fix:调整目录结构,优化hfc prompt,移除日程,移除动态和llm判断willing模式,

This commit is contained in:
SengokuCola
2025-05-13 18:37:55 +08:00
parent 6376da0682
commit fed71bccad
131 changed files with 422 additions and 1500 deletions

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from src.config.config import global_config
from src.chat.message_receive.message import MessageRecv, MessageSending, Message
from src.common.database import db
import time
import traceback
from typing import List
class InfoCatcher:
def __init__(self):
self.chat_history = [] # 聊天历史,长度为三倍使用的上下文喵~
self.context_length = global_config.observation_context_size
self.chat_history_in_thinking = [] # 思考期间的聊天内容喵~
self.chat_history_after_response = [] # 回复后的聊天内容,长度为一倍上下文喵~
self.chat_id = ""
self.trigger_response_text = ""
self.response_text = ""
self.trigger_response_time = 0
self.trigger_response_message = None
self.response_time = 0
self.response_messages = []
# 使用字典来存储 heartflow 模式的数据
self.heartflow_data = {
"heart_flow_prompt": "",
"sub_heartflow_before": "",
"sub_heartflow_now": "",
"sub_heartflow_after": "",
"sub_heartflow_model": "",
"prompt": "",
"response": "",
"model": "",
}
# 使用字典来存储 reasoning 模式的数据喵~
self.reasoning_data = {"thinking_log": "", "prompt": "", "response": "", "model": ""}
# 耗时喵~
self.timing_results = {
"interested_rate_time": 0,
"sub_heartflow_observe_time": 0,
"sub_heartflow_step_time": 0,
"make_response_time": 0,
}
def catch_decide_to_response(self, message: MessageRecv):
# 搜集决定回复时的信息
self.trigger_response_message = message
self.trigger_response_text = message.detailed_plain_text
self.trigger_response_time = time.time()
self.chat_id = message.chat_stream.stream_id
self.chat_history = self.get_message_from_db_before_msg(message)
def catch_after_observe(self, obs_duration: float): # 这里可以有更多信息
self.timing_results["sub_heartflow_observe_time"] = obs_duration
# def catch_shf
def catch_afer_shf_step(self, step_duration: float, past_mind: str, current_mind: str):
self.timing_results["sub_heartflow_step_time"] = step_duration
if len(past_mind) > 1:
self.heartflow_data["sub_heartflow_before"] = past_mind[-1]
self.heartflow_data["sub_heartflow_now"] = current_mind
else:
self.heartflow_data["sub_heartflow_before"] = past_mind[-1]
self.heartflow_data["sub_heartflow_now"] = current_mind
def catch_after_llm_generated(self, prompt: str, response: str, reasoning_content: str = "", model_name: str = ""):
# if self.response_mode == "heart_flow": # 条件判断不需要了喵~
# self.heartflow_data["prompt"] = prompt
# self.heartflow_data["response"] = response
# self.heartflow_data["model"] = model_name
# elif self.response_mode == "reasoning": # 条件判断不需要了喵~
# self.reasoning_data["thinking_log"] = reasoning_content
# self.reasoning_data["prompt"] = prompt
# self.reasoning_data["response"] = response
# self.reasoning_data["model"] = model_name
# 直接记录信息喵~
self.reasoning_data["thinking_log"] = reasoning_content
self.reasoning_data["prompt"] = prompt
self.reasoning_data["response"] = response
self.reasoning_data["model"] = model_name
# 如果 heartflow 数据也需要通用字段,可以取消下面的注释喵~
# self.heartflow_data["prompt"] = prompt
# self.heartflow_data["response"] = response
# self.heartflow_data["model"] = model_name
self.response_text = response
def catch_after_generate_response(self, response_duration: float):
self.timing_results["make_response_time"] = response_duration
def catch_after_response(
self, response_duration: float, response_message: List[str], first_bot_msg: MessageSending
):
self.timing_results["make_response_time"] = response_duration
self.response_time = time.time()
for msg in response_message:
self.response_messages.append(msg)
self.chat_history_in_thinking = self.get_message_from_db_between_msgs(
self.trigger_response_message, first_bot_msg
)
@staticmethod
def get_message_from_db_between_msgs(message_start: Message, message_end: Message):
try:
# 从数据库中获取消息的时间戳
time_start = message_start.message_info.time
time_end = message_end.message_info.time
chat_id = message_start.chat_stream.stream_id
print(f"查询参数: time_start={time_start}, time_end={time_end}, chat_id={chat_id}")
# 查询数据库,获取 chat_id 相同且时间在 start 和 end 之间的数据
messages_between = db.messages.find(
{"chat_id": chat_id, "time": {"$gt": time_start, "$lt": time_end}}
).sort("time", -1)
result = list(messages_between)
print(f"查询结果数量: {len(result)}")
if result:
print(f"第一条消息时间: {result[0]['time']}")
print(f"最后一条消息时间: {result[-1]['time']}")
return result
except Exception as e:
print(f"获取消息时出错: {str(e)}")
return []
def get_message_from_db_before_msg(self, message: MessageRecv):
# 从数据库中获取消息
message_id = message.message_info.message_id
chat_id = message.chat_stream.stream_id
# 查询数据库,获取 chat_id 相同且 message_id 小于当前消息的 30 条数据
messages_before = (
db.messages.find({"chat_id": chat_id, "message_id": {"$lt": message_id}})
.sort("time", -1)
.limit(self.context_length * 3)
) # 获取更多历史信息
return list(messages_before)
def message_list_to_dict(self, message_list):
# 存储简化的聊天记录
result = []
for message in message_list:
if not isinstance(message, dict):
message = self.message_to_dict(message)
# print(message)
lite_message = {
"time": message["time"],
"user_nickname": message["user_info"]["user_nickname"],
"processed_plain_text": message["processed_plain_text"],
}
result.append(lite_message)
return result
@staticmethod
def message_to_dict(message):
if not message:
return None
if isinstance(message, dict):
return message
return {
# "message_id": message.message_info.message_id,
"time": message.message_info.time,
"user_id": message.message_info.user_info.user_id,
"user_nickname": message.message_info.user_info.user_nickname,
"processed_plain_text": message.processed_plain_text,
# "detailed_plain_text": message.detailed_plain_text
}
def done_catch(self):
"""将收集到的信息存储到数据库的 thinking_log 集合中喵~"""
try:
# 将消息对象转换为可序列化的字典喵~
thinking_log_data = {
"chat_id": self.chat_id,
"trigger_text": self.trigger_response_text,
"response_text": self.response_text,
"trigger_info": {
"time": self.trigger_response_time,
"message": self.message_to_dict(self.trigger_response_message),
},
"response_info": {
"time": self.response_time,
"message": self.response_messages,
},
"timing_results": self.timing_results,
"chat_history": self.message_list_to_dict(self.chat_history),
"chat_history_in_thinking": self.message_list_to_dict(self.chat_history_in_thinking),
"chat_history_after_response": self.message_list_to_dict(self.chat_history_after_response),
"heartflow_data": self.heartflow_data,
"reasoning_data": self.reasoning_data,
}
# 根据不同的响应模式添加相应的数据喵~ # 现在直接都加上去好了喵~
# if self.response_mode == "heart_flow":
# thinking_log_data["mode_specific_data"] = self.heartflow_data
# elif self.response_mode == "reasoning":
# thinking_log_data["mode_specific_data"] = self.reasoning_data
# 将数据插入到 thinking_log 集合中喵~
db.thinking_log.insert_one(thinking_log_data)
return True
except Exception as e:
print(f"存储思考日志时出错: {str(e)} 喵~")
print(traceback.format_exc())
return False
class InfoCatcherManager:
def __init__(self):
self.info_catchers = {}
def get_info_catcher(self, thinking_id: str) -> InfoCatcher:
if thinking_id not in self.info_catchers:
self.info_catchers[thinking_id] = InfoCatcher()
return self.info_catchers[thinking_id]
info_catcher_manager = InfoCatcherManager()