From ef724b748e5af0d99a996aa1519661872e8105ae Mon Sep 17 00:00:00 2001 From: Windpicker-owo <3431391539@qq.com> Date: Thu, 13 Nov 2025 19:45:31 +0800 Subject: [PATCH 1/3] =?UTF-8?q?fix(affinity=5Finterest=5Fcalculator):=20?= =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E5=9B=9E=E5=A4=8D=E5=90=8E=E9=98=88=E5=80=BC?= =?UTF-8?q?=E8=B0=83=E6=95=B4=E9=80=BB=E8=BE=91=E5=B9=B6=E7=A7=BB=E9=99=A4?= =?UTF-8?q?=E5=BC=83=E7=94=A8=E6=96=B9=E6=B3=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../core/affinity_interest_calculator.py | 14 ++------------ 1 file changed, 2 insertions(+), 12 deletions(-) diff --git a/src/plugins/built_in/affinity_flow_chatter/core/affinity_interest_calculator.py b/src/plugins/built_in/affinity_flow_chatter/core/affinity_interest_calculator.py index 07d58ef6f..38ae6ad8c 100644 --- a/src/plugins/built_in/affinity_flow_chatter/core/affinity_interest_calculator.py +++ b/src/plugins/built_in/affinity_flow_chatter/core/affinity_interest_calculator.py @@ -307,6 +307,7 @@ class AffinityInterestCalculator(BaseInterestCalculator): self.post_reply_boost_max_count - self.post_reply_boost_remaining ) post_reply_reduction = self.post_reply_threshold_reduction * decay_factor + self.post_reply_boost_remaining -= 1 total_reduction += post_reply_reduction logger.debug( f"[阈值调整] 回复后降低: {post_reply_reduction:.3f} " @@ -319,17 +320,6 @@ class AffinityInterestCalculator(BaseInterestCalculator): return adjusted_reply_threshold, adjusted_action_threshold - def _apply_no_reply_boost(self, base_score: float) -> float: - """【已弃用】应用连续不回复的概率提升 - - 注意：此方法已被 _apply_no_reply_threshold_adjustment 替代 - 保留用于向后兼容 - """ - if self.no_reply_count > 0 and self.no_reply_count < self.max_no_reply_count: - boost = self.no_reply_count * self.probability_boost_per_no_reply - return min(1.0, base_score + boost) - return base_score - def _extract_keywords_from_database(self, message: "DatabaseMessages") -> list[str]: """从数据库消息中提取关键词""" keywords = [] @@ -394,7 +384,7 @@ class AffinityInterestCalculator(BaseInterestCalculator): def on_reply_sent(self): """当机器人发送回复后调用，激活回复后阈值降低机制""" - if self.enable_post_reply_boost: + if self.enable_post_reply_boost and not self.post_reply_boost_remaining: # 重置回复后降低计数器 self.post_reply_boost_remaining = self.post_reply_boost_max_count logger.debug( From 718584a7daa55dd1d63b947f50e7e5d37297bcd7 Mon Sep 17 00:00:00 2001 From: minecraft1024a Date: Thu, 13 Nov 2025 19:47:44 +0800 Subject: [PATCH 2/3] =?UTF-8?q?fix(statistic):=20=E5=A2=9E=E5=BC=BA?= =?UTF-8?q?=E7=BB=9F=E8=AE=A1=E6=95=B0=E6=8D=AE=E5=A4=84=E7=90=86=E7=9A=84?= =?UTF-8?q?=E5=81=A5=E5=A3=AE=E6=80=A7?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 此前的统计处理逻辑在某些统计项（如模型调用、消息数）在特定周期内未产生数据时，会因为直接访问字典键而引发 `KeyError`，导致统计任务失败。 本次提交通过以下方式解决了该问题： - 在访问统计字典时，全面使用 `dict.get()` 并提供默认值，避免因键不存在而崩溃。 - 使用 `setdefault` 来确保在计算平均耗时等指标前，相关的数据结构已被初始化。 - 简化并重构了历史总览数据的合并逻辑，使其在处理不完整数据时更加稳健。 --- src/chat/utils/statistic.py | 69 ++++++++++++++++++------------------- 1 file changed, 34 insertions(+), 35 deletions(-) diff --git a/src/chat/utils/statistic.py b/src/chat/utils/statistic.py index 3cf604675..fb9536f40 100644 --- a/src/chat/utils/statistic.py +++ b/src/chat/utils/statistic.py @@ -417,7 +417,12 @@ class StatisticOutputTask(AsyncTask): avg_key = f"AVG_TIME_COST_BY_{items.upper()}" std_key = f"STD_TIME_COST_BY_{items.upper()}" - for item_name in period_stats[category_key]: + # Ensure the stat dicts exist before trying to access them, making the process more robust. + period_stats.setdefault(time_cost_key, defaultdict(list)) + period_stats.setdefault(avg_key, defaultdict(float)) + period_stats.setdefault(std_key, defaultdict(float)) + + for item_name in period_stats.get(category_key, {}): time_costs = period_stats[time_cost_key].get(item_name, []) if time_costs: avg_time = sum(time_costs) / len(time_costs) @@ -614,37 +619,31 @@ class StatisticOutputTask(AsyncTask): # 统计数据合并 # 合并三类统计数据 for period_key, _ in stat_start_timestamp: - stat[period_key].update(model_req_stat[period_key]) - stat[period_key].update(online_time_stat[period_key]) - stat[period_key].update(message_count_stat[period_key]) + stat[period_key].update(model_req_stat.get(period_key, {})) + stat[period_key].update(online_time_stat.get(period_key, {})) + stat[period_key].update(message_count_stat.get(period_key, {})) if last_all_time_stat: # 若存在上次完整统计数据，则将其与当前统计数据合并 for key, val in last_all_time_stat.items(): - # 确保当前统计数据中存在该key + # If a key from old stats is not in the current period's stats, it means no new data was generated. + # In this case, we carry over the old data. if key not in stat["all_time"]: + stat["all_time"][key] = val continue + # If the key exists in both, we merge. if isinstance(val, dict): - # 是字典类型，则进行合并 + # It's a dictionary-like object (e.g., COST_BY_MODEL, TIME_COST_BY_TYPE) + current_dict = stat["all_time"][key] for sub_key, sub_val in val.items(): - # 普通的数值或字典合并 - if sub_key in stat["all_time"][key]: - # 检查是否为嵌套的字典类型（如版本统计） - if isinstance(sub_val, dict) and isinstance(stat["all_time"][key][sub_key], dict): - # 合并嵌套字典 - for nested_key, nested_val in sub_val.items(): - if nested_key in stat["all_time"][key][sub_key]: - stat["all_time"][key][sub_key][nested_key] += nested_val - else: - stat["all_time"][key][sub_key][nested_key] = nested_val - else: - # 普通数值累加 - stat["all_time"][key][sub_key] += sub_val + if sub_key in current_dict: + # For lists (like TIME_COST), this extends. For numbers, this adds. + current_dict[sub_key] += sub_val else: - stat["all_time"][key][sub_key] = sub_val + current_dict[sub_key] = sub_val else: - # 直接合并 + # It's a simple value (e.g., TOTAL_COST) stat["all_time"][key] += val # 更新上次完整统计数据的时间戳 @@ -686,10 +685,10 @@ class StatisticOutputTask(AsyncTask): """ output = [ - f"总在线时间: {_format_online_time(stats[ONLINE_TIME])}", - f"总消息数: {stats[TOTAL_MSG_CNT]}", - f"总请求数: {stats[TOTAL_REQ_CNT]}", - f"总花费: {stats[TOTAL_COST]:.4f}¥", + f"总在线时间: {_format_online_time(stats.get(ONLINE_TIME, 0))}", + f"总消息数: {stats.get(TOTAL_MSG_CNT, 0)}", + f"总请求数: {stats.get(TOTAL_REQ_CNT, 0)}", + f"总花费: {stats.get(TOTAL_COST, 0.0):.4f}¥", "", ] @@ -700,21 +699,21 @@ class StatisticOutputTask(AsyncTask): """ 格式化按模型分类的统计数据 """ - if stats[TOTAL_REQ_CNT] <= 0: + if stats.get(TOTAL_REQ_CNT, 0) <= 0: return "" data_fmt = "{:<32} {:>10} {:>12} {:>12} {:>12} {:>9.4f}¥ {:>10} {:>10}" output = [ " 模型名称 调用次数 输入Token 输出Token Token总量 累计花费 平均耗时(秒) 标准差(秒)", ] - for model_name, count in sorted(stats[REQ_CNT_BY_MODEL].items()): + for model_name, count in sorted(stats.get(REQ_CNT_BY_MODEL, {}).items()): name = f"{model_name[:29]}..." if len(model_name) > 32 else model_name - in_tokens = stats[IN_TOK_BY_MODEL][model_name] - out_tokens = stats[OUT_TOK_BY_MODEL][model_name] - tokens = stats[TOTAL_TOK_BY_MODEL][model_name] - cost = stats[COST_BY_MODEL][model_name] - avg_time_cost = stats[AVG_TIME_COST_BY_MODEL][model_name] - std_time_cost = stats[STD_TIME_COST_BY_MODEL][model_name] + in_tokens = stats.get(IN_TOK_BY_MODEL, {}).get(model_name, 0) + out_tokens = stats.get(OUT_TOK_BY_MODEL, {}).get(model_name, 0) + tokens = stats.get(TOTAL_TOK_BY_MODEL, {}).get(model_name, 0) + cost = stats.get(COST_BY_MODEL, {}).get(model_name, 0.0) + avg_time_cost = stats.get(AVG_TIME_COST_BY_MODEL, {}).get(model_name, 0.0) + std_time_cost = stats.get(STD_TIME_COST_BY_MODEL, {}).get(model_name, 0.0) output.append( data_fmt.format(name, count, in_tokens, out_tokens, tokens, cost, avg_time_cost, std_time_cost) ) @@ -726,12 +725,12 @@ class StatisticOutputTask(AsyncTask): """ 格式化聊天统计数据 """ - if stats[TOTAL_MSG_CNT] <= 0: + if stats.get(TOTAL_MSG_CNT, 0) <= 0: return "" output = ["聊天消息统计:", " 联系人/群组名称 消息数量"] output.extend( f"{self.name_mapping.get(chat_id, (chat_id, 0))[0][:32]:<32} {count:>10}" - for chat_id, count in sorted(stats[MSG_CNT_BY_CHAT].items()) + for chat_id, count in sorted(stats.get(MSG_CNT_BY_CHAT, {}).items()) ) output.append("") return "\n".join(output) From 2bbb03b5b0e247e11413f75f622150cdda6f9bb6 Mon Sep 17 00:00:00 2001 From: Windpicker-owo <3431391539@qq.com> Date: Thu, 13 Nov 2025 20:10:58 +0800 Subject: [PATCH 3/3] =?UTF-8?q?fix(planner):=20=E6=9B=B4=E6=96=B0=E5=8A=A8?= =?UTF-8?q?=E4=BD=9C=E8=BF=87=E6=BB=A4=E9=80=BB=E8=BE=91=E4=BB=A5=E7=A1=AE?= =?UTF-8?q?=E4=BF=9D=E5=9B=9E=E5=A4=8D=E5=8A=A8=E4=BD=9C=E5=9C=A8=E6=9C=AA?= =?UTF-8?q?=E8=BE=BE=E5=88=B0=E9=98=88=E5=80=BC=E6=97=B6=E8=A2=AB=E6=8E=92?= =?UTF-8?q?=E9=99=A4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../planner/plan_generator.py | 6 +-- .../affinity_flow_chatter/planner/planner.py | 49 +++---------------- 2 files changed, 10 insertions(+), 45 deletions(-) diff --git a/src/plugins/built_in/affinity_flow_chatter/planner/plan_generator.py b/src/plugins/built_in/affinity_flow_chatter/planner/plan_generator.py index 3cf36d046..f8142d696 100644 --- a/src/plugins/built_in/affinity_flow_chatter/planner/plan_generator.py +++ b/src/plugins/built_in/affinity_flow_chatter/planner/plan_generator.py @@ -107,15 +107,15 @@ class ChatterPlanGenerator: """ try: # 从组件注册表获取可用动作 - available_actions = component_registry.get_enabled_actions() + available_actions = self.action_manager.get_using_actions() # 根据聊天类型和模式筛选动作 filtered_actions = {} for action_name, action_info in available_actions.items(): # 检查动作是否支持当前聊天类型 - if chat_type in action_info.chat_types: + if chat_type == action_info.chat_type_allow: # 检查动作是否支持当前模式 - if mode in action_info.chat_modes: + if mode == action_info.mode_enable: filtered_actions[action_name] = action_info return filtered_actions diff --git a/src/plugins/built_in/affinity_flow_chatter/planner/planner.py b/src/plugins/built_in/affinity_flow_chatter/planner/planner.py index 0ac27bb93..2d42cc426 100644 --- a/src/plugins/built_in/affinity_flow_chatter/planner/planner.py +++ b/src/plugins/built_in/affinity_flow_chatter/planner/planner.py @@ -190,9 +190,13 @@ class ChatterActionPlanner: # 4. 生成初始计划 initial_plan = await self.generator.generate(ChatMode.FOCUS) - # 5. 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() - + # 5. 过滤回复动作（如果未达到回复阈值） + if reply_not_available: + initial_plan.available_actions = { + action_name: action_info + for action_name, action_info in initial_plan.available_actions.items() + if action_name not in ["reply", "respond"] + } # 6. 筛选 Plan available_actions = list(initial_plan.available_actions.keys()) plan_filter = ChatterPlanFilter(self.chat_id, available_actions) @@ -527,45 +531,6 @@ class ChatterActionPlanner: except Exception as e: logger.warning(f"同步chat_mode到ChatStream失败: {e}") - async def _flush_cached_messages_to_unread(self, context: "StreamContext | None") -> list: - """在planner开始时将缓存消息刷新到未读消息列表 - - 此方法在动作修改器执行后、生成初始计划前调用，确保计划阶段能看到所有积累的消息。 - - Args: - context: 流上下文 - - Returns: - list: 刷新的消息列表 - """ - if not context: - return [] - - try: - from src.chat.message_manager.message_manager import message_manager - - stream_id = context.stream_id - - if message_manager.is_running and message_manager.has_cached_messages(stream_id): - # 获取缓存消息 - cached_messages = message_manager.flush_cached_messages(stream_id) - - if cached_messages: - # 直接添加到上下文的未读消息列表 - for message in cached_messages: - context.unread_messages.append(message) - logger.info(f"Planner开始前刷新缓存消息到未读列表: stream={stream_id}, 数量={len(cached_messages)}") - return cached_messages - - return [] - - except ImportError: - logger.debug("MessageManager不可用，跳过缓存刷新") - return [] - except Exception as e: - logger.warning(f"Planner刷新缓存消息失败: error={e}") - return [] - 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: