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v0.4.0 支持任意替换的模型，改进配置文件

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好好好好好好好好好
This commit is contained in:
SengokuCola
2025-03-02 23:51:47 +08:00
parent 65d983f37f
commit 6462c3b360
16 changed files with 459 additions and 644 deletions
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287
src/plugins/chat/llm_generator.py
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@@ -13,249 +13,113 @@ from .prompt_builder import prompt_builder
from .config import global_config
from .utils import process_llm_response
from nonebot import get_driver
from ..models.utils_model import LLM_request
driver = get_driver()
config = driver.config
class LLMResponseGenerator:
class ResponseGenerator:
def __init__(self):
if global_config.API_USING == "siliconflow":
self.client = OpenAI(
api_key=config.siliconflow_key,
base_url=config.siliconflow_base_url
)
elif global_config.API_USING == "deepseek":
self.client = OpenAI(
api_key=config.deep_seek_key,
base_url=config.deep_seek_base_url
)
self.model_r1 = LLM_request(model=global_config.llm_reasoning, temperature=0.7)
self.model_v3 = LLM_request(model=global_config.llm_normal, temperature=0.7)
self.model_r1_distill = LLM_request(model=global_config.llm_reasoning_minor, temperature=0.7)
self.db = Database.get_instance()
# 当前使用的模型类型
self.current_model_type = 'r1' # 默认使用 R1
async def generate_response(self, message: Message) -> 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
model_r1_probability = global_config.MODEL_R1_PROBABILITY
model_v3_probability = global_config.MODEL_V3_PROBABILITY
model_r1_distill_probability = global_config.MODEL_R1_DISTILL_PROBABILITY
# 生成随机数并根据概率选择模型
# 从global_config中获取模型概率值并选择模型
rand = random.random()
if rand < model_r1_probability:
if rand < global_config.MODEL_R1_PROBABILITY:
self.current_model_type = 'r1'
elif rand < model_r1_probability + model_v3_probability:
current_model = self.model_r1
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' # 默认使用 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+++++++++++++++++")
if self.current_model_type == 'r1':
model_response = await self._generate_r1_response(message)
elif self.current_model_type == 'v3':
model_response = await self._generate_v3_response(message)
else:
model_response = await self._generate_r1_distill_response(message)
# 打印情感标签
print(f'{global_config.BOT_NICKNAME}的回复是：{model_response}')
model_response, emotion = await self._process_response(model_response)
model_response = await self._generate_response_with_model(message, current_model)
if model_response:
print(f"为 '{model_response}' 获取到的情感标签为：{emotion}")
return model_response, emotion
print(f'{global_config.BOT_NICKNAME}的回复是：{model_response}')
model_response, emotion = await self._process_response(model_response)
if model_response:
print(f"为 '{model_response}' 获取到的情感标签为：{emotion}")
return model_response, emotion
return None, []
async def _generate_base_response(
self,
message: Message,
model_name: str,
model_params: Optional[Dict[str, Any]] = None
) -> Optional[str]:
async def _generate_response_with_model(self, message: Message, 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}"
# 获取关系值
if relationship_manager.get_relationship(message.user_id):
relationship_value = relationship_manager.get_relationship(message.user_id).relationship_value
relationship_value = relationship_manager.get_relationship(message.user_id).relationship_value if relationship_manager.get_relationship(message.user_id) 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}")
else:
relationship_value = 0.0
''' 构建prompt '''
prompt,prompt_check = prompt_builder._build_prompt(
# 构建prompt
prompt, prompt_check = prompt_builder._build_prompt(
message_txt=message.processed_plain_text,
sender_name=sender_name,
relationship_value=relationship_value,
group_id=message.group_id
)
# 设置默认参数
default_params = {
"model": model_name,
"messages": [{"role": "user", "content": prompt}],
"stream": False,
"max_tokens": 1024,
"temperature": 0.7
}
default_params_check = {
"model": "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
"messages": [{"role": "user", "content": prompt_check}],
"stream": False,
"max_tokens": 1024,
"temperature": 0.7
}
# 更新参数
if model_params:
default_params.update(model_params)
def create_completion():
return self.client.chat.completions.create(**default_params)
def create_completion_check():
return self.client.chat.completions.create(**default_params_check)
loop = asyncio.get_event_loop()
# 读空气模块
air = 0
reasoning_content_check=''
content_check=''
if global_config.enable_kuuki_read:
response_check = await loop.run_in_executor(None, create_completion_check)
if response_check:
reasoning_content_check = ""
if hasattr(response_check.choices[0].message, "reasoning"):
reasoning_content_check = response_check.choices[0].message.reasoning or reasoning_content_check
elif hasattr(response_check.choices[0].message, "reasoning_content"):
reasoning_content_check = response_check.choices[0].message.reasoning_content or reasoning_content_check
content_check = response_check.choices[0].message.content
print(f"\033[1;32m[读空气]\033[0m 读空气结果为{content_check}")
if 'yes' not in content_check.lower():
air = 1
#稀释读空气的判定
if air == 1 and random.random() < 0.3:
self.db.db.reasoning_logs.insert_one({
'time': time.time(),
'group_id': message.group_id,
'user': sender_name,
'message': message.processed_plain_text,
'model': model_name,
'reasoning_check': reasoning_content_check,
'response_check': content_check,
'reasoning': "",
'response': "",
'prompt': prompt,
'prompt_check': prompt_check,
'model_params': default_params
})
return None
content_check, reasoning_content_check = await self.model_v3.generate_response(prompt_check)
print(f"\033[1;32m[读空气]\033[0m 读空气结果为{content_check}")
if 'yes' not in content_check.lower() and random.random() < 0.3:
self._save_to_db(
message=message,
sender_name=sender_name,
prompt=prompt,
prompt_check=prompt_check,
content="",
content_check=content_check,
reasoning_content="",
reasoning_content_check=reasoning_content_check
)
return None
response = await loop.run_in_executor(None, create_completion)
# 生成回复
content, reasoning_content = await model.generate_response(prompt)
# 检查响应内容
if not response:
print("请求未返回任何内容")
return None
if not response.choices or not response.choices[0].message.content:
print("请求返回的内容无效:", response)
return None
content = response.choices[0].message.content
# 获取推理内容
reasoning_content = ""
if hasattr(response.choices[0].message, "reasoning"):
reasoning_content = response.choices[0].message.reasoning or reasoning_content
elif hasattr(response.choices[0].message, "reasoning_content"):
reasoning_content = response.choices[0].message.reasoning_content or reasoning_content
# 保存到数据库
self._save_to_db(
message=message,
sender_name=sender_name,
prompt=prompt,
prompt_check=prompt_check,
content=content,
content_check=content_check if global_config.enable_kuuki_read else "",
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):
"""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': model_name,
'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,
'model_params': default_params
'prompt_check': prompt_check
})
return content
async def _generate_r1_response(self, message: Message) -> Optional[str]:
"""使用 DeepSeek-R1 模型生成回复"""
if global_config.API_USING == "deepseek":
return await self._generate_base_response(
message,
global_config.DEEPSEEK_MODEL_R1,
{"temperature": 0.7, "max_tokens": 1024}
)
else:
return await self._generate_base_response(
message,
global_config.SILICONFLOW_MODEL_R1,
{"temperature": 0.7, "max_tokens": 1024}
)
async def _generate_v3_response(self, message: Message) -> Optional[str]:
"""使用 DeepSeek-V3 模型生成回复"""
if global_config.API_USING == "deepseek":
return await self._generate_base_response(
message,
global_config.DEEPSEEK_MODEL_V3,
{"temperature": 0.8, "max_tokens": 1024}
)
else:
return await self._generate_base_response(
message,
global_config.SILICONFLOW_MODEL_V3,
{"temperature": 0.8, "max_tokens": 1024}
)
async def _generate_r1_distill_response(self, message: Message) -> Optional[str]:
"""使用 DeepSeek-R1-Distill-Qwen-32B 模型生成回复"""
return await self._generate_base_response(
message,
global_config.SILICONFLOW_MODEL_R1_DISTILL,
{"temperature": 0.7, "max_tokens": 1024}
)
async def _get_group_chat_context(self, message: Message) -> str:
"""获取群聊上下文"""
recent_messages = self.db.db.messages.find(
{"group_id": message.group_id}
).sort("time", -1).limit(15)
messages_list = list(recent_messages)[::-1]
group_chat = ""
for msg_dict in messages_list:
time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(msg_dict['time']))
display_name = msg_dict.get('user_nickname', f"用户{msg_dict['user_id']}")
content = msg_dict.get('processed_plain_text', msg_dict['plain_text'])
group_chat += f"[{time_str}] {display_name}: {content}\n"
return group_chat
async def _get_emotion_tags(self, content: str) -> List[str]:
"""提取情感标签"""
@@ -266,33 +130,12 @@ class LLMResponseGenerator:
输出：
'''
messages = [{"role": "user", "content": prompt}]
loop = asyncio.get_event_loop()
if global_config.API_USING == "deepseek":
model = global_config.DEEPSEEK_MODEL_V3
else:
model = global_config.SILICONFLOW_MODEL_V3
create_completion = partial(
self.client.chat.completions.create,
model=model,
messages=messages,
stream=False,
max_tokens=30,
temperature=0.6
)
response = await loop.run_in_executor(None, create_completion)
if response.choices[0].message.content:
# 确保返回的是列表格式
emotion_tag = response.choices[0].message.content.strip()
return [emotion_tag] # 将单个标签包装成列表返回
return ["neutral"] # 如果无法获取情感标签，返回默认值
content, _ = await self.model_v3.generate_response(prompt)
return [content.strip()] if content else ["neutral"]
except Exception as e:
print(f"获取情感标签时出错: {e}")
return ["neutral"] # 发生错误时返回默认值
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"""
@@ -300,10 +143,6 @@ class LLMResponseGenerator:
return None, []
emotion_tags = await self._get_emotion_tags(content)
processed_response = process_llm_response(content)
return processed_response, emotion_tags
# 创建全局实例
llm_response = LLMResponseGenerator()
return processed_response, emotion_tags