Soulter
5a11d8f0ee
* 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>
297 lines
10 KiB
Python
297 lines
10 KiB
Python
import abc
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import asyncio
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from collections.abc import AsyncGenerator
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from astrbot.core.agent.message import Message
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from astrbot.core.agent.tool import ToolSet
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from astrbot.core.provider.entities import (
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LLMResponse,
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ProviderMeta,
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RerankResult,
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ToolCallsResult,
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)
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from astrbot.core.provider.register import provider_cls_map
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class AbstractProvider(abc.ABC):
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"""Provider Abstract Class"""
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def __init__(self, provider_config: dict) -> None:
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super().__init__()
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self.model_name = ""
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self.provider_config = provider_config
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def set_model(self, model_name: str):
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"""Set the current model name"""
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self.model_name = model_name
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def get_model(self) -> str:
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"""Get the current model name"""
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return self.model_name
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def meta(self) -> ProviderMeta:
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"""Get the provider metadata"""
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provider_type_name = self.provider_config["type"]
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meta_data = provider_cls_map.get(provider_type_name)
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if not meta_data:
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raise ValueError(f"Provider type {provider_type_name} not registered")
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meta = ProviderMeta(
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id=self.provider_config.get("id", "default"),
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model=self.get_model(),
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type=provider_type_name,
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provider_type=meta_data.provider_type,
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)
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return meta
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class Provider(AbstractProvider):
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"""Chat Provider"""
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def __init__(
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self,
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provider_config: dict,
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provider_settings: dict,
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) -> None:
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super().__init__(provider_config)
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self.provider_settings = provider_settings
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@abc.abstractmethod
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def get_current_key(self) -> str:
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raise NotImplementedError
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def get_keys(self) -> list[str]:
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"""获得提供商 Key"""
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keys = self.provider_config.get("key", [""])
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return keys or [""]
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@abc.abstractmethod
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def set_key(self, key: str):
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raise NotImplementedError
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@abc.abstractmethod
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async def get_models(self) -> list[str]:
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"""获得支持的模型列表"""
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raise NotImplementedError
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@abc.abstractmethod
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async def text_chat(
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self,
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prompt: str | None = None,
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session_id: str | None = None,
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image_urls: list[str] | None = None,
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func_tool: ToolSet | None = None,
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contexts: list[Message] | list[dict] | None = None,
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system_prompt: str | None = None,
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tool_calls_result: ToolCallsResult | list[ToolCallsResult] | None = None,
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model: str | None = None,
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**kwargs,
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) -> LLMResponse:
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"""获得 LLM 的文本对话结果。会使用当前的模型进行对话。
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Args:
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prompt: 提示词，和 contexts 二选一使用，如果都指定，则会将 prompt（以及可能的 image_urls） 作为最新的一条记录添加到 contexts 中
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session_id: 会话 ID(此属性已经被废弃)
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image_urls: 图片 URL 列表
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tools: tool set
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contexts: 上下文，和 prompt 二选一使用
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tool_calls_result: 回传给 LLM 的工具调用结果。参考: https://platform.openai.com/docs/guides/function-calling
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kwargs: 其他参数
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Notes:
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- 如果传入了 image_urls，将会在对话时附上图片。如果模型不支持图片输入，将会抛出错误。
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- 如果传入了 tools，将会使用 tools 进行 Function-calling。如果模型不支持 Function-calling，将会抛出错误。
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"""
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...
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async def text_chat_stream(
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self,
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prompt: str | None = None,
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session_id: str | None = None,
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image_urls: list[str] | None = None,
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func_tool: ToolSet | None = None,
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contexts: list[Message] | list[dict] | None = None,
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system_prompt: str | None = None,
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tool_calls_result: ToolCallsResult | list[ToolCallsResult] | None = None,
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model: str | None = None,
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**kwargs,
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) -> AsyncGenerator[LLMResponse, None]:
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"""获得 LLM 的流式文本对话结果。会使用当前的模型进行对话。在生成的最后会返回一次完整的结果。
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Args:
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prompt: 提示词，和 contexts 二选一使用，如果都指定，则会将 prompt（以及可能的 image_urls） 作为最新的一条记录添加到 contexts 中
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session_id: 会话 ID(此属性已经被废弃)
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image_urls: 图片 URL 列表
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tools: tool set
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contexts: 上下文，和 prompt 二选一使用
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tool_calls_result: 回传给 LLM 的工具调用结果。参考: https://platform.openai.com/docs/guides/function-calling
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kwargs: 其他参数
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Notes:
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- 如果传入了 image_urls，将会在对话时附上图片。如果模型不支持图片输入，将会抛出错误。
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- 如果传入了 tools，将会使用 tools 进行 Function-calling。如果模型不支持 Function-calling，将会抛出错误。
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"""
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...
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async def pop_record(self, context: list):
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"""弹出 context 第一条非系统提示词对话记录"""
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poped = 0
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indexs_to_pop = []
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for idx, record in enumerate(context):
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if record["role"] == "system":
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continue
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indexs_to_pop.append(idx)
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poped += 1
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if poped == 2:
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break
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for idx in reversed(indexs_to_pop):
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context.pop(idx)
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def _ensure_message_to_dicts(
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self,
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messages: list[dict] | list[Message] | None,
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) -> list[dict]:
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"""Convert a list of Message objects to a list of dictionaries."""
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if not messages:
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return []
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dicts: list[dict] = []
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for message in messages:
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if isinstance(message, Message):
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dicts.append(message.model_dump())
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else:
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dicts.append(message)
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return dicts
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class STTProvider(AbstractProvider):
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def __init__(self, provider_config: dict, provider_settings: dict) -> None:
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super().__init__(provider_config)
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self.provider_config = provider_config
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self.provider_settings = provider_settings
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@abc.abstractmethod
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async def get_text(self, audio_url: str) -> str:
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"""获取音频的文本"""
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raise NotImplementedError
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class TTSProvider(AbstractProvider):
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def __init__(self, provider_config: dict, provider_settings: dict) -> None:
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super().__init__(provider_config)
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self.provider_config = provider_config
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self.provider_settings = provider_settings
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@abc.abstractmethod
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async def get_audio(self, text: str) -> str:
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"""获取文本的音频，返回音频文件路径"""
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raise NotImplementedError
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class EmbeddingProvider(AbstractProvider):
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def __init__(self, provider_config: dict, provider_settings: dict) -> None:
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super().__init__(provider_config)
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self.provider_config = provider_config
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self.provider_settings = provider_settings
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@abc.abstractmethod
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async def get_embedding(self, text: str) -> list[float]:
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"""获取文本的向量"""
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...
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@abc.abstractmethod
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async def get_embeddings(self, text: list[str]) -> list[list[float]]:
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"""批量获取文本的向量"""
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...
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@abc.abstractmethod
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def get_dim(self) -> int:
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"""获取向量的维度"""
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...
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async def get_embeddings_batch(
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self,
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texts: list[str],
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batch_size: int = 16,
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tasks_limit: int = 3,
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max_retries: int = 3,
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progress_callback=None,
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) -> list[list[float]]:
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"""批量获取文本的向量，分批处理以节省内存
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Args:
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texts: 文本列表
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batch_size: 每批处理的文本数量
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tasks_limit: 并发任务数量限制
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max_retries: 失败时的最大重试次数
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progress_callback: 进度回调函数，接收参数 (current, total)
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Returns:
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向量列表
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"""
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semaphore = asyncio.Semaphore(tasks_limit)
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all_embeddings: list[list[float]] = []
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failed_batches: list[tuple[int, list[str]]] = []
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completed_count = 0
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total_count = len(texts)
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async def process_batch(batch_idx: int, batch_texts: list[str]):
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nonlocal completed_count
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async with semaphore:
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for attempt in range(max_retries):
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try:
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batch_embeddings = await self.get_embeddings(batch_texts)
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all_embeddings.extend(batch_embeddings)
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completed_count += len(batch_texts)
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if progress_callback:
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await progress_callback(completed_count, total_count)
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return
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except Exception as e:
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if attempt == max_retries - 1:
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# 最后一次重试失败，记录失败的批次
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failed_batches.append((batch_idx, batch_texts))
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raise Exception(
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f"批次 {batch_idx} 处理失败，已重试 {max_retries} 次: {e!s}",
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)
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# 等待一段时间后重试，使用指数退避
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await asyncio.sleep(2**attempt)
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tasks = []
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for i in range(0, len(texts), batch_size):
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batch_texts = texts[i : i + batch_size]
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batch_idx = i // batch_size
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tasks.append(process_batch(batch_idx, batch_texts))
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# 收集所有任务的结果，包括失败的任务
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results = await asyncio.gather(*tasks, return_exceptions=True)
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# 检查是否有失败的任务
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errors = [r for r in results if isinstance(r, Exception)]
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if errors:
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error_msg = (
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f"有 {len(errors)} 个批次处理失败: {'; '.join(str(e) for e in errors)}"
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)
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raise Exception(error_msg)
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return all_embeddings
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class RerankProvider(AbstractProvider):
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def __init__(self, provider_config: dict, provider_settings: dict) -> None:
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super().__init__(provider_config)
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self.provider_config = provider_config
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self.provider_settings = provider_settings
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@abc.abstractmethod
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async def rerank(
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self,
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query: str,
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documents: list[str],
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top_n: int | None = None,
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) -> list[RerankResult]:
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"""获取查询和文档的重排序分数"""
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...
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