gardel/Mofox-Core
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

🤖 自动格式化代码 [skip ci]

Browse Source
This commit is contained in:
github-actions[bot]
2025-06-25 07:55:16 +00:00
parent b4440defeb
commit 284c248add
5 changed files with 50 additions and 51 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
bot.py
View File

@@ -2,6 +2,7 @@ import asyncio
import hashlib
import os
from dotenv import load_dotenv
if os.path.exists(".env"):
load_dotenv(".env", override=True)
print("成功加载环境变量配置")
@@ -13,9 +14,11 @@ import platform
import traceback
from pathlib import Path
from rich.traceback import install
# maim_message imports for console input
from maim_message import Seg, UserInfo, BaseMessageInfo, MessageBase
from src.chat.message_receive.bot import chat_bot
# 最早期初始化日志系统，确保所有后续模块都使用正确的日志格式
from src.common.logger import initialize_logging, get_logger, shutdown_logging
from src.main import MainSystem
@@ -26,7 +29,6 @@ initialize_logging()
logger = get_logger("main")
install(extra_lines=3)
# 设置工作目录为脚本所在目录

53
src/chat/express/expression_selector.py
View File

@@ -12,6 +12,7 @@ from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
logger = get_logger("expression_selector")
def init_prompt():
expression_evaluation_prompt = """
你的名字是{bot_name}
@@ -42,30 +43,32 @@ def init_prompt():
"""
Prompt(expression_evaluation_prompt, "expression_evaluation_prompt")
def weighted_sample(population: List[Dict], weights: List[float], k: int) -> List[Dict]:
"""按权重随机抽样"""
if not population or not weights or k <= 0:
return []
if len(population) <= k:
return population.copy()
# 使用累积权重的方法进行加权抽样
selected = []
population_copy = population.copy()
weights_copy = weights.copy()
for _ in range(k):
if not population_copy:
break
# 选择一个元素
chosen_idx = random.choices(range(len(population_copy)), weights=weights_copy)[0]
selected.append(population_copy.pop(chosen_idx))
weights_copy.pop(chosen_idx)
return selected
class ExpressionSelector:
def __init__(self):
self.expression_learner = get_expression_learner()
@@ -75,7 +78,9 @@ class ExpressionSelector:
request_type="expression.selector",
)
def get_random_expressions(self, chat_id: str, style_num: int, grammar_num: int, personality_num: int) -> Tuple[List[Dict[str, str]], List[Dict[str, str]]]:
def get_random_expressions(
self, chat_id: str, style_num: int, grammar_num: int, personality_num: int
) -> Tuple[List[Dict[str, str]], List[Dict[str, str]]]:
(
learnt_style_expressions,
learnt_grammar_expressions,
@@ -88,13 +93,13 @@ class ExpressionSelector:
selected_style = weighted_sample(learnt_style_expressions, style_weights, style_num)
else:
selected_style = []
if learnt_grammar_expressions:
grammar_weights = [expr.get("count", 1) for expr in learnt_grammar_expressions]
selected_grammar = weighted_sample(learnt_grammar_expressions, grammar_weights, grammar_num)
else:
selected_grammar = []
if personality_expressions:
personality_weights = [expr.get("count", 1) for expr in personality_expressions]
selected_personality = weighted_sample(personality_expressions, personality_weights, personality_num)
@@ -102,7 +107,7 @@ class ExpressionSelector:
selected_personality = []
return selected_style, selected_grammar, selected_personality
def update_expression_count(self, chat_id: str, expression: Dict[str, str], multiplier: float = 1.5):
"""更新表达方式的count值"""
if expression.get("type") == "style_personality":
@@ -117,29 +122,30 @@ class ExpressionSelector:
file_path = os.path.join("data", "expression", "learnt_grammar", str(chat_id), "expressions.json")
else:
return
if not os.path.exists(file_path):
return
try:
with open(file_path, "r", encoding="utf-8") as f:
expressions = json.load(f)
# 找到匹配的表达方式并更新count
for expr in expressions:
if (expr.get("situation") == expression.get("situation") and
expr.get("style") == expression.get("style")):
if expr.get("situation") == expression.get("situation") and expr.get("style") == expression.get(
"style"
):
expr["count"] = expr.get("count", 1) * multiplier
expr["last_active_time"] = time.time()
break
# 保存更新后的文件
with open(file_path, "w", encoding="utf-8") as f:
json.dump(expressions, f, ensure_ascii=False, indent=2)
except Exception as e:
logger.error(f"更新表达方式count失败: {e}")
async def select_suitable_expressions_llm(self, chat_id: str, chat_info: str) -> List[Dict[str, str]]:
"""使用LLM选择适合的表达方式"""
@@ -188,7 +194,7 @@ class ExpressionSelector:
)
print(prompt)
# 4. 调用LLM
try:
content, (_, _) = await self.llm_model.generate_response_async(prompt=prompt)
@@ -216,7 +222,7 @@ class ExpressionSelector:
if isinstance(idx, int) and 1 <= idx <= len(all_expressions):
expression = all_expressions[idx - 1] # 索引从1开始
valid_expressions.append(expression)
# 对选中的表达方式count数*1.5
self.update_expression_count(chat_id, expression, 1.5)
@@ -226,7 +232,7 @@ class ExpressionSelector:
except Exception as e:
logger.error(f"LLM处理表达方式选择时出错: {e}")
return []
init_prompt()
@@ -234,10 +240,3 @@ try:
expression_selector = ExpressionSelector()
except Exception as e:
print(f"ExpressionSelector初始化失败: {e}")

6
src/chat/focus_chat/info_processors/expression_selector_processor.py
View File

@@ -24,7 +24,6 @@ class ExpressionSelectorProcessor(BaseProcessor):
self.selection_interval = 10 # 40秒间隔
self.cached_expressions = [] # 缓存上一次选择的表达方式
name = get_chat_manager().get_stream_name(self.subheartflow_id)
self.log_prefix = f"[{name}] 表达选择器"
@@ -72,7 +71,9 @@ class ExpressionSelectorProcessor(BaseProcessor):
try:
# LLM模式：调用LLM选择5-10个，然后随机选5个
selected_expressions = await expression_selector.select_suitable_expressions_llm(self.subheartflow_id, chat_info)
selected_expressions = await expression_selector.select_suitable_expressions_llm(
self.subheartflow_id, chat_info
)
cache_size = len(selected_expressions) if selected_expressions else 0
mode_desc = f"LLM模式（已缓存{cache_size}个）"
@@ -93,4 +94,3 @@ class ExpressionSelectorProcessor(BaseProcessor):
except Exception as e:
logger.error(f"{self.log_prefix} 处理表达方式选择时出错: {e}")
return []

8
src/chat/normal_chat/normal_prompt.py
View File

@@ -148,7 +148,7 @@ class PromptBuilder:
read_mark=0.0,
show_actions=True,
)
message_list_before_now_half = get_raw_msg_before_timestamp_with_chat(
chat_id=chat_stream.stream_id,
timestamp=time.time(),
@@ -162,8 +162,10 @@ class PromptBuilder:
read_mark=0.0,
show_actions=True,
)
expressions = expression_selector.select_suitable_expressions_llm(chat_stream.stream_id, chat_talking_prompt_half)
expressions = expression_selector.select_suitable_expressions_llm(
chat_stream.stream_id, chat_talking_prompt_half
)
style_habbits = []
grammar_habbits = []
if expressions:

30
src/individuality/individuality.py
View File

@@ -31,7 +31,7 @@ class Individuality:
self.name = ""
self.bot_person_id = ""
self.meta_info_file_path = "data/personality/meta.json"
self.model = LLMRequest(
model=global_config.model.utils,
request_type="individuality.compress",
@@ -99,9 +99,7 @@ class Individuality:
logger.info("已将完整人设更新到bot的impression中")
# 创建压缩版本的short_impression
asyncio.create_task(self._create_compressed_impression(
personality_core, personality_sides, identity_detail
))
asyncio.create_task(self._create_compressed_impression(personality_core, personality_sides, identity_detail))
asyncio.create_task(self.express_style.extract_and_store_personality_expressions())
@@ -374,12 +372,12 @@ class Individuality:
self, personality_core: str, personality_sides: list, identity_detail: list
) -> str:
"""使用LLM创建压缩版本的impression
Args:
personality_core: 核心人格
personality_sides: 人格侧面列表
identity_detail: 身份细节列表
Returns:
str: 压缩后的impression文本
"""
@@ -387,23 +385,23 @@ class Individuality:
compressed_parts = []
if personality_core:
compressed_parts.append(f"{personality_core}")
# 准备需要压缩的内容
content_to_compress = []
if personality_sides:
content_to_compress.append(f"人格特质: {'、'.join(personality_sides)}")
if identity_detail:
content_to_compress.append(f"身份背景: {'、'.join(identity_detail)}")
if not content_to_compress:
# 如果没有需要压缩的内容，直接返回核心人格
result = "。".join(compressed_parts)
return result + "。" if result else ""
# 使用LLM压缩其他内容
try:
compress_content = "、".join(content_to_compress)
prompt = f"""请将以下人设信息进行简洁压缩，保留主要内容，用简练的中文表达：
{compress_content}
@@ -413,10 +411,10 @@ class Individuality:
2. 尽量简洁，不超过30字
3. 直接输出压缩后的内容，不要解释"""
response,(_,_) = await self.model.generate_response_async(
response, (_, _) = await self.model.generate_response_async(
prompt=prompt,
)
if response.strip():
compressed_parts.append(response.strip())
logger.info(f"精简人格侧面: {response.strip()}")
@@ -424,15 +422,13 @@ class Individuality:
logger.error(f"使用LLM压缩人设时出错: {response}")
except Exception as e:
logger.error(f"使用LLM压缩人设时出错: {e}")
result = "。".join(compressed_parts)
# 更新short_impression字段
if result:
person_info_manager = get_person_info_manager()
await person_info_manager.update_one_field(
self.bot_person_id, "short_impression", result
)
await person_info_manager.update_one_field(self.bot_person_id, "short_impression", result)
logger.info("已将压缩人设更新到bot的short_impression中")