feat: Enhance OpenAI compatibility and introduce unified LLM service data models
- Refactored model fetching logic to support various authentication methods for OpenAI-compatible APIs. - Introduced new data models for LLM service requests and responses to standardize interactions across layers. - Added an adapter base class for unified request execution across different providers. - Implemented utility functions for building OpenAI-compatible client configurations and request overrides.
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DrSmoothl
@@ -1,14 +1,15 @@
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import asyncio
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from dataclasses import dataclass
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from abc import ABC, abstractmethod
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from typing import Callable, Any, Optional
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from dataclasses import dataclass, field
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from typing import Any, Callable, Coroutine, Dict, List, Tuple, Type
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import asyncio
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from src.common.logger import get_logger
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from src.config.config import config_manager
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from src.config.model_configs import ModelInfo, APIProvider
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from ..payload_content.message import Message
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from ..payload_content.resp_format import RespFormat
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from ..payload_content.tool_option import ToolOption, ToolCall
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from src.config.model_configs import APIProvider, ModelInfo
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from src.llm_models.payload_content.message import Message
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from src.llm_models.payload_content.resp_format import RespFormat
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from src.llm_models.payload_content.tool_option import ToolCall, ToolOption
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logger = get_logger("model_client_registry")
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@@ -47,10 +48,10 @@ class APIResponse:
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reasoning_content: str | None = None
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"""推理内容"""
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tool_calls: list[ToolCall] | None = None
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tool_calls: List[ToolCall] | None = None
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"""工具调用 [(工具名称, 工具参数), ...]"""
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embedding: list[float] | None = None
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embedding: List[float] | None = None
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"""嵌入向量"""
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usage: UsageRecord | None = None
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@@ -60,6 +61,82 @@ class APIResponse:
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"""响应原始数据"""
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UsageTuple = Tuple[int, int, int]
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"""统一的使用量三元组类型，顺序为 `(prompt_tokens, completion_tokens, total_tokens)`。"""
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StreamResponseHandler = Callable[
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[Any, asyncio.Event | None],
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Coroutine[Any, Any, Tuple["APIResponse", UsageTuple | None]],
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]
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"""统一的流式响应处理函数类型。"""
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ResponseParser = Callable[[Any], Tuple["APIResponse", UsageTuple | None]]
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"""统一的非流式响应解析函数类型。"""
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@dataclass(slots=True)
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class ResponseRequest:
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"""统一的文本/多模态响应请求。"""
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model_info: ModelInfo
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message_list: List[Message]
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tool_options: List[ToolOption] | None = None
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max_tokens: int | None = None
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temperature: float | None = None
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response_format: RespFormat | None = None
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stream_response_handler: StreamResponseHandler | None = None
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async_response_parser: ResponseParser | None = None
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interrupt_flag: asyncio.Event | None = None
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extra_params: Dict[str, Any] = field(default_factory=dict)
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def copy_with(self, **changes: Any) -> "ResponseRequest":
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"""基于当前请求创建一个带局部变更的新请求。
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Args:
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**changes: 需要覆盖的字段值。
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Returns:
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ResponseRequest: 复制后的请求对象。
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"""
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payload = {
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"model_info": self.model_info,
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"message_list": list(self.message_list),
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"tool_options": None if self.tool_options is None else list(self.tool_options),
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"max_tokens": self.max_tokens,
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"temperature": self.temperature,
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"response_format": self.response_format,
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"stream_response_handler": self.stream_response_handler,
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"async_response_parser": self.async_response_parser,
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"interrupt_flag": self.interrupt_flag,
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"extra_params": dict(self.extra_params),
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}
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payload.update(changes)
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return ResponseRequest(**payload)
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@dataclass(slots=True)
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class EmbeddingRequest:
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"""统一的嵌入请求。"""
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model_info: ModelInfo
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embedding_input: str
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extra_params: Dict[str, Any] = field(default_factory=dict)
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@dataclass(slots=True)
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class AudioTranscriptionRequest:
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"""统一的音频转录请求。"""
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model_info: ModelInfo
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audio_base64: str
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max_tokens: int | None = None
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extra_params: Dict[str, Any] = field(default_factory=dict)
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ClientRequest = ResponseRequest | EmbeddingRequest | AudioTranscriptionRequest
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"""统一客户端请求类型。"""
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class BaseClient(ABC):
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"""
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基础客户端
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@@ -67,97 +144,82 @@ class BaseClient(ABC):
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api_provider: APIProvider
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def __init__(self, api_provider: APIProvider):
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def __init__(self, api_provider: APIProvider) -> None:
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"""初始化基础客户端。
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Args:
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api_provider: API 提供商配置。
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"""
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self.api_provider = api_provider
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@abstractmethod
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async def get_response(
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self,
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model_info: ModelInfo,
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message_list: list[Message],
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tool_options: list[ToolOption] | None = None,
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max_tokens: Optional[int] = None,
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temperature: Optional[float] = None,
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response_format: RespFormat | None = None,
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stream_response_handler: Optional[
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Callable[[Any, asyncio.Event | None], tuple[APIResponse, tuple[int, int, int]]]
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] = None,
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async_response_parser: Callable[[Any], tuple[APIResponse, tuple[int, int, int]]] | None = None,
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interrupt_flag: asyncio.Event | None = None,
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extra_params: dict[str, Any] | None = None,
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) -> APIResponse:
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"""
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获取对话响应
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:param model_info: 模型信息
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:param message_list: 对话体
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:param tool_options: 工具选项（可选，默认为None）
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:param max_tokens: 最大token数（可选，默认为1024）
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:param temperature: 温度（可选，默认为0.7）
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:param response_format: 响应格式（可选，默认为 NotGiven ）
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:param stream_response_handler: 流式响应处理函数（可选）
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:param async_response_parser: 响应解析函数（可选）
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:param interrupt_flag: 中断信号量（可选，默认为None）
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:return: (响应文本, 推理文本, 工具调用, 其他数据)
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async def get_response(self, request: ResponseRequest) -> APIResponse:
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"""获取对话响应。
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Args:
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request: 统一响应请求对象。
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Returns:
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APIResponse: 统一响应对象。
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"""
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raise NotImplementedError("'get_response' method should be overridden in subclasses")
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@abstractmethod
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async def get_embedding(
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self,
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model_info: ModelInfo,
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embedding_input: str,
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extra_params: dict[str, Any] | None = None,
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) -> APIResponse:
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"""
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获取文本嵌入
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:param model_info: 模型信息
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:param embedding_input: 嵌入输入文本
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:return: 嵌入响应
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async def get_embedding(self, request: EmbeddingRequest) -> APIResponse:
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"""获取文本嵌入。
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Args:
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request: 统一嵌入请求对象。
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Returns:
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APIResponse: 嵌入响应。
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"""
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raise NotImplementedError("'get_embedding' method should be overridden in subclasses")
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@abstractmethod
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async def get_audio_transcriptions(
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self,
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model_info: ModelInfo,
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audio_base64: str,
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max_tokens: Optional[int] = None,
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extra_params: dict[str, Any] | None = None,
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) -> APIResponse:
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"""
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获取音频转录
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:param model_info: 模型信息
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:param audio_base64: base64编码的音频数据
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:extra_params: 附加的请求参数
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:return: 音频转录响应
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async def get_audio_transcriptions(self, request: AudioTranscriptionRequest) -> APIResponse:
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"""获取音频转录。
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Args:
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request: 统一音频转录请求对象。
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Returns:
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APIResponse: 音频转录响应。
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"""
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raise NotImplementedError("'get_audio_transcriptions' method should be overridden in subclasses")
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@abstractmethod
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def get_support_image_formats(self) -> list[str]:
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"""
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获取支持的图片格式
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:return: 支持的图片格式列表
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def get_support_image_formats(self) -> List[str]:
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"""获取支持的图片格式。
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Returns:
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List[str]: 支持的图片格式列表。
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"""
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raise NotImplementedError("'get_support_image_formats' method should be overridden in subclasses")
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class ClientRegistry:
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"""客户端注册表。"""
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def __init__(self) -> None:
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self.client_registry: dict[str, type[BaseClient]] = {}
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"""初始化注册表并绑定配置重载回调。"""
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self.client_registry: Dict[str, Type[BaseClient]] = {}
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"""APIProvider.type -> BaseClient的映射表"""
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self.client_instance_cache: dict[str, BaseClient] = {}
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self.client_instance_cache: Dict[str, BaseClient] = {}
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"""APIProvider.name -> BaseClient的映射表"""
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config_manager.register_reload_callback(self.clear_client_instance_cache)
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def register_client_class(self, client_type: str):
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"""
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注册API客户端类
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def register_client_class(self, client_type: str) -> Callable[[Type[BaseClient]], Type[BaseClient]]:
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"""注册 API 客户端类。
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Args:
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client_class: API客户端类
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client_type: 客户端类型标识。
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Returns:
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Callable[[Type[BaseClient]], Type[BaseClient]]: 装饰器函数。
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"""
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def decorator(cls: type[BaseClient]) -> type[BaseClient]:
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def decorator(cls: Type[BaseClient]) -> Type[BaseClient]:
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if not issubclass(cls, BaseClient):
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raise TypeError(f"{cls.__name__} is not a subclass of BaseClient")
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self.client_registry[client_type] = cls
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@@ -165,14 +227,15 @@ class ClientRegistry:
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return decorator
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def get_client_class_instance(self, api_provider: APIProvider, force_new=False) -> BaseClient:
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"""
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获取注册的API客户端实例
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def get_client_class_instance(self, api_provider: APIProvider, force_new: bool = False) -> BaseClient:
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"""获取注册的 API 客户端实例。
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Args:
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api_provider: APIProvider实例
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force_new: 是否强制创建新实例（用于解决事件循环问题）
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api_provider: APIProvider 实例。
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force_new: 是否强制创建新实例。
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Returns:
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BaseClient: 注册的API客户端实例
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BaseClient: 注册的 API 客户端实例。
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"""
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from . import ensure_client_type_loaded
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@@ -194,6 +257,7 @@ class ClientRegistry:
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return self.client_instance_cache[api_provider.name]
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def clear_client_instance_cache(self) -> None:
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"""清空客户端实例缓存。"""
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self.client_instance_cache.clear()
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logger.info("检测到配置重载，已清空LLM客户端实例缓存")
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