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.

src/common/data_models/llm_service_data_models.py

@@ -0,0 +1,187 @@
+"""LLM 服务层与编排层共享数据模型。
+
+该模块集中定义 LLM 服务层与底层编排器共同使用的请求、选项与结果对象，
+用于替代散落在各层之间的复杂元组返回值。
+"""
+
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, TypeAlias
+
+import asyncio
+
+from src.common.data_models import BaseDataModel
+from src.llm_models.payload_content.resp_format import RespFormat
+from src.llm_models.payload_content.tool_option import ToolCall, ToolDefinitionInput
+
+if TYPE_CHECKING:
+    from src.llm_models.model_client.base_client import BaseClient
+    from src.llm_models.payload_content.message import Message
+
+
+PromptMessage: TypeAlias = Dict[str, Any]
+"""统一的原始提示消息结构。"""
+
+PromptInput: TypeAlias = str | List[PromptMessage]
+"""统一的提示输入类型。"""
+
+MessageFactory: TypeAlias = Callable[["BaseClient"], List["Message"]]
+"""统一的消息工厂类型。"""
+
+
+@dataclass(slots=True)
+class LLMServiceRequest(BaseDataModel):
+    """LLM 服务层统一请求对象。"""
+
+    task_name: str
+    request_type: str
+    prompt: PromptInput | None = None
+    message_factory: MessageFactory | None = None
+    tool_options: List[ToolDefinitionInput] | None = None
+    temperature: float | None = None
+    max_tokens: int | None = None
+    response_format: RespFormat | None = None
+    interrupt_flag: asyncio.Event | None = None
+
+    def __post_init__(self) -> None:
+        """校验请求对象的必要字段。
+
+        Raises:
+            ValueError: 当 `task_name` 为空，或 `prompt` 与 `message_factory`
+                的组合非法时抛出。
+        """
+        self.task_name = self.task_name.strip()
+        if not self.task_name:
+            raise ValueError("`task_name` 不能为空")
+        has_prompt = self.prompt is not None
+        has_message_factory = self.message_factory is not None
+        if has_prompt == has_message_factory:
+            raise ValueError("`prompt` 与 `message_factory` 必须且只能提供一个")
+
+
+@dataclass(slots=True)
+class LLMResponseResult(BaseDataModel):
+    """单次 LLM 响应结果。"""
+
+    response: str = field(default_factory=str)
+    reasoning: str = field(default_factory=str)
+    model_name: str = field(default_factory=str)
+    tool_calls: List[ToolCall] | None = None
+
+
+@dataclass(slots=True)
+class LLMServiceResult(BaseDataModel):
+    """LLM 服务层统一响应对象。"""
+
+    success: bool = False
+    completion: LLMResponseResult = field(default_factory=LLMResponseResult)
+    error: str | None = None
+
+    @classmethod
+    def from_response_result(cls, completion: LLMResponseResult) -> "LLMServiceResult":
+        """从单次 LLM 响应结果构建服务响应。
+
+        Args:
+            completion: 单次 LLM 响应结果。
+
+        Returns:
+            LLMServiceResult: 标记为成功的服务响应对象。
+        """
+        return cls(
+            success=True,
+            completion=completion,
+            error=None,
+        )
+
+    @classmethod
+    def from_error(cls, error_message: str, error_detail: str | None = None) -> "LLMServiceResult":
+        """构建失败的服务响应对象。
+
+        Args:
+            error_message: 对上层展示的错误消息。
+            error_detail: 底层错误详情。
+
+        Returns:
+            LLMServiceResult: 标记为失败的服务响应对象。
+        """
+        return cls(
+            success=False,
+            completion=LLMResponseResult(response=error_message),
+            error=error_detail or error_message,
+        )
+
+    def to_capability_payload(self) -> Dict[str, Any]:
+        """转换为插件能力层可直接返回的结构。
+
+        Returns:
+            Dict[str, Any]: 标准化后的能力返回值。
+        """
+        payload: Dict[str, Any] = {
+            "success": self.success,
+            "response": self.completion.response,
+            "reasoning": self.completion.reasoning,
+            "model_name": self.completion.model_name,
+        }
+        if self.completion.tool_calls is not None:
+            payload["tool_calls"] = [
+                {
+                    "id": tool_call.call_id,
+                    "function": {
+                        "name": tool_call.func_name,
+                        "arguments": tool_call.args or {},
+                    },
+                }
+                for tool_call in self.completion.tool_calls
+            ]
+        if self.error:
+            payload["error"] = self.error
+        return payload
+
+
+@dataclass(slots=True)
+class LLMGenerationOptions(BaseDataModel):
+    """LLM 文本生成选项。"""
+
+    temperature: float | None = None
+    max_tokens: int | None = None
+    tool_options: List[ToolDefinitionInput] | None = None
+    response_format: RespFormat | None = None
+    interrupt_flag: asyncio.Event | None = None
+    raise_when_empty: bool = True
+
+
+@dataclass(slots=True)
+class LLMImageOptions(BaseDataModel):
+    """LLM 图像理解选项。"""
+
+    temperature: float | None = None
+    max_tokens: int | None = None
+    interrupt_flag: asyncio.Event | None = None
+
+
+@dataclass(slots=True)
+class LLMAudioTranscriptionResult(BaseDataModel):
+    """LLM 音频转写结果。"""
+
+    text: str | None = None
+
+
+@dataclass(slots=True)
+class LLMEmbeddingResult(BaseDataModel):
+    """LLM 向量生成结果。"""
+
+    embedding: List[float] = field(default_factory=list)
+    model_name: str = field(default_factory=str)
+
+
+__all__ = [
+    "LLMAudioTranscriptionResult",
+    "LLMEmbeddingResult",
+    "LLMGenerationOptions",
+    "LLMImageOptions",
+    "LLMResponseResult",
+    "LLMServiceRequest",
+    "LLMServiceResult",
+    "MessageFactory",
+    "PromptInput",
+    "PromptMessage",
+]