259 lines
9.2 KiB
Python
259 lines
9.2 KiB
Python
import re
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import time
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from typing import List, Dict, Optional, Any
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from src.common.logger import get_logger
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from src.common.database.database_model import Jargon
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from src.llm_models.utils_model import LLMRequest
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from src.config.config import model_config, global_config
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from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
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from src.jargon.jargon_miner import search_jargon
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from src.jargon.jargon_utils import is_bot_message, contains_bot_self_name, parse_chat_id_list, chat_id_list_contains
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logger = get_logger("jargon")
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def _init_explainer_prompts() -> None:
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"""初始化黑话解释器相关的prompt"""
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# Prompt：概括黑话解释结果
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summarize_prompt_str = """上下文聊天内容:
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{chat_context}
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在上下文中提取到的黑话及其含义:
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{jargon_explanations}
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请根据上述信息，对黑话解释进行概括和整理。
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- 如果上下文中有黑话出现，请简要说明这些黑话在上下文中的使用情况
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- 将所有黑话解释整理成简洁、易读的一段话
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- 输出格式要自然，适合作为回复参考信息
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请输出概括后的黑话解释（直接输出一段平文本，不要标题，无特殊格式或markdown格式，不要使用JSON格式）：
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"""
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Prompt(summarize_prompt_str, "jargon_explainer_summarize_prompt")
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_init_explainer_prompts()
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class JargonExplainer:
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"""黑话解释器，用于在回复前识别和解释上下文中的黑话"""
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def __init__(self, chat_id: str) -> None:
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self.chat_id = chat_id
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self.llm = LLMRequest(
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model_set=model_config.model_task_config.utils,
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request_type="jargon.explain",
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)
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def match_jargon_from_messages(
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self, messages: List[Any]
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) -> List[Dict[str, str]]:
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"""
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通过直接匹配数据库中的jargon字符串来提取黑话
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Args:
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messages: 消息列表
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Returns:
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List[Dict[str, str]]: 提取到的黑话列表，每个元素包含content
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"""
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start_time = time.time()
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if not messages:
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return []
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# 收集所有消息的文本内容
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message_texts: List[str] = []
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for msg in messages:
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# 跳过机器人自己的消息
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if is_bot_message(msg):
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continue
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msg_text = (getattr(msg, "display_message", None) or getattr(msg, "processed_plain_text", None) or "").strip()
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if msg_text:
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message_texts.append(msg_text)
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if not message_texts:
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return []
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# 合并所有消息文本
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combined_text = " ".join(message_texts)
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# 查询所有有meaning的jargon记录
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query = Jargon.select().where(
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(Jargon.meaning.is_null(False)) & (Jargon.meaning != "")
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)
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# 根据all_global配置决定查询逻辑
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if global_config.jargon.all_global:
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# 开启all_global：只查询is_global=True的记录
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query = query.where(Jargon.is_global)
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else:
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# 关闭all_global：查询is_global=True或chat_id列表包含当前chat_id的记录
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# 这里先查询所有，然后在Python层面过滤
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pass
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# 按count降序排序，优先匹配出现频率高的
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query = query.order_by(Jargon.count.desc())
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# 执行查询并匹配
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matched_jargon: Dict[str, Dict[str, str]] = {}
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query_time = time.time()
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for jargon in query:
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content = jargon.content or ""
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if not content or not content.strip():
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continue
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# 跳过包含机器人昵称的词条
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if contains_bot_self_name(content):
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continue
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# 检查chat_id（如果all_global=False）
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if not global_config.jargon.all_global:
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if jargon.is_global:
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# 全局黑话，包含
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pass
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else:
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# 检查chat_id列表是否包含当前chat_id
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chat_id_list = parse_chat_id_list(jargon.chat_id)
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if not chat_id_list_contains(chat_id_list, self.chat_id):
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continue
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# 在文本中查找匹配（大小写不敏感）
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pattern = re.escape(content)
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# 使用单词边界或中文字符边界来匹配，避免部分匹配
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# 对于中文，使用Unicode字符类；对于英文，使用单词边界
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if re.search(r'[\u4e00-\u9fff]', content):
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# 包含中文，使用更宽松的匹配
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search_pattern = pattern
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else:
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# 纯英文/数字，使用单词边界
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search_pattern = r'\b' + pattern + r'\b'
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if re.search(search_pattern, combined_text, re.IGNORECASE):
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# 找到匹配，记录（去重）
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if content not in matched_jargon:
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matched_jargon[content] = {"content": content}
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match_time = time.time()
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total_time = match_time - start_time
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query_duration = query_time - start_time
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match_duration = match_time - query_time
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logger.info(
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f"黑话匹配完成: 查询耗时 {query_duration:.3f}s, 匹配耗时 {match_duration:.3f}s, "
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f"总耗时 {total_time:.3f}s, 匹配到 {len(matched_jargon)} 个黑话"
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)
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return list(matched_jargon.values())
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async def explain_jargon(
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self, messages: List[Any], chat_context: str
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) -> Optional[str]:
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"""
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解释上下文中的黑话
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Args:
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messages: 消息列表
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chat_context: 聊天上下文的文本表示
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Returns:
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Optional[str]: 黑话解释的概括文本，如果没有黑话则返回None
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"""
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if not messages:
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return None
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# 直接匹配方式：从数据库中查询jargon并在消息中匹配
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jargon_entries = self.match_jargon_from_messages(messages)
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if not jargon_entries:
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return None
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# 去重（按content）
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unique_jargon: Dict[str, Dict[str, str]] = {}
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for entry in jargon_entries:
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content = entry["content"]
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if content not in unique_jargon:
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unique_jargon[content] = entry
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jargon_list = list(unique_jargon.values())
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logger.info(f"从上下文中提取到 {len(jargon_list)} 个黑话: {[j['content'] for j in jargon_list]}")
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# 查询每个黑话的含义
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jargon_explanations: List[str] = []
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for entry in jargon_list:
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content = entry["content"]
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# 根据是否开启全局黑话，决定查询方式
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if global_config.jargon.all_global:
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# 开启全局黑话：查询所有is_global=True的记录
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results = search_jargon(
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keyword=content,
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chat_id=None, # 不指定chat_id，查询全局黑话
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limit=1,
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case_sensitive=False,
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fuzzy=False, # 精确匹配
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)
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else:
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# 关闭全局黑话：优先查询当前聊天或全局的黑话
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results = search_jargon(
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keyword=content,
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chat_id=self.chat_id,
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limit=1,
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case_sensitive=False,
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fuzzy=False, # 精确匹配
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)
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if results and len(results) > 0:
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meaning = results[0].get("meaning", "").strip()
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if meaning:
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jargon_explanations.append(f"- {content}: {meaning}")
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else:
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logger.info(f"黑话 {content} 没有找到含义")
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else:
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logger.info(f"黑话 {content} 未在数据库中找到")
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if not jargon_explanations:
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logger.info("没有找到任何黑话的含义，跳过解释")
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return None
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# 拼接所有黑话解释
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explanations_text = "\n".join(jargon_explanations)
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# 使用LLM概括黑话解释
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summarize_prompt = await global_prompt_manager.format_prompt(
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"jargon_explainer_summarize_prompt",
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chat_context=chat_context,
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jargon_explanations=explanations_text,
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)
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summary, _ = await self.llm.generate_response_async(summarize_prompt, temperature=0.3)
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if not summary:
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# 如果LLM概括失败，直接返回原始解释
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return f"上下文中的黑话解释：\n{explanations_text}"
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summary = summary.strip()
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if not summary:
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return f"上下文中的黑话解释：\n{explanations_text}"
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return summary
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async def explain_jargon_in_context(
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chat_id: str, messages: List[Any], chat_context: str
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) -> Optional[str]:
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"""
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解释上下文中的黑话（便捷函数）
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Args:
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chat_id: 聊天ID
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messages: 消息列表
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chat_context: 聊天上下文的文本表示
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Returns:
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Optional[str]: 黑话解释的概括文本，如果没有黑话则返回None
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"""
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explainer = JargonExplainer(chat_id)
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return await explainer.explain_jargon(messages, chat_context)
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