The framework design of the aios kernel has been basically completed, as well as the key logic code centered on workflow.
This commit is contained in:
Liu Zhicong
@@ -1 +1,3 @@
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TODO
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TODO:
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Embading Pipline
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Knowlege Base
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@@ -0,0 +1,7 @@
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from .environment import environment,environment_event
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from .agent import agent_msg,ai_agent,ai_agent_templete
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from .compute_kernel import compute_kernel,compute_task
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from .compute_node import compute_node,local_compute_node
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from .open_ai_node import open_ai_compute_node
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from .role import ai_role,ai_role_group
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from .workflow import ai_workflow
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+59
-29
@@ -1,58 +1,88 @@
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from typing import Optional
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import logging
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import llm_kernel,llm_work_task
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logger = logging.getLogger(__name__)
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class agent_msg:
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def __init__(self) -> None:
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self.sender = None
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self.target = None
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self.body = None
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def get_msg_id(self) -> str:
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pass
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def get_sender(self) -> str:
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return self.sender
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def get_target(self) -> str:
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return self.target
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# return workflow_name, role_name, session_id
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def parser_target(self,target:str) -> None:
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pass
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class agent_prompt:
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def __init__(self) -> None:
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pass
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def as_str()->str:
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def as_str(self)->str:
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pass
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class agent_chat_session:
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def append(self,prompt) -> None:
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pass
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# chat session store the chat history between owner and agent
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# chat session might be large, so can read / write at stream mode.
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class ai_chat_session:
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def __init__(self) -> None:
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self.llm_model_name = None
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self.llm_instance = None
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self.max_token_size = 0
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self.chat_msg_list = None
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self.enable_function = True
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pass
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def chat(self,message:str) -> None:
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def get_owner_id(self) -> str:
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pass
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# Key functions, let the AI Agent try to run.
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def completion(self)->llm_work_task:
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if self.llm_instance is None:
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self.llm_instance = llm_kernel.craete(self.llm_model_name)
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if self.llm_instance is None:
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logger.fatal(f"cann't get llm_kerenel : {self.llm_model_name}")
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return
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llm_work_task = self.llm_instance.completion(self._get_prompt(),self.max_token_size)
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return llm_work_task
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def _get_prompt(str) -> str:
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def append_post(self,msg:agent_msg) -> None:
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"""append msg to session, msg is post from session (owner => msg.target)"""
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pass
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def append_recv(self,msg:agent_msg) -> None:
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"""append msg to session, msg is recv from msg'sender (msg.sender => owner)"""
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pass
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def attach_event_handler(self,handler) -> None:
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"""chat session changed event handler"""
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pass
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#TODO : add iterator interface for read chat history
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class ai_agent_templete:
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def __init__(self) -> None:
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pass
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class ai_agent:
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def __init__(self) -> None:
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pass
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self.chat_sessions = None
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self.llm_model_name = None
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self.max_token_size = 0
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self.instance_id = None
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self.template_id = None
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def get_chat_session(self,chat_user_name:str,session_id:Optional[str]) -> agent_chat_session:
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def get_id(self) -> str:
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return self.instance_id
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def get_template_id(self) -> str:
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return self.template_id
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def get_chat_session_for_msg(self,msg:agent_msg) -> ai_chat_session:
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pass
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def get_chat_session(self,sender:str,session_id:str) -> ai_chat_session:
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pass
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def get_llm_model_name(self) -> str:
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return self.llm_model_name
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def get_max_token_size(self) -> int:
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return self.max_token_size
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#chat_session = agent.get_default_chat_session("master");
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#chat_session.chat("给我讲一个英文笑话!");
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#chat_session.completion();
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#print(chat_session.last_msg());
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@@ -1,20 +0,0 @@
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from abc import ABC, abstractmethod
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from typing import Any
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from .agent import agent,agent_msg
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class ai_role:
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def __init__(self) -> None:
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pass
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class agent_group:
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def __init__(self) -> None:
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self.roles = None
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pass
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def add_role(self,role_name:str,agent_id:str) -> None:
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pass
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def send_msg(self,role_name:str,msg:agent_msg) -> None:
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pass
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@@ -0,0 +1,32 @@
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class ai_function:
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def __init__(self) -> None:
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self.intro : str = None
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def load_from_config(self,config:dict) -> bool:
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pass
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def is_local(self) -> bool:
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pass
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def is_in_zone(self) -> bool:
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pass
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def is_readyonly(self) -> bool:
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pass
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def get_intro(self) -> str:
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return self.intro
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async def execute(self):
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pass
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# call chain is a combination of ai_function,group of ai_function.
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class call_chain:
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def __init__(self) -> None:
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pass
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def load_from_config(self,config:dict) -> bool:
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pass
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async def execute(self):
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pass
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@@ -1,6 +1,14 @@
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# aiso shell like bash of linux
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from .workflow import ai_workflow
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from agent import agent_msg
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class aios_shell:
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def send_msg(self,msg:agent_msg,target:ai_workflow) -> None:
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def __init__(self,username:str) -> None:
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pass
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async def send_msg(self,msg:str,target_workflow:str) -> str:
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pass
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async def install_workflow(self,workflow_id:ai_workflow) -> None:
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pass
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@@ -1,5 +1,12 @@
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from compute_node import compute_node
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from abc import ABC, abstractmethod
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from typing import Optional
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import logging
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import asyncio
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from .agent import agent_prompt
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from .compute_node import compute_node
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logger = logging.getLogger(__name__)
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# How to dispatch different computing tasks (some tasks may contain a large amount of state for correct execution)
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# to suitable computing nodes, achieving a balance of speed, cost, and power consumption,
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@@ -11,19 +18,60 @@ class compute_task(ABC):
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class compute_kernel:
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_instance = None
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def __new__(cls):
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if cls._instance is None:
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cls._instance = super(compute_kernel, cls).__new__(cls)
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return cls._instance
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def __init__(self) -> None:
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self.task_queue = []
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self.is_start = False
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pass
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def run(self,task:compute_task) -> None:
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# check there is compute node can support this task
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if self.is_task_support(task) is False:
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logger.error(f"task {task.display()} is not support by any compute node")
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return
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# add task to working_queue
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pass
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self.task_queue.append(task)
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def start(self):
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if self.is_start is True:
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logger.warn("compute_kernel is already start")
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return
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self.is_start = True
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async def _run_task_loop():
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while True:
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task = self.task_queue.pop(0)
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c_node:compute_node= await self._schedule(task)
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c_node.push_task(task)
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asyncio.create_task(_run_task_loop())
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async def _schedule(self,task) -> compute_node:
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pass
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def add_compute_node(self,node:compute_node):
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pass
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def disable_compute_node(self,):
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pass
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pass
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def is_task_support(self,task:compute_task) -> bool:
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pass
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# friendly interface for use:
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def llm_completion(self,prompt:agent_prompt,mode_name:Optional[str] = None,max_token:int = 0) -> compute_task:
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# craete a llm_work_task ,push on queue's end
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# then task_schedule would run this task.(might schedule some work_task to another host)
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pass
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async def do_llm_completion(self,prompt:agent_prompt,mode_name:Optional[str] = None,max_token:int = 0) -> str:
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pass
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@@ -1,13 +1,48 @@
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from abc import ABC, abstractmethod
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from .compute_kernel import compute_task
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class compute_node(ABC):
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@abstractmethod
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async def push_task(self,task:compute_task,proiority:int = 0):
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pass
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async def remove_task(self,task_id:str):
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pass
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@abstractmethod
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def get_task_state(self,task_id:str):
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pass
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@abstractmethod
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def display(self) -> str:
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pass
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@abstractmethod
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def get_capacity(self):
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pass
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@abstractmethod
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def is_support(self,task_type:str) -> bool:
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pass
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@abstractmethod
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def is_local(self) -> bool:
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pass
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@abstractmethod
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def is_trusted(self) -> bool:
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return True
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def get_fee_type(self) -> str:
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return "free"
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class local_compute_node(compute_node):
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def display(self) -> str:
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return super().display()
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def is_local(self) -> bool:
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return True
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@@ -1,12 +1,23 @@
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# basic environment class
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# we have some built-in environment: Calender(include timer),Home(connect to IoT device in your home), ,KnwoledgeBase,FileSystem,
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from abc import ABC, abstractmethod
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from typing import Callable
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from .agent import agent_msg
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class environment_event(ABC):
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@abstractmethod
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def display(self) -> str:
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pass
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class environment:
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def __init__(self) -> None:
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pass
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def event_to_msg(self,) -> environment_event:
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pass
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def get_id(self) -> str:
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pass
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def attach_event_handler(self,event_id:str,handler:Callable) -> None:
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pass
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@@ -1,14 +0,0 @@
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class llm_work_task:
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def __init__(self) -> None:
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pass
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class llm_kernel:
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def __init__(self) -> None:
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pass
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def completion(self,prompt:str,max_token:int) -> llm_work_task:
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# craete a llm_work_task ,push on queue's end
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# then task_schedule would run this task.(might schedule some work_task to another host)
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pass
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@@ -1,6 +1,8 @@
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from compute_node import compute_node
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from .compute_node import compute_node
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class open_ai_compute_node(compute_node):
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def display(self) -> str:
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return super().display()
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@@ -0,0 +1,25 @@
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from .agent import ai_agent
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class ai_role:
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def __init__(self) -> None:
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self.agent_instance_id : str = None
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self.role_name : str = None
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self.agent : ai_agent = None
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self.introduce : str = None
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def load_from_config(self,config:dict) -> bool:
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pass
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def get_intro(self) -> str:
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return self.introduce
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def get_name(self) -> str:
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return self.role_name
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class ai_role_group:
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def __init__(self) -> None:
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self.roles : dict[str,str]= None
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pass
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+182
-14
@@ -1,38 +1,206 @@
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import environment
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import agent_prompt,agent_msg
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import logging
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import asyncio
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from typing import Optional,Tuple
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from .environment import environment,environment_event
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from .agent import agent_prompt,agent_msg,ai_chat_session
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from .role import ai_role
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from .ai_function import call_chain
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from .compute_kernel import compute_kernel
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logger = logging.getLogger(__name__)
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class ai_message_filter:
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def __init__(self) -> None:
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pass
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def select(self,msg:agent_msg) -> ai_role:
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pass
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class ai_workflow:
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def __init__(self) -> None:
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self.rule_prompt : agent_prompt = None
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self.workflow_config = None
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self.context = None
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self.role_group = None
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self.input_filter : ai_message_filter= None
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self.msg_queue = []
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self.connected_environment = {}
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def load_from_disk(self,config_path:str,context_dir_path) -> int:
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pass
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def send_msg(self,msg:agent_msg,target_group:str = None) -> None:
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if target_group is None:
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target_group = self.get_default_group()
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#workflow is asynchronous.
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# When processing one message, it can process another message at the same time.
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# chatsession is synchronous, it has to wait for the previous message to finish processing before it can process the next message.
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# Therefore, post a message needs to specify the session_id explicitly, if not specified it will be automatically created by workflow.
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def post_msg(self,msg:agent_msg) -> None:
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self.msg_queue.append(msg)
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return
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async def send_msg(self,msg:agent_msg) -> str:
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pass
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async def run(self):
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# TODO add tracking design of msg processing
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while True:
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the_msg = await self._pop_msg()
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chatsession:ai_chat_session = self._get_chat_session_for_msg(the_msg)
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if chatsession is None:
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logger.error(f"get_chat_session_for_msg return None for :{the_msg}")
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continue
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chatsession.append_recv(the_msg)
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async def _process_msg(msg:agent_msg,the_role) -> None:
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# prompt generat progress is most important part of workflow(app) develope
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prompt = agent_prompt()
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prompt.append(the_role.get_prompt())
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prompt.append(self.get_workflow_rule_prompt())
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prompt.append(self._get_function_prompt(the_role.get_name()))
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prompt.append(self._get_knowlege_prompt(the_role.get_name()))
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prompt.append(await self._get_prompt_from_session(chatsession,the_role.get_name())) # chat context
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result = await compute_kernel().do_llm_completion(prompt,the_role.agent.get_llm_model_name(),the_role.agent.get_max_token_size())
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final_result = result
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result_type : str = self._get_llm_result_type(result)
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is_ignore = False
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match result_type:
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case "function":
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callchain:call_chain = self._parse_function_call_chain(result)
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resp = await callchain.exec()
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if callchain.have_result():
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# generator proc resp prompt with WAITING state
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proc_resp_prompt:agent_prompt = self._get_resp_prompt(resp,msg,the_role,prompt,chatsession)
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final_result = await compute_kernel().do_llm_completion(proc_resp_prompt,the_role.agent.get_llm_model_name(),the_role.agent.get_max_token_size())
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return final_result
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case "send_message":
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# send message to other / sub workflow
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next_msg:agent_msg = self._parse_to_msg(result)
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if next_msg is not None:
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# TODO: Next Target can be another role in workflow
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next_workflow:ai_workflow = self.get_workflow(next_msg.get_target())
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inner_chat_session = the_role.agent.get_chat_session(next_msg.get_target(),next_msg.get_session_id())
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inner_chat_session.append_post(next_msg)
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resp = await next_workflow.send_msg(next_msg)
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inner_chat_session.append_recv(resp)
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# generator proc resp prompt with WAITING state
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proc_resp_prompt:agent_prompt = self._get_resp_prompt(resp,msg,the_role,prompt,chatsession)
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final_result = await compute_kernel().do_llm_completion(proc_resp_prompt,the_role.agent.get_llm_model_name(),the_role.agent.get_max_token_size())
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return final_result
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case "post_message":
|
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# post message to other / sub workflow
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next_msg:agent_msg = self._parse_to_msg(result)
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if next_msg is not None:
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next_workflow:ai_workflow = self.get_workflow(next_msg.get_target())
|
||||
inner_chat_session = the_role.agent.get_chat_session(next_msg.get_target(),next_msg.get_session_id())
|
||||
inner_chat_session.append_post(next_msg)
|
||||
next_workflow.post_msg(next_msg)
|
||||
|
||||
case "ignore":
|
||||
is_ignore = True
|
||||
|
||||
if is_ignore is not True:
|
||||
# TODO : how to get inner chat session?
|
||||
inner_chat_session = the_role.agent.get_chat_session_for_msg(msg)
|
||||
if inner_chat_session is not None:
|
||||
inner_chat_session.append_input(msg)
|
||||
inner_chat_session.append_result(final_result)
|
||||
|
||||
return result
|
||||
|
||||
async def _workflow_process_msg(msg:agent_msg) -> None:
|
||||
final_result = None
|
||||
if self.input_filter is not None:
|
||||
select_role = self.input_filter.select(msg)
|
||||
if select_role is not None:
|
||||
result = await _process_msg(msg,select_role)
|
||||
if result is None:
|
||||
logger.error(f"_process_msg return None for :{msg}")
|
||||
return
|
||||
if chatsession is not None:
|
||||
chatsession.append_post(result)
|
||||
final_result = result
|
||||
|
||||
else:
|
||||
results = {}
|
||||
for this_role in self.role_group.roles:
|
||||
a_result = asyncio.create_task(_process_msg(msg,this_role))
|
||||
results[this_role.get_name()] = a_result
|
||||
|
||||
# merge result from all roles
|
||||
# TODO: one input msg can have multiple result msg, at this while ,we only support one result msg
|
||||
final_result:agent_msg = self._merge_msg_result(results)
|
||||
if chatsession is not None:
|
||||
chatsession.append_post(final_result)
|
||||
|
||||
if final_result is not None:
|
||||
# TODO post message to source
|
||||
pass
|
||||
|
||||
asyncio.create_task(_workflow_process_msg(the_msg))
|
||||
|
||||
async def _pop_msg(self) -> agent_msg:
|
||||
pass
|
||||
|
||||
def run(self):
|
||||
|
||||
def _get_chat_session_for_msg(self,msg:agent_msg) -> ai_chat_session:
|
||||
pass
|
||||
|
||||
def _pop_msg(self) -> Tuple[agent_msg,str]:
|
||||
async def _get_prompt_from_session(self,chatsession:ai_chat_session,role_name:str) -> agent_prompt:
|
||||
pass
|
||||
|
||||
def _get_msg_queue(self,session_id:str):
|
||||
pass
|
||||
|
||||
def get_default_group(self) -> agent_group:
|
||||
def _merge_msg_result(self,results:dict) -> agent_msg:
|
||||
pass
|
||||
|
||||
def get_group(self,group_name:str) -> agent_group:
|
||||
def _get_function_prompt(self,role_name:str) -> agent_prompt:
|
||||
pass
|
||||
|
||||
def _get_knowlege_prompt(self,role_name:str) -> agent_prompt:
|
||||
pass
|
||||
|
||||
def _get_resp_prompt(self,resp:str,msg:agent_msg,role:ai_role,prompt:agent_prompt,chatsession:ai_chat_session) -> agent_prompt:
|
||||
pass
|
||||
|
||||
def get_workflow_rule_prompt(self) -> agent_prompt:
|
||||
return self.rule_prompt
|
||||
|
||||
def _get_llm_result_type(self,llm_resp_str:str) -> str:
|
||||
pass
|
||||
|
||||
def get_inner_environment(self) -> environment:
|
||||
def _parse_function_call_chain(self,llm_resp_str) -> call_chain:
|
||||
pass
|
||||
|
||||
def _parse_to_msg(self,llm_resp_str) -> agent_msg:
|
||||
pass
|
||||
|
||||
def get_workflow(self,workflow_name:str) -> ai_workflow:
|
||||
"""get workflow from known workflow list or sub workflow list"""
|
||||
pass
|
||||
|
||||
|
||||
def _env_event_to_msg(self,env_event:environment_event) -> agent_msg:
|
||||
pass
|
||||
|
||||
def get_inner_environment(self,env_id:str) -> environment:
|
||||
pass
|
||||
|
||||
def connect_to_environment(self,env:environment) -> None:
|
||||
pass
|
||||
the_env = self.connected_environment.get(env.get_id())
|
||||
if the_env is None:
|
||||
self.connected_environment[env.get_id()] = env
|
||||
def _env_msg_handler(env_event:environment_event) -> None:
|
||||
the_msg:agent_msg= self._env_event_to_msg(env_event)
|
||||
self.post_msg(the_msg)
|
||||
|
||||
# register all event handler
|
||||
the_env.attach_event_handler(None,_env_msg_handler)
|
||||
else:
|
||||
logger.warn(f"environment {env.get_id()} already connected!")
|
||||
|
||||
|
||||
Reference in New Issue
Block a user