正确使用lpmm构建prompt

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