import time import json import asyncio from collections import OrderedDict from typing import List, Dict, Optional, Any from json_repair import repair_json from peewee import fn from src.common.logger import get_logger from src.common.database.database_model import Jargon from src.llm_models.utils_model import LLMRequest from src.config.config import model_config, global_config from src.chat.message_receive.chat_stream import get_chat_manager from src.plugin_system.apis import llm_api from src.chat.utils.chat_message_builder import ( build_readable_messages_with_id, get_raw_msg_by_timestamp_with_chat_inclusive, get_raw_msg_before_timestamp_with_chat, build_readable_messages_with_list, ) from src.chat.utils.prompt_builder import Prompt, global_prompt_manager from src.chat.utils.utils import parse_platform_accounts logger = get_logger("jargon") def _contains_bot_self_name(content: str) -> bool: """ 判断词条是否包含机器人的昵称或别名 """ if not content: return False bot_config = getattr(global_config, "bot", None) if not bot_config: return False target = content.strip().lower() nickname = str(getattr(bot_config, "nickname", "") or "").strip().lower() alias_names = [str(alias or "").strip().lower() for alias in getattr(bot_config, "alias_names", []) or []] candidates = [name for name in [nickname, *alias_names] if name] return any(name in target for name in candidates if target) def _format_context_message(msg: Any, seq_index: int) -> str: """ 将单条消息格式化为带序号的上下文行 """ if msg is None: return "" text = (getattr(msg, "display_message", None) or getattr(msg, "processed_plain_text", None) or "").strip() if not text: return "" user_info = getattr(msg, "user_info", None) nickname = "" if user_info: nickname = getattr(user_info, "user_nickname", "") or getattr(user_info, "user_id", "") if not nickname: nickname = getattr(msg, "user_nickname", "") or getattr(msg, "user_id", "") or "某人" return f"{nickname}: {text}" def _build_context_paragraph(messages: List[Any], center_index: int) -> Optional[str]: """ 构建包含中心消息上下文的段落（前3条+后3条） """ if not messages or center_index < 0 or center_index >= len(messages): return None context_start = max(0, center_index - 3) context_end = min(len(messages), center_index + 1 + 3) context_lines: List[str] = [] for idx in range(context_start, context_end): formatted_line = _format_context_message(messages[idx], idx + 1) if formatted_line: context_lines.append(formatted_line) if not context_lines: return None paragraph = "\n".join(context_lines).strip() return paragraph or None def _is_bot_message(msg: Any) -> bool: """判断消息是否来自机器人自身""" if msg is None: return False bot_config = getattr(global_config, "bot", None) if not bot_config: return False platform = ( str(getattr(msg, "user_platform", "") or getattr(getattr(msg, "user_info", None), "platform", "") or "") .strip() .lower() ) user_id = ( str(getattr(msg, "user_id", "") or getattr(getattr(msg, "user_info", None), "user_id", "") or "") .strip() ) if not platform or not user_id: return False platform_accounts = {} try: platform_accounts = parse_platform_accounts(getattr(bot_config, "platforms", []) or []) except Exception: platform_accounts = {} bot_accounts: Dict[str, str] = {} qq_account = str(getattr(bot_config, "qq_account", "") or "").strip() if qq_account: bot_accounts["qq"] = qq_account telegram_account = str(getattr(bot_config, "telegram_account", "") or "").strip() if telegram_account: bot_accounts["telegram"] = telegram_account for plat, account in platform_accounts.items(): if account and plat not in bot_accounts: bot_accounts[plat] = account bot_account = bot_accounts.get(platform) return bool(bot_account and user_id == bot_account) def _has_adjacent_bot_message(messages: List[Any], center_index: int) -> bool: """检查目标消息的上一条或下一条是否为机器人发言""" for neighbor in (center_index - 1, center_index + 1): if 0 <= neighbor < len(messages) and _is_bot_message(messages[neighbor]): return True return False def _init_prompt() -> None: prompt_str = """ **聊天内容，其中的{bot_name}的发言内容是你自己的发言，[msg_id] 是消息ID** {chat_str} 请从上面这段聊天内容中提取"可能是黑话"的候选项（黑话/俚语/网络缩写/口头禅）。 - 必须为对话中真实出现过的短词或短语 - 必须是你无法理解含义的词语，没有明确含义的词语 - 请不要选择有明确含义，或者含义清晰的词语 - 排除：人名、@、表情包/图片中的内容、纯标点、常规功能词（如的、了、呢、啊等） - 每个词条长度建议 2-8 个字符（不强制），尽量短小 - 合并重复项，去重 黑话必须为以下几种类型： - 由字母构成的，汉语拼音首字母的简写词，例如：nb、yyds、xswl - 英文词语的缩写，用英文字母概括一个词汇或含义，例如：CPU、GPU、API - 中文词语的缩写，用几个汉字概括一个词汇或含义，例如：社死、内卷 以 JSON 数组输出，元素为对象（严格按以下结构）： 请你提取出可能的黑话，最多10 [ {{"content": "词条", "msg_id": "m12"}}, // msg_id 必须与上方聊天中展示的ID完全一致 {{"content": "词条2", "msg_id": "m15"}} ] 现在请输出： """ Prompt(prompt_str, "extract_jargon_prompt") def _init_inference_prompts() -> None: """初始化含义推断相关的prompt""" # Prompt 1: 基于raw_content和content推断 prompt1_str = """ **词条内容** {content} **词条出现的上下文。其中的{bot_name}的发言内容是你自己的发言** {raw_content_list} 请根据上下文，推断"{content}"这个词条的含义。 - 如果这是一个黑话、俚语或网络用语，请推断其含义 - 如果含义明确（常规词汇），也请说明 - 如果上下文信息不足，无法推断含义，请设置 no_info 为 true 以 JSON 格式输出： {{ "meaning": "详细含义说明（包含使用场景、来源、具体解释等）", "no_info": false }} 注意：如果信息不足无法推断，请设置 "no_info": true，此时 meaning 可以为空字符串 """ Prompt(prompt1_str, "jargon_inference_with_context_prompt") # Prompt 2: 仅基于content推断 prompt2_str = """ **词条内容** {content} 请仅根据这个词条本身，推断其含义。 - 如果这是一个黑话、俚语或网络用语，请推断其含义 - 如果含义明确（常规词汇），也请说明 以 JSON 格式输出： {{ "meaning": "详细含义说明（包含使用场景、来源、具体解释等）" }} """ Prompt(prompt2_str, "jargon_inference_content_only_prompt") # Prompt 3: 比较两个推断结果 prompt3_str = """ **推断结果1（基于上下文）** {inference1} **推断结果2（仅基于词条）** {inference2} 请比较这两个推断结果，判断它们是否相同或类似。 - 如果两个推断结果的"含义"相同或类似，说明这个词条不是黑话（含义明确） - 如果两个推断结果有差异，说明这个词条可能是黑话（需要上下文才能理解） 以 JSON 格式输出： {{ "is_similar": true/false, "reason": "判断理由" }} """ Prompt(prompt3_str, "jargon_compare_inference_prompt") _init_prompt() _init_inference_prompts() async def _enrich_raw_content_if_needed( content: str, raw_content_list: List[str], chat_id: str, messages: List[Any], extraction_start_time: float, extraction_end_time: float, ) -> List[str]: """ 检查raw_content是否只包含黑话本身，如果是，则获取该消息的前三条消息作为原始内容 Args: content: 黑话内容 raw_content_list: 原始raw_content列表 chat_id: 聊天ID messages: 当前时间窗口内的消息列表 extraction_start_time: 提取开始时间 extraction_end_time: 提取结束时间 Returns: 处理后的raw_content列表 """ enriched_list = [] for raw_content in raw_content_list: # 检查raw_content是否只包含黑话本身（去除空白字符后比较） raw_content_clean = raw_content.strip() content_clean = content.strip() # 如果raw_content只包含黑话本身（可能有一些标点或空白），则尝试获取上下文 # 去除所有空白字符后比较，确保只包含黑话本身 raw_content_normalized = raw_content_clean.replace(" ", "").replace("\n", "").replace("\t", "") content_normalized = content_clean.replace(" ", "").replace("\n", "").replace("\t", "") if raw_content_normalized == content_normalized: # 在消息列表中查找只包含该黑话的消息（去除空白后比较） target_message = None for msg in messages: msg_content = (msg.processed_plain_text or msg.display_message or "").strip() msg_content_normalized = msg_content.replace(" ", "").replace("\n", "").replace("\t", "") # 检查消息内容是否只包含黑话本身（去除空白后完全匹配） if msg_content_normalized == content_normalized: target_message = msg break if target_message and target_message.time: # 获取该消息的前三条消息 try: previous_messages = get_raw_msg_before_timestamp_with_chat( chat_id=chat_id, timestamp=target_message.time, limit=3 ) if previous_messages: # 将前三条消息和当前消息一起格式化 context_messages = previous_messages + [target_message] # 按时间排序 context_messages.sort(key=lambda x: x.time or 0) # 格式化为可读消息 formatted_context, _ = await build_readable_messages_with_list( context_messages, replace_bot_name=True, timestamp_mode="relative", truncate=False, ) if formatted_context.strip(): enriched_list.append(formatted_context.strip()) logger.warning(f"为黑话 {content} 补充了上下文消息") else: # 如果格式化失败，使用原始raw_content enriched_list.append(raw_content) else: # 没有找到前三条消息，使用原始raw_content enriched_list.append(raw_content) except Exception as e: logger.warning(f"获取黑话 {content} 的上下文消息失败: {e}") # 出错时使用原始raw_content enriched_list.append(raw_content) else: # 没有找到包含黑话的消息，使用原始raw_content enriched_list.append(raw_content) else: # raw_content包含更多内容，直接使用 enriched_list.append(raw_content) return enriched_list def _should_infer_meaning(jargon_obj: Jargon) -> bool: """ 判断是否需要进行含义推断 在 count 达到 3,6, 10, 20, 40, 60, 100 时进行推断 并且count必须大于last_inference_count，避免重启后重复判定 如果is_complete为True，不再进行推断 """ # 如果已完成所有推断，不再推断 if jargon_obj.is_complete: return False count = jargon_obj.count or 0 last_inference = jargon_obj.last_inference_count or 0 # 阈值列表：3,6, 10, 20, 40, 60, 100 thresholds = [2, 4, 8, 12, 24, 60, 100] if count < thresholds[0]: return False # 如果count没有超过上次判定值，不需要判定 if count <= last_inference: return False # 找到第一个大于last_inference的阈值 next_threshold = None for threshold in thresholds: if threshold > last_inference: next_threshold = threshold break # 如果没有找到下一个阈值，说明已经超过100，不应该再推断 if next_threshold is None: return False # 检查count是否达到或超过这个阈值 return count >= next_threshold class JargonMiner: def __init__(self, chat_id: str) -> None: self.chat_id = chat_id self.last_learning_time: float = time.time() # 频率控制，可按需调整 self.min_messages_for_learning: int = 10 self.min_learning_interval: float = 20 self.llm = LLMRequest( model_set=model_config.model_task_config.utils, request_type="jargon.extract", ) # 初始化stream_name作为类属性，避免重复提取 chat_manager = get_chat_manager() stream_name = chat_manager.get_stream_name(self.chat_id) self.stream_name = stream_name if stream_name else self.chat_id self.cache_limit = 100 self.cache: OrderedDict[str, None] = OrderedDict() def _add_to_cache(self, content: str) -> None: """将提取到的黑话加入缓存，保持LRU语义""" if not content: return key = content.strip() if not key: return if key in self.cache: self.cache.move_to_end(key) else: self.cache[key] = None if len(self.cache) > self.cache_limit: self.cache.popitem(last=False) def _collect_cached_entries(self, messages: List[Any]) -> List[Dict[str, List[str]]]: """检查缓存中的黑话是否出现在当前消息窗口，生成对应上下文""" if not self.cache or not messages: return [] cached_entries: List[Dict[str, List[str]]] = [] processed_pairs = set() for idx, msg in enumerate(messages): msg_text = (getattr(msg, "display_message", None) or getattr(msg, "processed_plain_text", None) or "").strip() if not msg_text or _is_bot_message(msg): continue for content in self.cache.keys(): if not content: continue if (content, idx) in processed_pairs: continue if content in msg_text: if _has_adjacent_bot_message(messages, idx): continue paragraph = _build_context_paragraph(messages, idx) if not paragraph: continue cached_entries.append({"content": content, "raw_content": [paragraph]}) processed_pairs.add((content, idx)) return cached_entries async def _infer_meaning_by_id(self, jargon_id: int) -> None: """通过ID加载对象并推断""" try: jargon_obj = Jargon.get_by_id(jargon_id) # 再次检查is_complete，因为可能在异步任务执行时已被标记为完成 if jargon_obj.is_complete: logger.debug(f"jargon {jargon_obj.content} 已完成所有推断，跳过") return await self.infer_meaning(jargon_obj) except Exception as e: logger.error(f"通过ID推断jargon失败: {e}") async def infer_meaning(self, jargon_obj: Jargon) -> None: """ 对jargon进行含义推断 """ try: content = jargon_obj.content raw_content_str = jargon_obj.raw_content or "" # 解析raw_content列表 raw_content_list = [] if raw_content_str: try: raw_content_list = ( json.loads(raw_content_str) if isinstance(raw_content_str, str) else raw_content_str ) if not isinstance(raw_content_list, list): raw_content_list = [raw_content_list] if raw_content_list else [] except (json.JSONDecodeError, TypeError): raw_content_list = [raw_content_str] if raw_content_str else [] if not raw_content_list: logger.warning(f"jargon {content} 没有raw_content，跳过推断") return # 步骤1: 基于raw_content和content推断 raw_content_text = "\n".join(raw_content_list) prompt1 = await global_prompt_manager.format_prompt( "jargon_inference_with_context_prompt", content=content, bot_name = global_config.bot.nickname, raw_content_list=raw_content_text, ) response1, _ = await self.llm.generate_response_async(prompt1, temperature=0.3) if not response1: logger.warning(f"jargon {content} 推断1失败：无响应") return # 解析推断1结果 inference1 = None try: resp1 = response1.strip() if resp1.startswith("{") and resp1.endswith("}"): inference1 = json.loads(resp1) else: repaired = repair_json(resp1) inference1 = json.loads(repaired) if isinstance(repaired, str) else repaired if not isinstance(inference1, dict): logger.warning(f"jargon {content} 推断1结果格式错误") return except Exception as e: logger.error(f"jargon {content} 推断1解析失败: {e}") return # 检查推断1是否表示信息不足无法推断 no_info = inference1.get("no_info", False) meaning1 = inference1.get("meaning", "").strip() if no_info or not meaning1: logger.info(f"jargon {content} 推断1表示信息不足无法推断，放弃本次推断，待下次更新") # 更新最后一次判定的count值，避免在同一阈值重复尝试 jargon_obj.last_inference_count = jargon_obj.count or 0 jargon_obj.save() return # 步骤2: 仅基于content推断 prompt2 = await global_prompt_manager.format_prompt( "jargon_inference_content_only_prompt", content=content, ) response2, _ = await self.llm.generate_response_async(prompt2, temperature=0.3) if not response2: logger.warning(f"jargon {content} 推断2失败：无响应") return # 解析推断2结果 inference2 = None try: resp2 = response2.strip() if resp2.startswith("{") and resp2.endswith("}"): inference2 = json.loads(resp2) else: repaired = repair_json(resp2) inference2 = json.loads(repaired) if isinstance(repaired, str) else repaired if not isinstance(inference2, dict): logger.warning(f"jargon {content} 推断2结果格式错误") return except Exception as e: logger.error(f"jargon {content} 推断2解析失败: {e}") return # logger.info(f"jargon {content} 推断2提示词: {prompt2}") # logger.info(f"jargon {content} 推断2结果: {response2}") # logger.info(f"jargon {content} 推断1提示词: {prompt1}") # logger.info(f"jargon {content} 推断1结果: {response1}") if global_config.debug.show_jargon_prompt: logger.info(f"jargon {content} 推断2提示词: {prompt2}") logger.info(f"jargon {content} 推断2结果: {response2}") logger.info(f"jargon {content} 推断1提示词: {prompt1}") logger.info(f"jargon {content} 推断1结果: {response1}") else: logger.debug(f"jargon {content} 推断2提示词: {prompt2}") logger.debug(f"jargon {content} 推断2结果: {response2}") logger.debug(f"jargon {content} 推断1提示词: {prompt1}") logger.debug(f"jargon {content} 推断1结果: {response1}") # 步骤3: 比较两个推断结果 prompt3 = await global_prompt_manager.format_prompt( "jargon_compare_inference_prompt", inference1=json.dumps(inference1, ensure_ascii=False), inference2=json.dumps(inference2, ensure_ascii=False), ) if global_config.debug.show_jargon_prompt: logger.info(f"jargon {content} 比较提示词: {prompt3}") response3, _ = await self.llm.generate_response_async(prompt3, temperature=0.3) if not response3: logger.warning(f"jargon {content} 比较失败：无响应") return # 解析比较结果 comparison = None try: resp3 = response3.strip() if resp3.startswith("{") and resp3.endswith("}"): comparison = json.loads(resp3) else: repaired = repair_json(resp3) comparison = json.loads(repaired) if isinstance(repaired, str) else repaired if not isinstance(comparison, dict): logger.warning(f"jargon {content} 比较结果格式错误") return except Exception as e: logger.error(f"jargon {content} 比较解析失败: {e}") return # 判断是否为黑话 is_similar = comparison.get("is_similar", False) is_jargon = not is_similar # 如果相似，说明不是黑话；如果有差异，说明是黑话 # 更新数据库记录 jargon_obj.is_jargon = is_jargon if is_jargon: # 是黑话，使用推断1的结果（基于上下文，更准确） jargon_obj.meaning = inference1.get("meaning", "") else: # 不是黑话，清空含义，不再存储任何内容 jargon_obj.meaning = "" # 更新最后一次判定的count值，避免重启后重复判定 jargon_obj.last_inference_count = jargon_obj.count or 0 # 如果count>=100，标记为完成，不再进行推断 if (jargon_obj.count or 0) >= 100: jargon_obj.is_complete = True jargon_obj.save() logger.debug( f"jargon {content} 推断完成: is_jargon={is_jargon}, meaning={jargon_obj.meaning}, last_inference_count={jargon_obj.last_inference_count}, is_complete={jargon_obj.is_complete}" ) # 固定输出推断结果，格式化为可读形式 if is_jargon: # 是黑话，输出格式：[聊天名]xxx的含义是 xxxxxxxxxxx meaning = jargon_obj.meaning or "无详细说明" is_global = jargon_obj.is_global if is_global: logger.info(f"[黑话]{content}的含义是 {meaning}") else: logger.info(f"[{self.stream_name}]{content}的含义是 {meaning}") else: # 不是黑话，输出格式：[聊天名]xxx 不是黑话 logger.info(f"[{self.stream_name}]{content} 不是黑话") except Exception as e: logger.error(f"jargon推断失败: {e}") import traceback traceback.print_exc() def should_trigger(self) -> bool: # 冷却时间检查 if time.time() - self.last_learning_time < self.min_learning_interval: return False # 拉取最近消息数量是否足够 recent_messages = get_raw_msg_by_timestamp_with_chat_inclusive( chat_id=self.chat_id, timestamp_start=self.last_learning_time, timestamp_end=time.time(), ) return bool(recent_messages and len(recent_messages) >= self.min_messages_for_learning) async def run_once(self) -> None: try: if not self.should_trigger(): return chat_stream = get_chat_manager().get_stream(self.chat_id) if not chat_stream: return # 记录本次提取的时间窗口，避免重复提取 extraction_start_time = self.last_learning_time extraction_end_time = time.time() # 拉取学习窗口内的消息 messages = get_raw_msg_by_timestamp_with_chat_inclusive( chat_id=self.chat_id, timestamp_start=extraction_start_time, timestamp_end=extraction_end_time, limit=20, ) if not messages: return # 按时间排序，确保编号与上下文一致 messages = sorted(messages, key=lambda msg: msg.time or 0) chat_str, message_id_list = build_readable_messages_with_id( messages=messages, replace_bot_name=True, timestamp_mode="relative", truncate=False, show_actions=False, show_pic=True, pic_single=True, ) if not chat_str.strip(): return msg_id_to_index: Dict[str, int] = {} for idx, (msg_id, _msg) in enumerate(message_id_list or []): if not msg_id: continue msg_id_to_index[msg_id] = idx if not msg_id_to_index: logger.warning("未能生成消息ID映射，跳过本次提取") return prompt: str = await global_prompt_manager.format_prompt( "extract_jargon_prompt", bot_name=global_config.bot.nickname, chat_str=chat_str, ) response, _ = await self.llm.generate_response_async(prompt, temperature=0.2) if not response: return if global_config.debug.show_jargon_prompt: logger.info(f"jargon提取提示词: {prompt}") logger.info(f"jargon提取结果: {response}") # 解析为JSON entries: List[dict] = [] try: resp = response.strip() parsed = None if resp.startswith("[") and resp.endswith("]"): parsed = json.loads(resp) else: repaired = repair_json(resp) if isinstance(repaired, str): parsed = json.loads(repaired) else: parsed = repaired if isinstance(parsed, dict): parsed = [parsed] if not isinstance(parsed, list): return for item in parsed: if not isinstance(item, dict): continue content = str(item.get("content", "")).strip() msg_id_value = item.get("msg_id") if not content: continue if _contains_bot_self_name(content): logger.debug(f"解析阶段跳过包含机器人昵称/别名的词条: {content}") continue msg_id_str = str(msg_id_value or "").strip() if not msg_id_str: logger.warning(f"解析jargon失败：msg_id缺失，content={content}") continue msg_index = msg_id_to_index.get(msg_id_str) if msg_index is None: logger.warning(f"解析jargon失败：msg_id未找到，content={content}, msg_id={msg_id_str}") continue target_msg = messages[msg_index] if _is_bot_message(target_msg): logger.debug(f"解析阶段跳过引用机器人自身消息的词条: content={content}, msg_id={msg_id_str}") continue if _has_adjacent_bot_message(messages, msg_index): logger.debug( f"解析阶段跳过因邻近机器人发言的词条: content={content}, msg_id={msg_id_str}" ) continue context_paragraph = _build_context_paragraph(messages, msg_index) if not context_paragraph: logger.warning(f"解析jargon失败：上下文为空，content={content}, msg_id={msg_id_str}") continue entries.append({"content": content, "raw_content": [context_paragraph]}) cached_entries = self._collect_cached_entries(messages) if cached_entries: entries.extend(cached_entries) except Exception as e: logger.error(f"解析jargon JSON失败: {e}; 原始: {response}") return if not entries: return # 去重并合并raw_content（按 content 聚合） merged_entries: OrderedDict[str, Dict[str, List[str]]] = OrderedDict() for entry in entries: content_key = entry["content"] raw_list = entry.get("raw_content", []) or [] if content_key in merged_entries: merged_entries[content_key]["raw_content"].extend(raw_list) else: merged_entries[content_key] = { "content": content_key, "raw_content": list(raw_list), } uniq_entries = [] for merged_entry in merged_entries.values(): raw_content_list = merged_entry["raw_content"] if raw_content_list: merged_entry["raw_content"] = list(dict.fromkeys(raw_content_list)) uniq_entries.append(merged_entry) saved = 0 updated = 0 for entry in uniq_entries: content = entry["content"] raw_content_list = entry["raw_content"] # 已经是列表 # 检查并补充raw_content：如果只包含黑话本身，则获取前三条消息作为上下文 raw_content_list = await _enrich_raw_content_if_needed( content=content, raw_content_list=raw_content_list, chat_id=self.chat_id, messages=messages, extraction_start_time=extraction_start_time, extraction_end_time=extraction_end_time, ) try: # 根据all_global配置决定查询逻辑 if global_config.jargon.all_global: # 开启all_global：无视chat_id，查询所有content匹配的记录（所有记录都是全局的） query = Jargon.select().where(Jargon.content == content) else: # 关闭all_global：只查询chat_id匹配的记录（不考虑is_global） query = Jargon.select().where((Jargon.chat_id == self.chat_id) & (Jargon.content == content)) if query.exists(): obj = query.get() try: obj.count = (obj.count or 0) + 1 except Exception: obj.count = 1 # 合并raw_content列表：读取现有列表，追加新值，去重 existing_raw_content = [] if obj.raw_content: try: existing_raw_content = ( json.loads(obj.raw_content) if isinstance(obj.raw_content, str) else obj.raw_content ) if not isinstance(existing_raw_content, list): existing_raw_content = [existing_raw_content] if existing_raw_content else [] except (json.JSONDecodeError, TypeError): existing_raw_content = [obj.raw_content] if obj.raw_content else [] # 合并并去重 merged_list = list(dict.fromkeys(existing_raw_content + raw_content_list)) obj.raw_content = json.dumps(merged_list, ensure_ascii=False) # 开启all_global时，确保记录标记为is_global=True if global_config.jargon.all_global: obj.is_global = True # 关闭all_global时，保持原有is_global不变（不修改） obj.save() # 检查是否需要推断（达到阈值且超过上次判定值） if _should_infer_meaning(obj): # 异步触发推断，不阻塞主流程 # 重新加载对象以确保数据最新 jargon_id = obj.id asyncio.create_task(self._infer_meaning_by_id(jargon_id)) updated += 1 else: # 没找到匹配记录，创建新记录 if global_config.jargon.all_global: # 开启all_global：新记录默认为is_global=True is_global_new = True else: # 关闭all_global：新记录is_global=False is_global_new = False Jargon.create( content=content, raw_content=json.dumps(raw_content_list, ensure_ascii=False), chat_id=self.chat_id, is_global=is_global_new, count=1, ) saved += 1 except Exception as e: logger.error(f"保存jargon失败: chat_id={self.chat_id}, content={content}, err={e}") continue finally: self._add_to_cache(content) # 固定输出提取的jargon结果，格式化为可读形式（只要有提取结果就输出） if uniq_entries: # 收集所有提取的jargon内容 jargon_list = [entry["content"] for entry in uniq_entries] jargon_str = ",".join(jargon_list) # 输出格式化的结果（使用logger.info会自动应用jargon模块的颜色） logger.info(f"[{self.stream_name}]疑似黑话: {jargon_str}") # 更新为本次提取的结束时间，确保不会重复提取相同的消息窗口 self.last_learning_time = extraction_end_time if saved or updated: logger.info(f"jargon写入: 新增 {saved} 条，更新 {updated} 条，chat_id={self.chat_id}") except Exception as e: logger.error(f"JargonMiner 运行失败: {e}") class JargonMinerManager: def __init__(self) -> None: self._miners: dict[str, JargonMiner] = {} def get_miner(self, chat_id: str) -> JargonMiner: if chat_id not in self._miners: self._miners[chat_id] = JargonMiner(chat_id) return self._miners[chat_id] miner_manager = JargonMinerManager() async def extract_and_store_jargon(chat_id: str) -> None: miner = miner_manager.get_miner(chat_id) await miner.run_once() def search_jargon( keyword: str, chat_id: Optional[str] = None, limit: int = 10, case_sensitive: bool = False, fuzzy: bool = True ) -> List[Dict[str, str]]: """ 搜索jargon，支持大小写不敏感和模糊搜索 Args: keyword: 搜索关键词 chat_id: 可选的聊天ID - 如果开启了all_global：此参数被忽略，查询所有is_global=True的记录 - 如果关闭了all_global：如果提供则优先搜索该聊天或global的jargon limit: 返回结果数量限制，默认10 case_sensitive: 是否大小写敏感，默认False（不敏感） fuzzy: 是否模糊搜索，默认True（使用LIKE匹配） Returns: List[Dict[str, str]]: 包含content, meaning的字典列表 """ if not keyword or not keyword.strip(): return [] keyword = keyword.strip() # 构建查询 query = Jargon.select(Jargon.content, Jargon.meaning) # 构建搜索条件 if case_sensitive: # 大小写敏感 if fuzzy: # 模糊搜索 search_condition = Jargon.content.contains(keyword) else: # 精确匹配 search_condition = Jargon.content == keyword else: # 大小写不敏感 if fuzzy: # 模糊搜索（使用LOWER函数） search_condition = fn.LOWER(Jargon.content).contains(keyword.lower()) else: # 精确匹配（使用LOWER函数） search_condition = fn.LOWER(Jargon.content) == keyword.lower() query = query.where(search_condition) # 根据all_global配置决定查询逻辑 if global_config.jargon.all_global: # 开启all_global：所有记录都是全局的，查询所有is_global=True的记录（无视chat_id） query = query.where(Jargon.is_global) else: # 关闭all_global：如果提供了chat_id，优先搜索该聊天或global的jargon if chat_id: query = query.where((Jargon.chat_id == chat_id) | Jargon.is_global) # 只返回有meaning的记录 query = query.where((Jargon.meaning.is_null(False)) & (Jargon.meaning != "")) # 按count降序排序，优先返回出现频率高的 query = query.order_by(Jargon.count.desc()) # 限制结果数量 query = query.limit(limit) # 执行查询并返回结果 results = [] for jargon in query: results.append({"content": jargon.content or "", "meaning": jargon.meaning or ""}) return results async def store_jargon_from_answer(jargon_keyword: str, answer: str, chat_id: str) -> None: """将黑话存入jargon系统 Args: jargon_keyword: 黑话关键词 answer: 答案内容（将概括为raw_content） chat_id: 聊天ID """ try: # 概括答案为简短的raw_content summary_prompt = f"""请将以下答案概括为一句简短的话（不超过50字），作为黑话"{jargon_keyword}"的使用示例： 答案：{answer} 只输出概括后的内容，不要输出其他内容：""" success, summary, _, _ = await llm_api.generate_with_model( summary_prompt, model_config=model_config.model_task_config.utils_small, request_type="memory.summarize_jargon", ) logger.info(f"概括答案提示: {summary_prompt}") logger.info(f"概括答案: {summary}") if not success: logger.warning(f"概括答案失败，使用原始答案: {summary}") summary = answer[:100] # 截取前100字符作为备用 raw_content = summary.strip()[:200] # 限制长度 # 检查是否已存在 if global_config.jargon.all_global: query = Jargon.select().where(Jargon.content == jargon_keyword) else: query = Jargon.select().where((Jargon.chat_id == chat_id) & (Jargon.content == jargon_keyword)) if query.exists(): # 更新现有记录 obj = query.get() obj.count = (obj.count or 0) + 1 # 合并raw_content列表 existing_raw_content = [] if obj.raw_content: try: existing_raw_content = ( json.loads(obj.raw_content) if isinstance(obj.raw_content, str) else obj.raw_content ) if not isinstance(existing_raw_content, list): existing_raw_content = [existing_raw_content] if existing_raw_content else [] except (json.JSONDecodeError, TypeError): existing_raw_content = [obj.raw_content] if obj.raw_content else [] # 合并并去重 merged_list = list(dict.fromkeys(existing_raw_content + [raw_content])) obj.raw_content = json.dumps(merged_list, ensure_ascii=False) if global_config.jargon.all_global: obj.is_global = True obj.save() logger.info(f"更新jargon记录: {jargon_keyword}") else: # 创建新记录 is_global_new = True if global_config.jargon.all_global else False Jargon.create( content=jargon_keyword, raw_content=json.dumps([raw_content], ensure_ascii=False), chat_id=chat_id, is_global=is_global_new, count=1, ) logger.info(f"创建新jargon记录: {jargon_keyword}") except Exception as e: logger.error(f"存储jargon失败: {e}")