重构整个插件系统，尝试恢复可启动性，新增插件系统maibot-plugin-sdk依赖

src/services/llm_service.py

@@ -0,0 +1,155 @@
+"""LLM 服务模块
+
+提供与 LLM 模型交互的核心功能。
+"""
+
+from typing import Any, Callable, Dict, List, Optional, Tuple
+
+from src.common.logger import get_logger
+from src.config.config import config_manager
+from src.config.model_configs import TaskConfig
+from src.llm_models.model_client.base_client import BaseClient
+from src.llm_models.payload_content.message import Message
+from src.llm_models.payload_content.tool_option import ToolCall
+from src.llm_models.utils_model import LLMRequest
+
+logger = get_logger("llm_service")
+
+
+def get_available_models() -> Dict[str, TaskConfig]:
+    """获取所有可用的模型配置
+
+    Returns:
+        Dict[str, Any]: 模型配置字典，key为模型名称，value为模型配置
+    """
+    try:
+        models = config_manager.get_model_config().model_task_config
+        attrs = dir(models)
+        rets: Dict[str, TaskConfig] = {}
+        for attr in attrs:
+            if not attr.startswith("__"):
+                try:
+                    value = getattr(models, attr)
+                    if not callable(value) and isinstance(value, TaskConfig):
+                        rets[attr] = value
+                except Exception as e:
+                    logger.debug(f"[LLMService] 获取属性 {attr} 失败: {e}")
+                    continue
+        return rets
+
+    except Exception as e:
+        logger.error(f"[LLMService] 获取可用模型失败: {e}")
+        return {}
+
+
+async def generate_with_model(
+    prompt: str,
+    model_config: TaskConfig,
+    request_type: str = "plugin.generate",
+    temperature: Optional[float] = None,
+    max_tokens: Optional[int] = None,
+) -> Tuple[bool, str, str, str]:
+    """使用指定模型生成内容
+
+    Args:
+        prompt: 提示词
+        model_config: 模型配置（从 get_available_models 获取的模型配置）
+        request_type: 请求类型标识
+
+    Returns:
+        Tuple[bool, str, str, str]: (是否成功, 生成的内容, 推理过程, 模型名称)
+    """
+    try:
+        logger.debug(f"[LLMService] 完整提示词: {prompt}")
+
+        llm_request = LLMRequest(model_set=model_config, request_type=request_type)
+
+        response, (reasoning_content, model_name, _) = await llm_request.generate_response_async(
+            prompt, temperature=temperature, max_tokens=max_tokens
+        )
+        return True, response, reasoning_content, model_name
+
+    except Exception as e:
+        error_msg = f"生成内容时出错: {str(e)}"
+        logger.error(f"[LLMService] {error_msg}")
+        return False, error_msg, "", ""
+
+
+async def generate_with_model_with_tools(
+    prompt: str,
+    model_config: TaskConfig,
+    tool_options: List[Dict[str, Any]] | None = None,
+    request_type: str = "plugin.generate",
+    temperature: Optional[float] = None,
+    max_tokens: Optional[int] = None,
+) -> Tuple[bool, str, str, str, List[ToolCall] | None]:
+    """使用指定模型和工具生成内容
+
+    Args:
+        prompt: 提示词
+        model_config: 模型配置（从 get_available_models 获取的模型配置）
+        tool_options: 工具选项列表
+        request_type: 请求类型标识
+        temperature: 温度参数
+        max_tokens: 最大token数
+
+    Returns:
+        Tuple[bool, str, str, str, List[ToolCall] | None]: (是否成功, 生成的内容, 推理过程, 模型名称, 工具调用列表)
+    """
+    try:
+        model_name_list = model_config.model_list
+        logger.info(f"使用模型{model_name_list}生成内容")
+        logger.debug(f"完整提示词: {prompt}")
+
+        llm_request = LLMRequest(model_set=model_config, request_type=request_type)
+
+        response, (reasoning_content, model_name, tool_call) = await llm_request.generate_response_async(
+            prompt, tools=tool_options, temperature=temperature, max_tokens=max_tokens
+        )
+        return True, response, reasoning_content, model_name, tool_call
+
+    except Exception as e:
+        error_msg = f"生成内容时出错: {str(e)}"
+        logger.error(f"[LLMService] {error_msg}")
+        return False, error_msg, "", "", None
+
+
+async def generate_with_model_with_tools_by_message_factory(
+    message_factory: Callable[[BaseClient], List[Message]],
+    model_config: TaskConfig,
+    tool_options: List[Dict[str, Any]] | None = None,
+    request_type: str = "plugin.generate",
+    temperature: Optional[float] = None,
+    max_tokens: Optional[int] = None,
+) -> Tuple[bool, str, str, str, List[ToolCall] | None]:
+    """使用指定模型和工具生成内容（通过消息工厂构建消息列表）
+
+    Args:
+        message_factory: 消息工厂函数
+        model_config: 模型配置
+        tool_options: 工具选项列表
+        request_type: 请求类型标识
+        temperature: 温度参数
+        max_tokens: 最大token数
+
+    Returns:
+        Tuple[bool, str, str, str, List[ToolCall] | None]: (是否成功, 生成的内容, 推理过程, 模型名称, 工具调用列表)
+    """
+    try:
+        model_name_list = model_config.model_list
+        logger.info(f"使用模型 {model_name_list} 生成内容")
+
+        llm_request = LLMRequest(model_set=model_config, request_type=request_type)
+
+        response, (reasoning_content, model_name, tool_call) = await llm_request.generate_response_with_message_async(
+            message_factory=message_factory,
+            tools=tool_options,
+            temperature=temperature,
+            max_tokens=max_tokens,
+        )
+        return True, response, reasoning_content, model_name, tool_call
+
+    except Exception as e:
+        error_msg = f"生成内容时出错: {str(e)}"
+        logger.error(f"[LLMService] {error_msg}")
+        return False, error_msg, "", "", None