Losita/mai-bot
0
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Feat：添加对Action插件的支持，现在可以编写插件

Browse Source
This commit is contained in:
SengokuCola
2025-05-16 00:43:46 +08:00
parent ac6f96f805
commit cda9879bb2
28 changed files with 934 additions and 662 deletions
Download Patch File Download Diff File Expand all files Collapse all files

205
src/chat/focus_chat/heartflow_prompt_builder.py
View File

@@ -6,14 +6,13 @@ from src.chat.utils.chat_message_builder import build_readable_messages, get_raw
from src.chat.person_info.relationship_manager import relationship_manager
from src.chat.utils.utils import get_embedding
import time
from typing import Union, Optional, Dict, Any
from typing import Union, Optional
from src.common.database import db
from src.chat.utils.utils import get_recent_group_speaker
from src.manager.mood_manager import mood_manager
from src.chat.memory_system.Hippocampus import HippocampusManager
from src.chat.knowledge.knowledge_lib import qa_manager
from src.chat.focus_chat.expressors.exprssion_learner import expression_learner
import traceback
import random
@@ -21,27 +20,6 @@ logger = get_logger("prompt")
def init_prompt():
Prompt(
"""
你可以参考以下的语言习惯，如果情景合适就使用，不要盲目使用,不要生硬使用，而是结合到表达中：
{style_habbits}
你现在正在群里聊天，以下是群里正在进行的聊天内容：
{chat_info}
以上是聊天内容，你需要了解聊天记录中的内容
{chat_target}
你的名字是{bot_name}，{prompt_personality}，在这聊天中，"{target_message}"引起了你的注意，对这句话，你想表达：{in_mind_reply},原因是：{reason}。你现在要思考怎么回复
你需要使用合适的语法和句法，参考聊天内容，组织一条日常且口语化的回复。
请你根据情景使用以下句法：
{grammar_habbits}
回复尽量简短一些。可以参考贴吧，知乎和微博的回复风格，你可以完全重组回复，保留最基本的表达含义就好，但注意回复要简短，但重组后保持语意通顺。
回复不要浮夸，不要用夸张修辞，平淡一些。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 )，只输出一条回复就好。
现在，你说：
""",
"heart_flow_prompt",
)
Prompt(
"""
@@ -82,29 +60,6 @@ def init_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。
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}，不要复读自己说的话
{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}
@@ -126,118 +81,6 @@ def init_prompt():
)
async def _build_prompt_focus(
reason, current_mind_info, structured_info, chat_stream, sender_name, in_mind_reply, target_message
) -> str:
individuality = Individuality.get_instance()
prompt_personality = individuality.get_prompt(x_person=0, level=2)
# 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,
merge_messages=True,
timestamp_mode="relative",
read_mark=0.0,
truncate=True,
)
if structured_info:
structured_info_prompt = await global_prompt_manager.format_prompt(
"info_from_tools", structured_info=structured_info
)
else:
structured_info_prompt = ""
# 从/data/expression/对应chat_id/expressions.json中读取表达方式
(
learnt_style_expressions,
learnt_grammar_expressions,
personality_expressions,
) = await expression_learner.get_expression_by_chat_id(chat_stream.stream_id)
style_habbits = []
grammar_habbits = []
# 1. learnt_expressions加权随机选3条
if learnt_style_expressions:
weights = [expr["count"] for expr in learnt_style_expressions]
selected_learnt = weighted_sample_no_replacement(learnt_style_expressions, weights, 3)
for expr in selected_learnt:
if isinstance(expr, dict) and "situation" in expr and "style" in expr:
style_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}")
# 2. learnt_grammar_expressions加权随机选3条
if learnt_grammar_expressions:
weights = [expr["count"] for expr in learnt_grammar_expressions]
selected_learnt = weighted_sample_no_replacement(learnt_grammar_expressions, weights, 3)
for expr in selected_learnt:
if isinstance(expr, dict) and "situation" in expr and "style" in expr:
grammar_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}")
# 3. personality_expressions随机选1条
if personality_expressions:
expr = random.choice(personality_expressions)
if isinstance(expr, dict) and "situation" in expr and "style" in expr:
style_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}")
style_habbits_str = "\n".join(style_habbits)
grammar_habbits_str = "\n".join(grammar_habbits)
logger.debug("开始构建 focus 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,
style_habbits=style_habbits_str,
grammar_habbits=grammar_habbits_str,
chat_target=chat_target_1, # Used in group template
# chat_talking_prompt=chat_talking_prompt,
chat_info=chat_talking_prompt,
bot_name=global_config.BOT_NICKNAME,
# prompt_personality=prompt_personality,
prompt_personality="",
reason=reason,
in_mind_reply=in_mind_reply,
target_message=target_message,
# 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,
reason=reason,
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
class PromptBuilder:
def __init__(self):
self.prompt_built = ""
@@ -257,17 +100,6 @@ class PromptBuilder:
) -> Optional[str]:
if build_mode == "normal":
return await self._build_prompt_normal(chat_stream, message_txt or "", sender_name)
elif build_mode == "focus":
return await _build_prompt_focus(
reason,
current_mind_info,
structured_info,
chat_stream,
sender_name,
in_mind_reply,
target_message,
)
return None
async def _build_prompt_normal(self, chat_stream, message_txt: str, sender_name: str = "某人") -> str:
@@ -689,40 +521,5 @@ 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)
def weighted_sample_no_replacement(items, weights, k) -> list:
"""
加权且不放回地随机抽取k个元素。
参数：
items: 待抽取的元素列表
weights: 每个元素对应的权重（与items等长，且为正数）
k: 需要抽取的元素个数
返回：
selected: 按权重加权且不重复抽取的k个元素组成的列表
如果 items 中的元素不足 k 个，就只会返回所有可用的元素
实现思路：
每次从当前池中按权重加权随机选出一个元素，选中后将其从池中移除，重复k次。
这样保证了：
1. count越大被选中概率越高
2. 不会重复选中同一个元素
"""
selected = []
pool = list(zip(items, weights))
for _ in range(min(k, len(pool))):
total = sum(w for _, w in pool)
r = random.uniform(0, total)
upto = 0
for idx, (item, weight) in enumerate(pool):
upto += weight
if upto >= r:
selected.append(item)
pool.pop(idx)
break
return selected
init_prompt()
prompt_builder = PromptBuilder()