feat：拆分子心流的思考模块
This commit is contained in:
SengokuCola
17
src/heart_flow/chat_state_info.py
Normal file
17
src/heart_flow/chat_state_info.py
Normal file
@@ -0,0 +1,17 @@
|
||||
from src.plugins.moods.moods import MoodManager
|
||||
import enum
|
||||
|
||||
|
||||
class ChatState(enum.Enum):
|
||||
ABSENT = "没在看群"
|
||||
CHAT = "随便水群"
|
||||
FOCUSED = "激情水群"
|
||||
|
||||
|
||||
class ChatStateInfo:
|
||||
def __init__(self):
|
||||
self.chat_status: ChatState = ChatState.ABSENT
|
||||
self.current_state_time = 120
|
||||
|
||||
self.mood_manager = MoodManager()
|
||||
self.mood = self.mood_manager.get_prompt()
|
||||
@@ -1,24 +1,20 @@
|
||||
from .observation import Observation, ChattingObservation
|
||||
import asyncio
|
||||
from src.plugins.moods.moods import MoodManager
|
||||
from src.plugins.models.utils_model import LLMRequest
|
||||
from src.config.config import global_config
|
||||
import time
|
||||
from typing import Optional, List, Dict, Callable
|
||||
import traceback
|
||||
import enum
|
||||
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.plugins.chat.message import MessageRecv
|
||||
from src.plugins.chat.chat_stream import chat_manager
|
||||
import math
|
||||
from src.plugins.heartFC_chat.heartFC_chat import HeartFChatting
|
||||
from src.plugins.heartFC_chat.normal_chat import NormalChat
|
||||
from src.do_tool.tool_use import ToolUser
|
||||
from src.heart_flow.mai_state_manager import MaiStateInfo
|
||||
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 ChatState, ChatStateInfo
|
||||
from src.heart_flow.sub_mind import SubMind
|
||||
|
||||
|
||||
# 定义常量 (从 interest.py 移动过来)
|
||||
MAX_INTEREST = 15.0
|
||||
@@ -37,42 +33,6 @@ interest_log_config = LogConfig(
|
||||
interest_logger = get_module_logger("InterestChatting", config=interest_log_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 ChatState(enum.Enum):
|
||||
ABSENT = "没在看群"
|
||||
CHAT = "随便水群"
|
||||
FOCUSED = "激情水群"
|
||||
|
||||
|
||||
class ChatStateInfo:
|
||||
def __init__(self):
|
||||
self.chat_status: ChatState = ChatState.ABSENT
|
||||
self.current_state_time = 120
|
||||
|
||||
self.mood_manager = MoodManager()
|
||||
self.mood = self.mood_manager.get_prompt()
|
||||
|
||||
|
||||
base_reply_probability = 0.05
|
||||
probability_increase_rate_per_second = 0.08
|
||||
max_reply_probability = 1
|
||||
@@ -112,7 +72,7 @@ class InterestChatting:
|
||||
|
||||
self.above_threshold = False
|
||||
self.start_hfc_probability = 0.0
|
||||
|
||||
|
||||
def add_interest_dict(self, message: MessageRecv, interest_value: float, is_mentioned: bool):
|
||||
self.interest_dict[message.message_info.message_id] = (message, interest_value, is_mentioned)
|
||||
self.last_interaction_time = time.time()
|
||||
@@ -259,15 +219,11 @@ class SubHeartflow:
|
||||
|
||||
self.mai_states = mai_states
|
||||
|
||||
# 思维状态相关
|
||||
self.current_mind = "什么也没想" # 当前想法
|
||||
self.past_mind = [] # 历史想法记录
|
||||
|
||||
# 聊天状态管理
|
||||
self.chat_state: ChatStateInfo = ChatStateInfo() # 该sub_heartflow的聊天状态信息
|
||||
self.interest_chatting = InterestChatting(
|
||||
state_change_callback=self.set_chat_state
|
||||
) # 该sub_heartflow的兴趣系统
|
||||
)
|
||||
|
||||
# 活动状态管理
|
||||
self.last_active_time = time.time() # 最后活跃时间
|
||||
@@ -281,16 +237,15 @@ class SubHeartflow:
|
||||
self.running_knowledges = [] # 运行中的知识
|
||||
|
||||
# LLM模型配置
|
||||
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.sub_mind = SubMind(
|
||||
subheartflow_id=self.subheartflow_id,
|
||||
chat_state=self.chat_state,
|
||||
observations=self.observations
|
||||
)
|
||||
|
||||
self.log_prefix = chat_manager.get_stream_name(self.subheartflow_id) or self.subheartflow_id
|
||||
|
||||
self.structured_info = {}
|
||||
self.log_prefix = chat_manager.get_stream_name(self.subheartflow_id) or self.subheartflow_id
|
||||
|
||||
|
||||
async def add_time_current_state(self, add_time: float):
|
||||
self.current_state_time += add_time
|
||||
@@ -381,6 +336,8 @@ class SubHeartflow:
|
||||
try:
|
||||
self.heart_fc_instance = HeartFChatting(
|
||||
chat_id=self.chat_id,
|
||||
sub_mind=self.sub_mind,
|
||||
observations=self.observations
|
||||
)
|
||||
if await self.heart_fc_instance._initialize():
|
||||
await self.heart_fc_instance.start() # 初始化成功后启动循环
|
||||
@@ -477,188 +434,9 @@ class SubHeartflow:
|
||||
|
||||
logger.info(f"{self.log_prefix} 子心流后台任务已停止。")
|
||||
|
||||
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._get_primary_observation()
|
||||
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
|
||||
self.sub_mind.update_current_mind(response)
|
||||
|
||||
def add_observation(self, observation: Observation):
|
||||
for existing_obs in self.observations:
|
||||
@@ -705,7 +483,7 @@ class SubHeartflow:
|
||||
interest_state = await self.get_interest_state()
|
||||
return {
|
||||
"interest_state": interest_state,
|
||||
"current_mind": self.current_mind,
|
||||
"current_mind": self.sub_mind.current_mind,
|
||||
"chat_state": self.chat_state.chat_status.value,
|
||||
"last_active_time": self.last_active_time,
|
||||
}
|
||||
@@ -747,4 +525,4 @@ class SubHeartflow:
|
||||
logger.info(f"{self.log_prefix} 子心流关闭完成。")
|
||||
|
||||
|
||||
init_prompt()
|
||||
|
||||
|
||||
254
src/heart_flow/sub_mind.py
Normal file
254
src/heart_flow/sub_mind.py
Normal file
@@ -0,0 +1,254 @@
|
||||
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()
|
||||
@@ -456,7 +456,7 @@ class SubHeartflowManager:
|
||||
for subheartflow in self.subheartflows.values():
|
||||
# 检查子心流是否活跃(非ABSENT状态)
|
||||
if subheartflow.chat_state.chat_status != ChatState.ABSENT:
|
||||
minds.append(subheartflow.current_mind)
|
||||
minds.append(subheartflow.sub_mind.current_mind)
|
||||
return minds
|
||||
|
||||
def update_main_mind_in_subflows(self, main_mind: str):
|
||||
|
||||
@@ -1,9 +1,7 @@
|
||||
import asyncio
|
||||
import time
|
||||
import traceback
|
||||
from typing import List, Optional, Dict, Any, TYPE_CHECKING
|
||||
|
||||
# import json # 移除，因为使用了json_utils
|
||||
from typing import List, Optional, Dict, Any
|
||||
from src.plugins.chat.message import MessageRecv, BaseMessageInfo, MessageThinking, MessageSending
|
||||
from src.plugins.chat.message import MessageSet, Seg # Local import needed after move
|
||||
from src.plugins.chat.chat_stream import ChatStream
|
||||
@@ -19,6 +17,8 @@ from src.do_tool.tool_use import ToolUser
|
||||
from ..chat.message_sender import message_manager # <-- Import the global manager
|
||||
from src.plugins.chat.emoji_manager import emoji_manager
|
||||
from src.plugins.utils.json_utils import process_llm_tool_response # 导入新的JSON工具
|
||||
from src.heart_flow.sub_mind import SubMind
|
||||
from src.heart_flow.observation import Observation
|
||||
# --- End import ---
|
||||
|
||||
|
||||
@@ -33,13 +33,6 @@ interest_log_config = LogConfig(
|
||||
logger = get_module_logger("HeartFCLoop", config=interest_log_config) # Logger Name Changed
|
||||
|
||||
|
||||
# Forward declaration for type hinting
|
||||
if TYPE_CHECKING:
|
||||
# Keep this if HeartFCController methods are still needed elsewhere,
|
||||
# but the instance variable will be removed from HeartFChatting
|
||||
# from .heartFC_controler import HeartFCController
|
||||
from src.heart_flow.heartflow import SubHeartflow # <-- 同时导入 heartflow 实例用于类型检查
|
||||
|
||||
PLANNER_TOOL_DEFINITION = [
|
||||
{
|
||||
"type": "function",
|
||||
@@ -74,7 +67,12 @@ class HeartFChatting:
|
||||
其生命周期现在由其关联的 SubHeartflow 的 FOCUSED 状态控制。
|
||||
"""
|
||||
|
||||
def __init__(self, chat_id: str):
|
||||
def __init__(
|
||||
self,
|
||||
chat_id: str,
|
||||
sub_mind: SubMind,
|
||||
observations: Observation
|
||||
):
|
||||
"""
|
||||
HeartFChatting 初始化函数
|
||||
|
||||
@@ -84,7 +82,8 @@ class HeartFChatting:
|
||||
# 基础属性
|
||||
self.stream_id: str = chat_id # 聊天流ID
|
||||
self.chat_stream: Optional[ChatStream] = None # 关联的聊天流
|
||||
self.sub_hf: SubHeartflow = None # 关联的子心流
|
||||
self.sub_mind: SubMind = sub_mind # 关联的子思维
|
||||
self.observations: Observation = observations # 关联的观察
|
||||
|
||||
# 初始化状态控制
|
||||
self._initialized = False # 是否已初始化标志
|
||||
@@ -121,18 +120,10 @@ class HeartFChatting:
|
||||
log_prefix = self._get_log_prefix() # 获取前缀
|
||||
try:
|
||||
self.chat_stream = chat_manager.get_stream(self.stream_id)
|
||||
|
||||
if not self.chat_stream:
|
||||
logger.error(f"{log_prefix} 获取ChatStream失败。")
|
||||
return False
|
||||
|
||||
# <-- 在这里导入 heartflow 实例
|
||||
from src.heart_flow.heartflow import heartflow
|
||||
|
||||
self.sub_hf = heartflow.get_subheartflow(self.stream_id)
|
||||
if not self.sub_hf:
|
||||
logger.warning(f"{log_prefix} 获取SubHeartflow失败。一些功能可能受限。")
|
||||
|
||||
self._initialized = True
|
||||
logger.info(f"麦麦感觉到了，激发了HeartFChatting{log_prefix} 初始化成功。")
|
||||
return True
|
||||
@@ -321,8 +312,8 @@ class HeartFChatting:
|
||||
# --- 新增：等待新消息 ---
|
||||
logger.debug(f"{log_prefix} HeartFChatting: 开始等待新消息 (自 {planner_start_db_time})...")
|
||||
observation = None
|
||||
if self.sub_hf:
|
||||
observation = self.sub_hf._get_primary_observation()
|
||||
|
||||
observation = self.observations[0]
|
||||
|
||||
if observation:
|
||||
with Timer("Wait New Msg", cycle_timers): # <--- Start Wait timer
|
||||
@@ -427,7 +418,7 @@ class HeartFChatting:
|
||||
llm_error = False
|
||||
|
||||
try:
|
||||
observation = self.sub_hf._get_primary_observation()
|
||||
observation = self.observations[0]
|
||||
await observation.observe()
|
||||
observed_messages = observation.talking_message
|
||||
observed_messages_str = observation.talking_message_str
|
||||
@@ -435,7 +426,7 @@ class HeartFChatting:
|
||||
logger.error(f"{log_prefix}[Planner] 获取观察信息时出错: {e}")
|
||||
|
||||
try:
|
||||
current_mind, _past_mind = await self.sub_hf.do_thinking_before_reply()
|
||||
current_mind, _past_mind = await self.sub_mind.do_thinking_before_reply()
|
||||
except Exception as e_subhf:
|
||||
logger.error(f"{log_prefix}[Planner] SubHeartflow 思考失败: {e_subhf}")
|
||||
current_mind = "[思考时出错]"
|
||||
@@ -447,7 +438,7 @@ class HeartFChatting:
|
||||
llm_error = False # LLM错误标志
|
||||
|
||||
try:
|
||||
prompt = await self._build_planner_prompt(observed_messages_str, current_mind, self.sub_hf.structured_info)
|
||||
prompt = await self._build_planner_prompt(observed_messages_str, current_mind, self.sub_mind.structured_info)
|
||||
payload = {
|
||||
"model": self.planner_llm.model_name,
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
@@ -655,8 +646,8 @@ class HeartFChatting:
|
||||
response_set: Optional[List[str]] = None
|
||||
try:
|
||||
response_set = await self.gpt_instance.generate_response(
|
||||
structured_info=self.sub_hf.structured_info,
|
||||
current_mind_info=self.sub_hf.current_mind,
|
||||
structured_info=self.sub_mind.structured_info,
|
||||
current_mind_info=self.sub_mind.current_mind,
|
||||
reason=reason,
|
||||
message=anchor_message, # Pass anchor_message positionally (matches 'message' parameter)
|
||||
thinking_id=thinking_id, # Pass thinking_id positionally
|
||||
|
||||
Reference in New Issue
Block a user