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feat：拆分子心流的思考模块

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SengokuCola
2025-04-24 14:41:49 +08:00
parent a89be639d0
commit bb333e8feb
5 changed files with 306 additions and 266 deletions
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src/heart_flow/sub_mind.py Normal file
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from .observation import Observation
from src.plugins.models.utils_model import LLMRequest
from src.config.config import global_config
import time
import traceback
from src.common.logger import get_module_logger, LogConfig, SUB_HEARTFLOW_STYLE_CONFIG # noqa: E402
from src.individuality.individuality import Individuality
import random
from ..plugins.utils.prompt_builder import Prompt, global_prompt_manager
from src.do_tool.tool_use import ToolUser
from src.plugins.utils.json_utils import safe_json_dumps, normalize_llm_response, process_llm_tool_calls
from src.heart_flow.chat_state_info import ChatStateInfo
subheartflow_config = LogConfig(
console_format=SUB_HEARTFLOW_STYLE_CONFIG["console_format"],
file_format=SUB_HEARTFLOW_STYLE_CONFIG["file_format"],
)
logger = get_module_logger("subheartflow", config=subheartflow_config)
def init_prompt():
prompt = ""
# prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n"
prompt += "{extra_info}\n"
# prompt += "{prompt_schedule}\n"
# prompt += "{relation_prompt_all}\n"
prompt += "{prompt_personality}\n"
prompt += "刚刚你的想法是：\n我是{bot_name}，我想，{current_thinking_info}\n"
prompt += "-----------------------------------\n"
prompt += "现在是{time_now}，你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：\n{chat_observe_info}\n"
prompt += "\n你现在{mood_info}\n"
# prompt += "你注意到{sender_name}刚刚说：{message_txt}\n"
prompt += "现在请你根据刚刚的想法继续思考，思考时可以想想如何对群聊内容进行回复，要不要对群里的话题进行回复，关注新话题，可以适当转换话题，大家正在说的话才是聊天的主题。\n"
prompt += "回复的要求是：平淡一些，简短一些，说中文，如果你要回复，最好只回复一个人的一个话题\n"
prompt += "请注意不要输出多余内容(包括前后缀，冒号和引号，括号， 表情，等)，不要带有括号和动作描写。不要回复自己的发言，尽量不要说你说过的话。\n"
prompt += "现在请你先{hf_do_next}，不要分点输出,生成内心想法，文字不要浮夸"
prompt += "在输出完想法后，请你思考应该使用什么工具。如果你需要做某件事，来对消息和你的回复进行处理，请使用工具。\n"
Prompt(prompt, "sub_heartflow_prompt_before")
class SubMind:
def __init__(
self,
subheartflow_id: str,
chat_state: ChatStateInfo,
observations: Observation
):
self.subheartflow_id = subheartflow_id
self.llm_model = LLMRequest(
model=global_config.llm_sub_heartflow,
temperature=global_config.llm_sub_heartflow["temp"],
max_tokens=800,
request_type="sub_heart_flow",
)
self.chat_state = chat_state
self.observations = observations
self.current_mind = ""
self.past_mind = []
self.structured_info = {}
async def do_thinking_before_reply(self):
"""
在回复前进行思考，生成内心想法并收集工具调用结果
返回:
tuple: (current_mind, past_mind) 当前想法和过去的想法列表
"""
# 更新活跃时间
self.last_active_time = time.time()
# ---------- 1. 准备基础数据 ----------
# 获取现有想法和情绪状态
current_thinking_info = self.current_mind
mood_info = self.chat_state.mood
# 获取观察对象
observation = self.observations[0]
if not observation:
logger.error(f"[{self.subheartflow_id}] 无法获取观察对象")
self.update_current_mind("(我没看到任何聊天内容...)")
return self.current_mind, self.past_mind
# 获取观察内容
chat_observe_info = observation.get_observe_info()
# ---------- 2. 准备工具和个性化数据 ----------
# 初始化工具
tool_instance = ToolUser()
tools = tool_instance._define_tools()
# 获取个性化信息
individuality = Individuality.get_instance()
# 构建个性部分
prompt_personality = f"你的名字是{individuality.personality.bot_nickname}，你"
prompt_personality += individuality.personality.personality_core
# 随机添加个性侧面
if individuality.personality.personality_sides:
random_side = random.choice(individuality.personality.personality_sides)
prompt_personality += f"，{random_side}"
# 随机添加身份细节
if individuality.identity.identity_detail:
random_detail = random.choice(individuality.identity.identity_detail)
prompt_personality += f"，{random_detail}"
# 获取当前时间
time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
# ---------- 3. 构建思考指导部分 ----------
# 创建本地随机数生成器，基于分钟数作为种子
local_random = random.Random()
current_minute = int(time.strftime("%M"))
local_random.seed(current_minute)
# 思考指导选项和权重
hf_options = [
("继续生成你在这个聊天中的想法，在原来想法的基础上继续思考", 0.7),
("生成你在这个聊天中的想法，在原来的想法上尝试新的话题", 0.1),
("生成你在这个聊天中的想法，不要太深入", 0.1),
("继续生成你在这个聊天中的想法，进行深入思考", 0.1),
]
# 加权随机选择思考指导
hf_do_next = local_random.choices(
[option[0] for option in hf_options], weights=[option[1] for option in hf_options], k=1
)[0]
# ---------- 4. 构建最终提示词 ----------
# 获取提示词模板并填充数据
prompt = (await global_prompt_manager.get_prompt_async("sub_heartflow_prompt_before")).format(
extra_info="", # 可以在这里添加额外信息
prompt_personality=prompt_personality,
bot_name=individuality.personality.bot_nickname,
current_thinking_info=current_thinking_info,
time_now=time_now,
chat_observe_info=chat_observe_info,
mood_info=mood_info,
hf_do_next=hf_do_next,
)
logger.debug(f"[{self.subheartflow_id}] 心流思考提示词构建完成")
# ---------- 5. 执行LLM请求并处理响应 ----------
content = "" # 初始化内容变量
reasoning_content = "" # 初始化推理内容变量
try:
# 调用LLM生成响应
response = await self.llm_model.generate_response_tool_async(prompt=prompt, tools=tools)
# 标准化响应格式
success, normalized_response, error_msg = normalize_llm_response(
response, log_prefix=f"[{self.subheartflow_id}] "
)
if not success:
# 处理标准化失败情况
logger.warning(f"[{self.subheartflow_id}] {error_msg}")
content = "LLM响应格式无法处理"
else:
# 从标准化响应中提取内容
if len(normalized_response) >= 2:
content = normalized_response[0]
_reasoning_content = normalized_response[1] if len(normalized_response) > 1 else ""
# 处理可能的工具调用
if len(normalized_response) == 3:
# 提取并验证工具调用
success, valid_tool_calls, error_msg = process_llm_tool_calls(
normalized_response, log_prefix=f"[{self.subheartflow_id}] "
)
if success and valid_tool_calls:
# 记录工具调用信息
tool_calls_str = ", ".join(
[call.get("function", {}).get("name", "未知工具") for call in valid_tool_calls]
)
logger.info(
f"[{self.subheartflow_id}] 模型请求调用{len(valid_tool_calls)}个工具: {tool_calls_str}"
)
# 收集工具执行结果
await self._execute_tool_calls(valid_tool_calls, tool_instance)
elif not success:
logger.warning(f"[{self.subheartflow_id}] {error_msg}")
except Exception as e:
# 处理总体异常
logger.error(f"[{self.subheartflow_id}] 执行LLM请求或处理响应时出错: {e}")
logger.error(traceback.format_exc())
content = "思考过程中出现错误"
# 记录最终思考结果
logger.debug(f"[{self.subheartflow_id}] 心流思考结果:\n{content}\n")
# 处理空响应情况
if not content:
content = "(不知道该想些什么...)"
logger.warning(f"[{self.subheartflow_id}] LLM返回空结果，思考失败。")
# ---------- 6. 更新思考状态并返回结果 ----------
# 更新当前思考内容
self.update_current_mind(content)
return self.current_mind, self.past_mind
async def _execute_tool_calls(self, tool_calls, tool_instance):
"""
执行一组工具调用并收集结果
参数:
tool_calls: 工具调用列表
tool_instance: 工具使用器实例
"""
tool_results = []
structured_info = {} # 动态生成键
# 执行所有工具调用
for tool_call in tool_calls:
try:
result = await tool_instance._execute_tool_call(tool_call)
if result:
tool_results.append(result)
# 使用工具名称作为键
tool_name = result["name"]
if tool_name not in structured_info:
structured_info[tool_name] = []
structured_info[tool_name].append({"name": result["name"], "content": result["content"]})
except Exception as tool_e:
logger.error(f"[{self.subheartflow_id}] 工具执行失败: {tool_e}")
# 如果有工具结果，记录并更新结构化信息
if structured_info:
logger.debug(f"工具调用收集到结构化信息: {safe_json_dumps(structured_info, ensure_ascii=False)}")
self.structured_info = structured_info
def update_current_mind(self, response):
self.past_mind.append(self.current_mind)
self.current_mind = response
init_prompt()