Merge pull request #33 from waterflier/main
Complete basic ai kernel frame codes
This commit is contained in:
fiatrete
GitHub
@@ -13,7 +13,14 @@ This project is still in its very early stages, and there may be significant cha
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## **Updates**
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I've just refactored Jarvis's code, leveraging the `functions` support from ChatGPT. As a result, Jarvis now responds much faster and has more stable selection for the functional module. I encourage you to checkout the updated code to experience these improvements firsthand.
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### 1. Adding Knowledge Base Infrastructure
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We're currently working on implementing the infrastructure for the Knowledge Base, aiming to enhance AI agent's access to Personal Data stored on Personal Servers. Leveraging these new features, we want to empower everyone to build their own personal homepage in the AI era using OpenDAN.
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### 2. Development Based on SourceDAO Contract
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We are in the process of developing the OpenDAN's DAO page, which is based on the SourceDAO contract. The future of OpenDAN should be determined by the community.
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### 3. Welcoming waterflier as a Core Contributor
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We're excited to officially invite waterflier to join as a core contributor to our community. We recognize and appreciate his innovative ideas and deep insights in the realm of Personal AI OS. Waterflier will be spearheading the development of the MVP version. As a key contributor to the CYFS CoreDev Team, he brings extensive experience in the domain of Personal Servers.
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## **Intro video - What is OpenDAN?**
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Click the image below for a demo:
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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 @@
|
||||
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
|
||||
from .compute_node import compute_node,local_compute_node
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from .open_ai_node import open_ai_compute_node
|
||||
from .role import ai_role,ai_role_group
|
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from .workflow import ai_workflow
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@@ -0,0 +1,88 @@
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from typing import Optional
|
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|
||||
import logging
|
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|
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logger = logging.getLogger(__name__)
|
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|
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class agent_msg:
|
||||
def __init__(self) -> None:
|
||||
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
|
||||
|
||||
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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|
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# return workflow_name, role_name, session_id
|
||||
def parser_target(self,target:str) -> None:
|
||||
pass
|
||||
|
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class agent_prompt:
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def __init__(self) -> None:
|
||||
pass
|
||||
|
||||
def as_str(self)->str:
|
||||
pass
|
||||
|
||||
def append(self,prompt) -> None:
|
||||
pass
|
||||
|
||||
# chat session store the chat history between owner and agent
|
||||
# chat session might be large, so can read / write at stream mode.
|
||||
class ai_chat_session:
|
||||
def __init__(self) -> None:
|
||||
pass
|
||||
|
||||
def get_owner_id(self) -> str:
|
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pass
|
||||
|
||||
def append_post(self,msg:agent_msg) -> None:
|
||||
"""append msg to session, msg is post from session (owner => msg.target)"""
|
||||
pass
|
||||
|
||||
def append_recv(self,msg:agent_msg) -> None:
|
||||
"""append msg to session, msg is recv from msg'sender (msg.sender => owner)"""
|
||||
pass
|
||||
|
||||
def attach_event_handler(self,handler) -> None:
|
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"""chat session changed event handler"""
|
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pass
|
||||
|
||||
#TODO : add iterator interface for read chat history
|
||||
|
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class ai_agent_templete:
|
||||
def __init__(self) -> None:
|
||||
pass
|
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|
||||
class ai_agent:
|
||||
def __init__(self) -> None:
|
||||
self.chat_sessions = None
|
||||
self.llm_model_name = None
|
||||
self.max_token_size = 0
|
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self.instance_id = None
|
||||
self.template_id = None
|
||||
|
||||
def get_id(self) -> str:
|
||||
return self.instance_id
|
||||
|
||||
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:
|
||||
pass
|
||||
|
||||
def get_llm_model_name(self) -> str:
|
||||
return self.llm_model_name
|
||||
|
||||
def get_max_token_size(self) -> int:
|
||||
return self.max_token_size
|
||||
|
||||
@@ -0,0 +1,32 @@
|
||||
class ai_function:
|
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def __init__(self) -> None:
|
||||
self.intro : str = None
|
||||
|
||||
def load_from_config(self,config:dict) -> bool:
|
||||
pass
|
||||
|
||||
def is_local(self) -> bool:
|
||||
pass
|
||||
|
||||
def is_in_zone(self) -> bool:
|
||||
pass
|
||||
|
||||
def is_readyonly(self) -> bool:
|
||||
pass
|
||||
|
||||
def get_intro(self) -> str:
|
||||
return self.intro
|
||||
|
||||
async def execute(self):
|
||||
pass
|
||||
|
||||
# call chain is a combination of ai_function,group of ai_function.
|
||||
class call_chain:
|
||||
def __init__(self) -> None:
|
||||
pass
|
||||
|
||||
def load_from_config(self,config:dict) -> bool:
|
||||
pass
|
||||
|
||||
async def execute(self):
|
||||
pass
|
||||
@@ -0,0 +1,14 @@
|
||||
# aiso shell like bash of linux
|
||||
from .workflow import ai_workflow
|
||||
|
||||
|
||||
class aios_shell:
|
||||
def __init__(self,username:str) -> None:
|
||||
pass
|
||||
|
||||
async def send_msg(self,msg:str,target_workflow:str) -> str:
|
||||
pass
|
||||
|
||||
async def install_workflow(self,workflow_id:ai_workflow) -> None:
|
||||
pass
|
||||
|
||||
@@ -0,0 +1,77 @@
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Optional
|
||||
import logging
|
||||
import asyncio
|
||||
|
||||
from .agent import agent_prompt
|
||||
from .compute_node import compute_node
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# How to dispatch different computing tasks (some tasks may contain a large amount of state for correct execution)
|
||||
# to suitable computing nodes, achieving a balance of speed, cost, and power consumption,
|
||||
# is the CORE GOAL of the entire computing task schedule system (aios_kernel).
|
||||
class compute_task(ABC):
|
||||
@abstractmethod
|
||||
def display(self) -> str:
|
||||
pass
|
||||
|
||||
|
||||
class compute_kernel:
|
||||
_instance = None
|
||||
def __new__(cls):
|
||||
if cls._instance is None:
|
||||
cls._instance = super(compute_kernel, cls).__new__(cls)
|
||||
return cls._instance
|
||||
|
||||
def __init__(self) -> None:
|
||||
self.task_queue = []
|
||||
self.is_start = False
|
||||
pass
|
||||
|
||||
def run(self,task:compute_task) -> None:
|
||||
# check there is compute node can support this task
|
||||
if self.is_task_support(task) is False:
|
||||
logger.error(f"task {task.display()} is not support by any compute node")
|
||||
return
|
||||
# add task to working_queue
|
||||
self.task_queue.append(task)
|
||||
|
||||
|
||||
def start(self):
|
||||
if self.is_start is True:
|
||||
logger.warn("compute_kernel is already start")
|
||||
return
|
||||
|
||||
self.is_start = True
|
||||
async def _run_task_loop():
|
||||
while True:
|
||||
task = self.task_queue.pop(0)
|
||||
c_node:compute_node= await self._schedule(task)
|
||||
c_node.push_task(task)
|
||||
|
||||
asyncio.create_task(_run_task_loop())
|
||||
|
||||
|
||||
async def _schedule(self,task) -> compute_node:
|
||||
pass
|
||||
|
||||
def add_compute_node(self,node:compute_node):
|
||||
pass
|
||||
|
||||
def disable_compute_node(self,):
|
||||
pass
|
||||
|
||||
def is_task_support(self,task:compute_task) -> bool:
|
||||
pass
|
||||
|
||||
|
||||
# friendly interface for use:
|
||||
def llm_completion(self,prompt:agent_prompt,mode_name:Optional[str] = None,max_token:int = 0) -> compute_task:
|
||||
# craete a llm_work_task ,push on queue's end
|
||||
# then task_schedule would run this task.(might schedule some work_task to another host)
|
||||
pass
|
||||
|
||||
async def do_llm_completion(self,prompt:agent_prompt,mode_name:Optional[str] = None,max_token:int = 0) -> str:
|
||||
pass
|
||||
|
||||
@@ -0,0 +1,48 @@
|
||||
from abc import ABC, abstractmethod
|
||||
from .compute_kernel import compute_task
|
||||
|
||||
class compute_node(ABC):
|
||||
@abstractmethod
|
||||
async def push_task(self,task:compute_task,proiority:int = 0):
|
||||
pass
|
||||
|
||||
async def remove_task(self,task_id:str):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def get_task_state(self,task_id:str):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def display(self) -> str:
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def get_capacity(self):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def is_support(self,task_type:str) -> bool:
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def is_local(self) -> bool:
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def is_trusted(self) -> bool:
|
||||
return True
|
||||
|
||||
def get_fee_type(self) -> str:
|
||||
return "free"
|
||||
|
||||
|
||||
|
||||
class local_compute_node(compute_node):
|
||||
def display(self) -> str:
|
||||
return super().display()
|
||||
|
||||
def is_local(self) -> bool:
|
||||
return True
|
||||
|
||||
|
||||
@@ -0,0 +1,23 @@
|
||||
# basic environment class
|
||||
# we have some built-in environment: Calender(include timer),Home(connect to IoT device in your home), ,KnwoledgeBase,FileSystem,
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Callable
|
||||
|
||||
from .agent import agent_msg
|
||||
|
||||
class environment_event(ABC):
|
||||
@abstractmethod
|
||||
def display(self) -> str:
|
||||
pass
|
||||
|
||||
class environment:
|
||||
def __init__(self) -> None:
|
||||
pass
|
||||
|
||||
def get_id(self) -> str:
|
||||
pass
|
||||
|
||||
def attach_event_handler(self,event_id:str,handler:Callable) -> None:
|
||||
pass
|
||||
|
||||
@@ -0,0 +1,8 @@
|
||||
from .compute_node import compute_node
|
||||
|
||||
class open_ai_compute_node(compute_node):
|
||||
def display(self) -> str:
|
||||
return super().display()
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,25 @@
|
||||
from .agent import ai_agent
|
||||
|
||||
class ai_role:
|
||||
def __init__(self) -> None:
|
||||
self.agent_instance_id : str = None
|
||||
self.role_name : str = None
|
||||
self.agent : ai_agent = None
|
||||
self.introduce : str = None
|
||||
|
||||
def load_from_config(self,config:dict) -> bool:
|
||||
pass
|
||||
|
||||
def get_intro(self) -> str:
|
||||
return self.introduce
|
||||
|
||||
def get_name(self) -> str:
|
||||
return self.role_name
|
||||
|
||||
class ai_role_group:
|
||||
def __init__(self) -> None:
|
||||
self.roles : dict[str,str]= None
|
||||
pass
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,206 @@
|
||||
|
||||
import logging
|
||||
import asyncio
|
||||
from typing import Optional,Tuple
|
||||
|
||||
from .environment import environment,environment_event
|
||||
from .agent import agent_prompt,agent_msg,ai_chat_session
|
||||
from .role import ai_role
|
||||
from .ai_function import call_chain
|
||||
from .compute_kernel import compute_kernel
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class ai_message_filter:
|
||||
def __init__(self) -> None:
|
||||
pass
|
||||
def select(self,msg:agent_msg) -> ai_role:
|
||||
pass
|
||||
|
||||
class ai_workflow:
|
||||
def __init__(self) -> None:
|
||||
self.rule_prompt : agent_prompt = None
|
||||
self.workflow_config = None
|
||||
self.role_group = None
|
||||
self.input_filter : ai_message_filter= None
|
||||
self.msg_queue = []
|
||||
self.connected_environment = {}
|
||||
|
||||
def load_from_disk(self,config_path:str,context_dir_path) -> int:
|
||||
pass
|
||||
|
||||
#workflow is asynchronous.
|
||||
# When processing one message, it can process another message at the same time.
|
||||
# chatsession is synchronous, it has to wait for the previous message to finish processing before it can process the next message.
|
||||
# Therefore, post a message needs to specify the session_id explicitly, if not specified it will be automatically created by workflow.
|
||||
def post_msg(self,msg:agent_msg) -> None:
|
||||
self.msg_queue.append(msg)
|
||||
return
|
||||
|
||||
async def send_msg(self,msg:agent_msg) -> str:
|
||||
pass
|
||||
|
||||
async def run(self):
|
||||
# TODO add tracking design of msg processing
|
||||
while True:
|
||||
the_msg = await self._pop_msg()
|
||||
chatsession:ai_chat_session = self._get_chat_session_for_msg(the_msg)
|
||||
if chatsession is None:
|
||||
logger.error(f"get_chat_session_for_msg return None for :{the_msg}")
|
||||
continue
|
||||
|
||||
chatsession.append_recv(the_msg)
|
||||
|
||||
async def _process_msg(msg:agent_msg,the_role) -> None:
|
||||
# prompt generat progress is most important part of workflow(app) develope
|
||||
prompt = agent_prompt()
|
||||
prompt.append(the_role.get_prompt())
|
||||
prompt.append(self.get_workflow_rule_prompt())
|
||||
prompt.append(self._get_function_prompt(the_role.get_name()))
|
||||
prompt.append(self._get_knowlege_prompt(the_role.get_name()))
|
||||
prompt.append(await self._get_prompt_from_session(chatsession,the_role.get_name())) # chat context
|
||||
|
||||
result = await compute_kernel().do_llm_completion(prompt,the_role.agent.get_llm_model_name(),the_role.agent.get_max_token_size())
|
||||
final_result = result
|
||||
result_type : str = self._get_llm_result_type(result)
|
||||
is_ignore = False
|
||||
match result_type:
|
||||
case "function":
|
||||
callchain:call_chain = self._parse_function_call_chain(result)
|
||||
resp = await callchain.exec()
|
||||
if callchain.have_result():
|
||||
# generator proc resp prompt with WAITING state
|
||||
proc_resp_prompt:agent_prompt = self._get_resp_prompt(resp,msg,the_role,prompt,chatsession)
|
||||
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())
|
||||
return final_result
|
||||
|
||||
|
||||
case "send_message":
|
||||
# send message to other / sub workflow
|
||||
next_msg:agent_msg = self._parse_to_msg(result)
|
||||
if next_msg is not None:
|
||||
# TODO: Next Target can be another role in workflow
|
||||
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)
|
||||
resp = await next_workflow.send_msg(next_msg)
|
||||
inner_chat_session.append_recv(resp)
|
||||
# generator proc resp prompt with WAITING state
|
||||
proc_resp_prompt:agent_prompt = self._get_resp_prompt(resp,msg,the_role,prompt,chatsession)
|
||||
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())
|
||||
|
||||
return final_result
|
||||
|
||||
case "post_message":
|
||||
# post message to other / sub workflow
|
||||
next_msg:agent_msg = self._parse_to_msg(result)
|
||||
if next_msg is not None:
|
||||
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 _get_chat_session_for_msg(self,msg:agent_msg) -> ai_chat_session:
|
||||
pass
|
||||
|
||||
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 _merge_msg_result(self,results:dict) -> agent_msg:
|
||||
pass
|
||||
|
||||
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 _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:
|
||||
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!")
|
||||
|
||||
@@ -1,3 +0,0 @@
|
||||
class ai_agent:
|
||||
def __init__(self) -> None:
|
||||
pass
|
||||
@@ -1,11 +1,19 @@
|
||||
|
||||
import logging
|
||||
import toml
|
||||
|
||||
from .agent import ai_agent
|
||||
from .templete import ai_agent_templete
|
||||
from package_manager import pkg_env,pkg_env_mgr,pkg_media_info,media_reader,pkg_install_task
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class agent_manager:
|
||||
_instance = None
|
||||
|
||||
def __init__(self) -> None:
|
||||
self.loaded_agent_instance = {}
|
||||
pass
|
||||
|
||||
def __new__(cls):
|
||||
@@ -15,22 +23,51 @@ class agent_manager:
|
||||
|
||||
|
||||
def initial(self,root_dir:str) -> None:
|
||||
pass
|
||||
self.agent_templete_env : pkg_env = pkg_env_mgr().get_env(f"{root_dir}templetes/agent_templetes.cfg")
|
||||
self.agent_env : pkg_env = pkg_env_mgr().get_env(f"{root_dir}agents/agents.cfg")
|
||||
if self.agent_templete_env is None:
|
||||
raise Exception("agent_manager initial failed")
|
||||
|
||||
|
||||
|
||||
def get(self,agent_id) -> ai_agent:
|
||||
pass
|
||||
def get(self,agent_id:str) -> ai_agent:
|
||||
the_agent = self.loaded_agent_instance.get(agent_id)
|
||||
if the_agent:
|
||||
return the_agent
|
||||
|
||||
# try load from disk
|
||||
agent_media_info = self.agent_env.load(agent_id)
|
||||
if agent_media_info is None:
|
||||
return None
|
||||
|
||||
the_agent = self._load_agent_from_media(agent_media_info)
|
||||
if the_agent is None:
|
||||
logger.warn(f"load agent {agent_id} from media failed!")
|
||||
|
||||
return the_agent
|
||||
|
||||
|
||||
def remove(self,agent_id:str)->int:
|
||||
pass
|
||||
|
||||
def get_templete(self,templete_id) -> ai_agent_templete:
|
||||
template_media_info = self.agent_templete_env.get(templete_id)
|
||||
if template_media_info is None:
|
||||
return None
|
||||
return self._load_templete_from_media(template_media_info)
|
||||
|
||||
def install(self,templete_id) -> pkg_install_task:
|
||||
installer = self.agent_templete_env.get_installer()
|
||||
return installer.install(templete_id)
|
||||
|
||||
def uninstall(self,templete_id) -> int:
|
||||
pass
|
||||
|
||||
def _load_templete_from_media(self,templete_media:pkg_media_info) -> ai_agent_templete:
|
||||
pass
|
||||
|
||||
def install(self,templete_id) -> int:
|
||||
|
||||
installer = None
|
||||
#installer.install(templete_id)
|
||||
def _load_agent_from_media(self,agent_media:pkg_media_info) -> ai_agent:
|
||||
pass
|
||||
|
||||
|
||||
def create(self,template,agent_name,agent_last_name,agent_introduce) -> ai_agent:
|
||||
pass
|
||||
|
||||
|
||||
@@ -1,3 +0,0 @@
|
||||
class ai_agent_templete:
|
||||
def __init__(self) -> None:
|
||||
pass
|
||||
@@ -0,0 +1,3 @@
|
||||
from .env import pkg_env_mgr,pkg_env
|
||||
from .pkg import pkg_info,pkg_media_info
|
||||
from .installer import pkg_install_task
|
||||
@@ -19,7 +19,7 @@ INSTALL_TASK_STATE_DOWNLOADING = 3
|
||||
INSTALL_TASK_STATE_INSTALLING = 4
|
||||
INSTALL_TAKS_STATE_ERROR = 5
|
||||
|
||||
class install_task:
|
||||
class pkg_install_task:
|
||||
def __init__(self,owner:pkg_env) -> None:
|
||||
self.owner = owner
|
||||
self.state = INSTALL_TASK_STATE_CHECK_DEPENDENCY
|
||||
@@ -35,7 +35,7 @@ class pkg_installer:
|
||||
self.owner_env = owner_env
|
||||
|
||||
def install(self,pkg_name:str,
|
||||
install_from_dependency = False, can_upgrade = True,skip_depends = False,options = None)->Tuple[install_task,str]:
|
||||
install_from_dependency = False, can_upgrade = True,skip_depends = False,options = None)->Tuple[pkg_install_task,str]:
|
||||
|
||||
the_pkg_info : pkg_info = None
|
||||
is_upgrade : bool = False
|
||||
@@ -74,7 +74,7 @@ class pkg_installer:
|
||||
return result_task,"already installing"
|
||||
|
||||
logger.info(f"start download&install {pkg_name},install_from_dependency={install_from_dependency},upgrade={is_upgrade},backup={need_backup},target_pkg_info={the_pkg_info}")
|
||||
result_task = install_task()
|
||||
result_task = pkg_install_task()
|
||||
self.all_tasks[the_pkg_info.cid] = result_task
|
||||
async def download_and_install_pkg()->int:
|
||||
# check dependency
|
||||
@@ -149,7 +149,7 @@ class pkg_installer:
|
||||
if depend_task is not None:
|
||||
logger.debug(f"{pkg.name}'s depend pkg {depend_pkg_name} already in task list")
|
||||
continue
|
||||
depend_task = install_task()
|
||||
depend_task = pkg_install_task()
|
||||
task_list[depend_pkg_name] = depend_task
|
||||
|
||||
depend_pkg_info = self.owner_env.lookup(depend_pkg_name)
|
||||
|
||||
@@ -0,0 +1,9 @@
|
||||
class media_reader:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def load_from_media(self,media_info:str) -> int:
|
||||
pass
|
||||
|
||||
def read(self, inner_path:str):
|
||||
raise NotImplementedError
|
||||
@@ -9,12 +9,13 @@ class pkg_info:
|
||||
self.target_media_type = "dir"
|
||||
self.source_media_type = "7z"
|
||||
|
||||
|
||||
|
||||
def parse_pkg_name(pkg_name:str) -> Tuple[str, str, str]:
|
||||
#return pkg_id,version_str,cid
|
||||
@classmethod
|
||||
def parse_pkg_name(cls,pkg_name:str) -> Tuple[str, str, str]:
|
||||
"""parse pkg name like test-pkg#nightly#>0.2.31#sha1:323423423 to test-pkg,nightly#>0.2.31,sha1:323423423"""
|
||||
pass
|
||||
|
||||
|
||||
|
||||
@property
|
||||
def cid(self) -> str:
|
||||
return self.cid
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
TODO
|
||||
+2
-1
@@ -23,8 +23,9 @@ def test_agent():
|
||||
agent = am.create(agent_templete,"Tracy","Wang","Tracy Wang is my english teacher")
|
||||
|
||||
print("Agent Tracy Wang load success!");
|
||||
print(agent.get_introduce());
|
||||
|
||||
#print(agent.get_introduce());
|
||||
|
||||
|
||||
#chat_session = agent.get_default_chat_session("master");
|
||||
#chat_session.chat("给我讲一个英文笑话!");
|
||||
|
||||
@@ -0,0 +1,66 @@
|
||||
import asyncio
|
||||
import aiohttp
|
||||
import aiofiles
|
||||
STATE_DONE = 0
|
||||
STATE_DOWNLOADING = 1
|
||||
class install_task:
|
||||
def __init__(self) -> None:
|
||||
self.download_task = None
|
||||
self.state = STATE_DOWNLOADING
|
||||
self.recv_bytes = 0
|
||||
self.total_bytes = 0
|
||||
|
||||
class install_task_mgr:
|
||||
def __init__(self) -> None:
|
||||
self.all_tasks = {}
|
||||
|
||||
def create_install_task(self,url:str,target:str):
|
||||
owner = self
|
||||
this_task = self.all_tasks.get(url)
|
||||
if this_task is not None:
|
||||
return this_task
|
||||
|
||||
this_task = install_task()
|
||||
self.all_tasks[url] = this_task
|
||||
async def down_and_write():
|
||||
async with aiofiles.open(target, 'wb') as file:
|
||||
async with aiohttp.ClientSession() as session:
|
||||
async with session.get(url) as response:
|
||||
while True:
|
||||
chunk = await response.content.read(1024*1024*16)
|
||||
this_task.recv_bytes += len(chunk)
|
||||
if not chunk:
|
||||
break
|
||||
await file.write(chunk)
|
||||
print(f"download task {url} done!")
|
||||
this_task.state = STATE_DONE
|
||||
del owner.all_tasks[url]
|
||||
|
||||
this_task.download_task = asyncio.create_task(down_and_write())
|
||||
return this_task
|
||||
|
||||
|
||||
|
||||
async def test_wait_download(mgr):
|
||||
this_task = mgr.create_install_task("https://www.cyfs.com/download/beta/cyberchat/android/latest","test.pkg")
|
||||
await this_task.download_task
|
||||
|
||||
def test_timer_download(mgr):
|
||||
this_task = mgr.create_install_task("https://www.cyfs.com/download/beta/cyberchat/android/latest","test.pkg")
|
||||
# start timer
|
||||
async def check_timer():
|
||||
while this_task.state == STATE_DOWNLOADING:
|
||||
await r = asyncio.sleep(1)
|
||||
print(f"download bytes:{this_task.recv_bytes}")
|
||||
print("download complete!")
|
||||
|
||||
asyncio.create_task(check_timer())
|
||||
|
||||
async def test_main():
|
||||
mgr = install_task_mgr()
|
||||
test_timer_download(mgr)
|
||||
await test_wait_download(mgr)
|
||||
await asyncio.sleep(1)
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(test_main())
|
||||
@@ -0,0 +1,12 @@
|
||||
import asyncio
|
||||
|
||||
async def test_llm_completion_task():
|
||||
# compute task have engouh meta info to make sure compute_kernel can run it in most suitable compute container
|
||||
test_task = llm_completion_task()
|
||||
# add tset_task to compute_kernel's execute queue
|
||||
compute_kernel.run(test_task)
|
||||
# wait for test_task
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(test_llm_completion_task())
|
||||
@@ -0,0 +1,6 @@
|
||||
|
||||
|
||||
def test_workflow():
|
||||
|
||||
|
||||
pass
|
||||
Reference in New Issue
Block a user