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tools整合彻底完成

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UnCLAS-Prommer
2025-07-28 23:57:55 +08:00
parent 8bf7166aa4
commit af27d0dbf0
13 changed files with 189 additions and 601 deletions
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133
src/plugin_system/core/tool_use.py
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@@ -1,7 +1,8 @@
import json
import time
from typing import List, Dict, Tuple, Optional
from src.plugin_system.apis.tool_api import get_llm_available_tool_definitions,get_tool_instance
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.core.global_announcement_manager import global_announcement_manager
from src.llm_models.utils_model import LLMRequest
from src.config.config import global_config
from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
@@ -11,6 +12,7 @@ from src.common.logger import get_logger
logger = get_logger("tool_use")
def init_tool_executor_prompt():
"""初始化工具执行器的提示词"""
tool_executor_prompt = """
@@ -27,9 +29,11 @@ If you need to use a tool, please directly call the corresponding tool function.
"""
Prompt(tool_executor_prompt, "tool_executor_prompt")
# 初始化提示词
init_tool_executor_prompt()
class ToolExecutor:
"""独立的工具执行器组件
@@ -53,9 +57,6 @@ class ToolExecutor:
request_type="tool_executor",
)
# 初始化工具实例
self.tool_instance = ToolUser()
# 缓存配置
self.enable_cache = enable_cache
self.cache_ttl = cache_ttl
@@ -75,7 +76,7 @@ class ToolExecutor:
return_details: 是否返回详细信息(使用的工具列表和提示词)
Returns:
如果return_details为False: List[Dict] - 工具执行结果列表
如果return_details为False: Tuple[List[Dict], List[str], str] - (工具执行结果列表, 空, 空)
如果return_details为True: Tuple[List[Dict], List[str], str] - (结果列表, 使用的工具, 提示词)
"""
@@ -84,15 +85,15 @@ class ToolExecutor:
if cached_result := self._get_from_cache(cache_key):
logger.info(f"{self.log_prefix}使用缓存结果，跳过工具执行")
if not return_details:
return cached_result, [], "使用缓存结果"
return cached_result, [], ""
# 从缓存结果中提取工具名称
used_tools = [result.get("tool_name", "unknown") for result in cached_result]
return cached_result, used_tools, "使用缓存结果"
return cached_result, used_tools, ""
# 缓存未命中，执行工具调用
# 获取可用工具
tools = self.tool_instance._define_tools()
tools = self._get_tool_definitions()
# 获取当前时间
time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
@@ -114,6 +115,7 @@ class ToolExecutor:
# 调用LLM进行工具决策
response, other_info = await self.llm_model.generate_response_async(prompt=prompt, tools=tools)
# TODO: 在APIADA加入后完全修复这里！
# 解析LLM响应
if len(other_info) == 3:
reasoning_content, model_name, tool_calls = other_info
@@ -135,6 +137,11 @@ class ToolExecutor:
return tool_results, used_tools, prompt
else:
return tool_results, [], ""
def _get_tool_definitions(self) -> List[Dict[str, Any]]:
all_tools = get_llm_available_tool_definitions()
user_disabled_tools = global_announcement_manager.get_disabled_chat_tools(self.chat_id)
return [parameters for name, parameters in all_tools if name not in user_disabled_tools]
async def _execute_tool_calls(self, tool_calls) -> Tuple[List[Dict], List[str]]:
"""执行工具调用
@@ -174,7 +181,7 @@ class ToolExecutor:
logger.debug(f"{self.log_prefix}执行工具: {tool_name}")
# 执行工具
result = await self.tool_instance.execute_tool_call(tool_call)
result = await self._execute_tool_call(tool_call)
if result:
tool_info = {
@@ -207,6 +214,45 @@ class ToolExecutor:
return tool_results, used_tools
async def _execute_tool_call(self, tool_call: Dict[str, Any]) -> Optional[Dict]:
# sourcery skip: use-assigned-variable
"""执行单个工具调用
Args:
tool_call: 工具调用对象
Returns:
Optional[Dict]: 工具调用结果，如果失败则返回None
"""
try:
function_name = tool_call["function"]["name"]
function_args = json.loads(tool_call["function"]["arguments"])
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)
if not tool_instance:
logger.warning(f"未知工具名称: {function_name}")
return None
# 执行工具
result = await tool_instance.execute(function_args)
if result:
# 直接使用 function_name 作为 tool_type
tool_type = function_name
return {
"tool_call_id": tool_call["id"],
"role": "tool",
"name": function_name,
"type": tool_type,
"content": result["content"],
}
return None
except Exception as e:
logger.error(f"执行工具调用时发生错误: {str(e)}")
return None
def _generate_cache_key(self, target_message: str, chat_history: str, sender: str) -> str:
"""生成缓存键
@@ -274,15 +320,6 @@ class ToolExecutor:
if expired_keys:
logger.debug(f"{self.log_prefix}清理了{len(expired_keys)}个过期缓存")
def get_available_tools(self) -> List[str]:
"""获取可用工具列表
Returns:
List[str]: 可用工具名称列表
"""
tools = self.tool_instance._define_tools()
return [tool.get("function", {}).get("name", "unknown") for tool in tools]
async def execute_specific_tool(
self, tool_name: str, tool_args: Dict, validate_args: bool = True
) -> Optional[Dict]:
@@ -301,7 +338,7 @@ class ToolExecutor:
logger.info(f"{self.log_prefix}直接执行工具: {tool_name}")
result = await self.tool_instance.execute_tool_call(tool_call)
result = await self._execute_tool_call(tool_call)
if result:
tool_info = {
@@ -367,6 +404,7 @@ class ToolExecutor:
self.cache_ttl = cache_ttl
logger.info(f"{self.log_prefix}缓存TTL修改为: {cache_ttl}")
"""
ToolExecutor使用示例：
@@ -397,62 +435,7 @@ result = await executor.execute_specific_tool(
)
# 6. 缓存管理
available_tools = executor.get_available_tools()
cache_status = executor.get_cache_status() # 查看缓存状态
executor.clear_cache() # 清空缓存
executor.set_cache_config(cache_ttl=5) # 动态修改缓存配置
"""
class ToolUser:
@staticmethod
def _define_tools():
"""获取所有已注册工具的定义
Returns:
list: 工具定义列表
"""
return get_llm_available_tool_definitions()
@staticmethod
async def execute_tool_call(tool_call):
# sourcery skip: use-assigned-variable
"""执行特定的工具调用
Args:
tool_call: 工具调用对象
message_txt: 原始消息文本
Returns:
dict: 工具调用结果
"""
try:
function_name = tool_call["function"]["name"]
function_args = json.loads(tool_call["function"]["arguments"])
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)
if not tool_instance:
logger.warning(f"未知工具名称: {function_name}")
return None
# 执行工具
result = await tool_instance.execute(function_args)
if result:
# 直接使用 function_name 作为 tool_type
tool_type = function_name
return {
"tool_call_id": tool_call["id"],
"role": "tool",
"name": function_name,
"type": tool_type,
"content": result["content"],
}
return None
except Exception as e:
logger.error(f"执行工具调用时发生错误: {str(e)}")
return None
tool_user = ToolUser()