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🤖 自动格式化代码 [skip ci]

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github-actions[bot]
2025-07-01 05:46:54 +00:00
parent 0dad4a1d46
commit 087f4a6cbf
3 changed files with 52 additions and 49 deletions
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6
src/chat/focus_chat/heartFC_chat.py
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@@ -139,14 +139,14 @@ class HeartFChatting:
# 检查全局关系开关
if not global_config.relationship.enable_relationship:
continue
# 检查处理器特定配置，同时支持向后兼容
processor_enabled = getattr(config_processor_settings, config_key, True)
# 向后兼容：如果旧的person_impression_processor为True，则启用两个新处理器
if not processor_enabled and getattr(config_processor_settings, "person_impression_processor", True):
processor_enabled = True
if processor_enabled:
self.enabled_post_planning_processor_names.append(proc_name)
else:

89
src/chat/focus_chat/info_processors/real_time_info_processor.py
View File

@@ -63,20 +63,20 @@ def init_real_time_info_prompts():
class RealTimeInfoProcessor(BaseProcessor):
"""实时信息提取处理器
负责从对话中识别需要的用户信息，并从用户档案中实时提取相关信息
"""
log_prefix = "实时信息"
def __init__(self, subheartflow_id: str):
super().__init__()
self.subheartflow_id = subheartflow_id
# 信息获取缓存：记录正在获取的信息请求
self.info_fetching_cache: List[Dict[str, any]] = []
# 信息结果缓存：存储已获取的信息结果，带TTL
self.info_fetched_cache: Dict[str, Dict[str, any]] = {}
# 结构：{person_id: {info_type: {"info": str, "ttl": int, "start_time": float, "person_name": str, "unknow": bool}}}
@@ -94,6 +94,7 @@ class RealTimeInfoProcessor(BaseProcessor):
)
from src.chat.message_receive.chat_stream import get_chat_manager
name = get_chat_manager().get_stream_name(self.subheartflow_id)
self.log_prefix = f"[{name}] 实时信息"
@@ -105,21 +106,21 @@ class RealTimeInfoProcessor(BaseProcessor):
**kwargs,
) -> List[InfoBase]:
"""处理信息对象
Args:
observations: 观察对象列表
action_type: 动作类型
action_data: 动作数据
Returns:
List[InfoBase]: 处理后的结构化信息列表
"""
# 清理过期的信息缓存
self._cleanup_expired_cache()
# 执行实时信息识别和提取
relation_info_str = await self._identify_and_extract_info(observations, action_type, action_data)
if relation_info_str:
relation_info = RelationInfo()
relation_info.set_relation_info(relation_info_str)
@@ -144,12 +145,12 @@ class RealTimeInfoProcessor(BaseProcessor):
action_data: dict = None,
) -> str:
"""识别并提取用户信息
Args:
observations: 观察对象列表
action_type: 动作类型
action_data: 动作数据
Returns:
str: 提取到的用户信息字符串
"""
@@ -178,7 +179,7 @@ class RealTimeInfoProcessor(BaseProcessor):
# 识别需要提取的信息类型
info_type = await self._identify_needed_info(chat_observe_info, sender, text)
# 如果需要提取新信息，执行提取
if info_type:
await self._extract_single_info(person_id, info_type, sender)
@@ -188,10 +189,10 @@ class RealTimeInfoProcessor(BaseProcessor):
def _parse_reply_target(self, target_message: str) -> tuple:
"""解析回复目标消息
Args:
target_message: 目标消息，格式为 "用户名:消息内容"
Returns:
tuple: (发送者, 消息内容)
"""
@@ -213,16 +214,16 @@ class RealTimeInfoProcessor(BaseProcessor):
def _extract_chat_observe_info(self, observations: List[Observation]) -> str:
"""从观察对象中提取聊天信息
Args:
observations: 观察对象列表
Returns:
str: 聊天观察信息
"""
if not observations:
return ""
for observation in observations:
if isinstance(observation, ChattingObservation):
return observation.get_observe_info()
@@ -230,12 +231,12 @@ class RealTimeInfoProcessor(BaseProcessor):
async def _identify_needed_info(self, chat_observe_info: str, sender: str, text: str) -> str:
"""识别需要提取的信息类型
Args:
chat_observe_info: 聊天观察信息
sender: 发送者
text: 消息内容
Returns:
str: 需要提取的信息类型，如果不需要则返回None
"""
@@ -258,39 +259,41 @@ class RealTimeInfoProcessor(BaseProcessor):
try:
logger.debug(f"{self.log_prefix} 信息识别prompt: \n{prompt}\n")
content, _ = await self.llm_model.generate_response_async(prompt=prompt)
if content:
content_json = json.loads(repair_json(content))
# 检查是否返回了不需要查询的标志
if "none" in content_json:
logger.info(f"{self.log_prefix} LLM判断当前不需要查询任何信息：{content_json.get('none', '')}")
return None
info_type = content_json.get("info_type")
if info_type:
# 记录信息获取请求
self.info_fetching_cache.append({
"person_id": get_person_info_manager().get_person_id_by_person_name(sender),
"person_name": sender,
"info_type": info_type,
"start_time": time.time(),
"forget": False,
})
self.info_fetching_cache.append(
{
"person_id": get_person_info_manager().get_person_id_by_person_name(sender),
"person_name": sender,
"info_type": info_type,
"start_time": time.time(),
"forget": False,
}
)
# 限制缓存大小
if len(self.info_fetching_cache) > 20:
self.info_fetching_cache.pop(0)
logger.info(f"{self.log_prefix} 识别到需要调取用户 {sender} 的[{info_type}]信息")
return info_type
else:
logger.warning(f"{self.log_prefix} LLM未返回有效的info_type。响应: {content}")
except Exception as e:
logger.error(f"{self.log_prefix} 执行信息识别LLM请求时出错: {e}")
logger.error(traceback.format_exc())
return None
def _build_info_cache_block(self) -> str:
@@ -314,7 +317,7 @@ class RealTimeInfoProcessor(BaseProcessor):
async def _extract_single_info(self, person_id: str, info_type: str, person_name: str):
"""提取单个信息类型
Args:
person_id: 用户ID
info_type: 信息类型
@@ -353,7 +356,7 @@ class RealTimeInfoProcessor(BaseProcessor):
try:
person_impression = await person_info_manager.get_value(person_id, "impression")
points = await person_info_manager.get_value(person_id, "points")
# 构建印象信息块
if person_impression:
person_impression_block = (
@@ -387,7 +390,7 @@ class RealTimeInfoProcessor(BaseProcessor):
# 使用LLM提取信息
nickname_str = ",".join(global_config.bot.alias_names)
name_block = f"你的名字是{global_config.bot.nickname},你的昵称有{nickname_str}，有人也会用这些昵称称呼你。"
prompt = (await global_prompt_manager.get_prompt_async("real_time_fetch_person_info_prompt")).format(
name_block=name_block,
info_type=info_type,
@@ -426,14 +429,14 @@ class RealTimeInfoProcessor(BaseProcessor):
logger.info(f"{self.log_prefix} 思考了也不知道{person_name} 的 {info_type} 信息")
else:
logger.warning(f"{self.log_prefix} 小模型返回空结果，获取 {person_name} 的 {info_type} 信息失败。")
except Exception as e:
logger.error(f"{self.log_prefix} 执行信息提取时出错: {e}")
logger.error(traceback.format_exc())
async def _save_info_to_cache(self, person_id: str, info_type: str, info_content: str):
"""将提取到的信息保存到 person_info 的 info_list 字段中
Args:
person_id: 用户ID
info_type: 信息类型
@@ -476,12 +479,12 @@ class RealTimeInfoProcessor(BaseProcessor):
def _organize_known_info(self) -> str:
"""组织已知的用户信息为字符串
Returns:
str: 格式化的用户信息字符串
"""
persons_infos_str = ""
if self.info_fetched_cache:
persons_with_known_info = [] # 有已知信息的人员
persons_with_unknown_info = [] # 有未知信息的人员
@@ -534,7 +537,7 @@ class RealTimeInfoProcessor(BaseProcessor):
status_lines = [f"{self.log_prefix} 实时信息缓存状态："]
status_lines.append(f"获取请求缓存数：{len(self.info_fetching_cache)}")
status_lines.append(f"结果缓存用户数：{len(self.info_fetched_cache)}")
if self.info_fetched_cache:
for person_id, info_types in self.info_fetched_cache.items():
person_name = list(info_types.values())[0]["person_name"] if info_types else person_id
@@ -544,9 +547,9 @@ class RealTimeInfoProcessor(BaseProcessor):
unknow = info_data["unknow"]
status = "未知" if unknow else "已知"
status_lines.append(f" {info_type}: {status} (TTL: {ttl})")
return "\n".join(status_lines)
# 初始化提示词
init_real_time_info_prompts()
init_real_time_info_prompts()

6
src/chat/focus_chat/info_processors/relationship_processor.py
View File

@@ -1,6 +1,5 @@
from src.chat.heart_flow.observation.chatting_observation import ChattingObservation
from src.chat.heart_flow.observation.observation import Observation
from src.llm_models.utils_model import LLMRequest
from src.config.config import global_config
import time
import traceback
@@ -36,10 +35,10 @@ logger = get_logger("relationship_build_processor")
class RelationshipBuildProcessor(BaseProcessor):
"""关系构建处理器
负责跟踪用户消息活动、管理消息段、触发关系构建和印象更新
"""
log_prefix = "关系构建"
def __init__(self, subheartflow_id: str):
@@ -446,6 +445,7 @@ class RelationshipBuildProcessor(BaseProcessor):
segments = self.person_engaged_cache[person_id]
# 异步执行关系构建
import asyncio
asyncio.create_task(self.update_impression_on_segments(person_id, self.subheartflow_id, segments))
# 移除已处理的用户缓存
del self.person_engaged_cache[person_id]