mirror of
https://github.com/AstrBotDevs/AstrBot
synced 2026-07-16 01:40:15 +08:00
286 lines
9.7 KiB
Python
286 lines
9.7 KiB
Python
import abc
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import asyncio
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from typing import List
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from typing import AsyncGenerator
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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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ToolCallsResult,
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ProviderType,
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RerankResult,
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)
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from astrbot.core.provider.register import provider_cls_map
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from astrbot.core.db.po import Personality
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from dataclasses import dataclass
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@dataclass
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class ProviderMeta:
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id: str
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model: str
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type: str
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provider_type: ProviderType
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class AbstractProvider(abc.ABC):
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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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"""设置当前使用的模型名称"""
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self.model_name = model_name
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def get_model(self) -> str:
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"""获得当前使用的模型名称"""
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return self.model_name
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def meta(self) -> ProviderMeta:
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"""获取 Provider 的元数据"""
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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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provider_type = meta_data.provider_type if meta_data else None
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return ProviderMeta(
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id=self.provider_config["id"],
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model=self.get_model(),
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type=provider_type_name,
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provider_type=provider_type,
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)
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class Provider(AbstractProvider):
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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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default_persona: Personality | None = None,
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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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self.curr_personality = default_persona
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"""维护了当前的使用的 persona，即人格。可能为 None"""
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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,
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session_id: str = None,
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image_urls: list[str] = None,
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func_tool: ToolSet = None,
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contexts: list = None,
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system_prompt: str = None,
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tool_calls_result: ToolCallsResult | list[ToolCallsResult] = 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
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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: Function-calling 工具
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contexts: 上下文
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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,
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session_id: str = None,
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image_urls: list[str] = None,
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func_tool: ToolSet = None,
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contexts: list = None,
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system_prompt: str = None,
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tool_calls_result: ToolCallsResult | list[ToolCallsResult] = 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
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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: Function-calling 工具
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contexts: 上下文
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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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"""
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弹出 context 第一条非系统提示词对话记录
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"""
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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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else:
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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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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: {str(e)}"
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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, query: str, documents: list[str], 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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