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SengokuCola
2026-03-24 11:36:26 +08:00
parent 6a0b902e17
commit 03ed59e388
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src/learners/expression_learner.py Normal file
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from datetime import datetime
from sqlmodel import select
from typing import TYPE_CHECKING, List, Optional, Tuple
import asyncio
import difflib
import json
import re
from src.llm_models.utils_model import LLMRequest
from src.config.config import model_config, global_config
from src.prompt.prompt_manager import prompt_manager
from src.common.logger import get_logger
from src.common.database.database_model import Expression
from src.common.database.database import get_db_session
from src.common.data_models.expression_data_model import MaiExpression
from src.chat.utils.utils import is_bot_self
from src.common.utils.utils_message import MessageUtils
from .expression_utils import check_expression_suitability, parse_expression_response
if TYPE_CHECKING:
from src.chat.message_receive.message import SessionMessage
from .jargon_miner import JargonMiner, JargonEntry
logger = get_logger("expressor")
# TODO: 重构完LLM相关内容后，替换成新的模型调用方式
express_learn_model = LLMRequest(model_set=model_config.model_task_config.utils, request_type="expression.learner")
summary_model = LLMRequest(model_set=model_config.model_task_config.tool_use, request_type="expression.summary")
check_model = LLMRequest(model_set=model_config.model_task_config.tool_use, request_type="expression.check")
class ExpressionLearner:
def __init__(self, session_id: str) -> None:
self.session_id = session_id
# 学习锁，防止并发执行学习任务
self._learning_lock = asyncio.Lock()
# 消息缓存
self._messages_cache: List["SessionMessage"] = []
def add_messages(self, messages: List["SessionMessage"]) -> None:
"""添加消息到缓存"""
self._messages_cache.extend(messages)
def get_cache_size(self) -> int:
"""获取当前消息缓存的大小"""
return len(self._messages_cache)
async def learn(self, jargon_miner: Optional["JargonMiner"] = None):
"""学习主流程"""
if not self._messages_cache:
logger.debug("没有消息可供学习，跳过学习过程")
return
# 构建可读消息
readable_message, _, _ = await MessageUtils.build_readable_message(
self._messages_cache,
anonymize=True,
show_lineno=True,
extract_pictures=True,
replace_bot_name=True,
target_bot_name="SELF",
)
# 准备提示词
prompt_template = prompt_manager.get_prompt("learn_style")
prompt_template.add_context("bot_name", global_config.bot.nickname)
prompt_template.add_context("chat_str", readable_message)
prompt = await prompt_manager.render_prompt(prompt_template)
# 调用 LLM 学习表达方式
try:
response, _ = await express_learn_model.generate_response_async(prompt, temperature=0.3)
except Exception as e:
logger.error(f"学习表达方式失败，模型生成出错：{e}")
return
# 解析 LLM 返回的表达方式列表和黑话列表（包含来源行编号）
expressions: List[Tuple[str, str, str]]
jargon_entries: List[Tuple[str, str]] # (content, source_id)
expressions, jargon_entries = parse_expression_response(response)
# 从缓存中检查 jargon 是否出现在 messages 中
if cached_jargon_entries := self._check_cached_jargons_in_messages(jargon_miner):
# 合并缓存中的 jargon 条目（去重：如果 content 已存在则跳过）
existing_contents = {content for content, _ in jargon_entries}
for content, source_id in cached_jargon_entries:
if content not in existing_contents:
jargon_entries.append((content, source_id))
existing_contents.add(content)
logger.info(f"从缓存中检查到黑话：{content}")
# 检查表达方式数量，如果超过 20 个则放弃本次表达学习
if len(expressions) > 20:
logger.info(f"表达方式提取数量超过 20 个（实际{len(expressions)}个），放弃本次表达学习")
expressions = []
# 检查黑话数量，如果超过 30 个则放弃本次黑话学习
if len(jargon_entries) > 30:
logger.info(f"黑话提取数量超过 30 个（实际{len(jargon_entries)}个），放弃本次黑话学习")
jargon_entries = []
# 处理黑话条目，路由到 jargon_miner（即使没有表达方式也要处理黑话）
# TODO: 检测是否开启了
if jargon_entries:
await self._process_jargon_entries(jargon_entries, jargon_miner)
# 如果没有表达方式，直接返回
if not expressions:
logger.info("解析后没有可用的表达方式")
return
logger.info(f"学习的 expressions: {expressions}")
logger.info(f"学习的 jargon_entries: {jargon_entries}")
# 过滤表达方式，根据 source_id 溯源并应用各种过滤规则
learnt_expressions = self._filter_expressions(expressions)
if not learnt_expressions:
logger.info("没有学习到表达风格")
return
# 展示学到的表达方式
learnt_expressions_str = "\n".join(f"{situation}->{style}" for situation, style in learnt_expressions)
logger.info(f"在 {self.session_id} 学习到表达风格:\n{learnt_expressions_str}")
# 存储到数据库 Expression 表
for situation, style in learnt_expressions:
await self._upsert_expression_to_db(situation, style)
# ====== 黑话相关 ======
def _check_cached_jargons_in_messages(self, jargon_miner: Optional["JargonMiner"] = None) -> List[Tuple[str, str]]:
"""
检查缓存中的 jargon 是否出现在 messages 中
Args:
jargon_miner: JargonMiner 实例，用于获取缓存的黑话
Returns:
List[Tuple[str, str]]: 匹配到的黑话条目列表，每个元素是 (content, source_id)
"""
if not jargon_miner:
return []
# 获取缓存的所有 jargon 实例
cached_jargons = jargon_miner.get_cached_jargons()
if not cached_jargons:
return []
matched_entries: List[Tuple[str, str]] = []
for i, msg in enumerate(self._messages_cache):
# 跳过机器人自己的消息
if is_bot_self(msg.platform, msg.message_info.user_info.user_id):
continue
# 获取消息文本
msg_text = (msg.processed_plain_text or "").strip()
if not msg_text:
continue
# 检查每个缓存中的 jargon 是否出现在消息文本中
for jargon in cached_jargons:
if not jargon or not jargon.strip():
continue
jargon_content = jargon.strip()
# 使用正则匹配，考虑单词边界（类似 jargon_explainer 中的逻辑）
pattern = re.escape(jargon_content)
# 对于中文，使用更宽松的匹配；对于英文/数字，使用单词边界
if re.search(r"[\u4e00-\u9fff]", jargon_content):
# 包含中文，使用更宽松的匹配
search_pattern = pattern
else:
# 纯英文/数字，使用单词边界
search_pattern = r"\b" + pattern + r"\b"
if re.search(search_pattern, msg_text, re.IGNORECASE):
# 找到匹配，构建条目（source_id 从 1 开始，因为 build_readable_message 的编号从 1 开始）
source_id = str(i + 1)
matched_entries.append((jargon_content, source_id))
return matched_entries
async def _process_jargon_entries(
self, jargon_entries: List[Tuple[str, str]], jargon_miner: Optional["JargonMiner"] = None
):
"""
处理从 expression learner 提取的黑话条目，路由到 jargon_miner
Args:
jargon_entries: 黑话条目列表，每个元素是 (content, source_id)
jargon_miner: JargonMiner 实例
"""
if not jargon_entries or not self._messages_cache:
return
if not jargon_miner:
logger.warning("缺少 JargonMiner 实例，无法处理黑话条目")
return
# 构建黑话条目格式
entries: List["JargonEntry"] = []
for content, source_id in jargon_entries:
content = content.strip()
if not content:
continue
# 过滤掉包含 SELF 的黑话，不学习
if "SELF" in content:
logger.info(f"跳过包含 SELF 的黑话：{content}")
continue
# TODO: 多平台兼容
# 检查是否包含机器人名称
bot_nickname = global_config.bot.nickname
if bot_nickname and bot_nickname in content:
logger.info(f"跳过包含机器人昵称的黑话：{content}")
continue
# 解析 source_id
if not source_id.isdigit():
logger.warning(f"黑话条目 source_id 无效：content={content}, source_id={source_id}")
continue
# build_readable_message 的编号从 1 开始
line_index = int(source_id) - 1
if line_index < 0 or line_index >= len(self._messages_cache):
logger.warning(f"黑话条目 source_id 超出范围：content={content}, source_id={source_id}")
continue
# 检查是否是机器人自己的消息
target_msg = self._messages_cache[line_index]
if is_bot_self(target_msg.platform, target_msg.message_info.user_info.user_id):
logger.info(f"跳过引用机器人自身消息的黑话：content={content}, source_id={source_id}")
continue
# 构建上下文段落（取前后各 3 条消息）
start_idx = max(0, line_index - 3)
end_idx = min(len(self._messages_cache), line_index + 4)
context_msgs = self._messages_cache[start_idx:end_idx]
context_paragraph = "\n".join(
[f"[{i + 1}] {msg.processed_plain_text or ''}" for i, msg in enumerate(context_msgs)]
)
if not context_paragraph:
logger.warning(f"黑话条目上下文为空：content={content}, source_id={source_id}")
continue
entries.append({"content": content, "raw_content": {context_paragraph}}) # type: ignore
if not entries:
return
await jargon_miner.process_extracted_entries(entries)
logger.info(f"成功处理 {len(entries)} 个黑话条目")
# ====== 过滤方法 ======
def _filter_expressions(self, expressions: List[Tuple[str, str, str]]) -> List[Tuple[str, str]]:
"""
过滤表达方式，移除不符合条件的条目
Args:
expressions: 表达方式列表，每个元素是 (situation, style, source_id)
Returns:
过滤后的表达方式列表，每个元素是 (situation, style)
"""
filtered_expressions: List[Tuple[str, str]] = []
# 准备机器人名称集合（用于过滤 style 与机器人名称重复的表达）
# TODO: 完善这里的机器人名称检测逻辑（考虑别名、不同平台的名称等）
banned_names: set[str] = set()
bot_nickname = global_config.bot.nickname
if bot_nickname:
banned_names.add(bot_nickname)
alias_names = global_config.bot.alias_names or []
for alias in alias_names:
if alias_stripped := alias.strip():
banned_names.add(alias_stripped)
banned_casefold = {name.casefold() for name in banned_names if name}
for situation, style, source_id in expressions:
source_id_str = source_id.strip()
if not source_id_str.isdigit():
continue # 无效的来源行编号，跳过
line_index = int(source_id_str) - 1 # build_readable_message 的编号从 1 开始
if line_index < 0 or line_index >= len(self._messages_cache):
continue # 超出范围，跳过
# 当前行的原始消息
current_msg = self._messages_cache[line_index]
# 过滤掉从 bot 自己发言中提取到的表达方式
if is_bot_self(current_msg.platform, current_msg.message_info.user_info.user_id):
continue
# 过滤掉无上下文的表达方式
context = (current_msg.processed_plain_text or "").strip()
if not context:
continue
# 过滤掉包含 SELF 的内容（不学习）
if "SELF" in situation or "SELF" in style or "SELF" in context:
logger.info(f"跳过包含 SELF 的表达方式：situation={situation}, style={style}, source_id={source_id}")
continue
# 过滤掉 style 与机器人名称/昵称重复的表达
normalized_style = (style or "").strip()
if normalized_style and normalized_style.casefold() in banned_casefold:
logger.debug(
f"跳过 style 与机器人名称重复的表达方式：situation={situation}, style={style}, source_id={source_id}"
)
continue
# 过滤掉包含 "[表情" 的内容
if "[表情包" in situation or "[表情包" in style or "[表情包" in context:
logger.info(f"跳过包含表情标记的表达方式：situation={situation}, style={style}, source_id={source_id}")
continue
# 过滤掉包含 "[图片" 的内容
if "[图片" in situation or "[图片" in style or "[图片" in context:
logger.info(f"跳过包含图片标记的表达方式：situation={situation}, style={style}, source_id={source_id}")
continue
filtered_expressions.append((situation, style))
return filtered_expressions
# ====== DB 操作相关 ======
async def _upsert_expression_to_db(self, situation: str, style: str):
expr, similarity = self._find_similar_expression(situation) or (None, 0)
if expr:
# 根据相似度决定是否使用 LLM 总结
# 完全匹配（相似度 == 1.0）时不总结，相似匹配时总结
use_llm_summary = similarity < 1.0
await self._update_existing_expression(expr, situation, use_llm_summary=use_llm_summary)
return
# 没有找到匹配的记录，创建新记录
self._create_expression(situation, style)
def _create_expression(self, situation: str, style: str):
content_list = [situation]
try:
with get_db_session() as db:
new_expr = Expression(
situation=situation,
style=style,
content_list=json.dumps(content_list),
count=1,
session_id=self.session_id,
last_active_time=datetime.now(),
)
db.add(new_expr)
except Exception as e:
logger.error(f"创建表达方式失败: {e}")
async def _update_existing_expression(self, expr: "MaiExpression", situation: str, use_llm_summary: bool = True):
expr.content.append(situation)
expr.count += 1
expr.checked = False # count 增加时重置 checked 为 False
expr.last_active_time = datetime.now()
if use_llm_summary:
# 相似匹配时，使用 LLM 重新组合 situation
new_situation = await self._compose_situation_text(expr.content)
if new_situation:
expr.situation = new_situation
try:
with get_db_session() as session:
if expr.item_id is None:
raise ValueError("表达方式对象缺少 item_id，无法更新数据库记录")
statement = select(Expression).filter_by(id=expr.item_id).limit(1)
if db_expr := session.exec(statement).first():
db_expr.content_list = json.dumps(expr.content)
db_expr.count = expr.count
db_expr.checked = expr.checked
db_expr.last_active_time = expr.last_active_time
db_expr.situation = expr.situation # 更新 situation
session.add(db_expr)
else:
logger.warning(f"表达方式 ID {expr.item_id} 在数据库中未找到，无法更新")
except Exception as e:
logger.error(f"更新表达方式失败: {e}")
# count 增加后，立即进行一次检查
await self._check_expression(expr)
# ====== 概括方法 ======
async def _compose_situation_text(self, content_list: List[str]) -> Optional[str]:
texts = [c.strip() for c in content_list if c.strip()]
if not texts:
return None
description = "\n".join(f"- {s}" for s in texts[-10:]) # 只取最近10条进行概括
prompt = (
"请阅读以下多个聊天情境描述，并将它们概括成一句简短的话，长度不超过20个字，保留共同特点：\n"
f"{description}\n"
"只输出概括内容。"
)
try:
summary, _ = await summary_model.generate_response_async(prompt, temperature=0.2)
if summary := summary.strip():
return summary
except Exception as e:
logger.error(f"使用 LLM 生成表达方式概括失败: {e}")
return None
async def _check_expression(self, expr: "MaiExpression"):
"""
检查表达方式（在 count 增加后调用）
Args:
expr (MaiExpression): 要检查的表达方式对象
"""
if not global_config.expression.expression_self_reflect:
logger.debug("表达方式自我反思功能未启用，跳过检查")
return
suitable, reason, error = await check_expression_suitability(expr.situation, expr.style)
if error:
logger.error(f"检查表达方式时发生错误: {error}")
return
expr.checked = True
expr.rejected = not suitable
try:
with get_db_session() as session:
statement = select(Expression).filter_by(id=expr.item_id).limit(1)
if db_expr := session.exec(statement).first():
db_expr.checked = expr.checked
db_expr.rejected = expr.rejected
session.add(db_expr)
else:
logger.warning(f"表达方式 ID {expr.item_id} 在数据库中未找到，无法更新检查结果")
except Exception as e:
logger.error(f"更新表达方式检查结果失败: {e}")
status = "通过" if suitable else "不通过"
logger.info(
f"表达方式检查完成 [ID: {expr.item_id}] - {status} | "
f"Situation: {expr.situation[:30]}... | "
f"Style: {expr.style[:30]}... | "
f"Reason: {reason[:50] if reason else '无'}..."
)
def _find_similar_expression(
self, situation: str, similarity_threshold: float = 0.75
) -> Optional[Tuple[MaiExpression, float]]:
"""在数据库中查找相似的表达方式"""
try:
with get_db_session() as session:
statement = select(Expression).filter_by(session_id=self.session_id)
expressions = session.exec(statement).all()
best_match: Optional[Expression] = None
best_similarity = 0.0
for expr in expressions:
content_list = json.loads(expr.content_list)
for situation in content_list:
similarity = difflib.SequenceMatcher(None, situation, expr.situation).ratio()
if similarity > similarity_threshold and similarity > best_similarity:
best_similarity = similarity
best_match = expr
if best_match:
logger.debug(f"找到相似表达方式情景 [ID: {best_match.id}]，相似度: {best_similarity:.2f}")
return MaiExpression.from_db_instance(best_match), best_similarity
except Exception as e:
logger.error(f"查找相似表达方式失败: {e}")
return None