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Merge branch 'main-fix' into relationship

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This commit is contained in:
meng_xi_pan
2025-03-14 18:52:13 +08:00
parent a3927507dc 08499f747d
commit 3d1a58130d
11 changed files with 262 additions and 332 deletions
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133
src/plugins/chat/prompt_builder.py
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@@ -15,8 +15,8 @@ from .relationship_manager import relationship_manager
class PromptBuilder:
def __init__(self):
self.prompt_built = ''
self.activate_messages = ''
self.prompt_built = ""
self.activate_messages = ""
async def _build_prompt(self,
chat_stream,
@@ -24,13 +24,13 @@ class PromptBuilder:
sender_name: str = "某人",
stream_id: Optional[int] = None) -> tuple[str, str]:
"""构建prompt
Args:
message_txt: 消息文本
sender_name: 发送者昵称
# relationship_value: 关系值
group_id: 群组ID
Returns:
str: 构建好的prompt
"""
@@ -88,46 +88,43 @@ class PromptBuilder:
current_date = time.strftime("%Y-%m-%d", time.localtime())
current_time = time.strftime("%H:%M:%S", time.localtime())
bot_schedule_now_time, bot_schedule_now_activity = bot_schedule.get_current_task()
prompt_date = f'''今天是{current_date}，现在是{current_time}，你今天的日程是：\n{bot_schedule.today_schedule}\n你现在正在{bot_schedule_now_activity}\n'''
prompt_date = f"""今天是{current_date}，现在是{current_time}，你今天的日程是：\n{bot_schedule.today_schedule}\n你现在正在{bot_schedule_now_activity}\n"""
# 知识构建
start_time = time.time()
prompt_info = ''
promt_info_prompt = ''
prompt_info = ""
promt_info_prompt = ""
prompt_info = await self.get_prompt_info(message_txt, threshold=0.5)
if prompt_info:
prompt_info = f'''你有以下这些[知识]：{prompt_info}请你记住上面的[
知识]，之后可能会用到-'''
prompt_info = f"""你有以下这些[知识]：{prompt_info}请你记住上面的[
知识]，之后可能会用到-"""
end_time = time.time()
logger.debug(f"知识检索耗时: {(end_time - start_time):.3f}秒")
# 获取聊天上下文
chat_in_group=True
chat_talking_prompt = ''
chat_in_group = True
chat_talking_prompt = ""
if stream_id:
chat_talking_prompt = get_recent_group_detailed_plain_text(stream_id, limit=global_config.MAX_CONTEXT_SIZE,combine = True)
chat_stream=chat_manager.get_stream(stream_id)
chat_talking_prompt = get_recent_group_detailed_plain_text(
stream_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True
)
chat_stream = chat_manager.get_stream(stream_id)
if chat_stream.group_info:
chat_talking_prompt = f"以下是群里正在聊天的内容：\n{chat_talking_prompt}"
else:
chat_in_group=False
chat_in_group = False
chat_talking_prompt = f"以下是你正在和{sender_name}私聊的内容：\n{chat_talking_prompt}"
# print(f"\033[1;34m[调试]\033[0m 已从数据库获取群 {group_id} 的消息记录:{chat_talking_prompt}")
# 使用新的记忆获取方法
memory_prompt = ''
memory_prompt = ""
start_time = time.time()
# 调用 hippocampus 的 get_relevant_memories 方法
relevant_memories = await hippocampus.get_relevant_memories(
text=message_txt,
max_topics=5,
similarity_threshold=0.4,
max_memory_num=5
text=message_txt, max_topics=5, similarity_threshold=0.4, max_memory_num=5
)
if relevant_memories:
@@ -147,7 +144,7 @@ class PromptBuilder:
logger.info(f"回忆耗时: {(end_time - start_time):.3f}秒")
# 激活prompt构建
activate_prompt = ''
activate_prompt = ""
if chat_in_group:
activate_prompt = f"以上是群里正在进行的聊天，{memory_prompt}，\
{relation_prompt}{relation_prompt2}现在昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意。请分析聊天记录，根据你和他的关系和态度进行回复，明确你的立场和情感。"
@@ -155,20 +152,22 @@ class PromptBuilder:
activate_prompt = f"以上是你正在和{sender_name}私聊的内容，{memory_prompt} 现在昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意，{relation_prompt}{mood_prompt}，你的回复态度是{relation_prompt2}"
# 关键词检测与反应
keywords_reaction_prompt = ''
keywords_reaction_prompt = ""
for rule in global_config.keywords_reaction_rules:
if rule.get("enable", False):
if any(keyword in message_txt.lower() for keyword in rule.get("keywords", [])):
logger.info(f"检测到以下关键词之一：{rule.get('keywords', [])}，触发反应：{rule.get('reaction', '')}")
keywords_reaction_prompt += rule.get("reaction", "") + '，'
#人格选择
personality=global_config.PROMPT_PERSONALITY
logger.info(
f"检测到以下关键词之一：{rule.get('keywords', [])}，触发反应：{rule.get('reaction', '')}"
)
keywords_reaction_prompt += rule.get("reaction", "") + "，"
# 人格选择
personality = global_config.PROMPT_PERSONALITY
probability_1 = global_config.PERSONALITY_1
probability_2 = global_config.PERSONALITY_2
probability_3 = global_config.PERSONALITY_3
prompt_personality = f'{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，你还有很多别名:{"/".join(global_config.BOT_ALIAS_NAMES)}，'
prompt_personality = f"{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，你还有很多别名:{'/'.join(global_config.BOT_ALIAS_NAMES)}，"
personality_choice = random.random()
if personality_choice < probability_1: # 第一种人格
@@ -185,13 +184,13 @@ class PromptBuilder:
请你表达自己的见解和观点。可以有个性。'''
# 中文高手(新加的好玩功能)
prompt_ger = ''
prompt_ger = ""
if random.random() < 0.04:
prompt_ger += '你喜欢用倒装句'
prompt_ger += "你喜欢用倒装句"
if random.random() < 0.02:
prompt_ger += '你喜欢用反问句'
prompt_ger += "你喜欢用反问句"
if random.random() < 0.01:
prompt_ger += '你喜欢用文言文'
prompt_ger += "你喜欢用文言文"
# 额外信息要求
extra_info = f'''但是记得你的回复态度和你的立场，切记你回复的人是{sender_name}，不要输出你的思考过程，只需要输出最终的回复，务必简短一些，尤其注意在没明确提到时不要过多提及自身的背景, 不要直接回复别人发的表情包，记住不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只需要输出回复内容就好，不要输出其他任何内容'''
@@ -225,38 +224,38 @@ class PromptBuilder:
current_date = time.strftime("%Y-%m-%d", time.localtime())
current_time = time.strftime("%H:%M:%S", time.localtime())
bot_schedule_now_time, bot_schedule_now_activity = bot_schedule.get_current_task()
prompt_date = f'''今天是{current_date}，现在是{current_time}，你今天的日程是：\n{bot_schedule.today_schedule}\n你现在正在{bot_schedule_now_activity}\n'''
prompt_date = f"""今天是{current_date}，现在是{current_time}，你今天的日程是：\n{bot_schedule.today_schedule}\n你现在正在{bot_schedule_now_activity}\n"""
chat_talking_prompt = ''
chat_talking_prompt = ""
if group_id:
chat_talking_prompt = get_recent_group_detailed_plain_text(group_id,
limit=global_config.MAX_CONTEXT_SIZE,
combine=True)
chat_talking_prompt = get_recent_group_detailed_plain_text(
group_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True
)
chat_talking_prompt = f"以下是群里正在聊天的内容：\n{chat_talking_prompt}"
# print(f"\033[1;34m[调试]\033[0m 已从数据库获取群 {group_id} 的消息记录:{chat_talking_prompt}")
# 获取主动发言的话题
all_nodes = memory_graph.dots
all_nodes = filter(lambda dot: len(dot[1]['memory_items']) > 3, all_nodes)
all_nodes = filter(lambda dot: len(dot[1]["memory_items"]) > 3, all_nodes)
nodes_for_select = random.sample(all_nodes, 5)
topics = [info[0] for info in nodes_for_select]
infos = [info[1] for info in nodes_for_select]
# 激活prompt构建
activate_prompt = ''
activate_prompt = ""
activate_prompt = "以上是群里正在进行的聊天。"
personality = global_config.PROMPT_PERSONALITY
prompt_personality = ''
prompt_personality = ""
personality_choice = random.random()
if personality_choice < probability_1: # 第一种人格
prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[0]}'''
prompt_personality = f"""{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[0]}"""
elif personality_choice < probability_1 + probability_2: # 第二种人格
prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[1]}'''
prompt_personality = f"""{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[1]}"""
else: # 第三种人格
prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[2]}'''
prompt_personality = f"""{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[2]}"""
topics_str = ','.join(f"\"{topics}\"")
topics_str = ",".join(f'"{topics}"')
prompt_for_select = f"你现在想在群里发言，回忆了一下，想到几个话题，分别是{topics_str}，综合当前状态以及群内气氛，请你在其中选择一个合适的话题，注意只需要输出话题，除了话题什么也不要输出(双引号也不要输出)"
prompt_initiative_select = f"{prompt_date}\n{prompt_personality}\n{prompt_for_select}"
@@ -265,8 +264,8 @@ class PromptBuilder:
return prompt_initiative_select, nodes_for_select, prompt_regular
def _build_initiative_prompt_check(self, selected_node, prompt_regular):
memory = random.sample(selected_node['memory_items'], 3)
memory = '\n'.join(memory)
memory = random.sample(selected_node["memory_items"], 3)
memory = "\n".join(memory)
prompt_for_check = f"{prompt_regular}你现在想在群里发言，回忆了一下，想到一个话题,是{selected_node['concept']}，关于这个话题的记忆有\n{memory}\n，以这个作为主题发言合适吗？请在把握群里的聊天内容的基础上，综合群内的氛围，如果认为应该发言请输出yes，否则输出no，请注意是决定是否需要发言，而不是编写回复内容，除了yes和no不要输出任何回复内容。"
return prompt_for_check, memory
@@ -275,7 +274,7 @@ class PromptBuilder:
return prompt_for_initiative
async def get_prompt_info(self, message: str, threshold: float):
related_info = ''
related_info = ""
logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
embedding = await get_embedding(message)
related_info += self.get_info_from_db(embedding, threshold=threshold)
@@ -284,7 +283,7 @@ class PromptBuilder:
def get_info_from_db(self, query_embedding: list, limit: int = 1, threshold: float = 0.5) -> str:
if not query_embedding:
return ''
return ""
# 使用余弦相似度计算
pipeline = [
{
@@ -296,12 +295,14 @@ class PromptBuilder:
"in": {
"$add": [
"$$value",
{"$multiply": [
{"$arrayElemAt": ["$embedding", "$$this"]},
{"$arrayElemAt": [query_embedding, "$$this"]}
]}
{
"$multiply": [
{"$arrayElemAt": ["$embedding", "$$this"]},
{"$arrayElemAt": [query_embedding, "$$this"]},
]
},
]
}
},
}
},
"magnitude1": {
@@ -309,7 +310,7 @@ class PromptBuilder:
"$reduce": {
"input": "$embedding",
"initialValue": 0,
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]}
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
}
}
},
@@ -318,19 +319,13 @@ class PromptBuilder:
"$reduce": {
"input": query_embedding,
"initialValue": 0,
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]}
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
}
}
}
}
},
{
"$addFields": {
"similarity": {
"$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]
}
},
}
},
{"$addFields": {"similarity": {"$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]}}},
{
"$match": {
"similarity": {"$gte": threshold} # 只保留相似度大于等于阈值的结果
@@ -338,17 +333,17 @@ class PromptBuilder:
},
{"$sort": {"similarity": -1}},
{"$limit": limit},
{"$project": {"content": 1, "similarity": 1}}
{"$project": {"content": 1, "similarity": 1}},
]
results = list(db.knowledges.aggregate(pipeline))
# print(f"\033[1;34m[调试]\033[0m获取知识库内容结果: {results}")
if not results:
return ''
return ""
# 返回所有找到的内容，用换行分隔
return '\n'.join(str(result['content']) for result in results)
return "\n".join(str(result["content"]) for result in results)
prompt_builder = PromptBuilder()