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feat(chat): 实现批量动作存储并优化消息处理流程

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新增批量动作记录存储功能，提升数据库写入性能。重构消息预处理逻辑，改进兴趣度计算和同步机制，优化用户信息和群组信息处理。添加配置选项控制批量存储开关，更新相关模板配置。
This commit is contained in:
Windpicker-owo
2025-10-05 17:45:44 +08:00
parent a7bc1b4f20
commit dccf1cffc9
8 changed files with 307 additions and 93 deletions
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35
src/plugins/built_in/affinity_flow_chatter/affinity_interest_calculator.py
View File

@@ -4,6 +4,7 @@
"""
import time
import orjson
from typing import TYPE_CHECKING
from src.chat.interest_system import bot_interest_manager
@@ -42,6 +43,9 @@ class AffinityInterestCalculator(BaseInterestCalculator):
self.reply_threshold = affinity_config.reply_action_interest_threshold # 回复动作兴趣阈值
self.mention_threshold = affinity_config.mention_bot_adjustment_threshold # 提及bot后的调整阈值
# 兴趣匹配系统配置
self.use_smart_matching = True
# 连续不回复概率提升
self.no_reply_count = 0
self.max_no_reply_count = affinity_config.max_no_reply_count
@@ -71,7 +75,11 @@ class AffinityInterestCalculator(BaseInterestCalculator):
start_time = time.time()
message_id = getattr(message, "message_id", "")
content = getattr(message, "processed_plain_text", "")
user_id = getattr(message, "user_info", {}).user_id if hasattr(message, "user_info") and hasattr(message.user_info, "user_id") else ""
user_info = getattr(message, "user_info", None)
if user_info and hasattr(user_info, "user_id"):
user_id = user_info.user_id
else:
user_id = ""
logger.debug(f"[Affinity兴趣计算] 开始处理消息 {message_id}")
logger.debug(f"[Affinity兴趣计算] 消息内容: {content[:50]}...")
@@ -111,10 +119,18 @@ class AffinityInterestCalculator(BaseInterestCalculator):
logger.debug(f"[Affinity兴趣计算] 应用不回复提升后: {total_score:.3f} → {adjusted_score:.3f}")
# 6. 决定是否回复和执行动作
should_reply = adjusted_score > self.reply_threshold
should_take_action = adjusted_score > (self.reply_threshold + 0.1)
logger.debug(f"[Affinity兴趣计算] 阈值判断: {adjusted_score:.3f} > 回复阈值:{self.reply_threshold:.3f}? = {should_reply}")
logger.debug(f"[Affinity兴趣计算] 阈值判断: {adjusted_score:.3f} > 动作阈值:{self.reply_threshold + 0.1:.3f}? = {should_take_action}")
reply_threshold = self.reply_threshold
action_threshold = global_config.affinity_flow.non_reply_action_interest_threshold
should_reply = adjusted_score >= reply_threshold
should_take_action = adjusted_score >= action_threshold
logger.debug(
f"[Affinity兴趣计算] 阈值判断: {adjusted_score:.3f} >= 回复阈值:{reply_threshold:.3f}? = {should_reply}"
)
logger.debug(
f"[Affinity兴趣计算] 阈值判断: {adjusted_score:.3f} >= 动作阈值:{action_threshold:.3f}? = {should_take_action}"
)
calculation_time = time.time() - start_time
@@ -140,7 +156,7 @@ class AffinityInterestCalculator(BaseInterestCalculator):
error_message=str(e)
)
async def _calculate_interest_match_score(self, content: str, keywords: list[str] = None) -> float:
async def _calculate_interest_match_score(self, content: str, keywords: list[str] | None = None) -> float:
"""计算兴趣匹配度（使用智能兴趣匹配系统）"""
# 调试日志：检查各个条件
@@ -158,7 +174,7 @@ class AffinityInterestCalculator(BaseInterestCalculator):
try:
# 使用机器人的兴趣标签系统进行智能匹配
match_result = await bot_interest_manager.calculate_interest_match(content, keywords)
match_result = await bot_interest_manager.calculate_interest_match(content, keywords or [])
logger.debug(f"兴趣匹配结果: {match_result}")
if match_result:
@@ -241,7 +257,6 @@ class AffinityInterestCalculator(BaseInterestCalculator):
key_words = getattr(message, "key_words", "")
if key_words:
try:
import orjson
extracted = orjson.loads(key_words)
if isinstance(extracted, list):
keywords = extracted
@@ -253,7 +268,6 @@ class AffinityInterestCalculator(BaseInterestCalculator):
key_words_lite = getattr(message, "key_words_lite", "")
if key_words_lite:
try:
import orjson
extracted = orjson.loads(key_words_lite)
if isinstance(extracted, list):
keywords = extracted
@@ -296,6 +310,3 @@ class AffinityInterestCalculator(BaseInterestCalculator):
self.no_reply_count = 0
else:
self.no_reply_count = min(self.no_reply_count + 1, self.max_no_reply_count)
# 是否使用智能兴趣匹配（作为类属性）
use_smart_matching = True

34
src/plugins/built_in/affinity_flow_chatter/plan_executor.py
View File

@@ -69,6 +69,13 @@ class ChatterPlanExecutor:
action_types = [action.action_type for action in plan.decided_actions]
logger.info(f"选择动作: {', '.join(action_types) if action_types else '无'}")
# 根据配置决定是否启用批量存储模式
if global_config.database.batch_action_storage_enabled:
self.action_manager.enable_batch_storage(plan.chat_id)
logger.debug("已启用批量存储模式")
else:
logger.debug("批量存储功能已禁用，使用立即存储模式")
execution_results = []
reply_actions = []
other_actions = []
@@ -102,6 +109,9 @@ class ChatterPlanExecutor:
f"规划执行完成: 总数={len(plan.decided_actions)}, 成功={successful_count}, 失败={len(execution_results) - successful_count}"
)
# 批量存储所有待处理的动作
await self._flush_action_manager_batch_storage(plan)
return {
"executed_count": len(plan.decided_actions),
"successful_count": successful_count,
@@ -395,6 +405,7 @@ class ChatterPlanExecutor:
# 移除执行时间列表以避免返回过大数据
stats.pop("execution_times", None)
return stats
def reset_stats(self):
@@ -422,3 +433,26 @@ class ChatterPlanExecutor:
}
for i, time_val in enumerate(recent_times)
]
async def _flush_action_manager_batch_storage(self, plan: Plan):
"""使用 action_manager 的批量存储功能存储所有待处理的动作"""
try:
# 通过 chat_id 获取真实的 chat_stream 对象
from src.plugin_system.apis.chat_api import get_chat_manager
chat_manager = get_chat_manager()
chat_stream = chat_manager.get_stream(plan.chat_id)
if chat_stream:
# 调用 action_manager 的批量存储
await self.action_manager.flush_batch_storage(chat_stream)
logger.info("批量存储完成：通过 action_manager 存储所有动作记录")
# 禁用批量存储模式
self.action_manager.disable_batch_storage()
except Exception as e:
logger.error(f"批量存储动作记录时发生错误: {e}")
# 确保在出错时也禁用批量存储模式
self.action_manager.disable_batch_storage()

58
src/plugins/built_in/affinity_flow_chatter/planner.py
View File

@@ -13,6 +13,7 @@ from src.mood.mood_manager import mood_manager
from src.plugins.built_in.affinity_flow_chatter.plan_executor import ChatterPlanExecutor
from src.plugins.built_in.affinity_flow_chatter.plan_filter import ChatterPlanFilter
from src.plugins.built_in.affinity_flow_chatter.plan_generator import ChatterPlanGenerator
from src.plugin_system.base.component_types import ChatMode
if TYPE_CHECKING:
from src.chat.planner_actions.action_manager import ChatterActionManager
@@ -60,7 +61,7 @@ class ChatterActionPlanner:
"other_actions_executed": 0,
}
async def plan(self, context: "StreamContext" = None) -> tuple[list[dict], dict | None]:
async def plan(self, context: "StreamContext | None" = None) -> tuple[list[dict[str, Any]], Any | None]:
"""
执行完整的增强版规划流程。
@@ -82,7 +83,7 @@ class ChatterActionPlanner:
self.planner_stats["failed_plans"] += 1
return [], None
async def _enhanced_plan_flow(self, context: "StreamContext") -> tuple[list[dict], dict | None]:
async def _enhanced_plan_flow(self, context: "StreamContext | None") -> tuple[list[dict[str, Any]], Any | None]:
"""执行增强版规划流程"""
try:
# 在规划前，先进行动作修改
@@ -92,39 +93,48 @@ class ChatterActionPlanner:
await action_modifier.modify_actions()
# 1. 生成初始 Plan
initial_plan = await self.generator.generate(context.chat_mode)
chat_mode = context.chat_mode if context else ChatMode.NORMAL
initial_plan = await self.generator.generate(chat_mode)
# 确保Plan中包含所有当前可用的动作
initial_plan.available_actions = self.action_manager.get_using_actions()
unread_messages = context.get_unread_messages() if context else []
# 2. 使用新的兴趣度管理系统进行评分
message_interest = 0.0
reply_not_available = False
max_message_interest = 0.0
reply_not_available = True
interest_updates: list[dict[str, Any]] = []
message_should_act = False
message_should_reply = False
aggregate_should_act = False
aggregate_should_reply = False
if unread_messages:
# 直接使用消息中已计算的标志，无需重复计算兴趣值
for message in unread_messages:
try:
message_interest = getattr(message, "interest_value", 0.3)
raw_interest = getattr(message, "interest_value", 0.3)
if raw_interest is None:
raw_interest = 0.0
message_interest = float(raw_interest)
max_message_interest = max(max_message_interest, message_interest)
message_should_reply = getattr(message, "should_reply", False)
message_should_act = getattr(message, "should_act", False)
if not message_should_reply:
reply_not_available = True
# 如果should_act为false，强制设为no_action
if not message_should_act:
reply_not_available = True
logger.debug(
f"消息 {message.message_id} 预计算标志: interest={message_interest:.3f}, "
f"should_reply={message_should_reply}, should_act={message_should_act}"
)
if message_should_reply:
aggregate_should_reply = True
aggregate_should_act = True
reply_not_available = False
break
if message_should_act:
aggregate_should_act = True
except Exception as e:
logger.warning(f"处理消息 {message.message_id} 失败: {e}")
message.interest_value = 0.0
@@ -144,14 +154,18 @@ class ChatterActionPlanner:
# 检查兴趣度是否达到非回复动作阈值
non_reply_action_interest_threshold = global_config.affinity_flow.non_reply_action_interest_threshold
if not message_should_act:
logger.info(f"兴趣度 {message_interest:.3f} 低于阈值 {non_reply_action_interest_threshold:.3f}，不执行动作")
if not aggregate_should_act:
logger.info("所有未读消息低于兴趣度阈值，不执行动作")
# 直接返回 no_action
from src.common.data_models.info_data_model import ActionPlannerInfo
no_action = ActionPlannerInfo(
action_type="no_action",
reasoning=f"兴趣度评分 {message_interest:.3f} 未达阈值 {non_reply_action_interest_threshold:.3f}",
reasoning=(
"所有未读消息兴趣度未达阈值 "
f"{non_reply_action_interest_threshold:.3f}"
f"（最高兴趣度 {max_message_interest:.3f}）"
),
action_data={},
action_message=None,
)
@@ -204,7 +218,7 @@ class ChatterActionPlanner:
except Exception as e:
logger.warning(f"批量更新数据库兴趣度失败: {e}")
def _update_stats_from_execution_result(self, execution_result: dict[str, any]):
def _update_stats_from_execution_result(self, execution_result: dict[str, Any]):
"""根据执行结果更新规划器统计"""
if not execution_result:
return
@@ -228,7 +242,7 @@ class ChatterActionPlanner:
self.planner_stats["replies_generated"] += reply_count
self.planner_stats["other_actions_executed"] += other_count
def _build_return_result(self, plan: "Plan") -> tuple[list[dict], dict | None]:
def _build_return_result(self, plan: "Plan") -> tuple[list[dict[str, Any]], Any | None]:
"""构建返回结果"""
final_actions = plan.decided_actions or []
final_target_message = next((act.action_message for act in final_actions if act.action_message), None)
@@ -245,7 +259,7 @@ class ChatterActionPlanner:
return final_actions_dict, final_target_message_dict
def get_planner_stats(self) -> dict[str, any]:
def get_planner_stats(self) -> dict[str, Any]:
"""获取规划器统计"""
return self.planner_stats.copy()
@@ -254,7 +268,7 @@ class ChatterActionPlanner:
chat_mood = mood_manager.get_mood_by_chat_id(self.chat_id)
return chat_mood.mood_state
def get_mood_stats(self) -> dict[str, any]:
def get_mood_stats(self) -> dict[str, Any]:
"""获取情绪状态统计"""
chat_mood = mood_manager.get_mood_by_chat_id(self.chat_id)
return {