新增思维流
This commit is contained in:
SengokuCola
109
src/think_flow_demo/current_mind.py
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109
src/think_flow_demo/current_mind.py
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from .outer_world import outer_world
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import asyncio
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from .offline_llm import LLMModel
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class CuttentState:
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def __init__(self):
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self.willing = 0
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self.mood = 'TODO'
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self.current_state_info = ""
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class InnerWorld:
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def __init__(self):
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self.inner_world_info = ""
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self.current_state : CuttentState = CuttentState()
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class BRain:
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def __init__(self):
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self.current_mind = ""
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self.past_mind = []
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self.inner_world = InnerWorld()
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self.llm_model = LLMModel("Pro/Qwen/Qwen2.5-7B-Instruct")
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if not self.current_mind:
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self.current_mind = "你是麦麦,曾经是一个学习地质的女大学生，现在学习心理学和脑科学，你会刷贴吧,你现在正在浏览qq群，想着qq群里发生的事情，人们在谈论什么，以及和你有什么关系，你应该怎样在qq群中回复和发言。现在请麦麦你继续思考"
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async def brain_start_working(self):
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while True:
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await self.do_a_thinking()
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await asyncio.sleep(10)
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async def do_a_thinking(self):
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print("麦麦脑袋转起来了")
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current_thinking = self.current_mind
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outer_world_info = self.build_outer_world_info()
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inner_world_info = self.build_inner_world_info(self.inner_world)
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current_state_info = self.build_current_state_info(self.inner_world.current_state)
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# prompt += f"这是你当前的脑内状态{current_state_info}\n\n"
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prompt = f"这是你刚刚接触的内容：{outer_world_info}\n\n"
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# prompt += f"这是你当前的脑内状态{inner_world_info}\n\n"
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prompt += f"这是你之前的想法{current_thinking}\n\n"
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prompt += f"现在你接下去继续思考，产生新的想法，不要分点输出，输出连贯的内心独白，不要太长，注重当前的思考:"
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reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
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self.update_current_mind(reponse)
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self.current_mind = reponse
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print(f"麦麦的脑内状态：{self.current_mind}")
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async def do_after_reply(self,reply_content,chat_talking_prompt):
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print("麦麦脑袋转起来了")
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current_thinking = self.current_mind
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outer_world_info = self.build_outer_world_info()
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inner_world_info = self.build_inner_world_info(self.inner_world)
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current_state_info = self.build_current_state_info(self.inner_world.current_state)
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# prompt += f"这是你当前的脑内状态{current_state_info}\n\n"
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prompt = f"这是你刚刚接触的内容：{outer_world_info}\n\n"
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# prompt += f"这是你当前的脑内状态{inner_world_info}\n\n"
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prompt += f"这是你之前想要回复的内容：{chat_talking_prompt}\n\n"
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prompt += f"这是你之前的想法{current_thinking}\n\n"
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prompt += f"这是你自己刚刚回复的内容{reply_content}\n\n"
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prompt += f"现在你接下去继续思考，产生新的想法，不要分点输出，输出连贯的内心独白:"
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reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
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self.update_current_mind(reponse)
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self.current_mind = reponse
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print(f"麦麦的脑内状态：{self.current_mind}")
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def update_current_state_from_current_mind(self):
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self.inner_world.current_state.willing += 0.01
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def build_current_state_info(self,current_state):
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current_state_info = current_state.current_state_info
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return current_state_info
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def build_inner_world_info(self,inner_world):
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inner_world_info = inner_world.inner_world_info
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return inner_world_info
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def build_outer_world_info(self):
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outer_world_info = outer_world.outer_world_info
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return outer_world_info
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def update_current_mind(self,reponse):
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self.past_mind.append(self.current_mind)
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self.current_mind = reponse
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brain = BRain()
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async def main():
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# 创建两个任务
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brain_task = asyncio.create_task(brain.brain_start_working())
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outer_world_task = asyncio.create_task(outer_world.open_eyes())
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# 等待两个任务
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await asyncio.gather(brain_task, outer_world_task)
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if __name__ == "__main__":
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asyncio.run(main())
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123
src/think_flow_demo/offline_llm.py
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123
src/think_flow_demo/offline_llm.py
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import asyncio
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import os
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import time
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from typing import Tuple, Union
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import aiohttp
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import requests
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from src.common.logger import get_module_logger
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logger = get_module_logger("offline_llm")
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class LLMModel:
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def __init__(self, model_name="Pro/deepseek-ai/DeepSeek-V3", **kwargs):
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self.model_name = model_name
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self.params = kwargs
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self.api_key = os.getenv("SILICONFLOW_KEY")
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self.base_url = os.getenv("SILICONFLOW_BASE_URL")
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if not self.api_key or not self.base_url:
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raise ValueError("环境变量未正确加载：SILICONFLOW_KEY 或 SILICONFLOW_BASE_URL 未设置")
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logger.info(f"API URL: {self.base_url}") # 使用 logger 记录 base_url
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def generate_response(self, prompt: str) -> Union[str, Tuple[str, str]]:
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"""根据输入的提示生成模型的响应"""
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headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
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# 构建请求体
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data = {
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"model": self.model_name,
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"messages": [{"role": "user", "content": prompt}],
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"temperature": 0.5,
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**self.params,
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}
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# 发送请求到完整的 chat/completions 端点
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api_url = f"{self.base_url.rstrip('/')}/chat/completions"
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logger.info(f"Request URL: {api_url}") # 记录请求的 URL
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max_retries = 3
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base_wait_time = 15 # 基础等待时间（秒）
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for retry in range(max_retries):
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try:
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response = requests.post(api_url, headers=headers, json=data)
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if response.status_code == 429:
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wait_time = base_wait_time * (2**retry) # 指数退避
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logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...")
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time.sleep(wait_time)
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continue
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response.raise_for_status() # 检查其他响应状态
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result = response.json()
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if "choices" in result and len(result["choices"]) > 0:
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content = result["choices"][0]["message"]["content"]
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reasoning_content = result["choices"][0]["message"].get("reasoning_content", "")
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return content, reasoning_content
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return "没有返回结果", ""
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except Exception as e:
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if retry < max_retries - 1: # 如果还有重试机会
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wait_time = base_wait_time * (2**retry)
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logger.error(f"[回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}")
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time.sleep(wait_time)
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else:
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logger.error(f"请求失败: {str(e)}")
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return f"请求失败: {str(e)}", ""
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logger.error("达到最大重试次数，请求仍然失败")
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return "达到最大重试次数，请求仍然失败", ""
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async def generate_response_async(self, prompt: str) -> Union[str, Tuple[str, str]]:
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"""异步方式根据输入的提示生成模型的响应"""
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headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
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# 构建请求体
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data = {
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"model": self.model_name,
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"messages": [{"role": "user", "content": prompt}],
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"temperature": 0.5,
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**self.params,
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}
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# 发送请求到完整的 chat/completions 端点
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api_url = f"{self.base_url.rstrip('/')}/chat/completions"
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logger.info(f"Request URL: {api_url}") # 记录请求的 URL
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max_retries = 3
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base_wait_time = 15
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async with aiohttp.ClientSession() as session:
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for retry in range(max_retries):
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try:
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async with session.post(api_url, headers=headers, json=data) as response:
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if response.status == 429:
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wait_time = base_wait_time * (2**retry) # 指数退避
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logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...")
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await asyncio.sleep(wait_time)
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continue
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response.raise_for_status() # 检查其他响应状态
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result = await response.json()
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if "choices" in result and len(result["choices"]) > 0:
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content = result["choices"][0]["message"]["content"]
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reasoning_content = result["choices"][0]["message"].get("reasoning_content", "")
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return content, reasoning_content
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return "没有返回结果", ""
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except Exception as e:
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if retry < max_retries - 1: # 如果还有重试机会
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wait_time = base_wait_time * (2**retry)
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logger.error(f"[回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}")
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await asyncio.sleep(wait_time)
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else:
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logger.error(f"请求失败: {str(e)}")
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return f"请求失败: {str(e)}", ""
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logger.error("达到最大重试次数，请求仍然失败")
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return "达到最大重试次数，请求仍然失败", ""
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111
src/think_flow_demo/outer_world.py
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111
src/think_flow_demo/outer_world.py
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#定义了来自外部世界的信息
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import asyncio
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from datetime import datetime
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from src.common.database import db
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from .offline_llm import LLMModel
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#存储一段聊天的大致内容
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class Talking_info:
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def __init__(self,chat_id):
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self.chat_id = chat_id
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self.talking_message = []
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self.talking_message_str = ""
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self.talking_summary = ""
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self.last_message_time = None # 记录最新消息的时间
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self.llm_summary = LLMModel("Pro/Qwen/Qwen2.5-7B-Instruct")
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def update_talking_message(self):
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#从数据库取最近30条该聊天流的消息
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messages = db.messages.find({"chat_id": self.chat_id}).sort("time", -1).limit(15)
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self.talking_message = []
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self.talking_message_str = ""
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for message in messages:
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self.talking_message.append(message)
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self.talking_message_str += message["detailed_plain_text"]
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async def update_talking_summary(self,new_summary=""):
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#基于已经有的talking_summary，和新的talking_message，生成一个summary
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prompt = f"聊天内容：{self.talking_message_str}\n\n"
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prompt += f"以上是群里在进行的聊天，请你对这个聊天内容进行总结，总结内容要包含聊天的大致内容，以及聊天中的一些重要信息，记得不要分点，不要太长，精简的概括成一段文本\n\n"
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prompt += f"总结："
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self.talking_summary, reasoning_content = await self.llm_summary.generate_response_async(prompt)
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class SheduleInfo:
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def __init__(self):
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self.shedule_info = ""
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class OuterWorld:
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def __init__(self):
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self.talking_info_list = [] #装的一堆talking_info
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self.shedule_info = "无日程"
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self.interest_info = "麦麦你好"
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self.outer_world_info = ""
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self.start_time = int(datetime.now().timestamp())
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self.llm_summary = LLMModel("Qwen/Qwen2.5-32B-Instruct")
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async def open_eyes(self):
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while True:
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await asyncio.sleep(60)
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print("更新所有聊天信息")
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await self.update_all_talking_info()
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print("更新outer_world_info")
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await self.update_outer_world_info()
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print(self.outer_world_info)
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for talking_info in self.talking_info_list:
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# print(talking_info.talking_message_str)
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# print(talking_info.talking_summary)
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pass
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async def update_outer_world_info(self):
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print("总结当前outer_world_info")
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all_talking_summary = ""
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for talking_info in self.talking_info_list:
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all_talking_summary += talking_info.talking_summary
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prompt = f"聊天内容：{all_talking_summary}\n\n"
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prompt += f"以上是多个群里在进行的聊天，请你对所有聊天内容进行总结，总结内容要包含聊天的大致内容，以及聊天中的一些重要信息，记得不要分点，不要太长，精简的概括成一段文本\n\n"
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prompt += f"总结："
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self.outer_world_info, reasoning_content = await self.llm_summary.generate_response_async(prompt)
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async def update_talking_info(self,chat_id):
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# 查找现有的talking_info
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talking_info = next((info for info in self.talking_info_list if info.chat_id == chat_id), None)
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if talking_info is None:
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print("新聊天流")
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talking_info = Talking_info(chat_id)
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talking_info.update_talking_message()
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await talking_info.update_talking_summary()
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self.talking_info_list.append(talking_info)
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else:
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print("旧聊天流")
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talking_info.update_talking_message()
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await talking_info.update_talking_summary()
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async def update_all_talking_info(self):
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all_streams = db.chat_streams.find({})
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update_tasks = []
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for data in all_streams:
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stream_id = data.get("stream_id")
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# print(stream_id)
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last_active_time = data.get("last_active_time")
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if last_active_time > self.start_time or 1:
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update_tasks.append(self.update_talking_info(stream_id))
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# 并行执行所有更新任务
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if update_tasks:
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await asyncio.gather(*update_tasks)
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outer_world = OuterWorld()
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if __name__ == "__main__":
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asyncio.run(outer_world.open_eyes())
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