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v0.2修改了一些东西

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使概率配置生效
将一些模块解耦合
将组信息管理器合并到关系管理器，添加了可以全局调用的接口
精简了llm生成器的代码
精简了message代码
重写了回复后处理
This commit is contained in:
SengokuCola
2025-02-28 00:28:34 +08:00
parent 6938a97941
commit 870be0a426
14 changed files with 333 additions and 653 deletions
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256
src/plugins/chat/llm_generator.py
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@@ -14,8 +14,8 @@ from dotenv import load_dotenv
from .relationship_manager import relationship_manager
from ..schedule.schedule_generator import bot_schedule
from .prompt_builder import prompt_builder
from .config import llm_config
from .utils import get_embedding, split_into_sentences, process_text_with_typos
from .config import llm_config, global_config
from .utils import process_llm_response
# 获取当前文件的绝对路径
@@ -38,15 +38,20 @@ class LLMResponseGenerator:
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"""
# 使用随机数选择模型
# 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
rand = random.random()
if rand < 0.8: # 60%概率使用 R1
self.current_model_type = "r1"
elif rand < 0.5: # 20%概率使用 V3
self.current_model_type = "v3"
else: # 20%概率使用 R1-Distill
self.current_model_type = "r1_distill"
if rand < model_r1_probability:
self.current_model_type = 'r1'
elif rand < model_r1_probability + model_v3_probability:
self.current_model_type = 'v3'
else:
self.current_model_type = 'r1_distill' # 默认使用 R1-Distill
print(f"+++++++++++++++++麦麦{self.current_model_type}思考中+++++++++++++++++")
if self.current_model_type == 'r1':
model_response = await self._generate_r1_response(message)
@@ -64,18 +69,22 @@ class LLMResponseGenerator:
return model_response, emotion
async def _generate_r1_response(self, message: Message) -> Optional[str]:
"""使用 DeepSeek-R1 模型生成回复"""
# 获取群聊上下文
group_chat = await self._get_group_chat_context(message)
async def _generate_base_response(
self,
message: Message,
model_name: str,
model_params: Optional[Dict[str, Any]] = None
) -> Optional[str]:
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
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
# 构建prompt
prompt = prompt_builder._build_prompt(
message_txt=message.processed_plain_text,
sender_name=sender_name,
@@ -83,142 +92,75 @@ class LLMResponseGenerator:
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
}
# 更新参数
if model_params:
default_params.update(model_params)
def create_completion():
return self.client.chat.completions.create(
model="Pro/deepseek-ai/DeepSeek-R1",
messages=[{"role": "user", "content": prompt}],
stream=False,
max_tokens=1024
)
return self.client.chat.completions.create(**default_params)
loop = asyncio.get_event_loop()
response = await loop.run_in_executor(None, create_completion)
if response.choices[0].message.content:
content = response.choices[0].message.content
# 获取推理内容
reasoning_content = "模型思考过程：\n" + prompt
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
# 保存推理结果到数据库
self.db.db.reasoning_logs.insert_one({
'time': time.time(),
'group_id': message.group_id,
'user': sender_name,
'message': message.processed_plain_text,
'model': "DeepSeek-R1",
'reasoning': reasoning_content,
'response': content,
'prompt': prompt
})
else:
if not response.choices[0].message.content:
return None
content = response.choices[0].message.content
# 获取推理内容
reasoning_content = "模型思考过程：\n" + prompt
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.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': reasoning_content,
'response': content,
'prompt': prompt,
'model_params': default_params
})
return content
async def _generate_r1_response(self, message: Message) -> Optional[str]:
"""使用 DeepSeek-R1 模型生成回复"""
return await self._generate_base_response(
message,
"Pro/deepseek-ai/DeepSeek-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"""
# 获取群聊上下文
group_chat = await self._get_group_chat_context(message)
sender_name = message.user_nickname or f"用户{message.user_id}"
if relationship_manager.get_relationship(message.user_id):
relationship_value = relationship_manager.get_relationship(message.user_id).relationship_value
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
prompt = prompt_builder._build_prompt(message.processed_plain_text, sender_name, relationship_value, group_id=message.group_id)
messages = [{"role": "user", "content": prompt}]
loop = asyncio.get_event_loop()
create_completion = partial(
self.client.chat.completions.create,
model="Pro/deepseek-ai/DeepSeek-V3",
messages=messages,
stream=False,
max_tokens=1024,
temperature=0.8
return await self._generate_base_response(
message,
"Pro/deepseek-ai/DeepSeek-V3",
{"temperature": 0.8, "max_tokens": 1024}
)
response = await loop.run_in_executor(None, create_completion)
if response.choices[0].message.content:
content = response.choices[0].message.content
# 保存推理结果到数据库
self.db.db.reasoning_logs.insert_one({
'time': time.time(),
'group_id': message.group_id,
'user': sender_name,
'message': message.processed_plain_text,
'model': "DeepSeek-V3",
'reasoning': "V3模型无推理过程",
'response': content,
'prompt': prompt
})
return content
else:
print(f"[ERROR] V3 回复发送生成失败: {response}")
return None
async def _generate_r1_distill_response(self, message: Message) -> Optional[str]:
"""使用 DeepSeek-R1-Distill-Qwen-32B 模型生成回复"""
# 获取群聊上下文
group_chat = await self._get_group_chat_context(message)
sender_name = message.user_nickname or f"用户{message.user_id}"
if relationship_manager.get_relationship(message.user_id):
relationship_value = relationship_manager.get_relationship(message.user_id).relationship_value
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_builder._build_prompt(
message_txt=message.processed_plain_text,
sender_name=sender_name,
relationship_value=relationship_value,
group_id=message.group_id
return await self._generate_base_response(
message,
"deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
{"temperature": 0.7, "max_tokens": 1024}
)
def create_completion():
return self.client.chat.completions.create(
model="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
messages=[{"role": "user", "content": prompt}],
stream=False,
max_tokens=1024
)
loop = asyncio.get_event_loop()
response = await loop.run_in_executor(None, create_completion)
if response.choices[0].message.content:
content = response.choices[0].message.content
# 获取推理内容
reasoning_content = "模型思考过程：\n" + prompt
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
# 保存推理结果到数据库
self.db.db.reasoning_logs.insert_one({
'time': time.time(),
'group_id': message.group_id,
'user': sender_name,
'message': message.processed_plain_text,
'model': "DeepSeek-R1-Distill",
'reasoning': reasoning_content,
'response': content,
'prompt': prompt
})
else:
return None
return content
async def _get_group_chat_context(self, message: Message) -> str:
"""获取群聊上下文"""
@@ -271,46 +213,14 @@ class LLMResponseGenerator:
print(f"获取情感标签时出错: {e}")
return ["neutral"] # 发生错误时返回默认值
async def _process_response(self, content: str) -> Tuple[Union[str, List[str]], List[str]]:
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, []
# 检查回复是否过长（超过200个字符）
if len(content) > 200:
print(f"回复过长 ({len(content)} 字符)，返回默认回复")
return "麦麦不知道哦", ["angry"]
emotion_tags = await self._get_emotion_tags(content)
# 添加错别字和处理标点符号
processed_response = process_text_with_typos(content)
# 处理长消息
if len(processed_response) > 5:
sentences = split_into_sentences(processed_response)
print(f"分割后的句子: {sentences}")
messages = []
current_message = ""
for sentence in sentences:
if len(current_message) + len(sentence) <= 5:
current_message += ' '
current_message += sentence
else:
if current_message:
messages.append(current_message.strip())
current_message = sentence
if current_message:
messages.append(current_message.strip())
# 检查分割后的消息数量是否过多（超过3条）
if len(messages) > 3:
print(f"分割后消息数量过多 ({len(messages)} 条)，返回默认回复")
return "麦麦不知道哦", ["angry"]
return messages, emotion_tags
processed_response = process_llm_response(content)
return processed_response, emotion_tags