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正确使用lpmm构建prompt

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UnCLAS-Prommer
2025-08-03 19:52:31 +08:00
parent 9a63a8030e
commit 1e5db5d7e1
12 changed files with 141 additions and 249 deletions
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55
src/plugin_system/apis/llm_api.py
View File

@@ -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

4
src/plugin_system/base/base_plugin.py
View File

@@ -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

17
src/plugin_system/core/tool_use.py
View File

@@ -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:
@@ -158,7 +159,7 @@ 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 = {
@@ -191,7 +192,7 @@ 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
@@ -207,7 +208,7 @@ 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
@@ -294,7 +295,7 @@ 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:
@@ -314,7 +315,7 @@ 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 = {
@@ -405,7 +406,7 @@ 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"}
)