702 lines
29 KiB
Python
702 lines
29 KiB
Python
import time
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import json
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import asyncio
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from typing import List, Dict, Optional
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from json_repair import repair_json
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from peewee import fn
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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.message_receive.chat_stream import get_chat_manager
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from src.chat.utils.chat_message_builder import (
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build_anonymous_messages,
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get_raw_msg_by_timestamp_with_chat_inclusive,
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)
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from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
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logger = get_logger("jargon")
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def _init_prompt() -> None:
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prompt_str = """
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**聊天内容，其中的SELF是你自己的发言**
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{chat_str}
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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）。
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- 必须为对话中真实出现过的短词或短语
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- 必须是你无法理解含义的词语，没有明确含义的词语
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- 请不要选择有明确含义，或者含义清晰的词语
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- 必须是这几种类别之一：英文或中文缩写、中文拼音短语
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- 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）
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- 每个词条长度建议 2-8 个字符（不强制），尽量短小
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- 合并重复项，去重
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分类规则,type必须根据规则填写：
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- p（拼音缩写）：由字母构成的，汉语拼音首字母的简写词，例如：nb、yyds、xswl
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- e（英文缩写）：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
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- c（中文缩写）：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
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以 JSON 数组输出，元素为对象（严格按以下结构）：
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[
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{{"content": "词条", "raw_content": "包含该词条的完整对话上下文原文", "type": "p"}},
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{{"content": "词条2", "raw_content": "包含该词条的完整对话上下文原文", "type": "c"}}
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]
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现在请输出：
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"""
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Prompt(prompt_str, "extract_jargon_prompt")
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def _init_inference_prompts() -> None:
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"""初始化含义推断相关的prompt"""
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# Prompt 1: 基于raw_content和content推断
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prompt1_str = """
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**词条内容**
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{content}
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请根据以上词条内容和上下文，推断这个词条的含义。
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- 如果这是一个黑话、俚语或网络用语，请推断其含义
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- 如果含义明确（常规词汇），也请说明
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以 JSON 格式输出：
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{{
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"meaning": "详细含义说明（包含使用场景、来源、具体解释等）"
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}}
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"""
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Prompt(prompt1_str, "jargon_inference_with_context_prompt")
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# Prompt 2: 仅基于content推断
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prompt2_str = """
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**词条内容**
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{content}
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请仅根据这个词条本身，推断其含义。
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- 如果这是一个黑话、俚语或网络用语，请推断其含义
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- 如果含义明确（常规词汇），也请说明
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以 JSON 格式输出：
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{{
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"meaning": "详细含义说明（包含使用场景、来源、具体解释等）"
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}}
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"""
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Prompt(prompt2_str, "jargon_inference_content_only_prompt")
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# Prompt 3: 比较两个推断结果
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prompt3_str = """
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**推断结果1（基于上下文）**
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{inference1}
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**推断结果2（仅基于词条）**
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{inference2}
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请比较这两个推断结果，判断它们是否相同或类似。
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- 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）
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- 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）
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以 JSON 格式输出：
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{{
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"is_similar": true/false,
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"reason": "判断理由"
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}}
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"""
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Prompt(prompt3_str, "jargon_compare_inference_prompt")
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_init_prompt()
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_init_inference_prompts()
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def _should_infer_meaning(jargon_obj: Jargon) -> bool:
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"""
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判断是否需要进行含义推断
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在 count 达到 3,6, 10, 20, 40, 60, 100 时进行推断
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并且count必须大于last_inference_count，避免重启后重复判定
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如果is_complete为True，不再进行推断
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"""
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# 如果已完成所有推断，不再推断
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if jargon_obj.is_complete:
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return False
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count = jargon_obj.count or 0
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last_inference = jargon_obj.last_inference_count or 0
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# 阈值列表：3,6, 10, 20, 40, 60, 100
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thresholds = [3, 6, 10, 20, 40, 60, 100]
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if count < thresholds[0]:
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return False
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# 如果count没有超过上次判定值，不需要判定
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if count <= last_inference:
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return False
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# 找到第一个大于last_inference的阈值
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next_threshold = None
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for threshold in thresholds:
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if threshold > last_inference:
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next_threshold = threshold
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break
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# 如果没有找到下一个阈值，说明已经超过100，不应该再推断
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if next_threshold is None:
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return False
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# 检查count是否达到或超过这个阈值
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return count >= next_threshold
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class JargonMiner:
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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.last_learning_time: float = time.time()
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# 频率控制，可按需调整
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self.min_messages_for_learning: int = 20
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self.min_learning_interval: float = 30
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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.extract",
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)
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# 初始化stream_name作为类属性，避免重复提取
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chat_manager = get_chat_manager()
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stream_name = chat_manager.get_stream_name(self.chat_id)
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self.stream_name = stream_name if stream_name else self.chat_id
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async def _infer_meaning_by_id(self, jargon_id: int) -> None:
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"""通过ID加载对象并推断"""
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try:
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jargon_obj = Jargon.get_by_id(jargon_id)
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# 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
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if jargon_obj.is_complete:
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logger.debug(f"jargon {jargon_obj.content} 已完成所有推断，跳过")
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return
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await self.infer_meaning(jargon_obj)
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except Exception as e:
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logger.error(f"通过ID推断jargon失败: {e}")
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async def infer_meaning(self, jargon_obj: Jargon) -> None:
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"""
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对jargon进行含义推断
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"""
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try:
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content = jargon_obj.content
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raw_content_str = jargon_obj.raw_content or ""
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# 解析raw_content列表
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raw_content_list = []
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if raw_content_str:
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try:
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raw_content_list = (
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json.loads(raw_content_str) if isinstance(raw_content_str, str) else raw_content_str
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)
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if not isinstance(raw_content_list, list):
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raw_content_list = [raw_content_list] if raw_content_list else []
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except (json.JSONDecodeError, TypeError):
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raw_content_list = [raw_content_str] if raw_content_str else []
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if not raw_content_list:
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logger.warning(f"jargon {content} 没有raw_content，跳过推断")
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return
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# 步骤1: 基于raw_content和content推断
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raw_content_text = "\n".join(raw_content_list)
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prompt1 = await global_prompt_manager.format_prompt(
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"jargon_inference_with_context_prompt",
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content=content,
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raw_content_list=raw_content_text,
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)
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response1, _ = await self.llm.generate_response_async(prompt1, temperature=0.3)
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if not response1:
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logger.warning(f"jargon {content} 推断1失败：无响应")
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return
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# 解析推断1结果
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inference1 = None
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try:
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resp1 = response1.strip()
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if resp1.startswith("{") and resp1.endswith("}"):
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inference1 = json.loads(resp1)
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else:
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repaired = repair_json(resp1)
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inference1 = json.loads(repaired) if isinstance(repaired, str) else repaired
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if not isinstance(inference1, dict):
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logger.warning(f"jargon {content} 推断1结果格式错误")
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return
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except Exception as e:
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logger.error(f"jargon {content} 推断1解析失败: {e}")
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return
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# 步骤2: 仅基于content推断
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prompt2 = await global_prompt_manager.format_prompt(
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"jargon_inference_content_only_prompt",
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content=content,
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)
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response2, _ = await self.llm.generate_response_async(prompt2, temperature=0.3)
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if not response2:
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logger.warning(f"jargon {content} 推断2失败：无响应")
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return
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# 解析推断2结果
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inference2 = None
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try:
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resp2 = response2.strip()
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if resp2.startswith("{") and resp2.endswith("}"):
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inference2 = json.loads(resp2)
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else:
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repaired = repair_json(resp2)
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inference2 = json.loads(repaired) if isinstance(repaired, str) else repaired
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if not isinstance(inference2, dict):
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logger.warning(f"jargon {content} 推断2结果格式错误")
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return
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except Exception as e:
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logger.error(f"jargon {content} 推断2解析失败: {e}")
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return
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if global_config.debug.show_jargon_prompt:
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logger.info(f"jargon {content} 推断2提示词: {prompt2}")
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logger.info(f"jargon {content} 推断2结果: {response2}")
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# logger.info(f"jargon {content} 推断2结果: {inference2}")
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logger.info(f"jargon {content} 推断1提示词: {prompt1}")
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logger.info(f"jargon {content} 推断1结果: {response1}")
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# logger.info(f"jargon {content} 推断1结果: {inference1}")
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# 步骤3: 比较两个推断结果
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prompt3 = await global_prompt_manager.format_prompt(
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"jargon_compare_inference_prompt",
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inference1=json.dumps(inference1, ensure_ascii=False),
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inference2=json.dumps(inference2, ensure_ascii=False),
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)
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if global_config.debug.show_jargon_prompt:
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logger.info(f"jargon {content} 比较提示词: {prompt3}")
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response3, _ = await self.llm.generate_response_async(prompt3, temperature=0.3)
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if not response3:
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logger.warning(f"jargon {content} 比较失败：无响应")
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return
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# 解析比较结果
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comparison = None
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try:
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resp3 = response3.strip()
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if resp3.startswith("{") and resp3.endswith("}"):
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comparison = json.loads(resp3)
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else:
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repaired = repair_json(resp3)
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comparison = json.loads(repaired) if isinstance(repaired, str) else repaired
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if not isinstance(comparison, dict):
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logger.warning(f"jargon {content} 比较结果格式错误")
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return
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except Exception as e:
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logger.error(f"jargon {content} 比较解析失败: {e}")
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return
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# 判断是否为黑话
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is_similar = comparison.get("is_similar", False)
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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
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# 更新数据库记录
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jargon_obj.is_jargon = is_jargon
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if is_jargon:
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# 是黑话，使用推断1的结果（基于上下文，更准确）
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jargon_obj.meaning = inference1.get("meaning", "")
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else:
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# 不是黑话，也记录含义（使用推断2的结果，因为含义明确）
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jargon_obj.meaning = inference2.get("meaning", "")
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# 更新最后一次判定的count值，避免重启后重复判定
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jargon_obj.last_inference_count = jargon_obj.count or 0
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# 如果count>=100，标记为完成，不再进行推断
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if (jargon_obj.count or 0) >= 100:
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jargon_obj.is_complete = True
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jargon_obj.save()
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logger.info(
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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}"
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)
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# 固定输出推断结果，格式化为可读形式
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if is_jargon:
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# 是黑话，输出格式：[聊天名]xxx的含义是 xxxxxxxxxxx
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meaning = jargon_obj.meaning or "无详细说明"
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is_global = jargon_obj.is_global
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if is_global:
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logger.info(f"[通用黑话]{content}的含义是 {meaning}")
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else:
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logger.info(f"[{self.stream_name}]{content}的含义是 {meaning}")
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else:
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# 不是黑话，输出格式：[聊天名]xxx 不是黑话
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logger.info(f"[{self.stream_name}]{content} 不是黑话")
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except Exception as e:
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logger.error(f"jargon推断失败: {e}")
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import traceback
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traceback.print_exc()
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def should_trigger(self) -> bool:
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# 冷却时间检查
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if time.time() - self.last_learning_time < self.min_learning_interval:
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return False
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# 拉取最近消息数量是否足够
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recent_messages = get_raw_msg_by_timestamp_with_chat_inclusive(
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chat_id=self.chat_id,
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timestamp_start=self.last_learning_time,
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timestamp_end=time.time(),
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)
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return bool(recent_messages and len(recent_messages) >= self.min_messages_for_learning)
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async def run_once(self) -> None:
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try:
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if not self.should_trigger():
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return
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chat_stream = get_chat_manager().get_stream(self.chat_id)
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if not chat_stream:
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return
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# 记录本次提取的时间窗口，避免重复提取
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extraction_start_time = self.last_learning_time
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extraction_end_time = time.time()
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# 拉取学习窗口内的消息
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messages = get_raw_msg_by_timestamp_with_chat_inclusive(
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chat_id=self.chat_id,
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timestamp_start=extraction_start_time,
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timestamp_end=extraction_end_time,
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limit=20,
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)
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if not messages:
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return
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chat_str: str = await build_anonymous_messages(messages)
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if not chat_str.strip():
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return
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prompt: str = await global_prompt_manager.format_prompt(
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"extract_jargon_prompt",
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chat_str=chat_str,
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)
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response, _ = await self.llm.generate_response_async(prompt, temperature=0.2)
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if not response:
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return
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if global_config.debug.show_jargon_prompt:
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logger.info(f"jargon提取提示词: {prompt}")
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logger.info(f"jargon提取结果: {response}")
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# 解析为JSON
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entries: List[dict] = []
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try:
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resp = response.strip()
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parsed = None
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if resp.startswith("[") and resp.endswith("]"):
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parsed = json.loads(resp)
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else:
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repaired = repair_json(resp)
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if isinstance(repaired, str):
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parsed = json.loads(repaired)
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else:
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parsed = repaired
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if isinstance(parsed, dict):
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parsed = [parsed]
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if not isinstance(parsed, list):
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return
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for item in parsed:
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if not isinstance(item, dict):
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continue
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content = str(item.get("content", "")).strip()
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raw_content_value = item.get("raw_content", "")
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# 处理raw_content：可能是字符串或列表
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raw_content_list = []
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if isinstance(raw_content_value, list):
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raw_content_list = [str(rc).strip() for rc in raw_content_value if str(rc).strip()]
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# 去重
|
||
raw_content_list = list(dict.fromkeys(raw_content_list))
|
||
elif isinstance(raw_content_value, str):
|
||
raw_content_str = raw_content_value.strip()
|
||
if raw_content_str:
|
||
raw_content_list = [raw_content_str]
|
||
|
||
type_str = str(item.get("type", "")).strip().lower()
|
||
|
||
# 验证type是否为有效值
|
||
if type_str not in ["p", "c", "e"]:
|
||
type_str = "p" # 默认值
|
||
|
||
if content and raw_content_list:
|
||
entries.append({"content": content, "raw_content": raw_content_list, "type": type_str})
|
||
except Exception as e:
|
||
logger.error(f"解析jargon JSON失败: {e}; 原始: {response}")
|
||
return
|
||
|
||
if not entries:
|
||
return
|
||
|
||
# 去重并写入DB（按 chat_id + content 去重）
|
||
# 使用content作为去重键
|
||
seen = set()
|
||
uniq_entries = []
|
||
for entry in entries:
|
||
content_key = entry["content"]
|
||
if content_key not in seen:
|
||
seen.add(content_key)
|
||
uniq_entries.append(entry)
|
||
|
||
saved = 0
|
||
updated = 0
|
||
merged = 0
|
||
for entry in uniq_entries:
|
||
content = entry["content"]
|
||
raw_content_list = entry["raw_content"] # 已经是列表
|
||
type_str = entry["type"]
|
||
try:
|
||
# 步骤1: 检查同chat_id的记录，默认纳入global项目
|
||
# 查询条件：chat_id匹配 OR (is_global为True且content匹配)
|
||
query = Jargon.select().where(
|
||
((Jargon.chat_id == self.chat_id) | Jargon.is_global) & (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)
|
||
|
||
# 更新type（如果为空）
|
||
if type_str and not obj.type:
|
||
obj.type = type_str
|
||
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:
|
||
# 步骤2: 同chat_id没有找到，检查所有chat_id中是否有相同content的记录
|
||
# 查询所有非global的记录（global的已经在步骤1检查过了）
|
||
all_content_query = Jargon.select().where((Jargon.content == content) & (~Jargon.is_global))
|
||
all_matching = list(all_content_query)
|
||
|
||
# 如果找到3个或更多相同content的记录，合并它们
|
||
if len(all_matching) >= 3:
|
||
# 找到3个或更多已有记录，合并它们（新条目也会被包含在合并中）
|
||
total_count = sum((obj.count or 0) for obj in all_matching) + 1 # +1 是因为当前新条目
|
||
|
||
# 合并所有raw_content列表
|
||
all_raw_content = []
|
||
for obj in all_matching:
|
||
if obj.raw_content:
|
||
try:
|
||
obj_raw = (
|
||
json.loads(obj.raw_content)
|
||
if isinstance(obj.raw_content, str)
|
||
else obj.raw_content
|
||
)
|
||
if not isinstance(obj_raw, list):
|
||
obj_raw = [obj_raw] if obj_raw else []
|
||
all_raw_content.extend(obj_raw)
|
||
except (json.JSONDecodeError, TypeError):
|
||
if obj.raw_content:
|
||
all_raw_content.append(obj.raw_content)
|
||
|
||
# 添加当前新条目的raw_content
|
||
all_raw_content.extend(raw_content_list)
|
||
# 去重
|
||
merged_raw_content = list(dict.fromkeys(all_raw_content))
|
||
|
||
# 合并type：优先使用非空的值
|
||
merged_type = type_str
|
||
for obj in all_matching:
|
||
if obj.type and not merged_type:
|
||
merged_type = obj.type
|
||
break
|
||
|
||
# 合并其他字段：优先使用已有值
|
||
merged_meaning = None
|
||
merged_is_jargon = None
|
||
merged_last_inference_count = None
|
||
merged_is_complete = False
|
||
|
||
for obj in all_matching:
|
||
if obj.meaning and not merged_meaning:
|
||
merged_meaning = obj.meaning
|
||
if obj.is_jargon is not None and merged_is_jargon is None:
|
||
merged_is_jargon = obj.is_jargon
|
||
if obj.last_inference_count is not None and merged_last_inference_count is None:
|
||
merged_last_inference_count = obj.last_inference_count
|
||
if obj.is_complete:
|
||
merged_is_complete = True
|
||
|
||
# 删除旧的记录
|
||
for obj in all_matching:
|
||
obj.delete_instance()
|
||
|
||
# 创建新的global记录
|
||
Jargon.create(
|
||
content=content,
|
||
raw_content=json.dumps(merged_raw_content, ensure_ascii=False),
|
||
type=merged_type,
|
||
chat_id="global",
|
||
is_global=True,
|
||
count=total_count,
|
||
meaning=merged_meaning,
|
||
is_jargon=merged_is_jargon,
|
||
last_inference_count=merged_last_inference_count,
|
||
is_complete=merged_is_complete,
|
||
)
|
||
merged += 1
|
||
logger.info(
|
||
f"合并jargon为global: content={content}, 合并了{len(all_matching)}条已有记录+1条新记录（共{len(all_matching) + 1}条），总count={total_count}"
|
||
)
|
||
else:
|
||
# 找到少于3个已有记录，正常创建新记录
|
||
Jargon.create(
|
||
content=content,
|
||
raw_content=json.dumps(raw_content_list, ensure_ascii=False),
|
||
type=type_str,
|
||
chat_id=self.chat_id,
|
||
is_global=False,
|
||
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
|
||
|
||
# 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 or merged:
|
||
logger.info(
|
||
f"jargon写入: 新增 {saved} 条，更新 {updated} 条，合并为global {merged} 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)
|
||
|
||
# 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
|