AstrBot/astrbot/core/provider/provider.py

import abc
import asyncio
from collections.abc import AsyncGenerator
from dataclasses import dataclass

from astrbot.core.agent.message import Message
from astrbot.core.agent.tool import ToolSet
from astrbot.core.db.po import Personality
from astrbot.core.provider.entities import (
    LLMResponse,
    ProviderType,
    RerankResult,
    ToolCallsResult,
)
from astrbot.core.provider.register import provider_cls_map


@dataclass
class ProviderMeta:
    id: str
    model: str
    type: str
    provider_type: ProviderType


class AbstractProvider(abc.ABC):
    """Provider Abstract Class"""

    def __init__(self, provider_config: dict) -> None:
        super().__init__()
        self.model_name = ""
        self.provider_config = provider_config

    def set_model(self, model_name: str):
        """Set the current model name"""
        self.model_name = model_name

    def get_model(self) -> str:
        """Get the current model name"""
        return self.model_name

    def meta(self) -> ProviderMeta:
        """Get the provider metadata"""
        provider_type_name = self.provider_config["type"]
        meta_data = provider_cls_map.get(provider_type_name)
        provider_type = meta_data.provider_type if meta_data else None
        if provider_type is None:
            raise ValueError(f"Cannot find provider type: {provider_type_name}")
        return ProviderMeta(
            id=self.provider_config["id"],
            model=self.get_model(),
            type=provider_type_name,
            provider_type=provider_type,
        )


class Provider(AbstractProvider):
    """Chat Provider"""

    def __init__(
        self,
        provider_config: dict,
        provider_settings: dict,
        default_persona: Personality | None = None,
    ) -> None:
        super().__init__(provider_config)

        self.provider_settings = provider_settings

        self.curr_personality = default_persona
        """维护了当前的使用的 persona，即人格。可能为 None"""

    @abc.abstractmethod
    def get_current_key(self) -> str:
        raise NotImplementedError

    def get_keys(self) -> list[str]:
        """获得提供商 Key"""
        keys = self.provider_config.get("key", [""])
        return keys or [""]

    @abc.abstractmethod
    def set_key(self, key: str):
        raise NotImplementedError

    @abc.abstractmethod
    async def get_models(self) -> list[str]:
        """获得支持的模型列表"""
        raise NotImplementedError

    @abc.abstractmethod
    async def text_chat(
        self,
        prompt: str | None = None,
        session_id: str | None = None,
        image_urls: list[str] | None = None,
        func_tool: ToolSet | None = None,
        contexts: list[Message] | list[dict] | None = None,
        system_prompt: str | None = None,
        tool_calls_result: ToolCallsResult | list[ToolCallsResult] | None = None,
        model: str | None = None,
        **kwargs,
    ) -> LLMResponse:
        """获得 LLM 的文本对话结果。会使用当前的模型进行对话。

        Args:
            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
            session_id: 会话 ID(此属性已经被废弃)
            image_urls: 图片 URL 列表
            tools: tool set
            contexts: 上下文，和 prompt 二选一使用
            tool_calls_result: 回传给 LLM 的工具调用结果。参考: https://platform.openai.com/docs/guides/function-calling
            kwargs: 其他参数

        Notes:
            - 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。
            - 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。

        """
        ...

    async def text_chat_stream(
        self,
        prompt: str | None = None,
        session_id: str | None = None,
        image_urls: list[str] | None = None,
        func_tool: ToolSet | None = None,
        contexts: list[Message] | list[dict] | None = None,
        system_prompt: str | None = None,
        tool_calls_result: ToolCallsResult | list[ToolCallsResult] | None = None,
        model: str | None = None,
        **kwargs,
    ) -> AsyncGenerator[LLMResponse, None]:
        """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。

        Args:
            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
            session_id: 会话 ID(此属性已经被废弃)
            image_urls: 图片 URL 列表
            tools: tool set
            contexts: 上下文，和 prompt 二选一使用
            tool_calls_result: 回传给 LLM 的工具调用结果。参考: https://platform.openai.com/docs/guides/function-calling
            kwargs: 其他参数

        Notes:
            - 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。
            - 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。

        """
        ...

    async def pop_record(self, context: list):
        """弹出 context 第一条非系统提示词对话记录"""
        poped = 0
        indexs_to_pop = []
        for idx, record in enumerate(context):
            if record["role"] == "system":
                continue
            indexs_to_pop.append(idx)
            poped += 1
            if poped == 2:
                break

        for idx in reversed(indexs_to_pop):
            context.pop(idx)

    def _ensure_message_to_dicts(
        self,
        messages: list[dict] | list[Message] | None,
    ) -> list[dict]:
        """Convert a list of Message objects to a list of dictionaries."""
        if not messages:
            return []
        dicts: list[dict] = []
        for message in messages:
            if isinstance(message, Message):
                dicts.append(message.model_dump())
            else:
                dicts.append(message)

        return dicts


class STTProvider(AbstractProvider):
    def __init__(self, provider_config: dict, provider_settings: dict) -> None:
        super().__init__(provider_config)
        self.provider_config = provider_config
        self.provider_settings = provider_settings

    @abc.abstractmethod
    async def get_text(self, audio_url: str) -> str:
        """获取音频的文本"""
        raise NotImplementedError


class TTSProvider(AbstractProvider):
    def __init__(self, provider_config: dict, provider_settings: dict) -> None:
        super().__init__(provider_config)
        self.provider_config = provider_config
        self.provider_settings = provider_settings

    @abc.abstractmethod
    async def get_audio(self, text: str) -> str:
        """获取文本的音频，返回音频文件路径"""
        raise NotImplementedError


class EmbeddingProvider(AbstractProvider):
    def __init__(self, provider_config: dict, provider_settings: dict) -> None:
        super().__init__(provider_config)
        self.provider_config = provider_config
        self.provider_settings = provider_settings

    @abc.abstractmethod
    async def get_embedding(self, text: str) -> list[float]:
        """获取文本的向量"""
        ...

    @abc.abstractmethod
    async def get_embeddings(self, text: list[str]) -> list[list[float]]:
        """批量获取文本的向量"""
        ...

    @abc.abstractmethod
    def get_dim(self) -> int:
        """获取向量的维度"""
        ...

    async def get_embeddings_batch(
        self,
        texts: list[str],
        batch_size: int = 16,
        tasks_limit: int = 3,
        max_retries: int = 3,
        progress_callback=None,
    ) -> list[list[float]]:
        """批量获取文本的向量，分批处理以节省内存

        Args:
            texts: 文本列表
            batch_size: 每批处理的文本数量
            tasks_limit: 并发任务数量限制
            max_retries: 失败时的最大重试次数
            progress_callback: 进度回调函数，接收参数 (current, total)

        Returns:
            向量列表

        """
        semaphore = asyncio.Semaphore(tasks_limit)
        all_embeddings: list[list[float]] = []
        failed_batches: list[tuple[int, list[str]]] = []
        completed_count = 0
        total_count = len(texts)

        async def process_batch(batch_idx: int, batch_texts: list[str]):
            nonlocal completed_count
            async with semaphore:
                for attempt in range(max_retries):
                    try:
                        batch_embeddings = await self.get_embeddings(batch_texts)
                        all_embeddings.extend(batch_embeddings)
                        completed_count += len(batch_texts)
                        if progress_callback:
                            await progress_callback(completed_count, total_count)
                        return
                    except Exception as e:
                        if attempt == max_retries - 1:
                            # 最后一次重试失败，记录失败的批次
                            failed_batches.append((batch_idx, batch_texts))
                            raise Exception(
                                f"批次 {batch_idx} 处理失败，已重试 {max_retries} 次: {e!s}",
                            )
                        # 等待一段时间后重试，使用指数退避
                        await asyncio.sleep(2**attempt)

        tasks = []
        for i in range(0, len(texts), batch_size):
            batch_texts = texts[i : i + batch_size]
            batch_idx = i // batch_size
            tasks.append(process_batch(batch_idx, batch_texts))

        # 收集所有任务的结果，包括失败的任务
        results = await asyncio.gather(*tasks, return_exceptions=True)

        # 检查是否有失败的任务
        errors = [r for r in results if isinstance(r, Exception)]
        if errors:
            error_msg = (
                f"有 {len(errors)} 个批次处理失败: {'; '.join(str(e) for e in errors)}"
            )
            raise Exception(error_msg)

        return all_embeddings


class RerankProvider(AbstractProvider):
    def __init__(self, provider_config: dict, provider_settings: dict) -> None:
        super().__init__(provider_config)
        self.provider_config = provider_config
        self.provider_settings = provider_settings

    @abc.abstractmethod
    async def rerank(
        self,
        query: str,
        documents: list[str],
        top_n: int | None = None,
    ) -> list[RerankResult]:
        """获取查询和文档的重排序分数"""
        ...