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feat: 重构完成开始测试debug

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tcmofashi
2025-03-11 01:15:32 +08:00
parent 20b8778e2b
commit 7899e67cb2
13 changed files with 486 additions and 572 deletions
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153
src/plugins/chat/llm_generator.py
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@@ -7,7 +7,7 @@ from nonebot import get_driver
from ...common.database import Database
from ..models.utils_model import LLM_request
from .config import global_config
from .message_cq import Message
from .message import MessageRecv, MessageThinking, MessageSending
from .prompt_builder import prompt_builder
from .relationship_manager import relationship_manager
from .utils import process_llm_response
@@ -18,58 +18,88 @@ config = driver.config
class ResponseGenerator:
def __init__(self):
self.model_r1 = LLM_request(model=global_config.llm_reasoning, temperature=0.7,max_tokens=1000,stream=True)
self.model_v3 = LLM_request(model=global_config.llm_normal, temperature=0.7,max_tokens=1000)
self.model_r1_distill = LLM_request(model=global_config.llm_reasoning_minor, temperature=0.7,max_tokens=1000)
self.model_v25 = LLM_request(model=global_config.llm_normal_minor, temperature=0.7,max_tokens=1000)
self.model_r1 = LLM_request(
model=global_config.llm_reasoning,
temperature=0.7,
max_tokens=1000,
stream=True,
)
self.model_v3 = LLM_request(
model=global_config.llm_normal, temperature=0.7, max_tokens=1000
)
self.model_r1_distill = LLM_request(
model=global_config.llm_reasoning_minor, temperature=0.7, max_tokens=1000
)
self.model_v25 = LLM_request(
model=global_config.llm_normal_minor, temperature=0.7, max_tokens=1000
)
self.db = Database.get_instance()
self.current_model_type = 'r1' # 默认使用 R1
self.current_model_type = "r1" # 默认使用 R1
async def generate_response(self, message: Message) -> Optional[Union[str, List[str]]]:
async def generate_response(
self, message: MessageThinking
) -> Optional[Union[str, List[str]]]:
"""根据当前模型类型选择对应的生成函数"""
# 从global_config中获取模型概率值并选择模型
rand = random.random()
if rand < global_config.MODEL_R1_PROBABILITY:
self.current_model_type = 'r1'
self.current_model_type = "r1"
current_model = self.model_r1
elif rand < global_config.MODEL_R1_PROBABILITY + global_config.MODEL_V3_PROBABILITY:
self.current_model_type = 'v3'
elif (
rand
< global_config.MODEL_R1_PROBABILITY + global_config.MODEL_V3_PROBABILITY
):
self.current_model_type = "v3"
current_model = self.model_v3
else:
self.current_model_type = 'r1_distill'
self.current_model_type = "r1_distill"
current_model = self.model_r1_distill
print(f"+++++++++++++++++{global_config.BOT_NICKNAME}{self.current_model_type}思考中+++++++++++++++++")
model_response = await self._generate_response_with_model(message, current_model)
raw_content=model_response
print(
f"+++++++++++++++++{global_config.BOT_NICKNAME}{self.current_model_type}思考中+++++++++++++++++"
)
model_response = await self._generate_response_with_model(
message, current_model
)
raw_content = model_response
if model_response:
print(f'{global_config.BOT_NICKNAME}的回复是：{model_response}')
print(f"{global_config.BOT_NICKNAME}的回复是：{model_response}")
model_response = await self._process_response(model_response)
if model_response:
return model_response, raw_content
return None, raw_content
return model_response ,raw_content
return None,raw_content
async def _generate_response_with_model(self, message: Message, model: LLM_request) -> Optional[str]:
async def _generate_response_with_model(
self, message: MessageThinking, model: LLM_request
) -> Optional[str]:
"""使用指定的模型生成回复"""
sender_name = message.user_nickname or f"用户{message.user_id}"
if message.user_cardname:
sender_name=f"[({message.user_id}){message.user_nickname}]{message.user_cardname}"
sender_name = (
message.chat_stream.user_info.user_nickname
or f"用户{message.chat_stream.user_info.user_id}"
)
if message.chat_stream.user_info.user_cardname:
sender_name = f"[({message.chat_stream.user_info.user_id}){message.chat_stream.user_info.user_nickname}]{message.chat_stream.user_info.user_cardname}"
# 获取关系值
relationship_value = relationship_manager.get_relationship(message.user_id).relationship_value if relationship_manager.get_relationship(message.user_id) else 0.0
relationship_value = (
relationship_manager.get_relationship(
message.chat_stream
).relationship_value
if relationship_manager.get_relationship(message.chat_stream)
else 0.0
)
if relationship_value != 0.0:
# print(f"\033[1;32m[关系管理]\033[0m 回复中_当前关系值: {relationship_value}")
pass
# 构建prompt
prompt, prompt_check = await prompt_builder._build_prompt(
message_txt=message.processed_plain_text,
sender_name=sender_name,
relationship_value=relationship_value,
group_id=message.group_id
stream_id=message.chat_stream.stream_id,
)
# 读空气模块 简化逻辑，先停用
@@ -95,7 +125,7 @@ class ResponseGenerator:
except Exception as e:
print(f"生成回复时出错: {e}")
return None
# 保存到数据库
self._save_to_db(
message=message,
@@ -107,54 +137,71 @@ class ResponseGenerator:
reasoning_content=reasoning_content,
# reasoning_content_check=reasoning_content_check if global_config.enable_kuuki_read else ""
)
return content
# def _save_to_db(self, message: Message, sender_name: str, prompt: str, prompt_check: str,
# content: str, content_check: str, reasoning_content: str, reasoning_content_check: str):
def _save_to_db(self, message: Message, sender_name: str, prompt: str, prompt_check: str,
content: str, reasoning_content: str,):
def _save_to_db(
self,
message: Message,
sender_name: str,
prompt: str,
prompt_check: str,
content: str,
reasoning_content: str,
):
"""保存对话记录到数据库"""
self.db.db.reasoning_logs.insert_one({
'time': time.time(),
'group_id': message.group_id,
'user': sender_name,
'message': message.processed_plain_text,
'model': self.current_model_type,
# 'reasoning_check': reasoning_content_check,
# 'response_check': content_check,
'reasoning': reasoning_content,
'response': content,
'prompt': prompt,
'prompt_check': prompt_check
})
self.db.db.reasoning_logs.insert_one(
{
"time": time.time(),
"group_id": message.group_id,
"user": sender_name,
"message": message.processed_plain_text,
"model": self.current_model_type,
# 'reasoning_check': reasoning_content_check,
# 'response_check': content_check,
"reasoning": reasoning_content,
"response": content,
"prompt": prompt,
"prompt_check": prompt_check,
}
)
async def _get_emotion_tags(self, content: str) -> List[str]:
"""提取情感标签"""
try:
prompt = f'''请从以下内容中，从"happy,angry,sad,surprised,disgusted,fearful,neutral"中选出最匹配的1个情感标签并输出
prompt = f"""请从以下内容中，从"happy,angry,sad,surprised,disgusted,fearful,neutral"中选出最匹配的1个情感标签并输出
只输出标签就好，不要输出其他内容:
内容：{content}
输出：
'''
"""
content, _ = await self.model_v25.generate_response(prompt)
content=content.strip()
if content in ['happy','angry','sad','surprised','disgusted','fearful','neutral']:
content = content.strip()
if content in [
"happy",
"angry",
"sad",
"surprised",
"disgusted",
"fearful",
"neutral",
]:
return [content]
else:
return ["neutral"]
except Exception as e:
print(f"获取情感标签时出错: {e}")
return ["neutral"]
async def _process_response(self, content: str) -> Tuple[List[str], List[str]]:
"""处理响应内容，返回处理后的内容和情感标签"""
if not content:
return None, []
processed_response = process_llm_response(content)
return processed_response