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)