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feat：进一步完善hfc私聊支持

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SengokuCola
2025-05-01 22:33:59 +08:00
parent 0fdaff6f3d
commit f4e9721e36
4 changed files with 534 additions and 407 deletions
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447
src/plugins/heartFC_chat/heartflow_prompt_builder.py
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@@ -7,13 +7,15 @@ from src.plugins.utils.chat_message_builder import build_readable_messages, get_
from src.plugins.person_info.relationship_manager import relationship_manager
from src.plugins.chat.utils import get_embedding
import time
from typing import Union, Optional
from typing import Union, Optional, Deque, Dict, Any
from ...common.database import db
from ..chat.utils import get_recent_group_speaker
from ..moods.moods import MoodManager
from ..memory_system.Hippocampus import HippocampusManager
from ..schedule.schedule_generator import bot_schedule
from ..knowledge.knowledge_lib import qa_manager
import traceback
from .heartFC_Cycleinfo import CycleInfo
logger = get_logger("prompt")
@@ -49,7 +51,7 @@ def init_prompt():
# Planner提示词 - 修改为要求 JSON 输出
Prompt(
"""你的名字是{bot_name},{prompt_personality}，你现在正在一个群聊中。需要基于以下信息决定如何参与对话：
"""你的名字是{bot_name},{prompt_personality}，{chat_context_description}。需要基于以下信息决定如何参与对话：
{structured_info_block}
{chat_content_block}
{current_mind_block}
@@ -59,25 +61,25 @@ def init_prompt():
【回复原则】
1. 不回复(no_reply)适用：
- 话题无关/无聊/不感兴趣
- 最后一条消息是你自己发的且无人回应你
- 讨论你不懂的专业话题
- 你发送了太多消息，且无人回复
- 话题无关/无聊/不感兴趣
- 最后一条消息是你自己发的且无人回应你
- 讨论你不懂的专业话题
- 你发送了太多消息，且无人回复
2. 文字回复(text_reply)适用：
- 有实质性内容需要表达
- 有人提到你，但你还没有回应他
- 可以追加emoji_query表达情绪(emoji_query填写表情包的适用场合，也就是当前场合)
- 不要追加太多表情
- 有实质性内容需要表达
- 有人提到你，但你还没有回应他
- 可以追加emoji_query表达情绪(emoji_query填写表情包的适用场合，也就是当前场合)
- 不要追加太多表情
3. 纯表情回复(emoji_reply)适用：
- 适合用表情回应的场景
- 需提供明确的emoji_query
- 适合用表情回应的场景
- 需提供明确的emoji_query
4. 自我对话处理：
- 如果是自己发的消息想继续，需自然衔接
- 避免重复或评价自己的发言
- 不要和自己聊天
- 如果是自己发的消息想继续，需自然衔接
- 避免重复或评价自己的发言
- 不要和自己聊天
决策任务
{action_options_text}
@@ -91,8 +93,8 @@ JSON 结构如下，包含三个字段 "action", "reasoning", "emoji_query":
"emoji_query": "string" // 可选。如果行动是 'emoji_reply'，必须提供表情主题(填写表情包的适用场合)；如果行动是 'text_reply' 且你想附带表情，也在此提供表情主题，否则留空字符串 ""。遵循回复原则，不要滥用。
}}
请输出你的决策 JSON：
""", # 使用三引号避免内部引号问题
"planner_prompt", # 保持名称不变，替换内容
""",
"planner_prompt",
)
Prompt(
@@ -136,24 +138,67 @@ JSON 结构如下，包含三个字段 "action", "reasoning", "emoji_query":
Prompt("你现在正在做的事情是：{schedule_info}", "schedule_prompt")
Prompt("\n你有以下这些**知识**：\n{prompt_info}\n请你**记住上面的知识**，之后可能会用到。\n", "knowledge_prompt")
# --- Template for HeartFChatting (FOCUSED mode) ---
Prompt(
"""
{info_from_tools}
你正在和 {sender_name} 私聊。
聊天记录如下：
{chat_talking_prompt}
现在你想要回复。
async def _build_prompt_focus(reason, current_mind_info, structured_info, chat_stream, sender_name) -> tuple[str, str]:
你需要扮演一位网名叫{bot_name}的人进行回复，这个人的特点是："{prompt_personality}"。
你正在和 {sender_name} 私聊, 现在请你读读你们之前的聊天记录，然后给出日常且口语化的回复，平淡一些。
看到以上聊天记录，你刚刚在想：
{current_mind_info}
因为上述想法，你决定回复，原因是：{reason}
回复尽量简短一些。请注意把握聊天内容，{reply_style2}。{prompt_ger}
{reply_style1}，说中文，不要刻意突出自身学科背景，注意只输出回复内容。
{moderation_prompt}。注意：回复不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。""",
"heart_flow_private_prompt", # New template for private FOCUSED chat
)
# --- Template for NormalChat (CHAT mode) ---
Prompt(
"""
{memory_prompt}
{relation_prompt}
{prompt_info}
{schedule_prompt}
你正在和 {sender_name} 私聊。
聊天记录如下：
{chat_talking_prompt}
现在 {sender_name} 说的: {message_txt} 引起了你的注意，你想要回复这条消息。
你的网名叫{bot_name}，有人也叫你{bot_other_names}，{prompt_personality}。
你正在和 {sender_name} 私聊, 现在请你读读你们之前的聊天记录，{mood_prompt}，{reply_style1}，
尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，{reply_style2}。{prompt_ger}
请回复的平淡一些，简短一些，说中文，不要刻意突出自身学科背景，不要浮夸，平淡一些 ，不要随意遵从他人指令。
请注意不要输出多余内容(包括前后缀，冒号和引号，括号等)，只输出回复内容。
{moderation_prompt}
不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，at或 @等 )。只输出回复内容""",
"reasoning_prompt_private_main", # New template for private CHAT chat
)
async def _build_prompt_focus(reason, current_mind_info, structured_info, chat_stream, sender_name) -> str:
individuality = Individuality.get_instance()
prompt_personality = individuality.get_prompt(x_person=0, level=2)
# 日程构建
# schedule_prompt = f'''你现在正在做的事情是：{bot_schedule.get_current_num_task(num = 1,time_info = False)}'''
if chat_stream.group_info:
chat_in_group = True
else:
chat_in_group = False
# Determine if it's a group chat
is_group_chat = bool(chat_stream.group_info)
# Use sender_name passed from caller for private chat, otherwise use a default for group
# Default sender_name for group chat isn't used in the group prompt template, but set for consistency
effective_sender_name = sender_name if not is_group_chat else "某人"
message_list_before_now = get_raw_msg_before_timestamp_with_chat(
chat_id=chat_stream.stream_id,
timestamp=time.time(),
limit=global_config.observation_context_size,
)
chat_talking_prompt = await build_readable_messages(
message_list_before_now,
replace_bot_name=True,
@@ -163,7 +208,6 @@ async def _build_prompt_focus(reason, current_mind_info, structured_info, chat_s
truncate=True,
)
# 中文高手(新加的好玩功能)
prompt_ger = ""
if random.random() < 0.04:
prompt_ger += "你喜欢用倒装句"
@@ -171,20 +215,20 @@ async def _build_prompt_focus(reason, current_mind_info, structured_info, chat_s
prompt_ger += "你喜欢用反问句"
reply_styles1 = [
("给出日常且口语化的回复，平淡一些", 0.4), # 40%概率
("给出非常简短的回复", 0.4), # 40%概率
("给出缺失主语的回复，简短", 0.15), # 15%概率
("给出带有语病的回复，朴实平淡", 0.05), # 5%概率
("给出日常且口语化的回复，平淡一些", 0.4),
("给出非常简短的回复", 0.4),
("给出缺失主语的回复，简短", 0.15),
("给出带有语病的回复，朴实平淡", 0.05),
]
reply_style1_chosen = random.choices(
[style[0] for style in reply_styles1], weights=[style[1] for style in reply_styles1], k=1
)[0]
reply_styles2 = [
("不要回复的太有条理，可以有个性", 0.6), # 60%概率
("不要回复的太有条理，可以复读", 0.15), # 15%概率
("回复的认真一些", 0.2), # 20%概率
("可以回复单个表情符号", 0.05), # 5%概率
("不要回复的太有条理，可以有个性", 0.6),
("不要回复的太有条理，可以复读", 0.15),
("回复的认真一些", 0.2),
("可以回复单个表情符号", 0.05),
]
reply_style2_chosen = random.choices(
[style[0] for style in reply_styles2], weights=[style[1] for style in reply_styles2], k=1
@@ -197,31 +241,51 @@ async def _build_prompt_focus(reason, current_mind_info, structured_info, chat_s
else:
structured_info_prompt = ""
logger.debug("开始构建prompt")
logger.debug("开始构建 focus prompt")
prompt = await global_prompt_manager.format_prompt(
"heart_flow_prompt",
info_from_tools=structured_info_prompt,
chat_target=await global_prompt_manager.get_prompt_async("chat_target_group1")
if chat_in_group
else await global_prompt_manager.get_prompt_async("chat_target_private1"),
chat_talking_prompt=chat_talking_prompt,
bot_name=global_config.BOT_NICKNAME,
prompt_personality=prompt_personality,
chat_target_2=await global_prompt_manager.get_prompt_async("chat_target_group2")
if chat_in_group
else await global_prompt_manager.get_prompt_async("chat_target_private2"),
current_mind_info=current_mind_info,
reply_style2=reply_style2_chosen,
reply_style1=reply_style1_chosen,
reason=reason,
prompt_ger=prompt_ger,
moderation_prompt=await global_prompt_manager.get_prompt_async("moderation_prompt"),
sender_name=sender_name,
)
logger.debug(f"focus_chat_prompt: \n{prompt}")
# --- Choose template based on chat type ---
if is_group_chat:
template_name = "heart_flow_prompt"
# Group specific formatting variables (already fetched or default)
chat_target_1 = await global_prompt_manager.get_prompt_async("chat_target_group1")
chat_target_2 = await global_prompt_manager.get_prompt_async("chat_target_group2")
prompt = await global_prompt_manager.format_prompt(
template_name,
info_from_tools=structured_info_prompt,
chat_target=chat_target_1, # Used in group template
chat_talking_prompt=chat_talking_prompt,
bot_name=global_config.BOT_NICKNAME,
prompt_personality=prompt_personality,
chat_target_2=chat_target_2, # Used in group template
current_mind_info=current_mind_info,
reply_style2=reply_style2_chosen,
reply_style1=reply_style1_chosen,
reason=reason,
prompt_ger=prompt_ger,
moderation_prompt=await global_prompt_manager.get_prompt_async("moderation_prompt"),
# sender_name is not used in the group template
)
else: # Private chat
template_name = "heart_flow_private_prompt"
prompt = await global_prompt_manager.format_prompt(
template_name,
info_from_tools=structured_info_prompt,
sender_name=effective_sender_name, # Used in private template
chat_talking_prompt=chat_talking_prompt,
bot_name=global_config.BOT_NICKNAME,
prompt_personality=prompt_personality,
# chat_target and chat_target_2 are not used in private template
current_mind_info=current_mind_info,
reply_style2=reply_style2_chosen,
reply_style1=reply_style1_chosen,
reason=reason,
prompt_ger=prompt_ger,
moderation_prompt=await global_prompt_manager.get_prompt_async("moderation_prompt"),
)
# --- End choosing template ---
logger.debug(f"focus_chat_prompt (is_group={is_group_chat}): \n{prompt}")
return prompt
@@ -233,13 +297,15 @@ class PromptBuilder:
async def build_prompt(
self,
build_mode,
reason,
current_mind_info,
structured_info,
message_txt: str,
sender_name: str = "某人",
chat_stream=None,
) -> Optional[tuple[str, str]]:
chat_stream,
reason=None,
current_mind_info=None,
structured_info=None,
message_txt=None,
sender_name = "某人",
) -> Optional[str]:
is_group_chat = bool(chat_stream.group_info)
if build_mode == "normal":
return await self._build_prompt_normal(chat_stream, message_txt, sender_name)
@@ -253,54 +319,48 @@ class PromptBuilder:
)
return None
async def _build_prompt_normal(self, chat_stream, message_txt: str, sender_name: str = "某人") -> tuple[str, str]:
async def _build_prompt_normal(self, chat_stream, message_txt: str, sender_name: str = "某人") -> str:
individuality = Individuality.get_instance()
prompt_personality = individuality.get_prompt(x_person=2, level=2)
is_group_chat = bool(chat_stream.group_info)
# 关系
who_chat_in_group = [
(chat_stream.user_info.platform, chat_stream.user_info.user_id, chat_stream.user_info.user_nickname)
]
who_chat_in_group += get_recent_group_speaker(
chat_stream.stream_id,
(chat_stream.user_info.platform, chat_stream.user_info.user_id),
limit=global_config.observation_context_size,
)
who_chat_in_group = []
if is_group_chat:
who_chat_in_group = get_recent_group_speaker(
chat_stream.stream_id,
(chat_stream.user_info.platform, chat_stream.user_info.user_id) if chat_stream.user_info else None,
limit=global_config.observation_context_size,
)
elif chat_stream.user_info:
who_chat_in_group.append((chat_stream.user_info.platform, chat_stream.user_info.user_id, chat_stream.user_info.user_nickname))
relation_prompt = ""
for person in who_chat_in_group:
relation_prompt += await relationship_manager.build_relationship_info(person)
# print(f"relation_prompt: {relation_prompt}")
if len(person) >= 3 and person[0] and person[1]:
relation_prompt += await relationship_manager.build_relationship_info(person)
else:
logger.warning(f"Invalid person tuple encountered for relationship prompt: {person}")
# print(f"relat11111111ion_prompt: {relation_prompt}")
# 心情
mood_manager = MoodManager.get_instance()
mood_prompt = mood_manager.get_prompt()
# logger.info(f"心情prompt: {mood_prompt}")
reply_styles1 = [
("然后给出日常且口语化的回复，平淡一些", 0.4), # 40%概率
("给出非常简短的回复", 0.4), # 40%概率
("给出缺失主语的回复", 0.15), # 15%概率
("给出带有语病的回复", 0.05), # 5%概率
("然后给出日常且口语化的回复，平淡一些", 0.4),
("给出非常简短的回复", 0.4),
("给出缺失主语的回复", 0.15),
("给出带有语病的回复", 0.05),
]
reply_style1_chosen = random.choices(
[style[0] for style in reply_styles1], weights=[style[1] for style in reply_styles1], k=1
)[0]
reply_styles2 = [
("不要回复的太有条理，可以有个性", 0.6), # 60%概率
("不要回复的太有条理，可以复读", 0.15), # 15%概率
("回复的认真一些", 0.2), # 20%概率
("可以回复单个表情符号", 0.05), # 5%概率
("不要回复的太有条理，可以有个性", 0.6),
("不要回复的太有条理，可以复读", 0.15),
("回复的认真一些", 0.2),
("可以回复单个表情符号", 0.05),
]
reply_style2_chosen = random.choices(
[style[0] for style in reply_styles2], weights=[style[1] for style in reply_styles2], k=1
)[0]
# 调取记忆
memory_prompt = ""
related_memory = await HippocampusManager.get_instance().get_memory_from_text(
text=message_txt, max_memory_num=2, max_memory_length=2, max_depth=3, fast_retrieval=False
@@ -309,23 +369,14 @@ class PromptBuilder:
if related_memory:
for memory in related_memory:
related_memory_info += memory[1]
# memory_prompt = f"你想起你之前见过的事情：{related_memory_info}。\n以上是你的回忆，不一定是目前聊天里的人说的，也不一定是现在发生的事情，请记住。\n"
memory_prompt = await global_prompt_manager.format_prompt(
"memory_prompt", related_memory_info=related_memory_info
)
# 获取聊天上下文
if chat_stream.group_info:
chat_in_group = True
else:
chat_in_group = False
message_list_before_now = get_raw_msg_before_timestamp_with_chat(
chat_id=chat_stream.stream_id,
timestamp=time.time(),
limit=global_config.observation_context_size,
)
chat_talking_prompt = await build_readable_messages(
message_list_before_now,
replace_bot_name=True,
@@ -369,13 +420,12 @@ class PromptBuilder:
start_time = time.time()
prompt_info = await self.get_prompt_info(message_txt, threshold=0.38)
if prompt_info:
# prompt_info = f"""\n你有以下这些**知识**：\n{prompt_info}\n请你**记住上面的知识**，之后可能会用到。\n"""
prompt_info = await global_prompt_manager.format_prompt("knowledge_prompt", prompt_info=prompt_info)
end_time = time.time()
logger.debug(f"知识检索耗时: {(end_time - start_time):.3f}秒")
logger.debug("开始构建prompt")
if global_config.ENABLE_SCHEDULE_GEN:
schedule_prompt = await global_prompt_manager.format_prompt(
@@ -384,33 +434,60 @@ class PromptBuilder:
else:
schedule_prompt = ""
prompt = await global_prompt_manager.format_prompt(
"reasoning_prompt_main",
relation_prompt=relation_prompt,
sender_name=sender_name,
memory_prompt=memory_prompt,
prompt_info=prompt_info,
schedule_prompt=schedule_prompt,
chat_target=await global_prompt_manager.get_prompt_async("chat_target_group1")
if chat_in_group
else await global_prompt_manager.get_prompt_async("chat_target_private1"),
chat_target_2=await global_prompt_manager.get_prompt_async("chat_target_group2")
if chat_in_group
else await global_prompt_manager.get_prompt_async("chat_target_private2"),
chat_talking_prompt=chat_talking_prompt,
message_txt=message_txt,
bot_name=global_config.BOT_NICKNAME,
bot_other_names="/".join(
global_config.BOT_ALIAS_NAMES,
),
prompt_personality=prompt_personality,
mood_prompt=mood_prompt,
reply_style1=reply_style1_chosen,
reply_style2=reply_style2_chosen,
keywords_reaction_prompt=keywords_reaction_prompt,
prompt_ger=prompt_ger,
moderation_prompt=await global_prompt_manager.get_prompt_async("moderation_prompt"),
)
logger.debug("开始构建 normal prompt")
# --- Choose template and format based on chat type ---
if is_group_chat:
template_name = "reasoning_prompt_main"
effective_sender_name = sender_name
chat_target_1 = await global_prompt_manager.get_prompt_async("chat_target_group1")
chat_target_2 = await global_prompt_manager.get_prompt_async("chat_target_group2")
prompt = await global_prompt_manager.format_prompt(
template_name,
relation_prompt=relation_prompt,
sender_name=effective_sender_name,
memory_prompt=memory_prompt,
prompt_info=prompt_info,
schedule_prompt=schedule_prompt,
chat_target=chat_target_1,
chat_target_2=chat_target_2,
chat_talking_prompt=chat_talking_prompt,
message_txt=message_txt,
bot_name=global_config.BOT_NICKNAME,
bot_other_names="/".join(global_config.BOT_ALIAS_NAMES),
prompt_personality=prompt_personality,
mood_prompt=mood_prompt,
reply_style1=reply_style1_chosen,
reply_style2=reply_style2_chosen,
keywords_reaction_prompt=keywords_reaction_prompt,
prompt_ger=prompt_ger,
moderation_prompt=await global_prompt_manager.get_prompt_async("moderation_prompt"),
)
else:
template_name = "reasoning_prompt_private_main"
effective_sender_name = sender_name
prompt = await global_prompt_manager.format_prompt(
template_name,
relation_prompt=relation_prompt,
sender_name=effective_sender_name,
memory_prompt=memory_prompt,
prompt_info=prompt_info,
schedule_prompt=schedule_prompt,
chat_talking_prompt=chat_talking_prompt,
message_txt=message_txt,
bot_name=global_config.BOT_NICKNAME,
bot_other_names="/".join(global_config.BOT_ALIAS_NAMES),
prompt_personality=prompt_personality,
mood_prompt=mood_prompt,
reply_style1=reply_style1_chosen,
reply_style2=reply_style2_chosen,
keywords_reaction_prompt=keywords_reaction_prompt,
prompt_ger=prompt_ger,
moderation_prompt=await global_prompt_manager.get_prompt_async("moderation_prompt"),
)
# --- End choosing template ---
return prompt
@@ -670,6 +747,110 @@ class PromptBuilder:
# 返回所有找到的内容，用换行分隔
return "\n".join(str(result["content"]) for result in results)
async def build_planner_prompt(
self,
is_group_chat: bool, # Now passed as argument
chat_target_info: Optional[dict], # Now passed as argument
cycle_history: Deque["CycleInfo"], # Now passed as argument (Type hint needs import or string)
observed_messages_str: str,
current_mind: Optional[str],
structured_info: Dict[str, Any],
current_available_actions: Dict[str, str],
# replan_prompt: str, # Replan logic still simplified
) -> str:
"""构建 Planner LLM 的提示词 (获取模板并填充数据)"""
try:
# --- Determine chat context ---
chat_context_description = "你现在正在一个群聊中"
chat_target_name = None # Only relevant for private
if not is_group_chat and chat_target_info:
chat_target_name = chat_target_info.get('person_name') or chat_target_info.get('user_nickname') or "对方"
chat_context_description = f"你正在和 {chat_target_name} 私聊"
# --- End determining chat context ---
# ... (Copy logic from HeartFChatting._build_planner_prompt here) ...
# Structured info block
structured_info_block = ""
if structured_info:
structured_info_block = f"以下是一些额外的信息：\n{structured_info}\n"
# Chat content block
chat_content_block = ""
if observed_messages_str:
# Use triple quotes for multi-line string literal
chat_content_block = f"""观察到的最新聊天内容如下：
---
{observed_messages_str}
---"""
else:
chat_content_block = "当前没有观察到新的聊天内容。\\n"
# Current mind block
current_mind_block = ""
if current_mind:
current_mind_block = f"你的内心想法：\n{current_mind}"
else:
current_mind_block = "你的内心想法：\n[没有特别的想法]"
# Cycle info block (using passed cycle_history)
cycle_info_block = ""
recent_active_cycles = []
for cycle in reversed(cycle_history):
if cycle.action_taken:
recent_active_cycles.append(cycle)
if len(recent_active_cycles) == 3:
break
consecutive_text_replies = 0
responses_for_prompt = []
for cycle in recent_active_cycles:
if cycle.action_type == "text_reply":
consecutive_text_replies += 1
response_text = cycle.response_info.get("response_text", [])
formatted_response = "[空回复]" if not response_text else " ".join(response_text)
responses_for_prompt.append(formatted_response)
else:
break
if consecutive_text_replies >= 3:
cycle_info_block = f'你已经连续回复了三条消息（最近: "{responses_for_prompt[0]}"，第二近: "{responses_for_prompt[1]}"，第三近: "{responses_for_prompt[2]}"）。你回复的有点多了，请注意'
elif consecutive_text_replies == 2:
cycle_info_block = f'你已经连续回复了两条消息（最近: "{responses_for_prompt[0]}"，第二近: "{responses_for_prompt[1]}"），请注意'
elif consecutive_text_replies == 1:
cycle_info_block = f'你刚刚已经回复一条消息（内容: "{responses_for_prompt[0]}"）'
if cycle_info_block:
cycle_info_block = f"\n【近期回复历史】\n{cycle_info_block}\n"
else:
cycle_info_block = "\n【近期回复历史】\n(最近没有连续文本回复)\n"
individuality = Individuality.get_instance()
prompt_personality = individuality.get_prompt(x_person=2, level=2)
action_options_text = "当前你可以选择的行动有：\n"
action_keys = list(current_available_actions.keys())
for name in action_keys:
desc = current_available_actions[name]
action_options_text += f"- '{name}': {desc}\n"
example_action_key = action_keys[0] if action_keys else "no_reply"
planner_prompt_template = await global_prompt_manager.get_prompt_async("planner_prompt")
prompt = planner_prompt_template.format(
bot_name=global_config.BOT_NICKNAME,
prompt_personality=prompt_personality,
chat_context_description=chat_context_description,
structured_info_block=structured_info_block,
chat_content_block=chat_content_block,
current_mind_block=current_mind_block,
cycle_info_block=cycle_info_block,
action_options_text=action_options_text,
example_action=example_action_key,
)
return prompt
except Exception as e:
logger.error(f"[PromptBuilder] 构建 Planner 提示词时出错: {e}")
logger.error(traceback.format_exc())
return "[构建 Planner Prompt 时出错]"
init_prompt()
prompt_builder = PromptBuilder()