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refc：重构插件api，补全文档，合并expressor和replyer，分离reply和sender，新log浏览器

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SengokuCola
2025-06-19 20:20:34 +08:00
parent 7e05ede846
commit ab28b94e33
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534
src/chat/focus_chat/expressors/default_expressor.py
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import traceback
from typing import List, Optional, Dict, Any, Tuple
from src.chat.focus_chat.expressors.exprssion_learner import get_expression_learner
from src.chat.message_receive.message import MessageRecv, MessageThinking, MessageSending
from src.chat.message_receive.message import Seg # Local import needed after move
from src.chat.message_receive.message import UserInfo
from src.chat.message_receive.chat_stream import get_chat_manager
from src.common.logger import get_logger
from src.llm_models.utils_model import LLMRequest
from src.config.config import global_config
from src.chat.utils.utils_image import image_path_to_base64 # Local import needed after move
from src.chat.utils.timer_calculator import Timer # <--- Import Timer
from src.chat.emoji_system.emoji_manager import get_emoji_manager
from src.chat.focus_chat.heartFC_sender import HeartFCSender
from src.chat.utils.utils import process_llm_response
from src.chat.heart_flow.utils_chat import get_chat_type_and_target_info
from src.chat.message_receive.chat_stream import ChatStream
from src.chat.focus_chat.hfc_utils import parse_thinking_id_to_timestamp
from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat
import time
import random
logger = get_logger("expressor")
def init_prompt():
Prompt(
"""
你可以参考你的以下的语言习惯，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。请你修改你想表达的原句，符合你的表达风格和语言习惯
请你根据情景使用以下句法：
{grammar_habbits}
{config_expression_style}，你可以完全重组回复，保留最基本的表达含义就好，但重组后保持语意通顺。
不要浮夸，不要夸张修辞，平淡且不要输出多余内容(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：
""",
"default_expressor_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}
{config_expression_style}，你可以完全重组回复，保留最基本的表达含义就好，但重组后保持语意通顺。
不要浮夸，不要夸张修辞，平淡且不要输出多余内容(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：
""",
"default_expressor_private_prompt", # New template for private FOCUSED chat
)
class DefaultExpressor:
def __init__(self, chat_stream: ChatStream):
self.log_prefix = "expressor"
# TODO: API-Adapter修改标记
self.express_model = LLMRequest(
model=global_config.model.replyer_1,
request_type="focus.expressor",
)
self.heart_fc_sender = HeartFCSender()
self.chat_id = chat_stream.stream_id
self.chat_stream = chat_stream
self.is_group_chat, self.chat_target_info = get_chat_type_and_target_info(self.chat_id)
async def _create_thinking_message(self, anchor_message: Optional[MessageRecv], thinking_id: str):
"""创建思考消息 (尝试锚定到 anchor_message)"""
if not anchor_message or not anchor_message.chat_stream:
logger.error(f"{self.log_prefix} 无法创建思考消息，缺少有效的锚点消息或聊天流。")
return None
chat = anchor_message.chat_stream
messageinfo = anchor_message.message_info
thinking_time_point = parse_thinking_id_to_timestamp(thinking_id)
bot_user_info = UserInfo(
user_id=global_config.bot.qq_account,
user_nickname=global_config.bot.nickname,
platform=messageinfo.platform,
)
thinking_message = MessageThinking(
message_id=thinking_id,
chat_stream=chat,
bot_user_info=bot_user_info,
reply=anchor_message, # 回复的是锚点消息
thinking_start_time=thinking_time_point,
)
# logger.debug(f"创建思考消息thinking_message：{thinking_message}")
await self.heart_fc_sender.register_thinking(thinking_message)
return None
async def deal_reply(
self,
cycle_timers: dict,
action_data: Dict[str, Any],
reasoning: str,
anchor_message: MessageRecv,
thinking_id: str,
) -> tuple[bool, Optional[List[Tuple[str, str]]]]:
# 创建思考消息
await self._create_thinking_message(anchor_message, thinking_id)
reply = [] # 初始化 reply，防止未定义
try:
has_sent_something = False
# 处理文本部分
text_part = action_data.get("text", [])
if text_part:
with Timer("生成回复", cycle_timers):
# 可以保留原有的文本处理逻辑或进行适当调整
reply = await self.express(
in_mind_reply=text_part,
anchor_message=anchor_message,
thinking_id=thinking_id,
reason=reasoning,
action_data=action_data,
)
with Timer("选择表情", cycle_timers):
emoji_keyword = action_data.get("emojis", [])
emoji_base64 = await self._choose_emoji(emoji_keyword)
if emoji_base64:
reply.append(("emoji", emoji_base64))
if reply:
with Timer("发送消息", cycle_timers):
sent_msg_list = await self.send_response_messages(
anchor_message=anchor_message,
thinking_id=thinking_id,
response_set=reply,
)
has_sent_something = True
else:
logger.warning(f"{self.log_prefix} 文本回复生成失败")
if not has_sent_something:
logger.warning(f"{self.log_prefix} 回复动作未包含任何有效内容")
return has_sent_something, sent_msg_list
except Exception as e:
logger.error(f"回复失败: {e}")
traceback.print_exc()
return False, None
# --- 回复器 (Replier) 的定义 --- #
async def express(
self,
in_mind_reply: str,
reason: str,
anchor_message: MessageRecv,
thinking_id: str,
action_data: Dict[str, Any],
) -> Optional[List[str]]:
"""
回复器 (Replier): 核心逻辑，负责生成回复文本。
(已整合原 HeartFCGenerator 的功能)
"""
try:
# --- Determine sender_name for private chat ---
sender_name_for_prompt = "某人" # Default for group or if info unavailable
if not self.is_group_chat and self.chat_target_info:
# Prioritize person_name, then nickname
sender_name_for_prompt = (
self.chat_target_info.get("person_name")
or self.chat_target_info.get("user_nickname")
or sender_name_for_prompt
)
# --- End determining sender_name ---
target_message = action_data.get("target", "")
# 3. 构建 Prompt
with Timer("构建Prompt", {}): # 内部计时器，可选保留
prompt = await self.build_prompt_focus(
chat_stream=self.chat_stream, # Pass the stream object
in_mind_reply=in_mind_reply,
reason=reason,
sender_name=sender_name_for_prompt, # Pass determined name
target_message=target_message,
config_expression_style=global_config.expression.expression_style,
)
# 4. 调用 LLM 生成回复
content = None
reasoning_content = None
model_name = "unknown_model"
if not prompt:
logger.error(f"{self.log_prefix}[Replier-{thinking_id}] Prompt 构建失败，无法生成回复。")
return None
try:
with Timer("LLM生成", {}): # 内部计时器，可选保留
# TODO: API-Adapter修改标记
# logger.info(f"{self.log_prefix}[Replier-{thinking_id}]\nPrompt:\n{prompt}\n")
content, (reasoning_content, model_name) = await self.express_model.generate_response_async(prompt)
logger.info(f"想要表达：{in_mind_reply}||理由：{reason}")
logger.info(f"最终回复: {content}\n")
except Exception as llm_e:
# 精简报错信息
logger.error(f"{self.log_prefix}LLM 生成失败: {llm_e}")
return None # LLM 调用失败则无法生成回复
processed_response = process_llm_response(content)
# 5. 处理 LLM 响应
if not content:
logger.warning(f"{self.log_prefix}LLM 生成了空内容。")
return None
if not processed_response:
logger.warning(f"{self.log_prefix}处理后的回复为空。")
return None
reply_set = []
for str in processed_response:
reply_seg = ("text", str)
reply_set.append(reply_seg)
return reply_set
except Exception as e:
logger.error(f"{self.log_prefix}回复生成意外失败: {e}")
traceback.print_exc()
return None
async def build_prompt_focus(
self,
reason,
chat_stream,
sender_name,
in_mind_reply,
target_message,
config_expression_style,
) -> str:
is_group_chat = bool(chat_stream.group_info)
message_list_before_now = get_raw_msg_before_timestamp_with_chat(
chat_id=chat_stream.stream_id,
timestamp=time.time(),
limit=global_config.focus_chat.observation_context_size,
)
chat_talking_prompt = build_readable_messages(
message_list_before_now,
replace_bot_name=True,
merge_messages=True,
timestamp_mode="relative",
read_mark=0.0,
truncate=True,
)
expression_learner = get_expression_learner()
(
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 = "default_expressor_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,
style_habbits=style_habbits_str,
grammar_habbits=grammar_habbits_str,
chat_target=chat_target_1,
chat_info=chat_talking_prompt,
bot_name=global_config.bot.nickname,
prompt_personality="",
reason=reason,
in_mind_reply=in_mind_reply,
target_message=target_message,
config_expression_style=config_expression_style,
)
else: # Private chat
template_name = "default_expressor_private_prompt"
chat_target_1 = "你正在和人私聊"
prompt = await global_prompt_manager.format_prompt(
template_name,
style_habbits=style_habbits_str,
grammar_habbits=grammar_habbits_str,
chat_target=chat_target_1,
chat_info=chat_talking_prompt,
bot_name=global_config.bot.nickname,
prompt_personality="",
reason=reason,
in_mind_reply=in_mind_reply,
target_message=target_message,
config_expression_style=config_expression_style,
)
return prompt
# --- 发送器 (Sender) --- #
async def send_response_messages(
self,
anchor_message: Optional[MessageRecv],
response_set: List[Tuple[str, str]],
thinking_id: str = "",
display_message: str = "",
) -> Optional[MessageSending]:
"""发送回复消息 (尝试锚定到 anchor_message)，使用 HeartFCSender"""
chat = self.chat_stream
chat_id = self.chat_id
if chat is None:
logger.error(f"{self.log_prefix} 无法发送回复，chat_stream 为空。")
return None
if not anchor_message:
logger.error(f"{self.log_prefix} 无法发送回复，anchor_message 为空。")
return None
stream_name = get_chat_manager().get_stream_name(chat_id) or chat_id # 获取流名称用于日志
# 检查思考过程是否仍在进行，并获取开始时间
if thinking_id:
thinking_start_time = await self.heart_fc_sender.get_thinking_start_time(chat_id, thinking_id)
else:
thinking_id = "ds" + str(round(time.time(), 2))
thinking_start_time = time.time()
if thinking_start_time is None:
logger.error(f"[{stream_name}]expressor思考过程未找到或已结束，无法发送回复。")
return None
mark_head = False
# first_bot_msg: Optional[MessageSending] = None
reply_message_ids = [] # 记录实际发送的消息ID
sent_msg_list = []
for i, msg_text in enumerate(response_set):
# 为每个消息片段生成唯一ID
type = msg_text[0]
data = msg_text[1]
if global_config.experimental.debug_show_chat_mode and type == "text":
data += "ᶠ"
part_message_id = f"{thinking_id}_{i}"
message_segment = Seg(type=type, data=data)
if type == "emoji":
is_emoji = True
else:
is_emoji = False
reply_to = not mark_head
bot_message = await self._build_single_sending_message(
anchor_message=anchor_message,
message_id=part_message_id,
message_segment=message_segment,
display_message=display_message,
reply_to=reply_to,
is_emoji=is_emoji,
thinking_id=thinking_id,
thinking_start_time=thinking_start_time,
)
try:
if not mark_head:
mark_head = True
# first_bot_msg = bot_message # 保存第一个成功发送的消息对象
typing = False
else:
typing = True
if type == "emoji":
typing = False
if anchor_message.raw_message:
set_reply = True
else:
set_reply = False
sent_msg = await self.heart_fc_sender.send_message(
bot_message, has_thinking=True, typing=typing, set_reply=set_reply
)
reply_message_ids.append(part_message_id) # 记录我们生成的ID
sent_msg_list.append((type, sent_msg))
except Exception as e:
logger.error(f"{self.log_prefix}发送回复片段 {i} ({part_message_id}) 时失败: {e}")
traceback.print_exc()
# 这里可以选择是继续发送下一个片段还是中止
# 在尝试发送完所有片段后，完成原始的 thinking_id 状态
try:
await self.heart_fc_sender.complete_thinking(chat_id, thinking_id)
except Exception as e:
logger.error(f"{self.log_prefix}完成思考状态 {thinking_id} 时出错: {e}")
return sent_msg_list
async def _choose_emoji(self, send_emoji: str):
"""
选择表情，根据send_emoji文本选择表情，返回表情base64
"""
emoji_base64 = ""
emoji_raw = await get_emoji_manager().get_emoji_for_text(send_emoji)
if emoji_raw:
emoji_path, _description, _emotion = emoji_raw
emoji_base64 = image_path_to_base64(emoji_path)
return emoji_base64
async def _build_single_sending_message(
self,
anchor_message: MessageRecv,
message_id: str,
message_segment: Seg,
reply_to: bool,
is_emoji: bool,
thinking_id: str,
thinking_start_time: float,
display_message: str,
) -> MessageSending:
"""构建单个发送消息"""
bot_user_info = UserInfo(
user_id=global_config.bot.qq_account,
user_nickname=global_config.bot.nickname,
platform=self.chat_stream.platform,
)
bot_message = MessageSending(
message_id=message_id, # 使用片段的唯一ID
chat_stream=self.chat_stream,
bot_user_info=bot_user_info,
sender_info=anchor_message.message_info.user_info,
message_segment=message_segment,
reply=anchor_message, # 回复原始锚点
is_head=reply_to,
is_emoji=is_emoji,
thinking_start_time=thinking_start_time, # 传递原始思考开始时间
display_message=display_message,
)
return bot_message
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()

409
src/chat/focus_chat/expressors/exprssion_learner.py
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@@ -1,409 +0,0 @@
import time
import random
from typing import List, Dict, Optional, Any, Tuple
from src.common.logger import get_logger
from src.llm_models.utils_model import LLMRequest
from src.config.config import global_config
from src.chat.utils.chat_message_builder import get_raw_msg_by_timestamp_random, build_anonymous_messages
from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
import os
from src.chat.message_receive.chat_stream import get_chat_manager
import json
MAX_EXPRESSION_COUNT = 300
DECAY_DAYS = 30 # 30天衰减到0.01
DECAY_MIN = 0.01 # 最小衰减值
logger = get_logger("expressor")
def init_prompt() -> None:
learn_style_prompt = """
{chat_str}
请从上面这段群聊中概括除了人名为"SELF"之外的人的语言风格
1. 只考虑文字，不要考虑表情包和图片
2. 不要涉及具体的人名，只考虑语言风格
3. 语言风格包含特殊内容和情感
4. 思考有没有特殊的梗，一并总结成语言风格
5. 例子仅供参考，请严格根据群聊内容总结!!!
注意：总结成如下格式的规律，总结的内容要详细，但具有概括性：
当"xxxxxx"时，可以"xxxxxx", xxxxxx不超过20个字
例如：
当"对某件事表示十分惊叹，有些意外"时，使用"我嘞个xxxx"
当"表示讽刺的赞同，不想讲道理"时，使用"对对对"
当"想说明某个具体的事实观点，但懒得明说，或者不便明说，或表达一种默契"，使用"懂的都懂"
当"当涉及游戏相关时，表示意外的夸赞，略带戏谑意味"时，使用"这么强！"
注意不要总结你自己（SELF）的发言
现在请你概括
"""
Prompt(learn_style_prompt, "learn_style_prompt")
learn_grammar_prompt = """
{chat_str}
请从上面这段群聊中概括除了人名为"SELF"之外的人的语法和句法特点，只考虑纯文字，不要考虑表情包和图片
1.不要总结【图片】，【动画表情】，[图片]，[动画表情]，不总结 表情符号 at @ 回复 和[回复]
2.不要涉及具体的人名，只考虑语法和句法特点,
3.语法和句法特点要包括，句子长短（具体字数），有何种语病，如何拆分句子。
4. 例子仅供参考，请严格根据群聊内容总结!!!
总结成如下格式的规律，总结的内容要简洁，不浮夸：
当"xxx"时，可以"xxx"
例如：
当"表达观点较复杂"时，使用"省略主语(3-6个字)"的句法
当"不用详细说明的一般表达"时，使用"非常简洁的句子"的句法
当"需要单纯简单的确认"时，使用"单字或几个字的肯定(1-2个字)"的句法
注意不要总结你自己（SELF）的发言
现在请你概括
"""
Prompt(learn_grammar_prompt, "learn_grammar_prompt")
class ExpressionLearner:
def __init__(self) -> None:
# TODO: API-Adapter修改标记
self.express_learn_model: LLMRequest = LLMRequest(
model=global_config.model.replyer_1,
temperature=0.1,
request_type="expressor.learner",
)
async def get_expression_by_chat_id(self, chat_id: str) -> Tuple[List[Dict[str, str]], List[Dict[str, str]]]:
"""
读取/data/expression/learnt/{chat_id}/expressions.json和/data/expression/personality/expressions.json
返回(learnt_expressions, personality_expressions)
"""
learnt_style_file = os.path.join("data", "expression", "learnt_style", str(chat_id), "expressions.json")
learnt_grammar_file = os.path.join("data", "expression", "learnt_grammar", str(chat_id), "expressions.json")
personality_file = os.path.join("data", "expression", "personality", "expressions.json")
learnt_style_expressions = []
learnt_grammar_expressions = []
personality_expressions = []
if os.path.exists(learnt_style_file):
with open(learnt_style_file, "r", encoding="utf-8") as f:
learnt_style_expressions = json.load(f)
if os.path.exists(learnt_grammar_file):
with open(learnt_grammar_file, "r", encoding="utf-8") as f:
learnt_grammar_expressions = json.load(f)
if os.path.exists(personality_file):
with open(personality_file, "r", encoding="utf-8") as f:
personality_expressions = json.load(f)
return learnt_style_expressions, learnt_grammar_expressions, personality_expressions
def is_similar(self, s1: str, s2: str) -> bool:
"""
判断两个字符串是否相似（只考虑长度大于5且有80%以上重合，不考虑子串）
"""
if not s1 or not s2:
return False
min_len = min(len(s1), len(s2))
if min_len < 5:
return False
same = sum(1 for a, b in zip(s1, s2) if a == b)
return same / min_len > 0.8
async def learn_and_store_expression(self) -> List[Tuple[str, str, str]]:
"""
学习并存储表达方式，分别学习语言风格和句法特点
同时对所有已存储的表达方式进行全局衰减
"""
current_time = time.time()
# 全局衰减所有已存储的表达方式
for type in ["style", "grammar"]:
base_dir = os.path.join("data", "expression", f"learnt_{type}")
if not os.path.exists(base_dir):
continue
for chat_id in os.listdir(base_dir):
file_path = os.path.join(base_dir, chat_id, "expressions.json")
if not os.path.exists(file_path):
continue
try:
with open(file_path, "r", encoding="utf-8") as f:
expressions = json.load(f)
# 应用全局衰减
decayed_expressions = self.apply_decay_to_expressions(expressions, current_time)
# 保存衰减后的结果
with open(file_path, "w", encoding="utf-8") as f:
json.dump(decayed_expressions, f, ensure_ascii=False, indent=2)
except Exception as e:
logger.error(f"全局衰减{type}表达方式失败: {e}")
continue
# 学习新的表达方式（这里会进行局部衰减）
for _ in range(3):
learnt_style: Optional[List[Tuple[str, str, str]]] = await self.learn_and_store(type="style", num=25)
if not learnt_style:
return []
for _ in range(1):
learnt_grammar: Optional[List[Tuple[str, str, str]]] = await self.learn_and_store(type="grammar", num=10)
if not learnt_grammar:
return []
return learnt_style, learnt_grammar
def calculate_decay_factor(self, time_diff_days: float) -> float:
"""
计算衰减值
当时间差为0天时，衰减值为0.001
当时间差为7天时，衰减值为0
当时间差为30天时，衰减值为0.001
使用二次函数进行曲线插值
"""
if time_diff_days <= 0 or time_diff_days >= DECAY_DAYS:
return 0.001
# 使用二次函数进行插值
# 将7天作为顶点，0天和30天作为两个端点
# 使用顶点式：y = a(x-h)^2 + k，其中(h,k)为顶点
h = 7.0 # 顶点x坐标
k = 0.001 # 顶点y坐标
# 计算a值，使得x=0和x=30时y=0.001
# 0.001 = a(0-7)^2 + 0.001
# 解得a = 0
a = 0
# 计算衰减值
decay = a * (time_diff_days - h) ** 2 + k
return min(0.001, decay)
def apply_decay_to_expressions(
self, expressions: List[Dict[str, Any]], current_time: float
) -> List[Dict[str, Any]]:
"""
对表达式列表应用衰减
返回衰减后的表达式列表，移除count小于0的项
"""
result = []
for expr in expressions:
# 确保last_active_time存在，如果不存在则使用current_time
if "last_active_time" not in expr:
expr["last_active_time"] = current_time
last_active = expr["last_active_time"]
time_diff_days = (current_time - last_active) / (24 * 3600) # 转换为天
decay_value = self.calculate_decay_factor(time_diff_days)
expr["count"] = max(0.01, expr.get("count", 1) - decay_value)
if expr["count"] > 0:
result.append(expr)
return result
async def learn_and_store(self, type: str, num: int = 10) -> List[Tuple[str, str, str]]:
"""
选择从当前到最近1小时内的随机num条消息，然后学习这些消息的表达方式
type: "style" or "grammar"
"""
if type == "style":
type_str = "语言风格"
elif type == "grammar":
type_str = "句法特点"
else:
raise ValueError(f"Invalid type: {type}")
res = await self.learn_expression(type, num)
if res is None:
return []
learnt_expressions, chat_id = res
chat_stream = get_chat_manager().get_stream(chat_id)
if chat_stream.group_info:
group_name = chat_stream.group_info.group_name
else:
group_name = f"{chat_stream.user_info.user_nickname}的私聊"
learnt_expressions_str = ""
for _chat_id, situation, style in learnt_expressions:
learnt_expressions_str += f"{situation}->{style}\n"
logger.info(f"在 {group_name} 学习到{type_str}:\n{learnt_expressions_str}")
if not learnt_expressions:
logger.info(f"没有学习到{type_str}")
return []
# 按chat_id分组
chat_dict: Dict[str, List[Dict[str, str]]] = {}
for chat_id, situation, style in learnt_expressions:
if chat_id not in chat_dict:
chat_dict[chat_id] = []
chat_dict[chat_id].append({"situation": situation, "style": style})
current_time = time.time()
# 存储到/data/expression/对应chat_id/expressions.json
for chat_id, expr_list in chat_dict.items():
dir_path = os.path.join("data", "expression", f"learnt_{type}", str(chat_id))
os.makedirs(dir_path, exist_ok=True)
file_path = os.path.join(dir_path, "expressions.json")
# 若已存在，先读出合并
old_data: List[Dict[str, Any]] = []
if os.path.exists(file_path):
try:
with open(file_path, "r", encoding="utf-8") as f:
old_data = json.load(f)
except Exception:
old_data = []
# 应用衰减
# old_data = self.apply_decay_to_expressions(old_data, current_time)
# 合并逻辑
for new_expr in expr_list:
found = False
for old_expr in old_data:
if self.is_similar(new_expr["situation"], old_expr.get("situation", "")) and self.is_similar(
new_expr["style"], old_expr.get("style", "")
):
found = True
# 50%概率替换
if random.random() < 0.5:
old_expr["situation"] = new_expr["situation"]
old_expr["style"] = new_expr["style"]
old_expr["count"] = old_expr.get("count", 1) + 1
old_expr["last_active_time"] = current_time
break
if not found:
new_expr["count"] = 1
new_expr["last_active_time"] = current_time
old_data.append(new_expr)
# 处理超限问题
if len(old_data) > MAX_EXPRESSION_COUNT:
# 计算每个表达方式的权重（count的倒数，这样count越小的越容易被选中）
weights = [1 / (expr.get("count", 1) + 0.1) for expr in old_data]
# 随机选择要移除的表达方式，避免重复索引
remove_count = len(old_data) - MAX_EXPRESSION_COUNT
# 使用一种不会选到重复索引的方法
indices = list(range(len(old_data)))
# 方法1：使用numpy.random.choice
# 把列表转成一个映射字典，保证不会有重复
remove_set = set()
total_attempts = 0
# 尝试按权重随机选择，直到选够数量
while len(remove_set) < remove_count and total_attempts < len(old_data) * 2:
idx = random.choices(indices, weights=weights, k=1)[0]
remove_set.add(idx)
total_attempts += 1
# 如果没选够，随机补充
if len(remove_set) < remove_count:
remaining = set(indices) - remove_set
remove_set.update(random.sample(list(remaining), remove_count - len(remove_set)))
remove_indices = list(remove_set)
# 从后往前删除，避免索引变化
for idx in sorted(remove_indices, reverse=True):
old_data.pop(idx)
with open(file_path, "w", encoding="utf-8") as f:
json.dump(old_data, f, ensure_ascii=False, indent=2)
return learnt_expressions
async def learn_expression(self, type: str, num: int = 10) -> Optional[Tuple[List[Tuple[str, str, str]], str]]:
"""选择从当前到最近1小时内的随机num条消息，然后学习这些消息的表达方式
Args:
type: "style" or "grammar"
"""
if type == "style":
type_str = "语言风格"
prompt = "learn_style_prompt"
elif type == "grammar":
type_str = "句法特点"
prompt = "learn_grammar_prompt"
else:
raise ValueError(f"Invalid type: {type}")
current_time = time.time()
random_msg: Optional[List[Dict[str, Any]]] = get_raw_msg_by_timestamp_random(
current_time - 3600 * 24, current_time, limit=num
)
# print(random_msg)
if not random_msg or random_msg == []:
return None
# 转化成str
chat_id: str = random_msg[0]["chat_id"]
# random_msg_str: str = build_readable_messages(random_msg, timestamp_mode="normal")
random_msg_str: str = await build_anonymous_messages(random_msg)
# print(f"random_msg_str:{random_msg_str}")
prompt: str = await global_prompt_manager.format_prompt(
prompt,
chat_str=random_msg_str,
)
logger.debug(f"学习{type_str}的prompt: {prompt}")
try:
response, _ = await self.express_learn_model.generate_response_async(prompt)
except Exception as e:
logger.error(f"学习{type_str}失败: {e}")
return None
logger.debug(f"学习{type_str}的response: {response}")
expressions: List[Tuple[str, str, str]] = self.parse_expression_response(response, chat_id)
return expressions, chat_id
def parse_expression_response(self, response: str, chat_id: str) -> List[Tuple[str, str, str]]:
"""
解析LLM返回的表达风格总结，每一行提取"当"和"使用"之间的内容，存储为(situation, style)元组
"""
expressions: List[Tuple[str, str, str]] = []
for line in response.splitlines():
line = line.strip()
if not line:
continue
# 查找"当"和下一个引号
idx_when = line.find('当"')
if idx_when == -1:
continue
idx_quote1 = idx_when + 1
idx_quote2 = line.find('"', idx_quote1 + 1)
if idx_quote2 == -1:
continue
situation = line[idx_quote1 + 1 : idx_quote2]
# 查找"使用"
idx_use = line.find('使用"', idx_quote2)
if idx_use == -1:
continue
idx_quote3 = idx_use + 2
idx_quote4 = line.find('"', idx_quote3 + 1)
if idx_quote4 == -1:
continue
style = line[idx_quote3 + 1 : idx_quote4]
expressions.append((chat_id, situation, style))
return expressions
init_prompt()
expression_learner = None
def get_expression_learner():
global expression_learner
if expression_learner is None:
expression_learner = ExpressionLearner()
return expression_learner

14
src/chat/focus_chat/heartFC_chat.py
View File

@@ -21,8 +21,6 @@ from src.chat.heart_flow.observation.chatting_observation import ChattingObserva
from src.chat.heart_flow.observation.structure_observation import StructureObservation
from src.chat.heart_flow.observation.actions_observation import ActionObservation
from src.chat.focus_chat.info_processors.tool_processor import ToolProcessor
from src.chat.focus_chat.expressors.default_expressor import DefaultExpressor
from src.chat.focus_chat.replyer.default_replyer import DefaultReplyer
from src.chat.focus_chat.memory_activator import MemoryActivator
from src.chat.focus_chat.info_processors.base_processor import BaseProcessor
from src.chat.focus_chat.info_processors.self_processor import SelfProcessor
@@ -125,9 +123,6 @@ class HeartFChatting:
self.processors: List[BaseProcessor] = []
self._register_default_processors()
self.expressor = DefaultExpressor(chat_stream=self.chat_stream)
self.replyer = DefaultReplyer(chat_stream=self.chat_stream)
self.action_manager = ActionManager()
self.action_planner = PlannerFactory.create_planner(
log_prefix=self.log_prefix, action_manager=self.action_manager
@@ -543,6 +538,7 @@ class HeartFChatting:
async def _observe_process_plan_action_loop(self, cycle_timers: dict, thinking_id: str) -> dict:
try:
loop_start_time = time.time()
with Timer("观察", cycle_timers):
# 执行所有观察器的观察
for observation in self.observations:
@@ -583,7 +579,7 @@ class HeartFChatting:
}
with Timer("规划器", cycle_timers):
plan_result = await self.action_planner.plan(all_plan_info, running_memorys)
plan_result = await self.action_planner.plan(all_plan_info, running_memorys, loop_start_time)
loop_plan_info = {
"action_result": plan_result.get("action_result", {}),
@@ -607,7 +603,7 @@ class HeartFChatting:
logger.debug(f"{self.log_prefix} 麦麦想要：'{action_str}'")
success, reply_text, command = await self._handle_action(
action_type, reasoning, action_data, cycle_timers, thinking_id, self.observations
action_type, reasoning, action_data, cycle_timers, thinking_id
)
loop_action_info = {
@@ -646,7 +642,6 @@ class HeartFChatting:
action_data: dict,
cycle_timers: dict,
thinking_id: str,
observations: List[Observation],
) -> tuple[bool, str, str]:
"""
处理规划动作，使用动作工厂创建相应的动作处理器
@@ -670,9 +665,6 @@ class HeartFChatting:
reasoning=reasoning,
cycle_timers=cycle_timers,
thinking_id=thinking_id,
observations=observations,
expressor=self.expressor,
replyer=self.replyer,
chat_stream=self.chat_stream,
log_prefix=self.log_prefix,
shutting_down=self._shutting_down,

51
src/chat/focus_chat/heartFC_sender.py
View File

@@ -15,7 +15,7 @@ install(extra_lines=3)
logger = get_logger("sender")
async def send_message(message: MessageSending) -> str:
async def send_message(message: MessageSending) -> bool:
"""合并后的消息发送函数，包含WS发送和日志记录"""
message_preview = truncate_message(message.processed_plain_text, max_length=40)
@@ -23,7 +23,7 @@ async def send_message(message: MessageSending) -> str:
# 直接调用API发送消息
await get_global_api().send_message(message)
logger.info(f"已将消息 '{message_preview}' 发往平台'{message.message_info.platform}'")
return message.processed_plain_text
return True
except Exception as e:
logger.error(f"发送消息 '{message_preview}' 发往平台'{message.message_info.platform}' 失败: {str(e)}")
@@ -73,17 +73,15 @@ class HeartFCSender:
thinking_message = self.thinking_messages.get(chat_id, {}).get(message_id)
return thinking_message.thinking_start_time if thinking_message else None
async def send_message(self, message: MessageSending, has_thinking=False, typing=False, set_reply=False):
async def send_message(self, message: MessageSending, typing=False, set_reply=False, storage_message=True):
"""
处理、发送并存储一条消息。
参数：
message: MessageSending 对象，待发送的消息。
has_thinking: 是否管理思考状态，表情包无思考状态（如需调用 register_thinking/complete_thinking）。
typing: 是否模拟打字等待（根据 has_thinking 控制等待时长）。
typing: 是否模拟打字等待。
用法：
- has_thinking=True 时，自动处理思考消息的时间和清理。
- typing=True 时，发送前会有打字等待。
"""
if not message.chat_stream:
@@ -98,40 +96,29 @@ class HeartFCSender:
try:
if set_reply:
_ = message.update_thinking_time()
message.build_reply()
logger.debug(f"[{chat_id}] 选择回复引用消息: {message.processed_plain_text[:20]}...")
# --- 条件应用 set_reply 逻辑 ---
if (
message.is_head
and not message.is_private_message()
and message.reply.processed_plain_text != "[System Trigger Context]"
):
# message.set_reply(message.reply)
message.set_reply()
logger.debug(f"[{chat_id}] 应用 set_reply 逻辑: {message.processed_plain_text[:20]}...")
# print(f"message.display_message: {message.display_message}")
await message.process()
# print(f"message.display_message: {message.display_message}")
if typing:
if has_thinking:
typing_time = calculate_typing_time(
input_string=message.processed_plain_text,
thinking_start_time=message.thinking_start_time,
is_emoji=message.is_emoji,
)
await asyncio.sleep(typing_time)
else:
await asyncio.sleep(0.5)
typing_time = calculate_typing_time(
input_string=message.processed_plain_text,
thinking_start_time=message.thinking_start_time,
is_emoji=message.is_emoji,
)
await asyncio.sleep(typing_time)
sent_msg = await send_message(message)
await self.storage.store_message(message, message.chat_stream)
if not sent_msg:
return False
if sent_msg:
return sent_msg
else:
return "发送失败"
if storage_message:
await self.storage.store_message(message, message.chat_stream)
return sent_msg
except Exception as e:
logger.error(f"[{chat_id}] 处理或存储消息 {message_id} 时出错: {e}")

1
src/chat/focus_chat/heartflow_message_processor.py
View File

@@ -172,6 +172,7 @@ class HeartFCMessageReceiver:
return
# 5. 消息存储
print(f"message: {message.message_info.time}")
await self.storage.store_message(message, chat)
# 6. 兴趣度计算与更新

2
src/chat/focus_chat/info_processors/expression_selector_processor.py
View File

@@ -11,7 +11,7 @@ from src.chat.message_receive.chat_stream import get_chat_manager
from .base_processor import BaseProcessor
from src.chat.focus_chat.info.info_base import InfoBase
from src.chat.focus_chat.info.expression_selection_info import ExpressionSelectionInfo
from src.chat.focus_chat.expressors.exprssion_learner import get_expression_learner
from src.chat.express.exprssion_learner import get_expression_learner
from json_repair import repair_json
import json

248
src/chat/focus_chat/planners/action_manager.py
View File

@@ -1,13 +1,9 @@
from typing import Dict, List, Optional, Type, Any
from src.plugin_system.base.base_action import BaseAction
from src.chat.heart_flow.observation.observation import Observation
from src.chat.focus_chat.replyer.default_replyer import DefaultReplyer
from src.chat.focus_chat.expressors.default_expressor import DefaultExpressor
from src.chat.message_receive.chat_stream import ChatStream
from src.common.logger import get_logger
# 不再需要导入动作类，因为已经在main.py中导入
# import src.chat.actions.default_actions # noqa
from src.plugin_system.core.component_registry import component_registry
from src.plugin_system.base.component_types import ComponentType
logger = get_logger("action_manager")
@@ -15,87 +11,11 @@ logger = get_logger("action_manager")
ActionInfo = Dict[str, Any]
class PluginActionWrapper(BaseAction):
"""
新插件系统Action组件的兼容性包装器
将新插件系统的Action组件包装为旧系统兼容的BaseAction接口
"""
def __init__(
self, plugin_action, action_name: str, action_data: dict, reasoning: str, cycle_timers: dict, thinking_id: str
):
"""初始化包装器"""
# 调用旧系统BaseAction初始化，只传递它能接受的参数
super().__init__(
action_data=action_data, reasoning=reasoning, cycle_timers=cycle_timers, thinking_id=thinking_id
)
# 存储插件Action实例（它已经包含了所有必要的服务对象）
self.plugin_action = plugin_action
self.action_name = action_name
# 从插件Action实例复制属性到包装器
self._sync_attributes_from_plugin_action()
def _sync_attributes_from_plugin_action(self):
"""从插件Action实例同步属性到包装器"""
# 基本属性
self.action_name = getattr(self.plugin_action, "action_name", self.action_name)
# 设置兼容的默认值
self.action_description = f"插件Action: {self.action_name}"
self.action_parameters = {}
self.action_require = []
# 激活类型属性（从新插件系统转换）
plugin_focus_type = getattr(self.plugin_action, "focus_activation_type", None)
plugin_normal_type = getattr(self.plugin_action, "normal_activation_type", None)
if plugin_focus_type:
self.focus_activation_type = (
plugin_focus_type.value if hasattr(plugin_focus_type, "value") else str(plugin_focus_type)
)
if plugin_normal_type:
self.normal_activation_type = (
plugin_normal_type.value if hasattr(plugin_normal_type, "value") else str(plugin_normal_type)
)
# 其他属性
self.random_activation_probability = getattr(self.plugin_action, "random_activation_probability", 0.0)
self.llm_judge_prompt = getattr(self.plugin_action, "llm_judge_prompt", "")
self.activation_keywords = getattr(self.plugin_action, "activation_keywords", [])
self.keyword_case_sensitive = getattr(self.plugin_action, "keyword_case_sensitive", False)
# 模式和并行设置
plugin_mode = getattr(self.plugin_action, "mode_enable", None)
if plugin_mode:
self.mode_enable = plugin_mode.value if hasattr(plugin_mode, "value") else str(plugin_mode)
self.parallel_action = getattr(self.plugin_action, "parallel_action", True)
self.enable_plugin = True
async def execute(self) -> tuple[bool, str]:
"""实现抽象方法execute，委托给插件Action的execute方法"""
try:
# 调用插件Action的execute方法
success, response = await self.plugin_action.execute()
logger.debug(f"插件Action {self.action_name} 执行{'成功' if success else '失败'}: {response}")
return success, response
except Exception as e:
logger.error(f"插件Action {self.action_name} 执行异常: {e}")
return False, f"插件Action执行失败: {str(e)}"
async def handle_action(self) -> tuple[bool, str]:
"""兼容旧系统的动作处理接口，委托给execute方法"""
return await self.execute()
class ActionManager:
"""
动作管理器，用于管理各种类型的动作
现在统一使用新插件系统，简化了原有的新旧兼容逻辑。
"""
# 类常量
@@ -119,23 +39,20 @@ class ActionManager:
# 初始化时将默认动作加载到使用中的动作
self._using_actions = self._default_actions.copy()
# 添加系统核心动作
# self._add_system_core_actions()
def _load_plugin_actions(self) -> None:
"""
加载所有插件目录中的动作
加载所有插件系统中的动作
"""
try:
# 从新插件系统获取Action组件
self._load_plugin_system_actions()
logger.debug("从新插件系统加载Action组件成功")
logger.debug("从插件系统加载Action组件成功")
except Exception as e:
logger.error(f"加载插件动作失败: {e}")
def _load_plugin_system_actions(self) -> None:
"""从新插件系统的component_registry加载Action组件"""
"""从插件系统的component_registry加载Action组件"""
try:
from src.plugin_system.core.component_registry import component_registry
from src.plugin_system.base.component_types import ComponentType
@@ -148,7 +65,7 @@ class ActionManager:
logger.debug(f"Action组件 {action_name} 已存在，跳过")
continue
# 将新插件系统的ActionInfo转换为旧系统格式
# 将插件系统的ActionInfo转换为ActionManager格式
converted_action_info = {
"description": action_info.description,
"parameters": getattr(action_info, "action_parameters", {}),
@@ -165,8 +82,7 @@ class ActionManager:
# 模式和并行设置
"mode_enable": action_info.mode_enable.value,
"parallel_action": action_info.parallel_action,
# 标记这是来自新插件系统的组件
"_plugin_system_component": True,
# 插件信息
"_plugin_name": getattr(action_info, "plugin_name", ""),
}
@@ -180,7 +96,7 @@ class ActionManager:
f"从插件系统加载Action组件: {action_name} (插件: {getattr(action_info, 'plugin_name', 'unknown')})"
)
logger.info(f"从新插件系统加载了 {len(action_components)} 个Action组件")
logger.info(f"从插件系统加载了 {len(action_components)} 个Action组件")
except Exception as e:
logger.error(f"从插件系统加载Action组件失败: {e}")
@@ -195,12 +111,9 @@ class ActionManager:
reasoning: str,
cycle_timers: dict,
thinking_id: str,
observations: List[Observation],
chat_stream: ChatStream,
log_prefix: str,
shutting_down: bool = False,
expressor: DefaultExpressor = None,
replyer: DefaultReplyer = None,
) -> Optional[BaseAction]:
"""
创建动作处理器实例
@@ -211,9 +124,6 @@ class ActionManager:
reasoning: 执行理由
cycle_timers: 计时器字典
thinking_id: 思考ID
observations: 观察列表
expressor: 表达器
replyer: 回复器
chat_stream: 聊天流
log_prefix: 日志前缀
shutting_down: 是否正在关闭
@@ -221,122 +131,39 @@ class ActionManager:
Returns:
Optional[BaseAction]: 创建的动作处理器实例，如果动作名称未注册则返回None
"""
# 检查动作是否在当前使用的动作集中
# if action_name not in self._using_actions:
# logger.warning(f"当前不可用的动作类型: {action_name}")
# return None
# 检查是否是新插件系统的Action组件
action_info = self._registered_actions.get(action_name)
if action_info and action_info.get("_plugin_system_component", False):
return self._create_plugin_system_action(
action_name,
action_data,
reasoning,
cycle_timers,
thinking_id,
observations,
chat_stream,
log_prefix,
shutting_down,
expressor,
replyer,
)
# 旧系统的动作创建逻辑
from src.plugin_system.core.component_registry import component_registry
action_registry = component_registry.get_action_registry()
handler_class = action_registry.get(action_name)
if not handler_class:
logger.warning(f"未注册的动作类型: {action_name}")
return None
try:
# 创建动作实例
instance = handler_class(
action_data=action_data,
reasoning=reasoning,
cycle_timers=cycle_timers,
thinking_id=thinking_id,
observations=observations,
expressor=expressor,
replyer=replyer,
chat_stream=chat_stream,
log_prefix=log_prefix,
shutting_down=shutting_down,
)
return instance
except Exception as e:
logger.error(f"创建动作处理器实例失败: {e}")
return None
def _create_plugin_system_action(
self,
action_name: str,
action_data: dict,
reasoning: str,
cycle_timers: dict,
thinking_id: str,
observations: List[Observation],
chat_stream: ChatStream,
log_prefix: str,
shutting_down: bool = False,
expressor: DefaultExpressor = None,
replyer: DefaultReplyer = None,
) -> Optional["PluginActionWrapper"]:
"""
创建新插件系统的Action组件实例，并包装为兼容旧系统的接口
Returns:
Optional[PluginActionWrapper]: 包装后的Action实例
"""
try:
from src.plugin_system.core.component_registry import component_registry
# 获取组件类
component_class = component_registry.get_component_class(action_name)
# 获取组件类 - 明确指定查询Action类型
component_class = component_registry.get_component_class(action_name, ComponentType.ACTION)
if not component_class:
logger.error(f"未找到插件Action组件类: {action_name}")
logger.warning(f"{log_prefix} 未找到Action组件: {action_name}")
return None
# 获取组件信息
component_info = component_registry.get_component_info(action_name, ComponentType.ACTION)
if not component_info:
logger.warning(f"{log_prefix} 未找到Action组件信息: {action_name}")
return None
# 获取插件配置
component_info = component_registry.get_component_info(action_name)
plugin_config = None
if component_info and component_info.plugin_name:
plugin_config = component_registry.get_plugin_config(component_info.plugin_name)
plugin_config = component_registry.get_plugin_config(component_info.plugin_name)
# 创建插件Action实例
plugin_action_instance = component_class(
# 创建动作实例
instance = component_class(
action_data=action_data,
reasoning=reasoning,
cycle_timers=cycle_timers,
thinking_id=thinking_id,
chat_stream=chat_stream,
expressor=expressor,
replyer=replyer,
observations=observations,
log_prefix=log_prefix,
shutting_down=shutting_down,
plugin_config=plugin_config,
)
# 创建兼容性包装器
wrapper = PluginActionWrapper(
plugin_action=plugin_action_instance,
action_name=action_name,
action_data=action_data,
reasoning=reasoning,
cycle_timers=cycle_timers,
thinking_id=thinking_id,
)
logger.debug(f"创建插件Action实例成功: {action_name}")
return wrapper
logger.debug(f"创建Action实例成功: {action_name}")
return instance
except Exception as e:
logger.error(f"创建插件Action实例失败 {action_name}: {e}")
logger.error(f"创建Action实例失败 {action_name}: {e}")
import traceback
logger.error(traceback.format_exc())
@@ -366,19 +193,13 @@ class ActionManager:
"""
filtered_actions = {}
# print(self._using_actions)
for action_name, action_info in self._using_actions.items():
# print(f"action_info: {action_info}")
# print(f"action_name: {action_name}")
action_mode = action_info.get("mode_enable", "all")
# 检查动作是否在当前模式下启用
if action_mode == "all" or action_mode == mode:
filtered_actions[action_name] = action_info
logger.debug(f"动作 {action_name} 在模式 {mode} 下可用 (mode_enable: {action_mode})")
# else:
# logger.debug(f"动作 {action_name} 在模式 {mode} 下不可用 (mode_enable: {action_mode})")
logger.debug(f"模式 {mode} 下可用动作: {list(filtered_actions.keys())}")
return filtered_actions
@@ -474,20 +295,6 @@ class ActionManager:
def restore_default_actions(self) -> None:
"""恢复默认动作集到使用集"""
self._using_actions = self._default_actions.copy()
# 添加系统核心动作（即使enable_plugin为False的系统动作）
# self._add_system_core_actions()
# def _add_system_core_actions(self) -> None:
# """
# 添加系统核心动作到使用集
# 系统核心动作是那些enable_plugin为False但是系统必需的动作
# """
# system_core_actions = ["exit_focus_chat"] # 可以根据需要扩展
# for action_name in system_core_actions:
# if action_name in self._registered_actions and action_name not in self._using_actions:
# self._using_actions[action_name] = self._registered_actions[action_name]
# logger.debug(f"添加系统核心动作到使用集: {action_name}")
def add_system_action_if_needed(self, action_name: str) -> bool:
"""
@@ -517,5 +324,4 @@ class ActionManager:
"""
from src.plugin_system.core.component_registry import component_registry
action_registry = component_registry.get_action_registry()
return action_registry.get(action_name)
return component_registry.get_component_class(action_name)

4
src/chat/focus_chat/planners/base_planner.py
View File

@@ -12,14 +12,14 @@ class BasePlanner(ABC):
self.action_manager = action_manager
@abstractmethod
async def plan(self, all_plan_info: List[InfoBase], running_memorys: List[Dict[str, Any]]) -> Dict[str, Any]:
async def plan(self, all_plan_info: List[InfoBase], running_memorys: List[Dict[str, Any]], loop_start_time: float) -> Dict[str, Any]:
"""
规划下一步行动
Args:
all_plan_info: 所有计划信息
running_memorys: 回忆信息
loop_start_time: 循环开始时间
Returns:
Dict[str, Any]: 规划结果
"""

2
src/chat/focus_chat/planners/modify_actions.py
View File

@@ -242,6 +242,8 @@ class ActionModifier:
for action_name, action_info in actions_with_info.items():
activation_type = action_info.get("focus_activation_type", "always")
print(f"action_name: {action_name}, activation_type: {activation_type}")
# 现在统一是字符串格式的激活类型值
if activation_type == "always":

15
src/chat/focus_chat/planners/planner_simple.py
View File

@@ -32,11 +32,7 @@ def init_prompt():
{self_info_block}
请记住你的性格，身份和特点。
{extra_info_block}
{memory_str}
{time_block}
你是群内的一员，你现在正在参与群内的闲聊，以下是群内的聊天内容：
{chat_content_block}
@@ -86,13 +82,14 @@ class ActionPlanner(BasePlanner):
request_type="focus.planner", # 用于动作规划
)
async def plan(self, all_plan_info: List[InfoBase], running_memorys: List[Dict[str, Any]]) -> Dict[str, Any]:
async def plan(self, all_plan_info: List[InfoBase], running_memorys: List[Dict[str, Any]], loop_start_time: float) -> Dict[str, Any]:
"""
规划器 (Planner): 使用LLM根据上下文决定做出什么动作。
参数:
all_plan_info: 所有计划信息
running_memorys: 回忆信息
loop_start_time: 循环开始时间
"""
action = "no_reply" # 默认动作
@@ -246,6 +243,8 @@ class ActionPlanner(BasePlanner):
if selected_expressions:
action_data["selected_expressions"] = selected_expressions
logger.debug(f"{self.log_prefix} 传递{len(selected_expressions)}个选中的表达方式到action_data")
action_data["loop_start_time"] = loop_start_time
# 对于reply动作不需要额外处理，因为相关字段已经在上面的循环中添加到action_data
@@ -326,7 +325,7 @@ class ActionPlanner(BasePlanner):
chat_content_block = ""
if observed_messages_str:
chat_content_block = f"聊天记录：\n{observed_messages_str}"
chat_content_block = f"\n{observed_messages_str}"
else:
chat_content_block = "你还未开始聊天"
@@ -387,7 +386,7 @@ class ActionPlanner(BasePlanner):
prompt = planner_prompt_template.format(
relation_info_block=relation_info_block,
self_info_block=self_info_block,
memory_str=memory_str,
# memory_str=memory_str,
time_block=time_block,
# bot_name=global_config.bot.nickname,
prompt_personality=personality_block,
@@ -397,7 +396,7 @@ class ActionPlanner(BasePlanner):
cycle_info_block=cycle_info,
action_options_text=action_options_block,
# action_available_block=action_available_block,
extra_info_block=extra_info_block,
# extra_info_block=extra_info_block,
moderation_prompt=moderation_prompt_block,
)
return prompt

507
src/chat/focus_chat/replyer/default_replyer.py → src/chat/focus_chat/replyer/default_generator.py
View File

@@ -8,9 +8,7 @@ from src.chat.message_receive.chat_stream import get_chat_manager
from src.common.logger import get_logger
from src.llm_models.utils_model import LLMRequest
from src.config.config import global_config
from src.chat.utils.utils_image import image_path_to_base64 # Local import needed after move
from src.chat.utils.timer_calculator import Timer # <--- Import Timer
from src.chat.emoji_system.emoji_manager import get_emoji_manager
from src.chat.focus_chat.heartFC_sender import HeartFCSender
from src.chat.utils.utils import process_llm_response
from src.chat.heart_flow.utils_chat import get_chat_type_and_target_info
@@ -18,6 +16,7 @@ from src.chat.message_receive.chat_stream import ChatStream
from src.chat.focus_chat.hfc_utils import parse_thinking_id_to_timestamp
from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat
from src.chat.express.exprssion_learner import get_expression_learner
import time
import random
from datetime import datetime
@@ -50,7 +49,7 @@ def init_prompt():
不要浮夸，不要夸张修辞，只输出一条回复就好。
现在，你说：
""",
"default_replyer_prompt",
"default_generator_prompt",
)
Prompt(
@@ -70,7 +69,51 @@ def init_prompt():
不要浮夸，不要夸张修辞，只输出一条回复就好。
现在，你说：
""",
"default_replyer_private_prompt",
"default_generator_private_prompt",
)
Prompt(
"""
你可以参考你的以下的语言习惯，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，"{sender_name}"说的"{target_message}"引起了你的注意，对这句话，你想表达：{raw_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。请你修改你想表达的原句，符合你的表达风格和语言习惯
请你根据情景使用以下句法：
{grammar_habbits}
{config_expression_style}，你可以完全重组回复，保留最基本的表达含义就好，但重组后保持语意通顺。
不要浮夸，不要夸张修辞，平淡且不要输出多余内容(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：
""",
"default_expressor_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，"{sender_name}"说的"{target_message}"引起了你的注意，对这句话，你想表达：{raw_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}
{config_expression_style}，你可以完全重组回复，保留最基本的表达含义就好，但重组后保持语意通顺。
不要浮夸，不要夸张修辞，平淡且不要输出多余内容(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：
""",
"default_expressor_private_prompt", # New template for private FOCUSED chat
)
@@ -84,9 +127,8 @@ class DefaultReplyer:
)
self.heart_fc_sender = HeartFCSender()
self.chat_id = chat_stream.stream_id
self.chat_stream = chat_stream
self.is_group_chat, self.chat_target_info = get_chat_type_and_target_info(self.chat_id)
self.is_group_chat, self.chat_target_info = get_chat_type_and_target_info(self.chat_stream.stream_id)
async def _create_thinking_message(self, anchor_message: Optional[MessageRecv], thinking_id: str):
"""创建思考消息 (尝试锚定到 anchor_message)"""
@@ -114,213 +156,152 @@ class DefaultReplyer:
await self.heart_fc_sender.register_thinking(thinking_message)
return None
async def deal_reply(
async def generate_reply_with_context(
self,
cycle_timers: dict,
action_data: Dict[str, Any],
reasoning: str,
anchor_message: MessageRecv,
thinking_id: str,
) -> tuple[bool, Optional[List[Tuple[str, str]]]]:
# 创建思考消息
await self._create_thinking_message(anchor_message, thinking_id)
reply = [] # 初始化 reply，防止未定义
try:
has_sent_something = False
# 处理文本部分
# text_part = action_data.get("text", [])
# if text_part:
sent_msg_list = []
with Timer("生成回复", cycle_timers):
# 可以保留原有的文本处理逻辑或进行适当调整
reply = await self.reply(
# in_mind_reply=text_part,
anchor_message=anchor_message,
thinking_id=thinking_id,
reason=reasoning,
action_data=action_data,
)
if reply:
with Timer("发送消息", cycle_timers):
sent_msg_list = await self.send_response_messages(
anchor_message=anchor_message,
thinking_id=thinking_id,
response_set=reply,
)
has_sent_something = True
else:
logger.warning(f"{self.log_prefix} 文本回复生成失败")
if not has_sent_something:
logger.warning(f"{self.log_prefix} 回复动作未包含任何有效内容")
return has_sent_something, sent_msg_list
except Exception as e:
logger.error(f"回复失败: {e}")
traceback.print_exc()
return False, None
# --- 回复器 (Replier) 的定义 --- #
async def deal_emoji(
self,
anchor_message: MessageRecv,
thinking_id: str,
action_data: Dict[str, Any],
cycle_timers: dict,
) -> Optional[List[str]]:
"""
表情动作处理类
"""
await self._create_thinking_message(anchor_message, thinking_id)
try:
has_sent_something = False
sent_msg_list = []
reply = []
with Timer("选择表情", cycle_timers):
emoji_keyword = action_data.get("description", [])
emoji_base64, _description, emotion = await self._choose_emoji(emoji_keyword)
if emoji_base64:
# logger.info(f"选择表情: {_description}")
reply.append(("emoji", emoji_base64))
else:
logger.warning(f"{self.log_prefix} 没有找到合适表情")
if reply:
with Timer("发送表情", cycle_timers):
sent_msg_list = await self.send_response_messages(
anchor_message=anchor_message,
thinking_id=thinking_id,
response_set=reply,
)
has_sent_something = True
else:
logger.warning(f"{self.log_prefix} 表情发送失败")
if not has_sent_something:
logger.warning(f"{self.log_prefix} 表情发送失败")
return has_sent_something, sent_msg_list
except Exception as e:
logger.error(f"回复失败: {e}")
traceback.print_exc()
return False, None
async def reply(
self,
# in_mind_reply: str,
reason: str,
anchor_message: MessageRecv,
thinking_id: str,
action_data: Dict[str, Any],
) -> Optional[List[str]]:
reply_data: Dict[str, Any],
) -> Tuple[bool, Optional[List[str]]]:
"""
回复器 (Replier): 核心逻辑，负责生成回复文本。
(已整合原 HeartFCGenerator 的功能)
"""
try:
# 1. 获取情绪影响因子并调整模型温度
# arousal_multiplier = mood_manager.get_arousal_multiplier()
# current_temp = float(global_config.model.normal["temp"]) * arousal_multiplier
# self.express_model.params["temperature"] = current_temp # 动态调整温度
reply_to = action_data.get("reply_to", "none")
sender = ""
targer = ""
if ":" in reply_to or "：" in reply_to:
# 使用正则表达式匹配中文或英文冒号
parts = re.split(pattern=r"[:：]", string=reply_to, maxsplit=1)
if len(parts) == 2:
sender = parts[0].strip()
targer = parts[1].strip()
identity = action_data.get("identity", "")
extra_info_block = action_data.get("extra_info_block", "")
relation_info_block = action_data.get("relation_info_block", "")
# 3. 构建 Prompt
with Timer("构建Prompt", {}): # 内部计时器，可选保留
prompt = await self.build_prompt_focus(
chat_stream=self.chat_stream, # Pass the stream object
# in_mind_reply=in_mind_reply,
identity=identity,
extra_info_block=extra_info_block,
relation_info_block=relation_info_block,
reason=reason,
sender_name=sender, # Pass determined name
target_message=targer,
config_expression_style=global_config.expression.expression_style,
action_data=action_data, # 传递action_data
prompt = await self.build_prompt_reply_context(
reply_data=reply_data, # 传递action_data
)
# 4. 调用 LLM 生成回复
content = None
reasoning_content = None
model_name = "unknown_model"
if not prompt:
logger.error(f"{self.log_prefix}[Replier-{thinking_id}] Prompt 构建失败，无法生成回复。")
return None
try:
with Timer("LLM生成", {}): # 内部计时器，可选保留
logger.info(f"{self.log_prefix}Prompt:\n{prompt}\n")
content, (reasoning_content, model_name) = await self.express_model.generate_response_async(prompt)
# logger.info(f"prompt: {prompt}")
logger.info(f"最终回复: {content}")
except Exception as llm_e:
# 精简报错信息
logger.error(f"{self.log_prefix}LLM 生成失败: {llm_e}")
return None # LLM 调用失败则无法生成回复
return False, None # LLM 调用失败则无法生成回复
processed_response = process_llm_response(content)
# 5. 处理 LLM 响应
if not content:
logger.warning(f"{self.log_prefix}LLM 生成了空内容。")
return None
return False, None
if not processed_response:
logger.warning(f"{self.log_prefix}处理后的回复为空。")
return None
return False, None
reply_set = []
for str in processed_response:
reply_seg = ("text", str)
reply_set.append(reply_seg)
return reply_set
return True , reply_set
except Exception as e:
logger.error(f"{self.log_prefix}回复生成意外失败: {e}")
traceback.print_exc()
return None
async def build_prompt_focus(
return False, None
async def rewrite_reply_with_context(
self,
reason,
chat_stream,
sender_name,
# in_mind_reply,
extra_info_block,
relation_info_block,
identity,
target_message,
config_expression_style,
action_data=None,
# stuation,
reply_data: Dict[str, Any],
) -> Tuple[bool, Optional[List[str]]]:
"""
表达器 (Expressor): 核心逻辑，负责生成回复文本。
"""
try:
reply_to = reply_data.get("reply_to", "")
raw_reply = reply_data.get("raw_reply", "")
reason = reply_data.get("reason", "")
with Timer("构建Prompt", {}): # 内部计时器，可选保留
prompt = await self.build_prompt_rewrite_context(
raw_reply=raw_reply,
reason=reason,
reply_to=reply_to,
)
content = None
reasoning_content = None
model_name = "unknown_model"
if not prompt:
logger.error(f"{self.log_prefix}Prompt 构建失败，无法生成回复。")
return False, None
try:
with Timer("LLM生成", {}): # 内部计时器，可选保留
# TODO: API-Adapter修改标记
content, (reasoning_content, model_name) = await self.express_model.generate_response_async(prompt)
logger.info(f"想要表达：{raw_reply}||理由：{reason}")
logger.info(f"最终回复: {content}\n")
except Exception as llm_e:
# 精简报错信息
logger.error(f"{self.log_prefix}LLM 生成失败: {llm_e}")
return False, None # LLM 调用失败则无法生成回复
processed_response = process_llm_response(content)
# 5. 处理 LLM 响应
if not content:
logger.warning(f"{self.log_prefix}LLM 生成了空内容。")
return False, None
if not processed_response:
logger.warning(f"{self.log_prefix}处理后的回复为空。")
return False, None
reply_set = []
for str in processed_response:
reply_seg = ("text", str)
reply_set.append(reply_seg)
return True, reply_set
except Exception as e:
logger.error(f"{self.log_prefix}回复生成意外失败: {e}")
traceback.print_exc()
return False, None
async def build_prompt_reply_context(
self,
reply_data=None,
) -> str:
chat_stream = self.chat_stream
is_group_chat = bool(chat_stream.group_info)
identity = reply_data.get("identity", "")
extra_info_block = reply_data.get("extra_info_block", "")
relation_info_block = reply_data.get("relation_info_block", "")
reply_to = reply_data.get("reply_to", "none")
sender = ""
target = ""
if ":" in reply_to or "：" in reply_to:
# 使用正则表达式匹配中文或英文冒号
parts = re.split(pattern=r"[:：]", string=reply_to, maxsplit=1)
if len(parts) == 2:
sender = parts[0].strip()
target = parts[1].strip()
message_list_before_now = get_raw_msg_before_timestamp_with_chat(
chat_id=chat_stream.stream_id,
@@ -341,7 +322,7 @@ class DefaultReplyer:
grammar_habbits = []
# 使用从处理器传来的选中表达方式
selected_expressions = action_data.get("selected_expressions", []) if action_data else []
selected_expressions = reply_data.get("selected_expressions", []) if reply_data else []
if selected_expressions:
logger.info(f"{self.log_prefix} 使用处理器选中的{len(selected_expressions)}个表达方式")
@@ -371,7 +352,7 @@ class DefaultReplyer:
try:
# 处理关键词规则
for rule in global_config.keyword_reaction.keyword_rules:
if any(keyword in target_message for keyword in rule.keywords):
if any(keyword in target for keyword in rule.keywords):
logger.info(f"检测到关键词规则：{rule.keywords}，触发反应：{rule.reaction}")
keywords_reaction_prompt += f"{rule.reaction}，"
@@ -380,7 +361,7 @@ class DefaultReplyer:
for pattern_str in rule.regex:
try:
pattern = re.compile(pattern_str)
if result := pattern.search(target_message):
if result := pattern.search(target):
reaction = rule.reaction
for name, content in result.groupdict().items():
reaction = reaction.replace(f"[{name}]", content)
@@ -397,18 +378,18 @@ class DefaultReplyer:
# logger.debug("开始构建 focus prompt")
if sender_name:
if sender:
reply_target_block = (
f"现在{sender_name}说的:{target_message}。引起了你的注意，你想要在群里发言或者回复这条消息。"
f"现在{sender}说的:{target}。引起了你的注意，你想要在群里发言或者回复这条消息。"
)
elif target_message:
reply_target_block = f"现在{target_message}引起了你的注意，你想要在群里发言或者回复这条消息。"
elif target:
reply_target_block = f"现在{target}引起了你的注意，你想要在群里发言或者回复这条消息。"
else:
reply_target_block = "现在，你想要在群里发言或者回复消息。"
# --- Choose template based on chat type ---
if is_group_chat:
template_name = "default_replyer_prompt"
template_name = "default_generator_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")
@@ -422,18 +403,14 @@ class DefaultReplyer:
relation_info_block=relation_info_block,
time_block=time_block,
reply_target_block=reply_target_block,
# bot_name=global_config.bot.nickname,
# prompt_personality="",
# reason=reason,
# in_mind_reply=in_mind_reply,
keywords_reaction_prompt=keywords_reaction_prompt,
identity=identity,
target_message=target_message,
sender_name=sender_name,
config_expression_style=config_expression_style,
target_message=target,
sender_name=sender,
config_expression_style=global_config.expression.expression_style,
)
else: # Private chat
template_name = "default_replyer_private_prompt"
template_name = "default_generator_private_prompt"
chat_target_1 = "你正在和人私聊"
prompt = await global_prompt_manager.format_prompt(
template_name,
@@ -444,20 +421,125 @@ class DefaultReplyer:
relation_info_block=relation_info_block,
time_block=time_block,
reply_target_block=reply_target_block,
# bot_name=global_config.bot.nickname,
# prompt_personality="",
# reason=reason,
# in_mind_reply=in_mind_reply,
keywords_reaction_prompt=keywords_reaction_prompt,
identity=identity,
target_message=target_message,
sender_name=sender_name,
config_expression_style=config_expression_style,
target_message=target,
sender_name=sender,
config_expression_style=global_config.expression.expression_style,
)
return prompt
# --- 发送器 (Sender) --- #
async def build_prompt_rewrite_context(
self,
reason,
raw_reply,
reply_to,
) -> str:
sender = ""
target = ""
if ":" in reply_to or "：" in reply_to:
# 使用正则表达式匹配中文或英文冒号
parts = re.split(pattern=r"[:：]", string=reply_to, maxsplit=1)
if len(parts) == 2:
sender = parts[0].strip()
target = parts[1].strip()
chat_stream = self.chat_stream
is_group_chat = bool(chat_stream.group_info)
message_list_before_now = get_raw_msg_before_timestamp_with_chat(
chat_id=chat_stream.stream_id,
timestamp=time.time(),
limit=global_config.focus_chat.observation_context_size,
)
chat_talking_prompt = build_readable_messages(
message_list_before_now,
replace_bot_name=True,
merge_messages=True,
timestamp_mode="relative",
read_mark=0.0,
truncate=True,
)
expression_learner = get_expression_learner()
(
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 = "default_expressor_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,
style_habbits=style_habbits_str,
grammar_habbits=grammar_habbits_str,
chat_target=chat_target_1,
chat_info=chat_talking_prompt,
bot_name=global_config.bot.nickname,
prompt_personality="",
reason=reason,
raw_reply=raw_reply,
sender_name=sender,
target_message=target,
config_expression_style=global_config.expression.expression_style,
)
else: # Private chat
template_name = "default_expressor_private_prompt"
chat_target_1 = "你正在和人私聊"
prompt = await global_prompt_manager.format_prompt(
template_name,
style_habbits=style_habbits_str,
grammar_habbits=grammar_habbits_str,
chat_target=chat_target_1,
chat_info=chat_talking_prompt,
bot_name=global_config.bot.nickname,
prompt_personality="",
reason=reason,
raw_reply=raw_reply,
sender_name=sender,
target_message=target,
config_expression_style=global_config.expression.expression_style,
)
return prompt
async def send_response_messages(
self,
@@ -468,7 +550,7 @@ class DefaultReplyer:
) -> Optional[MessageSending]:
"""发送回复消息 (尝试锚定到 anchor_message)，使用 HeartFCSender"""
chat = self.chat_stream
chat_id = self.chat_id
chat_id = self.chat_stream.stream_id
if chat is None:
logger.error(f"{self.log_prefix} 无法发送回复，chat_stream 为空。")
return None
@@ -514,7 +596,7 @@ class DefaultReplyer:
is_emoji = False
reply_to = not mark_head
bot_message = await self._build_single_sending_message(
bot_message: MessageSending = await self._build_single_sending_message(
anchor_message=anchor_message,
message_id=part_message_id,
message_segment=message_segment,
@@ -526,22 +608,22 @@ class DefaultReplyer:
)
try:
if (bot_message.is_private_message() or
bot_message.reply.processed_plain_text != "[System Trigger Context]" or
mark_head):
set_reply = False
else:
set_reply = True
if not mark_head:
mark_head = True
# first_bot_msg = bot_message # 保存第一个成功发送的消息对象
typing = False
else:
typing = True
if type == "emoji":
typing = False
if anchor_message.raw_message:
set_reply = True
else:
set_reply = False
sent_msg = await self.heart_fc_sender.send_message(
bot_message, has_thinking=True, typing=typing, set_reply=set_reply
bot_message, typing=typing, set_reply=set_reply
)
reply_message_ids.append(part_message_id) # 记录我们生成的ID
@@ -562,30 +644,15 @@ class DefaultReplyer:
return sent_msg_list
async def _choose_emoji(self, send_emoji: str):
"""
选择表情，根据send_emoji文本选择表情，返回表情base64
"""
emoji_base64 = ""
description = ""
emoji_raw = await get_emoji_manager().get_emoji_for_text(send_emoji)
if emoji_raw:
emoji_path, description, _emotion = emoji_raw
emoji_base64 = image_path_to_base64(emoji_path)
return emoji_base64, description, _emotion
else:
return None, None, None
async def _build_single_sending_message(
self,
anchor_message: MessageRecv,
message_id: str,
message_segment: Seg,
reply_to: bool,
is_emoji: bool,
thinking_id: str,
thinking_start_time: float,
display_message: str,
anchor_message: MessageRecv = None
) -> MessageSending:
"""构建单个发送消息"""
@@ -596,12 +663,16 @@ class DefaultReplyer:
)
# await anchor_message.process()
if anchor_message:
sender_info = anchor_message.message_info.user_info
else:
sender_info = None
bot_message = MessageSending(
message_id=message_id, # 使用片段的唯一ID
chat_stream=self.chat_stream,
bot_user_info=bot_user_info,
sender_info=anchor_message.message_info.user_info,
sender_info=sender_info,
message_segment=message_segment,
reply=anchor_message, # 回复原始锚点
is_head=reply_to,