173 lines
6.6 KiB
Python
173 lines
6.6 KiB
Python
from src.plugins.models.utils_model import LLM_request
|
||
from src.plugins.config.config import global_config
|
||
from src.plugins.chat.chat_stream import ChatStream
|
||
from src.common.database import db
|
||
import time
|
||
import json
|
||
from src.common.logger import get_module_logger, TOOL_USE_STYLE_CONFIG, LogConfig
|
||
from src.do_tool.tool_can_use import get_all_tool_definitions, get_tool_instance
|
||
|
||
tool_use_config = LogConfig(
|
||
# 使用消息发送专用样式
|
||
console_format=TOOL_USE_STYLE_CONFIG["console_format"],
|
||
file_format=TOOL_USE_STYLE_CONFIG["file_format"],
|
||
)
|
||
logger = get_module_logger("tool_use", config=tool_use_config)
|
||
|
||
|
||
class ToolUser:
|
||
def __init__(self):
|
||
self.llm_model_tool = LLM_request(
|
||
model=global_config.llm_heartflow, temperature=0.2, max_tokens=1000, request_type="tool_use"
|
||
)
|
||
|
||
async def _build_tool_prompt(self, message_txt: str, sender_name: str, chat_stream: ChatStream):
|
||
"""构建工具使用的提示词
|
||
|
||
Args:
|
||
message_txt: 用户消息文本
|
||
sender_name: 发送者名称
|
||
chat_stream: 聊天流对象
|
||
|
||
Returns:
|
||
str: 构建好的提示词
|
||
"""
|
||
new_messages = list(
|
||
db.messages.find({"chat_id": chat_stream.stream_id, "time": {"$gt": time.time()}}).sort("time", 1).limit(15)
|
||
)
|
||
new_messages_str = ""
|
||
for msg in new_messages:
|
||
if "detailed_plain_text" in msg:
|
||
new_messages_str += f"{msg['detailed_plain_text']}"
|
||
|
||
# 这些信息应该从调用者传入，而不是从self获取
|
||
bot_name = global_config.BOT_NICKNAME
|
||
prompt = ""
|
||
prompt += "你正在思考如何回复群里的消息。\n"
|
||
prompt += f"你注意到{sender_name}刚刚说：{message_txt}\n"
|
||
prompt += f"注意你就是{bot_name}，{bot_name}指的就是你。"
|
||
|
||
prompt += "你现在需要对群里的聊天内容进行回复，现在请你思考，你是否需要额外的信息，或者一些工具来帮你回复，不要使用危险功能（比如文件操作或者系统操作爬虫），比如回忆或者搜寻已有的知识，或者了解你现在正在做什么，请输出你需要的工具，或者你需要的额外信息。"
|
||
return prompt
|
||
|
||
def _define_tools(self):
|
||
"""获取所有已注册工具的定义
|
||
|
||
Returns:
|
||
list: 工具定义列表
|
||
"""
|
||
return get_all_tool_definitions()
|
||
|
||
async def _execute_tool_call(self, tool_call, message_txt: str):
|
||
"""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"])
|
||
|
||
# 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, message_txt)
|
||
if result:
|
||
return {
|
||
"tool_call_id": tool_call["id"],
|
||
"role": "tool",
|
||
"name": function_name,
|
||
"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
|
||
|
||
async def use_tool(self, message_txt: str, sender_name: str, chat_stream: ChatStream):
|
||
"""使用工具辅助思考，判断是否需要额外信息
|
||
|
||
Args:
|
||
message_txt: 用户消息文本
|
||
sender_name: 发送者名称
|
||
chat_stream: 聊天流对象
|
||
|
||
Returns:
|
||
dict: 工具使用结果
|
||
"""
|
||
try:
|
||
# 构建提示词
|
||
prompt = await self._build_tool_prompt(message_txt, sender_name, chat_stream)
|
||
|
||
# 定义可用工具
|
||
tools = self._define_tools()
|
||
|
||
# 使用llm_model_tool发送带工具定义的请求
|
||
payload = {
|
||
"model": self.llm_model_tool.model_name,
|
||
"messages": [{"role": "user", "content": prompt}],
|
||
"max_tokens": global_config.max_response_length,
|
||
"tools": tools,
|
||
"temperature": 0.2,
|
||
}
|
||
|
||
logger.debug(f"发送工具调用请求，模型: {self.llm_model_tool.model_name}")
|
||
# 发送请求获取模型是否需要调用工具
|
||
response = await self.llm_model_tool._execute_request(
|
||
endpoint="/chat/completions", payload=payload, prompt=prompt
|
||
)
|
||
|
||
# 根据返回值数量判断是否有工具调用
|
||
if len(response) == 3:
|
||
content, reasoning_content, tool_calls = response
|
||
logger.info(f"工具思考: {tool_calls}")
|
||
|
||
# 检查响应中工具调用是否有效
|
||
if not tool_calls:
|
||
logger.info("模型返回了空的tool_calls列表")
|
||
return {"used_tools": False}
|
||
|
||
logger.info(f"模型请求调用{len(tool_calls)}个工具")
|
||
tool_results = []
|
||
collected_info = ""
|
||
|
||
# 执行所有工具调用
|
||
for tool_call in tool_calls:
|
||
result = await self._execute_tool_call(tool_call, message_txt)
|
||
if result:
|
||
tool_results.append(result)
|
||
# 将工具结果添加到收集的信息中
|
||
collected_info += f"\n{result['name']}返回结果: {result['content']}\n"
|
||
|
||
# 如果有工具结果，直接返回收集的信息
|
||
if collected_info:
|
||
logger.info(f"工具调用收集到信息: {collected_info}")
|
||
return {
|
||
"used_tools": True,
|
||
"collected_info": collected_info,
|
||
}
|
||
else:
|
||
# 没有工具调用
|
||
content, reasoning_content = response
|
||
logger.info("模型没有请求调用任何工具")
|
||
|
||
# 如果没有工具调用或处理失败，直接返回原始思考
|
||
return {
|
||
"used_tools": False,
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"工具调用过程中出错: {str(e)}")
|
||
return {
|
||
"used_tools": False,
|
||
"error": str(e),
|
||
}
|