Merge branch 'dev' of https://github.com/Windpicker-owo/MaiBot into dev

src/plugin_system/apis/llm_api.py

@@ -7,8 +7,9 @@
     success, response, reasoning, model_name = await llm_api.generate_with_model(prompt, model_config)
 """
 
-from typing import Tuple, Dict
+from typing import Tuple, Dict, List, Any, Optional
 from src.common.logger import get_logger
+from src.llm_models.payload_content.tool_option import ToolCall
 from src.llm_models.utils_model import LLMRequest
 from src.config.config import global_config, model_config
 from src.config.api_ada_configs import TaskConfig
@@ -52,7 +53,11 @@ def get_available_models() -> Dict[str, TaskConfig]:
 
 
 async def generate_with_model(
-    prompt: str, model_config: TaskConfig, request_type: str = "plugin.generate", **kwargs
+    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
 
@@ -60,7 +65,6 @@ async def generate_with_model(
         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
-        **kwargs: 其他模型特定参数，如temperature、max_tokens等
 
     Returns:
         Tuple[bool, str, str, str]: (是否成功, 生成的内容, 推理过程, 模型名称)
@@ -70,12 +74,53 @@ async def generate_with_model(
         logger.info(f"[LLMAPI] 使用模型集合 {model_name_list} 生成内容")
         logger.debug(f"[LLMAPI] 完整提示词: {prompt}")
 
-        llm_request = LLMRequest(model_set=model_config, request_type=request_type, **kwargs)
+        llm_request = LLMRequest(model_set=model_config, request_type=request_type)
 
-        response, (reasoning_content, model_name, _) = await llm_request.generate_response_async(prompt)
+        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"[LLMAPI] {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]: (是否成功, 生成的内容, 推理过程, 模型名称)
+    """
+    try:
+        model_name_list = model_config.model_list
+        logger.info(f"[LLMAPI] 使用模型集合 {model_name_list} 生成内容")
+        logger.debug(f"[LLMAPI] 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"[LLMAPI] {error_msg}")
+        return False, error_msg, "", "", None

src/plugin_system/base/base_plugin.py

@@ -3,10 +3,11 @@ from typing import List, Type, Tuple, Union
 from .plugin_base import PluginBase
 
 from src.common.logger import get_logger
-from src.plugin_system.base.component_types import ActionInfo, CommandInfo, EventHandlerInfo
+from src.plugin_system.base.component_types import ActionInfo, CommandInfo, EventHandlerInfo, ToolInfo
 from .base_action import BaseAction
 from .base_command import BaseCommand
 from .base_events_handler import BaseEventHandler
+from .base_tool import BaseTool
 
 logger = get_logger("base_plugin")
 
@@ -31,6 +32,7 @@ class BasePlugin(PluginBase):
             Tuple[ActionInfo, Type[BaseAction]],
             Tuple[CommandInfo, Type[BaseCommand]],
             Tuple[EventHandlerInfo, Type[BaseEventHandler]],
+            Tuple[ToolInfo, Type[BaseTool]],
         ]
     ]:
         """获取插件包含的组件列表

src/plugin_system/core/tool_use.py

@@ -1,6 +1,7 @@
 import time
 from typing import List, Dict, Tuple, Optional, Any
 from src.plugin_system.apis.tool_api import get_llm_available_tool_definitions, get_tool_instance
+from src.plugin_system.base.base_tool import BaseTool
 from src.plugin_system.core.global_announcement_manager import global_announcement_manager
 from src.llm_models.utils_model import LLMRequest
 from src.llm_models.payload_content import ToolCall
@@ -114,7 +115,7 @@ class ToolExecutor:
         )
 
         # 执行工具调用
-        tool_results, used_tools = await self._execute_tool_calls(tool_calls)
+        tool_results, used_tools = await self.execute_tool_calls(tool_calls)
 
         # 缓存结果
         if tool_results:
@@ -133,7 +134,7 @@ class ToolExecutor:
         user_disabled_tools = global_announcement_manager.get_disabled_chat_tools(self.chat_id)
         return [definition for name, definition in all_tools if name not in user_disabled_tools]
 
-    async def _execute_tool_calls(self, tool_calls: Optional[List[ToolCall]]) -> Tuple[List[Dict[str, Any]], List[str]]:
+    async def execute_tool_calls(self, tool_calls: Optional[List[ToolCall]]) -> Tuple[List[Dict[str, Any]], List[str]]:
         """执行工具调用
 
         Args:
@@ -161,7 +162,7 @@ class ToolExecutor:
                 logger.debug(f"{self.log_prefix}执行工具: {tool_name}")
 
                 # 执行工具
-                result = await self._execute_tool_call(tool_call)
+                result = await self.execute_tool_call(tool_call)
 
                 if result:
                     tool_info = {
@@ -194,7 +195,7 @@ class ToolExecutor:
 
         return tool_results, used_tools
 
-    async def _execute_tool_call(self, tool_call: ToolCall) -> Optional[Dict[str, Any]]:
+    async def execute_tool_call(self, tool_call: ToolCall, tool_instance: Optional[BaseTool] = None) -> Optional[Dict[str, Any]]:
         # sourcery skip: use-assigned-variable
         """执行单个工具调用
 
@@ -210,7 +211,7 @@ class ToolExecutor:
             function_args["llm_called"] = True  # 标记为LLM调用
 
             # 获取对应工具实例
-            tool_instance = get_tool_instance(function_name)
+            tool_instance = tool_instance or get_tool_instance(function_name)
             if not tool_instance:
                 logger.warning(f"未知工具名称: {function_name}")
                 return None
@@ -297,7 +298,7 @@ class ToolExecutor:
         if expired_keys:
             logger.debug(f"{self.log_prefix}清理了{len(expired_keys)}个过期缓存")
 
-    async def execute_specific_tool(self, tool_name: str, tool_args: Dict) -> Optional[Dict]:
+    async def execute_specific_tool_simple(self, tool_name: str, tool_args: Dict) -> Optional[Dict]:
         """直接执行指定工具
 
         Args:
@@ -317,7 +318,7 @@ class ToolExecutor:
 
             logger.info(f"{self.log_prefix}直接执行工具: {tool_name}")
 
-            result = await self._execute_tool_call(tool_call)
+            result = await self.execute_tool_call(tool_call)
 
             if result:
                 tool_info = {
@@ -408,7 +409,7 @@ results, used_tools, prompt = await executor.execute_from_chat_message(
 )
 
 # 5. 直接执行特定工具
-result = await executor.execute_specific_tool(
+result = await executor.execute_specific_tool_simple(
     tool_name="get_knowledge",
     tool_args={"query": "机器学习"}
 )