2b07c9e81b
- 新增LPMM知识库模块，包括实体提取、RDF构建、Embedding存储、KG管理等功能 - 新增`lpmm_get_knowledge`工具，支持从LPMM知识库中检索相关信息 - 新增OpenIE数据处理模块，支持信息提取、数据导入等功能 - 新增知识库初始化脚本，支持从原始数据到知识库的完整处理流程 - 新增配置文件`lpmm_config.toml`，支持自定义知识库相关参数 - 新增日志模块`LPMM_STYLE_CONFIG`，支持知识库相关日志输出 - 新增`raw_data_preprocessor.py`、`info_extraction.py`、`import_openie.py`等脚本，支持知识库数据预处理
198 lines
7.9 KiB
Python
198 lines
7.9 KiB
Python
from src.plugins.models.utils_model import LLMRequest
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from src.config.config import global_config
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import json
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from src.common.logger import get_module_logger, TOOL_USE_STYLE_CONFIG, LogConfig
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from src.do_tool.tool_can_use import get_all_tool_definitions, get_tool_instance
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import traceback
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from src.plugins.person_info.relationship_manager import relationship_manager
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from src.plugins.chat.utils import parse_text_timestamps
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from src.plugins.chat.chat_stream import ChatStream
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from src.heart_flow.observation import ChattingObservation
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tool_use_config = LogConfig(
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# 使用消息发送专用样式
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console_format=TOOL_USE_STYLE_CONFIG["console_format"],
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file_format=TOOL_USE_STYLE_CONFIG["file_format"],
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)
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logger = get_module_logger("tool_use", config=tool_use_config)
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class ToolUser:
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def __init__(self):
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self.llm_model_tool = LLMRequest(
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model=global_config.llm_tool_use, temperature=0.2, max_tokens=1000, request_type="tool_use"
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)
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@staticmethod
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async def _build_tool_prompt(
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message_txt: str, chat_stream: ChatStream = None, observation: ChattingObservation = None
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):
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"""构建工具使用的提示词
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Args:
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message_txt: 用户消息文本
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subheartflow: 子心流对象
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Returns:
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str: 构建好的提示词
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"""
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if observation:
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mid_memory_info = observation.mid_memory_info
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# print(f"intol111111111111111111111111111111111222222222222mid_memory_info：{mid_memory_info}")
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# 这些信息应该从调用者传入，而不是从self获取
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bot_name = global_config.BOT_NICKNAME
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prompt = ""
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prompt += mid_memory_info
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prompt += "你正在思考如何回复群里的消息。\n"
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prompt += "之前群里进行了如下讨论:\n"
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prompt += message_txt
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# prompt += f"你注意到{sender_name}刚刚说：{message_txt}\n"
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prompt += f"注意你就是{bot_name}，{bot_name}是你的名字。根据之前的聊天记录补充问题信息，搜索时避开你的名字。\n"
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prompt += "必须调用 'lpmm_get_knowledge' 工具来获取知识。\n"
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prompt += "你现在需要对群里的聊天内容进行回复，现在选择工具来对消息和你的回复进行处理，你是否需要额外的信息，比如回忆或者搜寻已有的知识，改变关系和情感，或者了解你现在正在做什么。"
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prompt = await relationship_manager.convert_all_person_sign_to_person_name(prompt)
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prompt = parse_text_timestamps(prompt, mode="lite")
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return prompt
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@staticmethod
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def _define_tools():
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"""获取所有已注册工具的定义
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Returns:
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list: 工具定义列表
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"""
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return get_all_tool_definitions()
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@staticmethod
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async def _execute_tool_call(tool_call, message_txt: str):
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"""执行特定的工具调用
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Args:
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tool_call: 工具调用对象
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message_txt: 原始消息文本
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Returns:
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dict: 工具调用结果
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"""
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try:
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function_name = tool_call["function"]["name"]
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function_args = json.loads(tool_call["function"]["arguments"])
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# 获取对应工具实例
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tool_instance = get_tool_instance(function_name)
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if not tool_instance:
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logger.warning(f"未知工具名称: {function_name}")
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return None
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# 执行工具
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result = await tool_instance.execute(function_args, message_txt)
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if result:
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# 直接使用 function_name 作为 tool_type
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tool_type = function_name
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return {
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"tool_call_id": tool_call["id"],
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"role": "tool",
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"name": function_name,
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"type": tool_type,
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"content": result["content"],
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}
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return None
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except Exception as e:
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logger.error(f"执行工具调用时发生错误: {str(e)}")
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return None
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async def use_tool(self, message_txt: str, chat_stream: ChatStream = None, observation: ChattingObservation = None):
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"""使用工具辅助思考，判断是否需要额外信息
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Args:
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message_txt: 用户消息文本
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sender_name: 发送者名称
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chat_stream: 聊天流对象
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observation: 观察对象（可选）
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Returns:
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dict: 工具使用结果，包含结构化的信息
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"""
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try:
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# 构建提示词
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prompt = await self._build_tool_prompt(
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message_txt=message_txt,
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chat_stream=chat_stream,
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observation=observation,
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)
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# 定义可用工具
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tools = self._define_tools()
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logger.trace(f"工具定义: {tools}")
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# 使用llm_model_tool发送带工具定义的请求
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payload = {
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"model": self.llm_model_tool.model_name,
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"messages": [{"role": "user", "content": prompt}],
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"max_tokens": global_config.max_response_length,
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"tools": tools,
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"temperature": 0.2,
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}
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logger.trace(f"发送工具调用请求，模型: {self.llm_model_tool.model_name}")
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# 发送请求获取模型是否需要调用工具
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response = await self.llm_model_tool._execute_request(
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endpoint="/chat/completions", payload=payload, prompt=prompt
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)
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# 根据返回值数量判断是否有工具调用
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if len(response) == 3:
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content, reasoning_content, tool_calls = response
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# logger.info(f"工具思考: {tool_calls}")
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# logger.debug(f"工具思考: {content}")
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# 检查响应中工具调用是否有效
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if not tool_calls:
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logger.debug("模型返回了空的tool_calls列表")
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return {"used_tools": False}
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tool_calls_str = ""
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for tool_call in tool_calls:
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tool_calls_str += f"{tool_call['function']['name']}\n"
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logger.info(f"根据:\n{prompt}\n模型请求调用{len(tool_calls)}个工具: {tool_calls_str}")
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tool_results = []
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structured_info = {} # 动态生成键
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# 执行所有工具调用
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for tool_call in tool_calls:
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result = await self._execute_tool_call(tool_call, message_txt)
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if result:
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tool_results.append(result)
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# 使用工具名称作为键
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tool_name = result["name"]
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if tool_name not in structured_info:
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structured_info[tool_name] = []
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structured_info[tool_name].append({"name": result["name"], "content": result["content"]})
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# 如果有工具结果，返回结构化的信息
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if structured_info:
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logger.debug(f"工具调用收集到结构化信息: {json.dumps(structured_info, ensure_ascii=False)}")
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return {"used_tools": True, "structured_info": structured_info}
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else:
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# 没有工具调用
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content, reasoning_content = response
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logger.debug("模型没有请求调用任何工具")
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# 如果没有工具调用或处理失败，直接返回原始思考
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return {
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"used_tools": False,
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}
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except Exception as e:
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logger.error(f"工具调用过程中出错: {str(e)}")
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logger.error(f"工具调用过程中出错: {traceback.format_exc()}")
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return {
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"used_tools": False,
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"error": str(e),
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}
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