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feat(affinity-flow): 重构消息处理以使用StreamContext对象

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重构AFC消息处理系统，将基于字典的消息数据传递改为直接使用StreamContext对象。主要变更包括：

- 修改AFCManager的process_message方法为process_stream_context，直接接收StreamContext对象
- 在chatter中重构消息处理逻辑，直接从StreamContext获取未读和历史消息
- 移除批量消息处理功能，改为单次StreamContext处理
- 在message_manager中简化消息处理流程，直接传递StreamContext对象
- 添加未读消息清理机制，防止异常情况下消息一直未读

同时优化兴趣度评分系统的参数：
- 调整回复阈值从0.55到0.56
- 增加最大不回复次数从15到20
- 提升每次不回复的概率增加从0.01到0.02
- 优化提及奖励从3.0降到1.0
- 调整回复后的不回复计数减少从1到3

BREAKING CHANGE: AFCManager的process_message方法已重命名为process_stream_context，参数从message_data改为context对象
This commit is contained in:
Windpicker-owo
2025-09-18 22:27:29 +08:00
parent ddf0d08fac
commit a2225cad3a
12 changed files with 476 additions and 277 deletions
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224
src/chat/replyer/default_generator.py
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@@ -83,13 +83,13 @@ def init_prompt():
- {schedule_block}
## 历史记录
### 当前群聊中的所有人的聊天记录：
{background_dialogue_prompt}
### 📜 已读历史消息（仅供参考）
{read_history_prompt}
{cross_context_block}
### 当前群聊中正在与你对话的聊天记录
{core_dialogue_prompt}
### 📬 未读历史消息（动作执行对象）
{unread_history_prompt}
## 表达方式
- *你需要参考你的回复风格：*
@@ -119,6 +119,11 @@ def init_prompt():
## 规则
{safety_guidelines_block}
**重要提醒：**
- **已读历史消息仅作为当前聊天情景的参考**
- **动作执行对象只能是未读历史消息中的消息**
- **请优先对兴趣值高的消息做出回复**（兴趣度标注在未读消息末尾）
在回应之前，首先分析消息的针对性：
1. **直接针对你**：@你、回复你、明确询问你 → 必须回应
2. **间接相关**：涉及你感兴趣的话题但未直接问你 → 谨慎参与
@@ -657,78 +662,195 @@ class DefaultReplyer:
duration = end_time - start_time
return name, result, duration
def build_s4u_chat_history_prompts(
self, message_list_before_now: List[Dict[str, Any]], target_user_id: str, sender: str
async def build_s4u_chat_history_prompts(
self, message_list_before_now: List[Dict[str, Any]], target_user_id: str, sender: str, chat_id: str
) -> Tuple[str, str]:
"""
构建 s4u 风格的分离对话 prompt
构建 s4u 风格的已读/未读历史消息 prompt
Args:
message_list_before_now: 历史消息列表
target_user_id: 目标用户ID（当前对话对象）
sender: 发送者名称
chat_id: 聊天ID
Returns:
Tuple[str, str]: (核心对话prompt, 背景对话prompt)
Tuple[str, str]: (已读历史消息prompt, 未读历史消息prompt)
"""
core_dialogue_list = []
try:
# 从message_manager获取真实的已读/未读消息
from src.chat.message_manager.message_manager import message_manager
# 获取聊天流的上下文
stream_context = message_manager.stream_contexts.get(chat_id)
if stream_context:
# 使用真正的已读和未读消息
read_messages = stream_context.history_messages # 已读消息
unread_messages = stream_context.get_unread_messages() # 未读消息
# 构建已读历史消息 prompt
read_history_prompt = ""
if read_messages:
read_content = build_readable_messages(
[msg.flatten() for msg in read_messages[-50:]], # 限制数量
replace_bot_name=True,
timestamp_mode="normal_no_YMD",
truncate=True,
)
read_history_prompt = f"这是已读历史消息，仅作为当前聊天情景的参考：\n{read_content}"
else:
read_history_prompt = "暂无已读历史消息"
# 构建未读历史消息 prompt（包含兴趣度）
unread_history_prompt = ""
if unread_messages:
# 尝试获取兴趣度评分
interest_scores = await self._get_interest_scores_for_messages([msg.flatten() for msg in unread_messages])
unread_lines = []
for msg in unread_messages:
msg_id = msg.message_id
msg_time = time.strftime('%H:%M:%S', time.localtime(msg.time))
msg_content = msg.processed_plain_text
# 添加兴趣度信息
interest_score = interest_scores.get(msg_id, 0.0)
interest_text = f" [兴趣度: {interest_score:.3f}]" if interest_score > 0 else ""
unread_lines.append(f"{msg_time}: {msg_content}{interest_text}")
unread_history_prompt_str = "\n".join(unread_lines)
unread_history_prompt = f"这是未读历史消息，包含兴趣度评分，请优先对兴趣值高的消息做出动作：\n{unread_history_prompt_str}"
else:
unread_history_prompt = "暂无未读历史消息"
return read_history_prompt, unread_history_prompt
else:
# 回退到传统方法
return await self._fallback_build_chat_history_prompts(message_list_before_now, target_user_id, sender)
except Exception as e:
logger.warning(f"获取已读/未读历史消息失败，使用回退方法: {e}")
return await self._fallback_build_chat_history_prompts(message_list_before_now, target_user_id, sender)
async def _fallback_build_chat_history_prompts(
self, message_list_before_now: List[Dict[str, Any]], target_user_id: str, sender: str
) -> Tuple[str, str]:
"""
回退的已读/未读历史消息构建方法
"""
# 通过is_read字段分离已读和未读消息
read_messages = []
unread_messages = []
bot_id = str(global_config.bot.qq_account)
# 过滤消息：分离bot和目标用户的对话 vs 其他用户的对话
for msg_dict in message_list_before_now:
try:
msg_user_id = str(msg_dict.get("user_id"))
reply_to = msg_dict.get("reply_to", "")
_platform, reply_to_user_id = self._parse_reply_target(reply_to)
if (msg_user_id == bot_id and reply_to_user_id == target_user_id) or msg_user_id == target_user_id:
# bot 和目标用户的对话
core_dialogue_list.append(msg_dict)
if msg_dict.get("is_read", False):
read_messages.append(msg_dict)
else:
unread_messages.append(msg_dict)
except Exception as e:
logger.error(f"处理消息记录时出错: {msg_dict}, 错误: {e}")
# 构建背景对话 prompt
all_dialogue_prompt = ""
if message_list_before_now:
latest_25_msgs = message_list_before_now[-int(global_config.chat.max_context_size) :]
all_dialogue_prompt_str = build_readable_messages(
latest_25_msgs,
# 如果没有is_read字段，使用原有的逻辑
if not read_messages and not unread_messages:
# 使用原有的核心对话逻辑
core_dialogue_list = []
for msg_dict in message_list_before_now:
try:
msg_user_id = str(msg_dict.get("user_id"))
reply_to = msg_dict.get("reply_to", "")
_platform, reply_to_user_id = self._parse_reply_target(reply_to)
if (msg_user_id == bot_id and reply_to_user_id == target_user_id) or msg_user_id == target_user_id:
core_dialogue_list.append(msg_dict)
except Exception as e:
logger.error(f"处理消息记录时出错: {msg_dict}, 错误: {e}")
read_messages = [msg for msg in message_list_before_now if msg not in core_dialogue_list]
unread_messages = core_dialogue_list
# 构建已读历史消息 prompt
read_history_prompt = ""
if read_messages:
read_content = build_readable_messages(
read_messages[-50:],
replace_bot_name=True,
timestamp_mode="normal",
timestamp_mode="normal_no_YMD",
truncate=True,
)
all_dialogue_prompt = f"所有用户的发言：\n{all_dialogue_prompt_str}"
read_history_prompt = f"这是已读历史消息，仅作为当前聊天情景的参考：\n{read_content}"
else:
read_history_prompt = "暂无已读历史消息"
# 构建核心对话 prompt
core_dialogue_prompt = ""
if core_dialogue_list:
# 检查最新五条消息中是否包含bot自己说的消息
latest_5_messages = core_dialogue_list[-5:] if len(core_dialogue_list) >= 5 else core_dialogue_list
has_bot_message = any(str(msg.get("user_id")) == bot_id for msg in latest_5_messages)
# 构建未读历史消息 prompt
unread_history_prompt = ""
if unread_messages:
# 尝试获取兴趣度评分
interest_scores = await self._get_interest_scores_for_messages(unread_messages)
# logger.info(f"最新五条消息：{latest_5_messages}")
# logger.info(f"最新五条消息中是否包含bot自己说的消息：{has_bot_message}")
unread_lines = []
for msg in unread_messages:
msg_id = msg.get("message_id", "")
msg_time = time.strftime('%H:%M:%S', time.localtime(msg.get("time", time.time())))
msg_content = msg.get("processed_plain_text", "")
# 如果最新五条消息中不包含bot的消息，则返回空字符串
if not has_bot_message:
core_dialogue_prompt = ""
else:
core_dialogue_list = core_dialogue_list[-int(global_config.chat.max_context_size * 2) :] # 限制消息数量
# 添加兴趣度信息
interest_score = interest_scores.get(msg_id, 0.0)
interest_text = f" [兴趣度: {interest_score:.3f}]" if interest_score > 0 else ""
core_dialogue_prompt_str = build_readable_messages(
core_dialogue_list,
replace_bot_name=True,
merge_messages=False,
timestamp_mode="normal_no_YMD",
read_mark=0.0,
truncate=True,
show_actions=True,
)
core_dialogue_prompt = f"""--------------------------------
这是你和{sender}的对话，你们正在交流中：
{core_dialogue_prompt_str}
--------------------------------
"""
unread_lines.append(f"{msg_time}: {msg_content}{interest_text}")
return core_dialogue_prompt, all_dialogue_prompt
unread_history_prompt_str = "\n".join(unread_lines)
unread_history_prompt = f"这是未读历史消息，包含兴趣度评分，请优先对兴趣值高的消息做出动作：\n{unread_history_prompt_str}"
else:
unread_history_prompt = "暂无未读历史消息"
return read_history_prompt, unread_history_prompt
async def _get_interest_scores_for_messages(self, messages: List[dict]) -> dict[str, float]:
"""为消息获取兴趣度评分"""
interest_scores = {}
try:
from src.chat.affinity_flow.interest_scoring import interest_scoring_system
from src.common.data_models.database_data_model import DatabaseMessages
# 转换消息格式
db_messages = []
for msg_dict in messages:
try:
db_msg = DatabaseMessages(
message_id=msg_dict.get("message_id", ""),
time=msg_dict.get("time", time.time()),
chat_id=msg_dict.get("chat_id", ""),
processed_plain_text=msg_dict.get("processed_plain_text", ""),
user_id=msg_dict.get("user_id", ""),
user_nickname=msg_dict.get("user_nickname", ""),
user_platform=msg_dict.get("platform", "qq"),
chat_info_group_id=msg_dict.get("group_id", ""),
chat_info_group_name=msg_dict.get("group_name", ""),
chat_info_group_platform=msg_dict.get("platform", "qq"),
)
db_messages.append(db_msg)
except Exception as e:
logger.warning(f"转换消息格式失败: {e}")
continue
# 计算兴趣度评分
if db_messages:
bot_nickname = global_config.bot.nickname or "麦麦"
scores = await interest_scoring_system.calculate_interest_scores(db_messages, bot_nickname)
# 构建兴趣度字典
for score in scores:
interest_scores[score.message_id] = score.total_score
except Exception as e:
logger.warning(f"获取兴趣度评分失败: {e}")
return interest_scores
def build_mai_think_context(
self,