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This commit is contained in:
墨梓柒
2025-12-13 17:14:09 +08:00
parent ef377bb0cd
commit e680a4d1f5
60 changed files with 1546 additions and 1532 deletions
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83
src/bw_learner/expression_learner.py
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@@ -13,7 +13,12 @@ from src.chat.utils.chat_message_builder import (
)
from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
from src.chat.message_receive.chat_stream import get_chat_manager
from src.bw_learner.learner_utils import filter_message_content, is_bot_message, build_context_paragraph, contains_bot_self_name
from src.bw_learner.learner_utils import (
filter_message_content,
is_bot_message,
build_context_paragraph,
contains_bot_self_name,
)
from src.bw_learner.jargon_miner import miner_manager
from json_repair import repair_json
@@ -77,8 +82,6 @@ def init_prompt() -> None:
Prompt(learn_style_prompt, "learn_style_prompt")
class ExpressionLearner:
def __init__(self, chat_id: str) -> None:
self.express_learn_model: LLMRequest = LLMRequest(
@@ -95,12 +98,12 @@ class ExpressionLearner:
self._learning_lock = asyncio.Lock()
async def learn_and_store(
self,
self,
messages: List[Any],
) -> List[Tuple[str, str, str]]:
"""
学习并存储表达方式
Args:
messages: 外部传入的消息列表（必需）
num: 学习数量
@@ -108,7 +111,7 @@ class ExpressionLearner:
"""
if not messages:
return None
random_msg = messages
# 学习用（开启行编号，便于溯源）
@@ -134,26 +137,26 @@ class ExpressionLearner:
jargon_entries: List[Tuple[str, str]] # (content, source_id)
expressions, jargon_entries = self.parse_expression_response(response)
expressions = self._filter_self_reference_styles(expressions)
# 检查表达方式数量，如果超过10个则放弃本次表达学习
if len(expressions) > 10:
logger.info(f"表达方式提取数量超过10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）
if jargon_entries:
await self._process_jargon_entries(jargon_entries, random_msg)
# 如果没有表达方式，直接返回
if not expressions:
logger.info("过滤后没有可用的表达方式（style 与机器人名称重复）")
return []
logger.info(f"学习的prompt: {prompt}")
logger.info(f"学习的expressions: {expressions}")
logger.info(f"学习的jargon_entries: {jargon_entries}")
@@ -175,18 +178,17 @@ class ExpressionLearner:
# 当前行的原始内容
current_msg = random_msg[line_index]
# 过滤掉从bot自己发言中提取到的表达方式
if is_bot_message(current_msg):
continue
context = filter_message_content(current_msg.processed_plain_text or "")
if not context:
continue
filtered_expressions.append((situation, style, context))
learnt_expressions = filtered_expressions
if learnt_expressions is None:
@@ -270,37 +272,38 @@ class ExpressionLearner:
# 如果解析失败，尝试修复中文引号问题
# 使用状态机方法，在 JSON 字符串值内部将中文引号替换为转义的英文引号
try:
def fix_chinese_quotes_in_json(text):
"""使用状态机修复 JSON 字符串值中的中文引号"""
result = []
i = 0
in_string = False
escape_next = False
while i < len(text):
char = text[i]
if escape_next:
# 当前字符是转义字符后的字符，直接添加
result.append(char)
escape_next = False
i += 1
continue
if char == '\\':
if char == "\\":
# 转义字符
result.append(char)
escape_next = True
i += 1
continue
if char == '"' and not escape_next:
# 遇到英文引号，切换字符串状态
in_string = not in_string
result.append(char)
i += 1
continue
if in_string:
# 在字符串值内部，将中文引号替换为转义的英文引号
if char == '"': # 中文左引号 U+201C
@@ -312,13 +315,13 @@ class ExpressionLearner:
else:
# 不在字符串内，直接添加
result.append(char)
i += 1
return ''.join(result)
return "".join(result)
fixed_raw = fix_chinese_quotes_in_json(raw)
# 再次尝试解析
if fixed_raw.startswith("[") and fixed_raw.endswith("]"):
parsed = json.loads(fixed_raw)
@@ -346,12 +349,12 @@ class ExpressionLearner:
for item in parsed_list:
if not isinstance(item, dict):
continue
# 检查是否是表达方式条目（有 situation 和 style）
situation = str(item.get("situation", "")).strip()
style = str(item.get("style", "")).strip()
source_id = str(item.get("source_id", "")).strip()
if situation and style and source_id:
# 表达方式条目
expressions.append((situation, style, source_id))
@@ -503,59 +506,59 @@ class ExpressionLearner:
async def _process_jargon_entries(self, jargon_entries: List[Tuple[str, str]], messages: List[Any]) -> 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)
messages: 消息列表，用于构建上下文
"""
if not jargon_entries or not messages:
return
# 获取 jargon_miner 实例
jargon_miner = miner_manager.get_miner(self.chat_id)
# 构建黑话条目格式，与 jargon_miner.run_once 中的格式一致
entries: List[Dict[str, List[str]]] = []
for content, source_id in jargon_entries:
content = content.strip()
if not content:
continue
# 检查是否包含机器人名称
if contains_bot_self_name(content):
logger.info(f"跳过包含机器人昵称/别名的黑话: {content}")
continue
# 解析 source_id
source_id_str = (source_id or "").strip()
if not source_id_str.isdigit():
logger.warning(f"黑话条目 source_id 无效: content={content}, source_id={source_id_str}")
continue
# build_anonymous_messages 的编号从 1 开始
line_index = int(source_id_str) - 1
if line_index < 0 or line_index >= len(messages):
logger.warning(f"黑话条目 source_id 超出范围: content={content}, source_id={source_id_str}")
continue
# 检查是否是机器人自己的消息
target_msg = messages[line_index]
if is_bot_message(target_msg):
logger.info(f"跳过引用机器人自身消息的黑话: content={content}, source_id={source_id_str}")
continue
# 构建上下文段落
context_paragraph = build_context_paragraph(messages, line_index)
if not context_paragraph:
logger.warning(f"黑话条目上下文为空: content={content}, source_id={source_id_str}")
continue
entries.append({"content": content, "raw_content": [context_paragraph]})
if not entries:
return
# 调用 jargon_miner 处理这些条目
await jargon_miner.process_extracted_entries(entries)