diff --git a/src/chat/utils/statistic.py b/src/chat/utils/statistic.py index 9b5497e9..fa865d48 100644 --- a/src/chat/utils/statistic.py +++ b/src/chat/utils/statistic.py @@ -227,6 +227,8 @@ class StatisticOutputTask(AsyncTask): "", self._format_model_classified_stat(stats["last_hour"]), "", + self._format_module_classified_stat(stats["last_hour"]), + "", self._format_chat_stat(stats["last_hour"]), self.SEP_LINE, "", @@ -737,11 +739,13 @@ class StatisticOutputTask(AsyncTask): """ if stats[TOTAL_REQ_CNT] <= 0: return "" - data_fmt = "{:<32} {:>10} {:>12} {:>12} {:>12} {:>9.2f}¥ {:>10.1f} {:>10.1f}" + data_fmt = "{:<32} {:>10} {:>12} {:>12} {:>12} {:>9.2f}¥ {:>10.1f} {:>10.1f} {:>12} {:>12}" + total_replies = stats.get(TOTAL_REPLY_CNT, 0) + output = [ "按模型分类统计:", - " 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)", + " 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) 每次回复平均调用次数 每次回复平均Token数", ] for model_name, count in sorted(stats[REQ_CNT_BY_MODEL].items()): name = f"{model_name[:29]}..." if len(model_name) > 32 else model_name @@ -751,11 +755,19 @@ class StatisticOutputTask(AsyncTask): 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] + + # 计算每次回复平均值 + avg_count_per_reply = count / total_replies if total_replies > 0 else 0.0 + avg_tokens_per_reply = tokens / total_replies if total_replies > 0 else 0.0 + # 格式化大数字 formatted_count = _format_large_number(count) formatted_in_tokens = _format_large_number(in_tokens) formatted_out_tokens = _format_large_number(out_tokens) formatted_tokens = _format_large_number(tokens) + formatted_avg_count = _format_large_number(avg_count_per_reply) if total_replies > 0 else "N/A" + formatted_avg_tokens = _format_large_number(avg_tokens_per_reply) if total_replies > 0 else "N/A" + output.append( data_fmt.format( name, @@ -766,6 +778,62 @@ class StatisticOutputTask(AsyncTask): cost, avg_time_cost, std_time_cost, + formatted_avg_count, + formatted_avg_tokens, + ) + ) + + output.append("") + return "\n".join(output) + + @staticmethod + def _format_module_classified_stat(stats: Dict[str, Any]) -> str: + """ + 格式化按模块分类的统计数据 + """ + if stats[TOTAL_REQ_CNT] <= 0: + return "" + data_fmt = "{:<32} {:>10} {:>12} {:>12} {:>12} {:>9.2f}¥ {:>10.1f} {:>10.1f} {:>12} {:>12}" + + total_replies = stats.get(TOTAL_REPLY_CNT, 0) + + output = [ + "按模块分类统计:", + " 模块名称 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) 每次回复平均调用次数 每次回复平均Token数", + ] + for module_name, count in sorted(stats[REQ_CNT_BY_MODULE].items()): + name = f"{module_name[:29]}..." if len(module_name) > 32 else module_name + in_tokens = stats[IN_TOK_BY_MODULE][module_name] + out_tokens = stats[OUT_TOK_BY_MODULE][module_name] + tokens = stats[TOTAL_TOK_BY_MODULE][module_name] + cost = stats[COST_BY_MODULE][module_name] + avg_time_cost = stats[AVG_TIME_COST_BY_MODULE][module_name] + std_time_cost = stats[STD_TIME_COST_BY_MODULE][module_name] + + # 计算每次回复平均值 + avg_count_per_reply = count / total_replies if total_replies > 0 else 0.0 + avg_tokens_per_reply = tokens / total_replies if total_replies > 0 else 0.0 + + # 格式化大数字 + formatted_count = _format_large_number(count) + formatted_in_tokens = _format_large_number(in_tokens) + formatted_out_tokens = _format_large_number(out_tokens) + formatted_tokens = _format_large_number(tokens) + formatted_avg_count = _format_large_number(avg_count_per_reply) if total_replies > 0 else "N/A" + formatted_avg_tokens = _format_large_number(avg_tokens_per_reply) if total_replies > 0 else "N/A" + + output.append( + data_fmt.format( + name, + formatted_count, + formatted_in_tokens, + formatted_out_tokens, + formatted_tokens, + cost, + avg_time_cost, + std_time_cost, + formatted_avg_count, + formatted_avg_tokens, ) ) @@ -849,6 +917,7 @@ class StatisticOutputTask(AsyncTask): # format总在线时间 # 按模型分类统计 + total_replies = stat_data.get(TOTAL_REPLY_CNT, 0) model_rows = "\n".join( [ f"" @@ -860,11 +929,13 @@ class StatisticOutputTask(AsyncTask): f"{stat_data[COST_BY_MODEL][model_name]:.2f} ¥" f"{stat_data[AVG_TIME_COST_BY_MODEL][model_name]:.1f} 秒" f"{stat_data[STD_TIME_COST_BY_MODEL][model_name]:.1f} 秒" + f"{_format_large_number(count / total_replies, html=True) if total_replies > 0 else 'N/A'}" + f"{_format_large_number(stat_data[TOTAL_TOK_BY_MODEL][model_name] / total_replies, html=True) if total_replies > 0 else 'N/A'}" f"" for model_name, count in sorted(stat_data[REQ_CNT_BY_MODEL].items()) ] if stat_data[REQ_CNT_BY_MODEL] - else ["暂无数据"] + else ["暂无数据"] ) # 按请求类型分类统计 type_rows = "\n".join( @@ -878,11 +949,13 @@ class StatisticOutputTask(AsyncTask): f"{stat_data[COST_BY_TYPE][req_type]:.2f} ¥" f"{stat_data[AVG_TIME_COST_BY_TYPE][req_type]:.1f} 秒" f"{stat_data[STD_TIME_COST_BY_TYPE][req_type]:.1f} 秒" + f"{_format_large_number(count / total_replies, html=True) if total_replies > 0 else 'N/A'}" + f"{_format_large_number(stat_data[TOTAL_TOK_BY_TYPE][req_type] / total_replies, html=True) if total_replies > 0 else 'N/A'}" f"" for req_type, count in sorted(stat_data[REQ_CNT_BY_TYPE].items()) ] if stat_data[REQ_CNT_BY_TYPE] - else ["暂无数据"] + else ["暂无数据"] ) # 按模块分类统计 module_rows = "\n".join( @@ -896,11 +969,13 @@ class StatisticOutputTask(AsyncTask): f"{stat_data[COST_BY_MODULE][module_name]:.2f} ¥" f"{stat_data[AVG_TIME_COST_BY_MODULE][module_name]:.1f} 秒" f"{stat_data[STD_TIME_COST_BY_MODULE][module_name]:.1f} 秒" + f"{_format_large_number(count / total_replies, html=True) if total_replies > 0 else 'N/A'}" + f"{_format_large_number(stat_data[TOTAL_TOK_BY_MODULE][module_name] / total_replies, html=True) if total_replies > 0 else 'N/A'}" f"" for module_name, count in sorted(stat_data[REQ_CNT_BY_MODULE].items()) ] if stat_data[REQ_CNT_BY_MODULE] - else ["暂无数据"] + else ["暂无数据"] ) # 聊天消息统计 @@ -975,7 +1050,7 @@ class StatisticOutputTask(AsyncTask):

按模型分类统计

- + {model_rows} @@ -986,7 +1061,7 @@ class StatisticOutputTask(AsyncTask):
模型名称调用次数输入Token输出TokenToken总量累计花费平均耗时(秒)标准差(秒)
模型名称调用次数输入Token输出TokenToken总量累计花费平均耗时(秒)标准差(秒)每次回复平均调用次数每次回复平均Token数
- + {module_rows} @@ -998,7 +1073,7 @@ class StatisticOutputTask(AsyncTask):
模块名称调用次数输入Token输出TokenToken总量累计花费平均耗时(秒)标准差(秒)
模块名称调用次数输入Token输出TokenToken总量累计花费平均耗时(秒)标准差(秒)每次回复平均调用次数每次回复平均Token数
- + {type_rows} diff --git a/src/chat/utils/utils_image.py b/src/chat/utils/utils_image.py index a244fecb..56a3ae0f 100644 --- a/src/chat/utils/utils_image.py +++ b/src/chat/utils/utils_image.py @@ -130,10 +130,12 @@ class ImageManager: try: # 清理Images表中type为emoji的记录 deleted_images = Images.delete().where(Images.type == "emoji").execute() - + # 清理ImageDescriptions表中type为emoji的记录 - deleted_descriptions = ImageDescriptions.delete().where(ImageDescriptions.type == "emoji").execute() - + deleted_descriptions = ( + ImageDescriptions.delete().where(ImageDescriptions.type == "emoji").execute() + ) + total_deleted = deleted_images + deleted_descriptions if total_deleted > 0: logger.info( @@ -162,6 +164,47 @@ class ImageManager: tag_str = ",".join(emotion_list) return f"[表情包：{tag_str}]" + async def _save_emoji_file_if_needed(self, image_base64: str, image_hash: str, image_format: str) -> None: + """如果启用了steal_emoji且表情包未注册，保存文件到data/emoji目录 + + Args: + image_base64: 图片的base64编码 + image_hash: 图片的MD5哈希值 + image_format: 图片格式 + """ + if not global_config.emoji.steal_emoji: + return + + try: + from src.chat.emoji_system.emoji_manager import EMOJI_DIR + from src.chat.emoji_system.emoji_manager import get_emoji_manager + + # 确保目录存在 + os.makedirs(EMOJI_DIR, exist_ok=True) + + # 检查是否已存在该表情包（通过哈希值） + emoji_manager = get_emoji_manager() + existing_emoji = await emoji_manager.get_emoji_from_manager(image_hash) + if existing_emoji: + logger.debug(f"[自动保存] 表情包已注册，跳过保存: {image_hash[:8]}...") + return + + # 生成文件名：使用哈希值前8位 + 格式 + filename = f"{image_hash[:8]}.{image_format}" + file_path = os.path.join(EMOJI_DIR, filename) + + # 检查文件是否已存在（可能之前保存过但未注册） + if not os.path.exists(file_path): + # 保存文件 + if base64_to_image(image_base64, file_path): + logger.info(f"[自动保存] 表情包已保存到 {file_path} (Hash: {image_hash[:8]}...)") + else: + logger.warning(f"[自动保存] 保存表情包文件失败: {file_path}") + else: + logger.debug(f"[自动保存] 表情包文件已存在，跳过: {file_path}") + except Exception as save_error: + logger.warning(f"[自动保存] 保存表情包文件时出错: {save_error}") + async def get_emoji_description(self, image_base64: str) -> str: """获取表情包描述，优先使用EmojiDescriptionCache表中的缓存数据""" try: @@ -191,16 +234,18 @@ class ImageManager: cache_record = EmojiDescriptionCache.get_or_none(EmojiDescriptionCache.emoji_hash == image_hash) if cache_record: # 优先使用情感标签，如果没有则使用详细描述 + result_text = "" if cache_record.emotion_tags: - logger.info( - f"[缓存命中] 使用EmojiDescriptionCache表中的情感标签: {cache_record.emotion_tags[:50]}..." - ) - return f"[表情包：{cache_record.emotion_tags}]" + logger.info(f"[缓存命中] 使用EmojiDescriptionCache表中的情感标签: {cache_record.emotion_tags[:50]}...") + result_text = f"[表情包：{cache_record.emotion_tags}]" elif cache_record.description: - logger.info( - f"[缓存命中] 使用EmojiDescriptionCache表中的描述: {cache_record.description[:50]}..." - ) - return f"[表情包：{cache_record.description}]" + logger.info(f"[缓存命中] 使用EmojiDescriptionCache表中的描述: {cache_record.description[:50]}...") + result_text = f"[表情包：{cache_record.description}]" + + # 即使缓存命中，如果启用了steal_emoji，也检查是否需要保存文件 + if result_text: + await self._save_emoji_file_if_needed(image_base64, image_hash, image_format) + return result_text except Exception as e: logger.debug(f"查询EmojiDescriptionCache时出错: {e}") @@ -292,6 +337,9 @@ class ImageManager: except Exception as e: logger.error(f"保存表情包描述和情感标签缓存失败: {str(e)}") + # 如果启用了steal_emoji，自动保存表情包文件到data/emoji目录 + await self._save_emoji_file_if_needed(image_base64, image_hash, image_format) + return f"[表情包：{final_emotion}]" except Exception as e: diff --git a/src/config/config.py b/src/config/config.py index 3f5db816..9342cbb4 100644 --- a/src/config/config.py +++ b/src/config/config.py @@ -57,7 +57,7 @@ TEMPLATE_DIR = os.path.join(PROJECT_ROOT, "template") # 考虑到，实际上配置文件中的mai_version是不会自动更新的,所以采用硬编码 # 对该字段的更新，请严格参照语义化版本规范：https://semver.org/lang/zh-CN/ -MMC_VERSION = "0.11.6-snapshot.1" +MMC_VERSION = "0.11.6" def get_key_comment(toml_table, key): diff --git a/src/hippo_memorizer/chat_history_summarizer.py b/src/hippo_memorizer/chat_history_summarizer.py index 840f349d..357f46dd 100644 --- a/src/hippo_memorizer/chat_history_summarizer.py +++ b/src/hippo_memorizer/chat_history_summarizer.py @@ -8,8 +8,9 @@ import json import time import re from pathlib import Path -from typing import Dict, List, Optional, Set +from typing import Any, Dict, List, Optional, Set from dataclasses import dataclass, field +from json_repair import repair_json from src.common.logger import get_logger from src.common.data_models.database_data_model import DatabaseMessages @@ -315,9 +316,7 @@ class ChatHistorySummarizer: before_count = len(self.current_batch.messages) self.current_batch.messages.extend(new_messages) self.current_batch.end_time = current_time - logger.info( - f"{self.log_prefix} 更新聊天检查批次: {before_count} -> {len(self.current_batch.messages)} 条消息" - ) + logger.info(f"{self.log_prefix} 更新聊天检查批次: {before_count} -> {len(self.current_batch.messages)} 条消息") # 更新批次后持久化 self._persist_topic_cache() else: @@ -363,18 +362,20 @@ class ChatHistorySummarizer: else: time_str = f"{time_since_last_check / 3600:.1f}小时" - logger.info(f"{self.log_prefix} 批次状态检查 | 消息数: {message_count} | 距上次检查: {time_str}") + logger.info( + f"{self.log_prefix} 批次状态检查 | 消息数: {message_count} | 距上次检查: {time_str}" + ) # 检查“话题检查”触发条件 should_check = False # 条件1: 消息数量 >= 100，触发一次检查 - if message_count >= 50: + if message_count >= 80: should_check = True logger.info(f"{self.log_prefix} 触发检查条件: 消息数量达到 {message_count} 条（阈值: 100条）") # 条件2: 距离上一次检查 > 3600 秒（1小时），触发一次检查 - elif time_since_last_check > 1200: + elif time_since_last_check > 2400: should_check = True logger.info(f"{self.log_prefix} 触发检查条件: 距上次检查 {time_str}（阈值: 1小时）") @@ -413,7 +414,7 @@ class ChatHistorySummarizer: # 说明 bot 没有参与这段对话，不应该记录 bot_user_id = str(global_config.bot.qq_account) has_bot_message = False - + for msg in messages: if msg.user_info.user_id == bot_user_id: has_bot_message = True @@ -426,9 +427,7 @@ class ChatHistorySummarizer: return # 2. 构造编号后的消息字符串和参与者信息 - numbered_lines, index_to_msg_str, index_to_msg_text, index_to_participants = ( - self._build_numbered_messages_for_llm(messages) - ) + numbered_lines, index_to_msg_str, index_to_msg_text, index_to_participants = self._build_numbered_messages_for_llm(messages) # 3. 调用 LLM 识别话题，并得到 topic -> indices existing_topics = list(self.topic_cache.keys()) @@ -485,11 +484,11 @@ class ChatHistorySummarizer: topics_to_finalize: List[str] = [] for topic, item in self.topic_cache.items(): if item.no_update_checks >= 3: - logger.info(f"{self.log_prefix} 话题[{topic}] 连续 5 次检查无新增内容，触发打包存储") + logger.info(f"{self.log_prefix} 话题[{topic}] 连续 3 次检查无新增内容，触发打包存储") topics_to_finalize.append(topic) continue - if len(item.messages) > 8: - logger.info(f"{self.log_prefix} 话题[{topic}] 消息条数超过 30，触发打包存储") + if len(item.messages) > 5: + logger.info(f"{self.log_prefix} 话题[{topic}] 消息条数超过 4，触发打包存储") topics_to_finalize.append(topic) for topic in topics_to_finalize: @@ -590,7 +589,9 @@ class ChatHistorySummarizer: if not numbered_lines: return False, {} - history_topics_block = "\n".join(f"- {t}" for t in existing_topics) if existing_topics else "（当前无历史话题）" + history_topics_block = ( + "\n".join(f"- {t}" for t in existing_topics) if existing_topics else "（当前无历史话题）" + ) messages_block = "\n".join(numbered_lines) prompt = await global_prompt_manager.format_prompt( @@ -606,19 +607,42 @@ class ChatHistorySummarizer: max_tokens=800, ) - import re - logger.info(f"{self.log_prefix} 话题识别LLM Prompt: {prompt}") logger.info(f"{self.log_prefix} 话题识别LLM Response: {response}") - json_str = response.strip() - # 移除可能的 markdown 代码块标记 - json_str = re.sub(r"^```json\s*", "", json_str, flags=re.MULTILINE) - json_str = re.sub(r"^```\s*", "", json_str, flags=re.MULTILINE) - json_str = json_str.strip() + # 尝试从响应中提取JSON代码块 + json_str = None + json_pattern = r"```json\s*(.*?)\s*```" + matches = re.findall(json_pattern, response, re.DOTALL) + + if matches: + # 找到JSON代码块，使用第一个匹配 + json_str = matches[0].strip() + else: + # 如果没有找到代码块，尝试查找JSON数组的开始和结束位置 + # 查找第一个 [ 和最后一个 ] + start_idx = response.find('[') + end_idx = response.rfind(']') + if start_idx != -1 and end_idx != -1 and end_idx > start_idx: + json_str = response[start_idx:end_idx + 1].strip() + else: + # 如果还是找不到，尝试直接使用整个响应（移除可能的markdown标记） + json_str = response.strip() + json_str = re.sub(r"^```json\s*", "", json_str, flags=re.MULTILINE) + json_str = re.sub(r"^```\s*", "", json_str, flags=re.MULTILINE) + json_str = json_str.strip() - # 尝试直接解析为 JSON 数组 - result = json.loads(json_str) + # 使用json_repair修复可能的JSON错误 + if json_str: + try: + repaired_json = repair_json(json_str) + result = json.loads(repaired_json) if isinstance(repaired_json, str) else repaired_json + except Exception as repair_error: + # 如果repair失败，尝试直接解析 + logger.warning(f"{self.log_prefix} JSON修复失败，尝试直接解析: {repair_error}") + result = json.loads(json_str) + else: + raise ValueError("无法从响应中提取JSON内容") if not isinstance(result, list): logger.error(f"{self.log_prefix} 话题识别返回的 JSON 不是列表: {result}") @@ -723,41 +747,30 @@ class ChatHistorySummarizer: ) # 解析JSON响应 - import re - - # 移除可能的markdown代码块标记 json_str = response.strip() json_str = re.sub(r"^```json\s*", "", json_str, flags=re.MULTILINE) json_str = re.sub(r"^```\s*", "", json_str, flags=re.MULTILINE) json_str = json_str.strip() - # 尝试找到JSON对象的开始和结束位置 - # 查找第一个 { 和最后一个匹配的 } + # 查找JSON对象的开始与结束 start_idx = json_str.find("{") if start_idx == -1: raise ValueError("未找到JSON对象开始标记") - # 从后往前查找最后一个 } end_idx = json_str.rfind("}") if end_idx == -1 or end_idx <= start_idx: - raise ValueError("未找到JSON对象结束标记") + logger.warning(f"{self.log_prefix} JSON缺少结束标记，尝试自动修复") + extracted_json = json_str[start_idx:] + else: + extracted_json = json_str[start_idx : end_idx + 1] - # 提取JSON字符串 - json_str = json_str[start_idx : end_idx + 1] - - # 尝试解析JSON - try: - result = json.loads(json_str) - except json.JSONDecodeError: - # 如果解析失败，尝试修复字符串值中的中文引号 - # 简单方法：将字符串值中的中文引号替换为转义的英文引号 - # 使用状态机方法：遍历字符串，在字符串值内部替换中文引号 - fixed_chars = [] + def _parse_with_quote_fix(payload: str) -> Dict[str, Any]: + fixed_chars: List[str] = [] in_string = False escape_next = False i = 0 - while i < len(json_str): - char = json_str[i] + while i < len(payload): + char = payload[i] if escape_next: fixed_chars.append(char) escape_next = False @@ -767,16 +780,28 @@ class ChatHistorySummarizer: elif char == '"' and not escape_next: fixed_chars.append(char) in_string = not in_string - elif in_string and (char == '"' or char == '"'): + elif in_string and char in {"“", "”"}: # 在字符串值内部，将中文引号替换为转义的英文引号 fixed_chars.append('\\"') else: fixed_chars.append(char) i += 1 - json_str = "".join(fixed_chars) - # 再次尝试解析 - result = json.loads(json_str) + repaired = "".join(fixed_chars) + return json.loads(repaired) + + try: + result = json.loads(extracted_json) + except json.JSONDecodeError: + try: + repaired_json = repair_json(extracted_json) + if isinstance(repaired_json, str): + result = json.loads(repaired_json) + else: + result = repaired_json + except Exception as repair_error: + logger.warning(f"{self.log_prefix} repair_json 失败，使用引号修复: {repair_error}") + result = _parse_with_quote_fix(extracted_json) keywords = result.get("keywords", []) summary = result.get("summary", "无概括") @@ -896,3 +921,4 @@ class ChatHistorySummarizer: init_prompt() + diff --git a/src/plugin_system/base/plugin_base.py b/src/plugin_system/base/plugin_base.py index 30d33ec2..f21c2a40 100644 --- a/src/plugin_system/base/plugin_base.py +++ b/src/plugin_system/base/plugin_base.py @@ -212,6 +212,22 @@ class PluginBase(ABC): return value + def _format_toml_value(self, value: Any) -> str: + """将Python值格式化为合法的TOML字符串""" + if isinstance(value, str): + return json.dumps(value, ensure_ascii=False) + if isinstance(value, bool): + return str(value).lower() + if isinstance(value, (int, float)): + return str(value) + if isinstance(value, list): + inner = ", ".join(self._format_toml_value(item) for item in value) + return f"[{inner}]" + if isinstance(value, dict): + items = [f"{k} = {self._format_toml_value(v)}" for k, v in value.items()] + return "{ " + ", ".join(items) + " }" + return json.dumps(value, ensure_ascii=False) + def _generate_and_save_default_config(self, config_file_path: str): """根据插件的Schema生成并保存默认配置文件""" if not self.config_schema: @@ -251,12 +267,7 @@ class PluginBase(ABC): # 添加字段值 value = field.default - if isinstance(value, str): - toml_str += f'{field_name} = "{value}"\n' - elif isinstance(value, bool): - toml_str += f"{field_name} = {str(value).lower()}\n" - else: - toml_str += f"{field_name} = {value}\n" + toml_str += f"{field_name} = {self._format_toml_value(value)}\n" toml_str += "\n" toml_str += "\n" @@ -429,19 +440,7 @@ class PluginBase(ABC): # 添加字段值（使用迁移后的值） value = section_data.get(field_name, field.default) - if isinstance(value, str): - toml_str += f'{field_name} = "{value}"\n' - elif isinstance(value, bool): - toml_str += f"{field_name} = {str(value).lower()}\n" - elif isinstance(value, list): - # 格式化列表 - if all(isinstance(item, str) for item in value): - formatted_list = "[" + ", ".join(f'"{item}"' for item in value) + "]" - else: - formatted_list = str(value) - toml_str += f"{field_name} = {formatted_list}\n" - else: - toml_str += f"{field_name} = {value}\n" + toml_str += f"{field_name} = {self._format_toml_value(value)}\n" toml_str += "\n" toml_str += "\n"
请求类型调用次数输入Token输出TokenToken总量累计花费平均耗时(秒)标准差(秒)
请求类型调用次数输入Token输出TokenToken总量累计花费平均耗时(秒)标准差(秒)每次回复平均调用次数每次回复平均Token数