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feat：心流查重和心流关系启用，关系prompt优化

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This commit is contained in:
SengokuCola
2025-04-29 23:47:22 +08:00
parent 4b7ce01c0d
commit be84017972
9 changed files with 237 additions and 43 deletions
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8
src/do_tool/tool_can_use/get_memory.py
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@@ -46,11 +46,13 @@ class GetMemoryTool(BaseTool):
if related_memory:
for memory in related_memory:
memory_info += memory[1] + "\n"
if memory_info:
content = f"你记得这些事情: {memory_info}"
content = f"你记得这些事情: {memory_info}\n"
content += "以上是你的回忆，不一定是目前聊天里的人说的，也不一定是现在发生的事情，请记住。\n"
else:
content = f"你不太记得有关{topic}的记忆，你对此不太了解"
content = f"{topic}的记忆，你记不太清"
return {"name": "get_memory", "content": content}
except Exception as e:

9
src/heart_flow/observation.py
View File

@@ -10,6 +10,7 @@ from src.plugins.utils.chat_message_builder import (
build_readable_messages,
get_raw_msg_by_timestamp_with_chat,
num_new_messages_since,
get_person_id_list,
)
logger = get_logger("observation")
@@ -45,6 +46,8 @@ class ChattingObservation(Observation):
self.mid_memorys = []
self.max_mid_memory_len = global_config.compress_length_limit
self.mid_memory_info = ""
self.person_list = []
self.llm_summary = LLMRequest(
model=global_config.llm_observation, temperature=0.7, max_tokens=300, request_type="chat_observation"
@@ -153,6 +156,12 @@ class ChattingObservation(Observation):
truncate=True,
)
self.person_list = await get_person_id_list(self.talking_message)
# print(f"self.11111person_list: {self.person_list}")
logger.trace(
f"Chat {self.chat_id} - 压缩早期记忆：{self.mid_memory_info}\n现在聊天内容：{self.talking_message_str}"
)

135
src/heart_flow/sub_mind.py
View File

@@ -12,6 +12,9 @@ from src.plugins.utils.json_utils import safe_json_dumps, process_llm_tool_calls
from src.heart_flow.chat_state_info import ChatStateInfo
from src.plugins.chat.chat_stream import chat_manager
from src.plugins.heartFC_chat.heartFC_Cycleinfo import CycleInfo
import difflib
from src.plugins.person_info.relationship_manager import relationship_manager
logger = get_logger("sub_heartflow")
@@ -20,6 +23,7 @@ logger = get_logger("sub_heartflow")
def init_prompt():
prompt = ""
prompt += "{extra_info}\n"
prompt += "{relation_prompt}\n"
prompt += "你的名字是{bot_name},{prompt_personality}\n"
prompt += "{last_loop_prompt}\n"
prompt += "{cycle_info_block}\n"
@@ -47,6 +51,39 @@ def init_prompt():
Prompt(prompt, "last_loop")
def calculate_similarity(text_a: str, text_b: str) -> float:
"""
计算两个文本字符串的相似度。
"""
if not text_a or not text_b:
return 0.0
matcher = difflib.SequenceMatcher(None, text_a, text_b)
return matcher.ratio()
def calculate_replacement_probability(similarity: float) -> float:
"""
根据相似度计算替换的概率。
规则：
- 相似度 <= 0.4: 概率 = 0
- 相似度 >= 0.9: 概率 = 1
- 相似度 == 0.6: 概率 = 0.7
- 0.4 < 相似度 <= 0.6: 线性插值 (0.4, 0) 到 (0.6, 0.7)
- 0.6 < 相似度 < 0.9: 线性插值 (0.6, 0.7) 到 (0.9, 1.0)
"""
if similarity <= 0.4:
return 0.0
elif similarity >= 0.9:
return 1.0
elif 0.4 < similarity <= 0.6:
# p = 3.5 * s - 1.4
probability = 3.5 * similarity - 1.4
return max(0.0, probability)
elif 0.6 < similarity < 0.9:
# p = s + 0.1
probability = similarity + 0.1
return min(1.0, max(0.0, probability))
class SubMind:
def __init__(self, subheartflow_id: str, chat_state: ChatStateInfo, observations: Observation):
self.subheartflow_id = subheartflow_id
@@ -80,7 +117,7 @@ class SubMind:
# ---------- 1. 准备基础数据 ----------
# 获取现有想法和情绪状态
current_thinking_info = self.current_mind
previous_mind = self.current_mind if self.current_mind else ""
mood_info = self.chat_state.mood
# 获取观察对象
@@ -92,6 +129,7 @@ class SubMind:
# 获取观察内容
chat_observe_info = observation.get_observe_info()
person_list = observation.person_list
# ---------- 2. 准备工具和个性化数据 ----------
# 初始化工具
@@ -100,6 +138,14 @@ class SubMind:
# 获取个性化信息
individuality = Individuality.get_instance()
relation_prompt = ""
print(f"person_list: {person_list}")
for person in person_list:
relation_prompt += await relationship_manager.build_relationship_info(person, is_id=True)
print(f"relat22222ion_prompt: {relation_prompt}")
# 构建个性部分
prompt_personality = individuality.get_prompt(x_person=2, level=2)
@@ -136,9 +182,9 @@ class SubMind:
last_reasoning = ""
is_replan = False
if_replan_prompt = ""
if current_thinking_info:
if previous_mind:
last_loop_prompt = (await global_prompt_manager.get_prompt_async("last_loop")).format(
current_thinking_info=current_thinking_info, if_replan_prompt=if_replan_prompt
current_thinking_info=previous_mind, if_replan_prompt=if_replan_prompt
)
else:
last_loop_prompt = ""
@@ -196,6 +242,7 @@ class SubMind:
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,
relation_prompt=relation_prompt,
bot_name=individuality.name,
time_now=time_now,
chat_observe_info=chat_observe_info,
@@ -205,8 +252,6 @@ class SubMind:
cycle_info_block=cycle_info_block,
)
# 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
@@ -240,7 +285,7 @@ class SubMind:
elif not success:
logger.warning(f"{self.log_prefix} 处理工具调用时出错: {error_msg}")
else:
logger.info(f"{self.log_prefix} 心流未使用工具") # 修改日志信息，明确是未使用工具而不是未处理
logger.info(f"{self.log_prefix} 心流未使用工具")
except Exception as e:
# 处理总体异常
@@ -248,15 +293,87 @@ class SubMind:
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.log_prefix} \nPrompt:\n{prompt}\n\n心流思考结果:\n{content}\n")
# 记录初步思考结果
logger.debug(f"{self.log_prefix} 初步心流思考结果: {content}\nprompt: {prompt}\n")
# 处理空响应情况
if not content:
content = "(不知道该想些什么...)"
logger.warning(f"{self.log_prefix} LLM返回空结果，思考失败。")
# ---------- 6. 更新思考状态并返回结果 ----------
# ---------- 6. 应用概率性去重和修饰 ----------
new_content = content # 保存 LLM 直接输出的结果
try:
similarity = calculate_similarity(previous_mind, new_content)
replacement_prob = calculate_replacement_probability(similarity)
logger.debug(f"{self.log_prefix} 新旧想法相似度: {similarity:.2f}, 替换概率: {replacement_prob:.2f}")
# 定义词语列表 (移到判断之前)
yu_qi_ci_liebiao = ["嗯", "哦", "啊", "唉", "哈", "唔"]
zhuan_zhe_liebiao = ["但是", "不过", "然而", "可是", "只是"]
cheng_jie_liebiao = ["然后", "接着", "此外", "而且", "另外"]
zhuan_jie_ci_liebiao = zhuan_zhe_liebiao + cheng_jie_liebiao
if random.random() < replacement_prob:
# 相似度非常高时，尝试去重或特殊处理
if similarity == 1.0:
logger.debug(f"{self.log_prefix} 想法完全重复 (相似度 1.0)，执行特殊处理...")
# 随机截取大约一半内容
if len(new_content) > 1: # 避免内容过短无法截取
split_point = max(1, len(new_content) // 2 + random.randint(-len(new_content)//4, len(new_content)//4))
truncated_content = new_content[:split_point]
else:
truncated_content = new_content # 如果只有一个字符或者为空，就不截取了
# 添加语气词和转折/承接词
yu_qi_ci = random.choice(yu_qi_ci_liebiao)
zhuan_jie_ci = random.choice(zhuan_jie_ci_liebiao)
content = f"{yu_qi_ci}{zhuan_jie_ci}，{truncated_content}"
logger.debug(f"{self.log_prefix} 想法重复，特殊处理后: {content}")
else:
# 相似度较高但非100%，执行标准去重逻辑
logger.debug(f"{self.log_prefix} 执行概率性去重 (概率: {replacement_prob:.2f})...")
matcher = difflib.SequenceMatcher(None, previous_mind, new_content)
deduplicated_parts = []
last_match_end_in_b = 0
for _i, j, n in matcher.get_matching_blocks():
if last_match_end_in_b < j:
deduplicated_parts.append(new_content[last_match_end_in_b:j])
last_match_end_in_b = j + n
deduplicated_content = "".join(deduplicated_parts).strip()
if deduplicated_content:
# 根据概率决定是否添加词语
prefix_str = ""
if random.random() < 0.3: # 30% 概率添加语气词
prefix_str += random.choice(yu_qi_ci_liebiao)
if random.random() < 0.7: # 70% 概率添加转折/承接词
prefix_str += random.choice(zhuan_jie_ci_liebiao)
# 组合最终结果
if prefix_str:
content = f"{prefix_str}，{deduplicated_content}" # 更新 content
logger.debug(f"{self.log_prefix} 去重并添加引导词后: {content}")
else:
content = deduplicated_content # 更新 content
logger.debug(f"{self.log_prefix} 去重后 (未添加引导词): {content}")
else:
logger.warning(f"{self.log_prefix} 去重后内容为空，保留原始LLM输出: {new_content}")
content = new_content # 保留原始 content
else:
logger.debug(f"{self.log_prefix} 未执行概率性去重 (概率: {replacement_prob:.2f})")
# content 保持 new_content 不变
except Exception as e:
logger.error(f"{self.log_prefix} 应用概率性去重或特殊处理时出错: {e}")
logger.error(traceback.format_exc())
# 出错时保留原始 content
content = new_content
# ---------- 7. 更新思考状态并返回结果 ----------
logger.info(f"{self.log_prefix} 最终心流思考结果: {content}")
# 更新当前思考内容
self.update_current_mind(content)

3
src/plugins/heartFC_chat/heartflow_prompt_builder.py
View File

@@ -260,6 +260,9 @@ class PromptBuilder:
relation_prompt = ""
for person in who_chat_in_group:
relation_prompt += await relationship_manager.build_relationship_info(person)
print(f"relation_prompt: {relation_prompt}")
print(f"relat11111111ion_prompt: {relation_prompt}")
# 心情
mood_manager = MoodManager.get_instance()

63
src/plugins/person_info/person_info.py
View File

@@ -137,34 +137,55 @@ class PersonInfoManager:
@staticmethod
def _extract_json_from_text(text: str) -> dict:
"""从文本中提取JSON数据的高容错方法"""
parsed_json = None
try:
# 尝试直接解析
return json.loads(text)
parsed_json = json.loads(text)
# 如果解析结果是列表，尝试取第一个元素
if isinstance(parsed_json, list):
if parsed_json: # 检查列表是否为空
parsed_json = parsed_json[0]
else: # 如果列表为空，重置为 None，走后续逻辑
parsed_json = None
# 确保解析结果是字典
if isinstance(parsed_json, dict):
return parsed_json
except json.JSONDecodeError:
try:
# 尝试找到JSON格式的部分
json_pattern = r"\{[^{}]*\}"
matches = re.findall(json_pattern, text)
if matches:
return json.loads(matches[0])
# 解析失败，继续尝试其他方法
pass
except Exception as e:
logger.warning(f"尝试直接解析JSON时发生意外错误: {e}")
pass # 继续尝试其他方法
# 如果上面都失败了，尝试提取键值对
nickname_pattern = r'"nickname"[:\s]+"([^"]+)"'
reason_pattern = r'"reason"[:\s]+"([^"]+)"'
# 如果直接解析失败或结果不是字典
try:
# 尝试找到JSON对象格式的部分
json_pattern = r"\{[^{}]*\}"
matches = re.findall(json_pattern, text)
if matches:
parsed_obj = json.loads(matches[0])
if isinstance(parsed_obj, dict): # 确保是字典
return parsed_obj
nickname_match = re.search(nickname_pattern, text)
reason_match = re.search(reason_pattern, text)
# 如果上面都失败了，尝试提取键值对
nickname_pattern = r'"nickname"[:\s]+"([^"]+)"'
reason_pattern = r'"reason"[:\s]+"([^"]+)"'
if nickname_match:
return {
"nickname": nickname_match.group(1),
"reason": reason_match.group(1) if reason_match else "未提供理由",
}
except Exception as e:
logger.error(f"JSON提取失败: {str(e)}")
nickname_match = re.search(nickname_pattern, text)
reason_match = re.search(reason_pattern, text)
# 如果所有方法都失败了，返回空结果
return {"nickname": "", "reason": ""}
if nickname_match:
return {
"nickname": nickname_match.group(1),
"reason": reason_match.group(1) if reason_match else "未提供理由",
}
except Exception as e:
logger.error(f"后备JSON提取失败: {str(e)}")
# 如果所有方法都失败了，返回默认字典
logger.warning(f"无法从文本中提取有效的JSON字典: {text}")
return {"nickname": "", "reason": ""}
async def qv_person_name(self, person_id: str, user_nickname: str, user_cardname: str, user_avatar: str):
"""给某个用户取名"""

13
src/plugins/person_info/relationship_manager.py
View File

@@ -278,12 +278,19 @@ class RelationshipManager:
return chat_stream.user_info.user_nickname, value, relationship_level[level_num]
async def build_relationship_info(self, person) -> str:
person_id = person_info_manager.get_person_id(person[0], person[1])
async def build_relationship_info(self, person, is_id: bool = False) -> str:
if is_id:
person_id = person
else:
print(f"person: {person}")
person_id = person_info_manager.get_person_id(person[0], person[1])
person_name = await person_info_manager.get_value(person_id, "person_name")
print(f"person_name: {person_name}")
relationship_value = await person_info_manager.get_value(person_id, "relationship_value")
level_num = self.calculate_level_num(relationship_value)
print(f"person_name: {person_name}, relationship_value: {relationship_value}, level_num: {level_num}")
if level_num == 0 or level_num == 5:
relationship_level = ["厌恶", "冷漠以对", "认识", "友好对待", "喜欢", "暧昧"]
relation_prompt2_list = [
@@ -298,7 +305,7 @@ class RelationshipManager:
elif level_num == 2:
return ""
else:
if random.random() < 0.5:
if random.random() < 0.6:
relationship_level = ["厌恶", "冷漠以对", "认识", "友好对待", "喜欢", "暧昧"]
relation_prompt2_list = [
"忽视的回应",

30
src/plugins/utils/chat_message_builder.py
View File

@@ -364,3 +364,33 @@ async def build_readable_messages(
else:
# 理论上不应该发生，但作为保险
return read_mark_line.strip() # 如果前后都无消息，只返回标记行
async def get_person_id_list(messages: List[Dict[str, Any]]) -> List[str]:
"""
从消息列表中提取不重复的 person_id 列表 (忽略机器人自身)。
Args:
messages: 消息字典列表。
Returns:
一个包含唯一 person_id 的列表。
"""
person_ids_set = set() # 使用集合来自动去重
for msg in messages:
user_info = msg.get("user_info", {})
platform = user_info.get("platform")
user_id = user_info.get("user_id")
# 检查必要信息是否存在 且 不是机器人自己
if not all([platform, user_id]) or user_id == global_config.BOT_QQ:
continue
person_id = person_info_manager.get_person_id(platform, user_id)
# 只有当获取到有效 person_id 时才添加
if person_id:
person_ids_set.add(person_id)
return list(person_ids_set) # 将集合转换为列表返回