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feat：支持回复和at正确解析

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This commit is contained in:
SengokuCola
2025-05-12 23:17:09 +08:00
parent 3a11c0136b
commit 7a46b56781
6 changed files with 84 additions and 12 deletions
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2
src/heart_flow/chat_state_info.py
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@@ -10,7 +10,7 @@ class ChatState(enum.Enum):
class ChatStateInfo:
def __init__(self):
self.chat_status: ChatState = ChatState.ABSENT
self.chat_status: ChatState = ChatState.CHAT
self.current_state_time = 120
self.mood_manager = MoodManager()

3
src/heart_flow/sub_heartflow.py
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@@ -295,9 +295,6 @@ class SubHeartflow:
def get_all_observations(self) -> list[Observation]:
return self.observations
def clear_observations(self):
self.observations.clear()
def _get_primary_observation(self) -> Optional[ChattingObservation]:
if self.observations and isinstance(self.observations[0], ChattingObservation):
return self.observations[0]

47
src/plugins/heartFC_chat/expressors/exprssion_learner.py
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@@ -1,4 +1,5 @@
import time
import random
from typing import List, Dict, Optional, Any, Tuple, Coroutine
from src.common.logger_manager import get_logger
from src.plugins.models.utils_model import LLMRequest
@@ -8,6 +9,9 @@ from src.plugins.heartFC_chat.heartflow_prompt_builder import Prompt, global_pro
import os
import json
MAX_EXPRESSION_COUNT = 300
logger = get_logger("expressor")
@@ -52,6 +56,18 @@ class ExpressionLearner:
expressions: List[dict] = json.load(f)
return expressions
def is_similar(self, s1: str, s2: str) -> bool:
"""
判断两个字符串是否相似（只考虑长度大于5且有80%以上重合，不考虑子串）
"""
if not s1 or not s2:
return False
min_len = min(len(s1), len(s2))
if min_len < 5:
return False
same = sum(1 for a, b in zip(s1, s2) if a == b)
return same / min_len > 0.8
async def learn_and_store_expression(self) -> List[Tuple[str, str, str]]:
"""选择从当前到最近1小时内的随机10条消息，然后学习这些消息的表达方式"""
logger.info("开始学习表达方式...")
@@ -74,15 +90,40 @@ class ExpressionLearner:
file_path = os.path.join(dir_path, "expressions.json")
# 若已存在，先读出合并
if os.path.exists(file_path):
old_data: List[Dict[str, str]] = []
old_data: List[Dict[str, str, str]] = []
try:
with open(file_path, "r", encoding="utf-8") as f:
old_data = json.load(f)
except Exception:
old_data = []
expr_list = old_data + expr_list
else:
old_data = []
# 超过最大数量时，20%概率移除count=1的项
if len(old_data) >= MAX_EXPRESSION_COUNT:
delete = True
new_old_data = []
for item in old_data:
if item.get("count", 1) == 1 and random.random() < 0.2:
continue # 20%概率移除
new_old_data.append(item)
old_data = new_old_data
# 合并逻辑
for new_expr in expr_list:
found = False
for old_expr in old_data:
if self.is_similar(new_expr["situation"], old_expr.get("situation", "")) and self.is_similar(new_expr["style"], old_expr.get("style", "")):
found = True
# 50%概率替换
if random.random() < 0.5:
old_expr["situation"] = new_expr["situation"]
old_expr["style"] = new_expr["style"]
old_expr["count"] = old_expr.get("count", 1) + 1
break
if not found:
new_expr["count"] = 1
old_data.append(new_expr)
with open(file_path, "w", encoding="utf-8") as f:
json.dump(expr_list, f, ensure_ascii=False, indent=2)
json.dump(old_data, f, ensure_ascii=False, indent=2)
return expressions
async def learn_expression(self) -> Optional[List[Tuple[str, str, str]]]:

2
src/plugins/heartFC_chat/heartflow_prompt_builder.py
View File

@@ -86,7 +86,7 @@ def init_prompt():
{{
"action": "reply",
"text": "你想表达的内容",
"emojis": "表情关键词",
"emojis": "描述当前使用表情包的场景",
"target": "你想要回复的原始文本内容（非必须，仅文本，不包含发送者)",
"reasoning": "你的决策理由",
}}

36
src/plugins/utils/chat_message_builder.py
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@@ -8,6 +8,7 @@ from src.config.config import global_config
from typing import List, Dict, Any, Tuple # 确保类型提示被导入
import time # 导入 time 模块以获取当前时间
import random
import re
# 导入新的 repository 函数
from src.common.message_repository import find_messages, count_messages
@@ -215,10 +216,43 @@ async def _build_readable_messages_internal(
else:
person_name = "某人"
# 检查是否有 回复<aaa:bbb> 字段
reply_pattern = r"回复<([^:<>]+):([^:<>]+)>"
match = re.search(reply_pattern, content)
if match:
aaa = match.group(1)
bbb = match.group(2)
reply_person_id = person_info_manager.get_person_id(platform, bbb)
reply_person_name = await person_info_manager.get_value(reply_person_id, "person_name")
if not reply_person_name:
reply_person_name = aaa
# 在内容前加上回复信息
content = re.sub(reply_pattern, f"回复 {reply_person_name}", content, count=1)
# 检查是否有 @<aaa:bbb> 字段 @<{member_info.get('nickname')}:{member_info.get('user_id')}>
at_pattern = r"@<([^:<>]+):([^:<>]+)>"
at_matches = list(re.finditer(at_pattern, content))
if at_matches:
new_content = ""
last_end = 0
for m in at_matches:
new_content += content[last_end:m.start()]
aaa = m.group(1)
bbb = m.group(2)
at_person_id = person_info_manager.get_person_id(platform, bbb)
at_person_name = await person_info_manager.get_value(at_person_id, "person_name")
if not at_person_name:
at_person_name = aaa
new_content += f"@{at_person_name}"
last_end = m.end()
new_content += content[last_end:]
content = new_content
message_details_raw.append((timestamp, person_name, content))
if not message_details_raw:
return "", []
return "", []
message_details_raw.sort(key=lambda x: x[0]) # 按时间戳(第一个元素)升序排序，越早的消息排在前面

6
template/bot_config_template.toml
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@@ -66,11 +66,11 @@ time_zone = "Asia/Shanghai" # 给你的机器人设置时区，可以解决运
nonebot-qq="http://127.0.0.1:18002/api/message"
[chat] #麦麦的聊天通用设置
allow_focus_mode = true # 是否允许专注聊天状态
allow_focus_mode = false # 是否允许专注聊天状态
# 是否启用heart_flowC(HFC)模式
# 启用后麦麦会自主选择进入heart_flowC模式（持续一段时间），进行主动的观察和回复，并给出回复，比较消耗token
base_normal_chat_num = 8 # 最多允许多少个群进行普通聊天
base_focused_chat_num = 5 # 最多允许多少个群进行专注聊天
base_normal_chat_num = 999 # 最多允许多少个群进行普通聊天
base_focused_chat_num = 4 # 最多允许多少个群进行专注聊天
observation_context_size = 15 # 观察到的最长上下文大小,建议15，太短太长都会导致脑袋尖尖
message_buffer = true # 启用消息缓冲器？启用此项以解决消息的拆分问题，但会使麦麦的回复延迟