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Update heartflow_message_processor.py

pull/1228/head
CNMr.Sunshine 2025-09-08 22:52:22 +08:00 committed by GitHub
parent ac2936d5fc
commit ad35c3fa4f
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1 changed files with 14 additions and 9 deletions
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src/chat/heart_flow/heartflow_message_processor.py
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@ -37,16 +37,21 @@ async def _calculate_interest(message: MessageRecv) -> Tuple[float, list[str]]:
is_mentioned,is_at,reply_probability_boost = is_mentioned_bot_in_message(message) is_mentioned,is_at,reply_probability_boost = is_mentioned_bot_in_message(message)
interested_rate = 0.0 interested_rate = 0.0
keywords = []
keywords_lite = []
with Timer("记忆激活"): if global_config.memory.enable_memory:
interested_rate, keywords,keywords_lite = await hippocampus_manager.get_activate_from_text( with Timer("记忆激活"):
message.processed_plain_text, interested_rate, keywords,keywords_lite = await hippocampus_manager.get_activate_from_text(
max_depth= 4, message.processed_plain_text,
fast_retrieval=global_config.chat.interest_rate_mode == "fast", max_depth= 4,
) fast_retrieval=global_config.chat.interest_rate_mode == "fast",
message.key_words = keywords )
message.key_words_lite = keywords_lite logger.debug(f"记忆激活率: {interested_rate:.2f}, 关键词: {keywords}")
logger.debug(f"记忆激活率: {interested_rate:.2f}, 关键词: {keywords}")
# 无论是否启用记忆系统，都为消息对象写入关键词字段（禁用时为空）
message.key_words = keywords
message.key_words_lite = keywords_lite
text_len = len(message.processed_plain_text) text_len = len(message.processed_plain_text)
# 根据文本长度分布调整兴趣度，采用分段函数实现更精确的兴趣度计算 # 根据文本长度分布调整兴趣度，采用分段函数实现更精确的兴趣度计算