Refactor the code to make it comply with PEP-8 standards:Convert all class definitions to CamelCase style.
(issue 37)
This commit is contained in:
Liu Zhicong
+43
-43
@@ -3,26 +3,26 @@ 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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from .environment import Environment,EnvironmentEvent
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from .agent import AgentPrompt,AgentMsg,AIChatSession
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from .role import AIRole
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from .ai_function import CallChain
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from .compute_kernel import ComputeKernel
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logger = logging.getLogger(__name__)
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class ai_message_filter:
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class MessageFilter:
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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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def select(self,msg:AgentMsg) -> AIRole:
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pass
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class ai_workflow:
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class Workflow:
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def __init__(self) -> None:
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self.rule_prompt : agent_prompt = None
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self.rule_prompt : AgentPrompt = None
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self.workflow_config = 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.input_filter : MessageFilter= None
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self.msg_queue = []
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self.connected_environment = {}
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@@ -33,70 +33,70 @@ class ai_workflow:
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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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def post_msg(self,msg:AgentMsg) -> 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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async def send_msg(self,msg:AgentMsg) -> 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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chatsession:AIChatSession = 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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async def _process_msg(msg:AgentMsg,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 = AgentPrompt()
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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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result = await ComputeKernel().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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callchain:CallChain = 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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proc_resp_prompt:AgentPrompt = self._get_resp_prompt(resp,msg,the_role,prompt,chatsession)
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final_result = await ComputeKernel().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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next_msg:AgentMsg = 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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next_workflow: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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proc_resp_prompt:AgentPrompt = self._get_resp_prompt(resp,msg,the_role,prompt,chatsession)
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final_result = await ComputeKernel().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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next_msg:AgentMsg = 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())
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next_workflow: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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next_workflow.post_msg(next_msg)
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@@ -113,7 +113,7 @@ class ai_workflow:
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return result
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async def _workflow_process_msg(msg:agent_msg) -> None:
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async def _workflow_process_msg(msg:AgentMsg) -> None:
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final_result = None
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if self.input_filter is not None:
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select_role = self.input_filter.select(msg)
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@@ -134,7 +134,7 @@ class ai_workflow:
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# merge result from all roles
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# TODO: one input msg can have multiple result msg, at this while ,we only support one result msg
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final_result:agent_msg = self._merge_msg_result(results)
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final_result:AgentMsg = self._merge_msg_result(results)
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if chatsession is not None:
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chatsession.append_post(final_result)
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@@ -144,59 +144,59 @@ class ai_workflow:
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asyncio.create_task(_workflow_process_msg(the_msg))
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async def _pop_msg(self) -> agent_msg:
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async def _pop_msg(self) -> AgentMsg:
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pass
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def _get_chat_session_for_msg(self,msg:agent_msg) -> ai_chat_session:
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def _get_chat_session_for_msg(self,msg:AgentMsg) -> AIChatSession:
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pass
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async def _get_prompt_from_session(self,chatsession:ai_chat_session,role_name:str) -> agent_prompt:
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async def _get_prompt_from_session(self,chatsession:AIChatSession,role_name:str) -> AgentPrompt:
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pass
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def _get_msg_queue(self,session_id:str):
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pass
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def _merge_msg_result(self,results:dict) -> agent_msg:
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def _merge_msg_result(self,results:dict) -> AgentMsg:
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pass
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def _get_function_prompt(self,role_name:str) -> agent_prompt:
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def _get_function_prompt(self,role_name:str) -> AgentPrompt:
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pass
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def _get_knowlege_prompt(self,role_name:str) -> agent_prompt:
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def _get_knowlege_prompt(self,role_name:str) -> AgentPrompt:
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pass
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def _get_resp_prompt(self,resp:str,msg:agent_msg,role:ai_role,prompt:agent_prompt,chatsession:ai_chat_session) -> agent_prompt:
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def _get_resp_prompt(self,resp:str,msg:AgentMsg,role:AIRole,prompt:AgentPrompt,chatsession:AIChatSession) -> AgentPrompt:
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pass
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def get_workflow_rule_prompt(self) -> agent_prompt:
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def get_workflow_rule_prompt(self) -> AgentPrompt:
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return self.rule_prompt
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def _get_llm_result_type(self,llm_resp_str:str) -> str:
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pass
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def _parse_function_call_chain(self,llm_resp_str) -> call_chain:
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def _parse_function_call_chain(self,llm_resp_str) -> CallChain:
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pass
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def _parse_to_msg(self,llm_resp_str) -> agent_msg:
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def _parse_to_msg(self,llm_resp_str) -> AgentMsg:
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pass
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def get_workflow(self,workflow_name:str) -> ai_workflow:
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def get_workflow(self,workflow_name:str) -> Workflow:
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"""get workflow from known workflow list or sub workflow list"""
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pass
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def _env_event_to_msg(self,env_event:environment_event) -> agent_msg:
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def _env_event_to_msg(self,env_event:EnvironmentEvent) -> AgentMsg:
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pass
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def get_inner_environment(self,env_id:str) -> environment:
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def get_inner_environment(self,env_id:str) -> Environment:
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pass
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def connect_to_environment(self,env:environment) -> None:
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def connect_to_environment(self,env:Environment) -> None:
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the_env = self.connected_environment.get(env.get_id())
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if the_env is None:
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self.connected_environment[env.get_id()] = env
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def _env_msg_handler(env_event:environment_event) -> None:
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the_msg:agent_msg= self._env_event_to_msg(env_event)
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def _env_msg_handler(env_event:EnvironmentEvent) -> None:
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the_msg:AgentMsg= self._env_event_to_msg(env_event)
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self.post_msg(the_msg)
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# register all event handler
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