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Merge branch 'main-fix' into relationship

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This commit is contained in:
meng_xi_pan
2025-03-14 18:52:13 +08:00
parent a3927507dc 08499f747d
commit 3d1a58130d
11 changed files with 262 additions and 332 deletions
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51
src/plugins/chat/llm_generator.py
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@@ -25,30 +25,19 @@ class ResponseGenerator:
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_v3 = LLM_request(model=global_config.llm_normal, temperature=0.7, max_tokens=3000)
self.model_r1_distill = LLM_request(model=global_config.llm_reasoning_minor, temperature=0.7, max_tokens=3000)
self.model_v25 = LLM_request(model=global_config.llm_normal_minor, temperature=0.7, max_tokens=3000)
self.current_model_type = "r1" # 默认使用 R1
async def generate_response(
self, message: MessageThinking
) -> 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"
current_model = self.model_r1
elif (
rand
< global_config.MODEL_R1_PROBABILITY + global_config.MODEL_V3_PROBABILITY
):
elif rand < global_config.MODEL_R1_PROBABILITY + global_config.MODEL_V3_PROBABILITY:
self.current_model_type = "v3"
current_model = self.model_v3
else:
@@ -57,24 +46,20 @@ class ResponseGenerator:
logger.info(f"{global_config.BOT_NICKNAME}{self.current_model_type}思考中")
model_response = await self._generate_response_with_model(
message, current_model
)
model_response = await self._generate_response_with_model(message, current_model)
raw_content = model_response
# print(f"raw_content: {raw_content}")
# print(f"model_response: {model_response}")
if model_response:
logger.info(f'{global_config.BOT_NICKNAME}的回复是：{model_response}')
logger.info(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
async def _generate_response_with_model(
self, message: MessageThinking, 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 = ""
if message.chat_stream.user_info.user_cardname and message.chat_stream.user_info.user_nickname:
@@ -219,7 +204,7 @@ class ResponseGenerator:
return None, []
processed_response = process_llm_response(content)
# print(f"得到了处理后的llm返回{processed_response}")
return processed_response
@@ -229,13 +214,11 @@ class InitiativeMessageGenerate:
def __init__(self):
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.model_r1_distill = LLM_request(model=global_config.llm_reasoning_minor, temperature=0.7)
def gen_response(self, message: Message):
topic_select_prompt, dots_for_select, prompt_template = (
prompt_builder._build_initiative_prompt_select(message.group_id)
topic_select_prompt, dots_for_select, prompt_template = prompt_builder._build_initiative_prompt_select(
message.group_id
)
content_select, reasoning = self.model_v3.generate_response(topic_select_prompt)
logger.debug(f"{content_select} {reasoning}")
@@ -247,16 +230,12 @@ class InitiativeMessageGenerate:
return None
else:
return None
prompt_check, memory = prompt_builder._build_initiative_prompt_check(
select_dot[1], prompt_template
)
prompt_check, memory = prompt_builder._build_initiative_prompt_check(select_dot[1], prompt_template)
content_check, reasoning_check = self.model_v3.generate_response(prompt_check)
logger.info(f"{content_check} {reasoning_check}")
if "yes" not in content_check.lower():
return None
prompt = prompt_builder._build_initiative_prompt(
select_dot, prompt_template, memory
)
prompt = prompt_builder._build_initiative_prompt(select_dot, prompt_template, memory)
content, reasoning = self.model_r1.generate_response_async(prompt)
logger.debug(f"[DEBUG] {content} {reasoning}")
return content