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dev：修复记忆构建文本名称问题

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This commit is contained in:
SengokuCola
2025-04-29 01:49:51 +08:00
parent f83e151d40
commit 286fef79c4
14 changed files with 494 additions and 367 deletions
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116
src/plugins/heartFC_chat/heartFC_chat.py
View File

@@ -1,7 +1,6 @@
import asyncio
import time
import traceback
import random # <-- 添加导入
from typing import List, Optional, Dict, Any, Deque, Callable, Coroutine
from collections import deque
from src.plugins.chat.message import MessageRecv, BaseMessageInfo, MessageThinking, MessageSending
@@ -14,17 +13,20 @@ from src.plugins.models.utils_model import LLMRequest
from src.config.config import global_config
from src.plugins.chat.utils_image import image_path_to_base64 # Local import needed after move
from src.plugins.utils.timer_calculator import Timer # <--- Import Timer
from src.plugins.heartFC_chat.heartFC_generator import HeartFCGenerator
from src.do_tool.tool_use import ToolUser
from src.plugins.emoji_system.emoji_manager import emoji_manager
from src.plugins.utils.json_utils import process_llm_tool_calls, extract_tool_call_arguments
from src.heart_flow.sub_mind import SubMind
from src.heart_flow.observation import Observation
from src.plugins.heartFC_chat.heartflow_prompt_builder import global_prompt_manager
from src.plugins.heartFC_chat.heartflow_prompt_builder import global_prompt_manager, prompt_builder
import contextlib
from src.plugins.utils.chat_message_builder import num_new_messages_since
from src.plugins.heartFC_chat.heartFC_Cycleinfo import CycleInfo
from .heartFC_sender import HeartFCSender
from src.plugins.chat.utils import process_llm_response
from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
from src.plugins.moods.moods import MoodManager
from src.individuality.individuality import Individuality
INITIAL_DURATION = 60.0
@@ -181,12 +183,18 @@ class HeartFChatting:
self.action_manager = ActionManager()
# 初始化状态控制
self._initialized = False # 是否已初始化标志
self._processing_lock = asyncio.Lock() # 处理锁(确保单次Plan-Replier-Sender周期)
self._initialized = False
self._processing_lock = asyncio.Lock()
# 依赖注入存储
self.gpt_instance = HeartFCGenerator() # 文本回复生成器
self.tool_user = ToolUser() # 工具使用实例
# --- 移除 gpt_instance, 直接初始化 LLM 模型 ---
# self.gpt_instance = HeartFCGenerator() # <-- 移除
self.model_normal = LLMRequest( # <-- 新增 LLM 初始化
model=global_config.llm_normal,
temperature=global_config.llm_normal["temp"],
max_tokens=256,
request_type="response_heartflow",
)
self.tool_user = ToolUser()
self.heart_fc_sender = HeartFCSender()
# LLM规划器配置
@@ -401,16 +409,15 @@ class HeartFChatting:
with Timer("决策", cycle_timers):
planner_result = await self._planner(current_mind, cycle_timers)
# 效果不太好，还没处理replan导致观察时间点改变的问题
# action = planner_result.get("action", "error")
# reasoning = planner_result.get("reasoning", "未提供理由")
# self._current_cycle.set_action_info(action, reasoning, False)
# 在获取规划结果后检查新消息
# if await self._check_new_messages(planner_start_db_time):
# if random.random() < 0.2:
# logger.info(f"{self.log_prefix} 看到了新消息，麦麦决定重新观察和规划...")
@@ -742,8 +749,8 @@ class HeartFChatting:
# --- 使用 LLM 进行决策 --- #
reasoning = "默认决策或获取决策失败"
llm_error = False # LLM错误标志
arguments = None # 初始化参数变量
emoji_query = "" # <--- 在这里初始化 emoji_query
arguments = None # 初始化参数变量
emoji_query = "" # <--- 在这里初始化 emoji_query
try:
# --- 构建提示词 ---
@@ -756,7 +763,7 @@ class HeartFChatting:
observed_messages_str, current_mind, self.sub_mind.structured_info, replan_prompt_str
)
# --- 调用 LLM ---
# --- 调用 LLM ---
try:
planner_tools = self.action_manager.get_planner_tool_definition()
_response_text, _reasoning_content, tool_calls = await self.planner_llm.generate_response_tool_async(
@@ -794,7 +801,7 @@ class HeartFChatting:
first_tool_call = valid_tool_calls[0]
tool_name = first_tool_call.get("function", {}).get("name")
arguments = extract_tool_call_arguments(first_tool_call, None)
# 3. 检查名称和参数
expected_tool_name = "decide_reply_action"
if tool_name == expected_tool_name and arguments is not None:
@@ -808,13 +815,13 @@ class HeartFChatting:
action = "no_reply"
reasoning = f"LLM返回了未授权的动作: {extracted_action}"
emoji_query = ""
llm_error = False # 视为非LLM错误，只是逻辑修正
llm_error = False # 视为非LLM错误，只是逻辑修正
else:
# 动作有效，使用提取的值
action = extracted_action
reasoning = arguments.get("reasoning", "未提供理由")
emoji_query = arguments.get("emoji_query", "")
llm_error = False # 成功处理
llm_error = False # 成功处理
# 记录决策结果
logger.debug(
f"{self.log_prefix}[要做什么]\nPrompt:\n{prompt}\n\n决策结果: {action}, 理由: {reasoning}, 表情查询: '{emoji_query}'"
@@ -822,13 +829,13 @@ class HeartFChatting:
elif tool_name != expected_tool_name:
reasoning = f"LLM返回了非预期的工具: {tool_name}"
logger.warning(f"{self.log_prefix}[Planner] {reasoning}")
else: # arguments is None
else: # arguments is None
reasoning = f"无法提取工具 {tool_name} 的参数"
logger.warning(f"{self.log_prefix}[Planner] {reasoning}")
elif not success:
reasoning = f"验证工具调用失败: {error_msg}"
logger.warning(f"{self.log_prefix}[Planner] {reasoning}")
else: # not valid_tool_calls
else: # not valid_tool_calls
reasoning = "LLM未返回有效的工具调用"
logger.warning(f"{self.log_prefix}[Planner] {reasoning}")
# 如果 llm_error 仍然是 True，说明在处理过程中有错误发生
@@ -1058,9 +1065,13 @@ class HeartFChatting:
# 如果最近的活动循环不是文本回复，或者没有活动循环
cycle_info_block = "\n【近期回复历史】\n(最近没有连续文本回复)\n"
individuality = Individuality.get_instance()
prompt_personality = individuality.get_prompt(x_person=2, level=2)
# 获取提示词模板并填充数据
prompt = (await global_prompt_manager.get_prompt_async("planner_prompt")).format(
bot_name=global_config.BOT_NICKNAME,
prompt_personality=prompt_personality,
structured_info_block=structured_info_block,
chat_content_block=chat_content_block,
current_mind_block=current_mind_block,
@@ -1083,27 +1094,66 @@ class HeartFChatting:
thinking_id: str,
) -> Optional[List[str]]:
"""
回复器 (Replier): 核心逻辑用于生成回复。
回复器 (Replier): 核心逻辑，负责生成回复文本。
(已整合原 HeartFCGenerator 的功能)
"""
response_set: Optional[List[str]] = None
try:
response_set = await self.gpt_instance.generate_response(
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
)
# 1. 获取情绪影响因子并调整模型温度
arousal_multiplier = MoodManager.get_instance().get_arousal_multiplier()
current_temp = global_config.llm_normal["temp"] * arousal_multiplier
self.model_normal.temperature = current_temp # 动态调整温度
if not response_set:
logger.warning(f"{self.log_prefix}[Replier-{thinking_id}] LLM生成了一个空回复集。")
# 2. 获取信息捕捉器
info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
# 3. 构建 Prompt
with Timer("构建Prompt", {}): # 内部计时器，可选保留
prompt = await prompt_builder.build_prompt(
build_mode="focus",
reason=reason,
current_mind_info=self.sub_mind.current_mind,
structured_info=self.sub_mind.structured_info,
message_txt="", # 似乎是固定的空字符串
sender_name="", # 似乎是固定的空字符串
chat_stream=anchor_message.chat_stream,
)
# 4. 调用 LLM 生成回复
content = None
reasoning_content = None
model_name = "unknown_model"
try:
with Timer("LLM生成", {}): # 内部计时器，可选保留
content, reasoning_content, model_name = await self.model_normal.generate_response(prompt)
logger.info(f"{self.log_prefix}[Replier-{thinking_id}]\\nPrompt:\\n{prompt}\\n生成回复: {content}\\n")
# 捕捉 LLM 输出信息
info_catcher.catch_after_llm_generated(
prompt=prompt, response=content, reasoning_content=reasoning_content, model_name=model_name
)
except Exception as llm_e:
# 精简报错信息
logger.error(f"{self.log_prefix}[Replier-{thinking_id}] LLM 生成失败: {llm_e}")
return None # LLM 调用失败则无法生成回复
# 5. 处理 LLM 响应
if not content:
logger.warning(f"{self.log_prefix}[Replier-{thinking_id}] LLM 生成了空内容。")
return None
return response_set
with Timer("处理响应", {}): # 内部计时器，可选保留
processed_response = process_llm_response(content)
if not processed_response:
logger.warning(f"{self.log_prefix}[Replier-{thinking_id}] 处理后的回复为空。")
return None
return processed_response
except Exception as e:
logger.error(f"{self.log_prefix}[Replier-{thinking_id}] Unexpected error in replier_work: {e}")
logger.error(traceback.format_exc())
# 更通用的错误处理，精简信息
logger.error(f"{self.log_prefix}[Replier-{thinking_id}] 回复生成意外失败: {e}")
# logger.error(traceback.format_exc()) # 可以取消注释这行以在调试时查看完整堆栈
return None
# --- Methods moved from HeartFCController start ---

107
src/plugins/heartFC_chat/heartFC_generator.py
View File

@@ -1,107 +0,0 @@
from typing import List, Optional
from ..models.utils_model import LLMRequest
from ...config.config import global_config
from ..chat.message import MessageRecv
from .heartflow_prompt_builder import prompt_builder
from ..chat.utils import process_llm_response
from src.common.logger_manager import get_logger
from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
from ..utils.timer_calculator import Timer
from src.plugins.moods.moods import MoodManager
logger = get_logger("llm")
class HeartFCGenerator:
def __init__(self):
self.model_normal = LLMRequest(
model=global_config.llm_normal,
temperature=global_config.llm_normal["temp"],
max_tokens=256,
request_type="response_heartflow",
)
self.model_sum = LLMRequest(
model=global_config.llm_summary_by_topic, temperature=0.6, max_tokens=2000, request_type="relation"
)
self.current_model_type = "r1" # 默认使用 R1
self.current_model_name = "unknown model"
async def generate_response(
self,
structured_info: str,
current_mind_info: str,
reason: str,
message: MessageRecv,
thinking_id: str,
) -> Optional[List[str]]:
"""根据当前模型类型选择对应的生成函数"""
arousal_multiplier = MoodManager.get_instance().get_arousal_multiplier()
current_model = self.model_normal
current_model.temperature = global_config.llm_normal["temp"] * arousal_multiplier # 激活度越高，温度越高
model_response = await self._generate_response_with_model(
structured_info, current_mind_info, reason, message, current_model, thinking_id
)
if model_response:
model_processed_response = await self._process_response(model_response)
return model_processed_response
else:
logger.info(f"{self.current_model_type}思考，失败")
return None
async def _generate_response_with_model(
self,
structured_info: str,
current_mind_info: str,
reason: str,
message: MessageRecv,
model: LLMRequest,
thinking_id: str,
) -> str:
info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
with Timer() as _build_prompt:
prompt = await prompt_builder.build_prompt(
build_mode="focus",
reason=reason,
current_mind_info=current_mind_info,
structured_info=structured_info,
message_txt="",
sender_name="",
chat_stream=message.chat_stream,
)
# logger.info(f"构建prompt时间: {t_build_prompt.human_readable}")
try:
content, reasoning_content, self.current_model_name = await model.generate_response(prompt)
logger.info(f"\nprompt:{prompt}\n生成回复{content}\n")
info_catcher.catch_after_llm_generated(
prompt=prompt, response=content, reasoning_content=reasoning_content, model_name=self.current_model_name
)
except Exception:
logger.exception("生成回复时出错")
return None
return content
async def _process_response(self, content: str) -> List[str]:
"""处理响应内容，返回处理后的内容和情感标签"""
if not content:
return None
processed_response = process_llm_response(content)
# print(f"得到了处理后的llm返回{processed_response}")
return processed_response

24
src/plugins/heartFC_chat/heartflow_prompt_builder.py
View File

@@ -25,7 +25,7 @@ def init_prompt():
{chat_target}
{chat_talking_prompt}
现在你想要在群里发言或者回复。\n
你需要扮演一位网名叫{bot_name}的人进行回复，这个人的特点是："{prompt_personality} {prompt_identity}"。
你需要扮演一位网名叫{bot_name}的人进行回复，这个人的特点是："{prompt_personality}"。
你正在{chat_target_2},现在请你读读之前的聊天记录，然后给出日常且口语化的回复，平淡一些，你可以参考贴吧，知乎或者微博的回复风格。
看到以上聊天记录，你刚刚在想：
@@ -49,7 +49,7 @@ def init_prompt():
# Planner提示词 - 优化版
Prompt(
"""你的名字是{bot_name}，一名大二女生，正在QQ群聊中。需要基于以下信息决定如何参与对话：
"""你的名字是{bot_name},{prompt_personality}，你现在正在一个群聊中。需要基于以下信息决定如何参与对话：
{structured_info_block}
{chat_content_block}
你的内心想法：
@@ -164,9 +164,7 @@ class PromptBuilder:
async def _build_prompt_focus(self, reason, current_mind_info, structured_info, chat_stream) -> tuple[str, str]:
individuality = Individuality.get_instance()
prompt_personality = individuality.get_prompt(type="personality", x_person=2, level=1)
prompt_identity = individuality.get_prompt(type="identity", x_person=2, level=1)
prompt_personality = individuality.get_prompt(x_person=0, level=2)
# 日程构建
# schedule_prompt = f'''你现在正在做的事情是：{bot_schedule.get_current_num_task(num = 1,time_info = False)}'''
@@ -214,7 +212,6 @@ class PromptBuilder:
chat_talking_prompt=chat_talking_prompt,
bot_name=global_config.BOT_NICKNAME,
prompt_personality=prompt_personality,
prompt_identity=prompt_identity,
chat_target_2=await global_prompt_manager.get_prompt_async("chat_target_group2")
if chat_in_group
else await global_prompt_manager.get_prompt_async("chat_target_private2"),
@@ -230,21 +227,8 @@ class PromptBuilder:
return prompt
async def _build_prompt_normal(self, chat_stream, message_txt: str, sender_name: str = "某人") -> tuple[str, str]:
# 开始构建prompt
prompt_personality = "你"
# person
individuality = Individuality.get_instance()
personality_core = individuality.personality.personality_core
prompt_personality += personality_core
personality_sides = individuality.personality.personality_sides
random.shuffle(personality_sides)
prompt_personality += f",{personality_sides[0]}"
identity_detail = individuality.identity.identity_detail
random.shuffle(identity_detail)
prompt_personality += f",{identity_detail[0]}"
prompt_personality = individuality.get_prompt(x_person=2, level=2)
# 关系
who_chat_in_group = [