm_response
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async def _query_stream(
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self,
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payloads: dict,
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tools: ToolSet | None,
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) -> AsyncGenerator[LLMResponse, None]:
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if tools:
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if tool_list := tools.get_func_desc_anthropic_style():
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payloads["tools"] = tool_list
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# 用于累积工具调用信息
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tool_use_buffer = {}
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# 用于累积最终结果
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final_text = ""
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final_tool_calls = []
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async with self.client.messages.stream(**payloads) as stream:
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assert isinstance(stream, anthropic.AsyncMessageStream)
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async for event in stream:
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if event.type == "content_block_start":
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if event.content_block.type == "text":
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# 文本块开始
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yield LLMResponse(
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role="assistant",
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completion_text="",
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is_chunk=True,
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)
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elif event.content_block.type == "tool_use":
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# 工具使用块开始，初始化缓冲区
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tool_use_buffer[event.index] = {
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"id": event.content_block.id,
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"name": event.content_block.name,
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"input": {},
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}
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elif event.type == "content_block_delta":
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if event.delta.type == "text_delta":
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# 文本增量
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final_text += event.delta.text
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yield LLMResponse(
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role="assistant",
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completion_text=event.delta.text,
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is_chunk=True,
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)
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elif event.delta.type == "input_json_delta":
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# 工具调用参数增量
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if event.index in tool_use_buffer:
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# 累积 JSON 输入
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if "input_json" not in tool_use_buffer[event.index]:
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tool_use_buffer[event.index]["input_json"] = ""
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tool_use_buffer[event.index]["input_json"] += (
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event.delta.partial_json
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)
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elif event.type == "content_block_stop":
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# 内容块结束
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if event.index in tool_use_buffer:
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# 解析完整的工具调用
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tool_info = tool_use_buffer[event.index]
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try:
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if "input_json" in tool_info:
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tool_info["input"] = json.loads(tool_info["input_json"])
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# 添加到最终结果
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final_tool_calls.append(
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{
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"id": tool_info["id"],
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"name": tool_info["name"],
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"input": tool_info["input"],
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},
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)
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yield LLMResponse(
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role="tool",
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completion_text="",
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tools_call_args=[tool_info["input"]],
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tools_call_name=[tool_info["name"]],
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tools_call_ids=[tool_info["id"]],
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is_chunk=True,
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)
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except json.JSONDecodeError:
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# JSON 解析失败，跳过这个工具调用
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logger.warning(f"工具调用参数 JSON 解析失败: {tool_info}")
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# 清理缓冲区
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del tool_use_buffer[event.index]
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# 返回最终的完整结果
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final_response = LLMResponse(
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role="assistant",
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completion_text=final_text,
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is_chunk=False,
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)
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if final_tool_calls:
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final_response.tools_call_args = [
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call["input"] for call in final_tool_calls
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]
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final_response.tools_call_name = [call["name"] for call in final_tool_calls]
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final_response.tools_call_ids = [call["id"] for call in final_tool_calls]
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yield final_response
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async def text_chat(
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self,
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prompt=None,
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session_id=None,
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image_urls=None,
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func_tool=None,
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contexts=None,
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system_prompt=None,
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tool_calls_result=None,
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model=None,
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**kwargs,
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) -> LLMResponse:
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if contexts is None:
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contexts = []
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new_record = None
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if prompt is not None:
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new_record = await self.assemble_context(prompt, image_urls)
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context_query = self._ensure_message_to_dicts(contexts)
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if new_record:
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context_query.append(new_record)
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if system_prompt:
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context_query.insert(0, {"role": "system", "content": system_prompt})
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for part in context_query:
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if "_no_save" in part:
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del part["_no_save"]
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# tool calls result
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if tool_calls_result:
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if not isinstance(tool_calls_result, list):
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context_query.extend(tool_calls_result.to_openai_messages())
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else:
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for tcr in tool_calls_result:
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context_query.extend(tcr.to_openai_messages())
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system_prompt, new_messages = self._prepare_payload(context_query)
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model_config = self.provider_config.get("model_config", {})
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model_config["model"] = model or self.get_model()
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payloads = {"messages": new_messages, **model_config}
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# Anthropic has a different way of handling system prompts
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if system_prompt:
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payloads["system"] = system_prompt
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llm_response = None
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try:
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llm_response = await self._query(payloads, func_tool)
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except Exception as e:
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logger.error(f"发生了错误。Provider 配置如下: {model_config}")
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raise e
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return llm_response
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async def text_chat_stream(
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self,
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prompt,
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session_id=None,
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image_urls=...,
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func_tool=None,
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contexts=...,
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system_prompt=None,
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tool_calls_result=None,
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model=None,
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**kwargs,
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):
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if contexts is None:
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contexts = []
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new_record = None
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if prompt is not None:
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new_record = await self.assemble_context(prompt, image_urls)
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context_query = self._ensure_message_to_dicts(contexts)
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if new_record:
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context_query.append(new_record)
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if system_prompt:
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context_query.insert(0, {"role": "system", "content": system_prompt})
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for part in context_query:
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if "_no_save" in part:
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del part["_no_save"]
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# tool calls result
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if tool_calls_result:
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if not isinstance(tool_calls_result, list):
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context_query.extend(tool_calls_result.to_openai_messages())
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else:
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for tcr in tool_calls_result:
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context_query.extend(tcr.to_openai_messages())
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system_prompt, new_messages = self._prepare_payload(context_query)
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model_config = self.provider_config.get("model_config", {})
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model_config["model"] = model or self.get_model()
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payloads = {"messages": new_messages, **model_config}
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# Anthropic has a different way of handling system prompts
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if system_prompt:
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payloads["system"] = system_prompt
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async for llm_response in self._query_stream(payloads, func_tool):
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yield llm_response
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async def assemble_context(self, text: str, image_urls: list[str] | None = None):
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"""组装上下文，支持文本和图片"""
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if not image_urls:
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return {"role": "user", "content": text}
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content = []
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content.append({"type": "text", "text": text})
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for image_url in image_urls:
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if image_url.startswith("http"):
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image_path = await download_image_by_url(image_url)
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image_data = await self.encode_image_bs64(image_path)
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elif image_url.startswith("file:///"):
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image_path = image_url.replace("file:///", "")
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image_data = await self.encode_image_bs64(image_path)
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else:
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image_data = await self.encode_image_bs64(image_url)
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if not image_data:
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logger.warning(f"图片 {image_url} 得到的结果为空，将忽略。")
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continue
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# Get mime type for the image
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mime_type, _ = guess_type(image_url)
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if not mime_type:
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mime_type = "image/jpeg" # Default to JPEG if can't determine
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content.append(
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{
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"type": "image",
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"source": {
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"type": "base64",
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"media_type": mime_type,
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"data": (
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image_data.split("base64,")[1]
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if "base64," in image_data
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else image_data
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),
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},
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},
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)
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return {"role": "user", "content": content}
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async def encode_image_bs64(self, image_url: str) -> str:
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"""将图片转换为 base64"""
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if image_url.startswith("base64://"):
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return image_url.replace("base64://", "data:image/jpeg;base64,")
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with open(image_url, "rb") as f:
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image_bs64 = base64.b64encode(f.read()).decode("utf-8")
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return "data:image/jpeg;base64," + image_bs64
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return ""
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def get_current_key(self) -> str:
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return self.chosen_api_key
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async def get_models(self) -> list[str]:
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models_str = []
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models = await self.client.models.list()
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models = sorted(models.data, key=lambda x: x.id)
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for model in models:
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models_str.append(model.id)
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return models_str
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def set_key(self, key: str):
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self.chosen_api_key = key
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