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read mail with issue tree pipeline works

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tsukasa
2023-11-20 23:36:28 +08:00
parent a63e9b6745
commit d7cdf2576e
4 changed files with 40 additions and 133 deletions
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src/aios_kernel/agent_base.py
+29 -54
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@@ -567,38 +567,6 @@ class BaseAIAgent(abc.ABC):
def get_max_token_size(self) -> int:
pass
@abstractmethod
def get_llm_learn_token_limit(self) -> int:
pass
@abstractmethod
async def _process_msg(self,msg:AgentMsg,workspace = None) -> AgentMsg:
pass
class CustomAIAgent(BaseAIAgent):
def __init__(self, agent_id: str, llm_model_name: str, max_token_size: int, llm_learn_token_limit: int) -> None:
self.agent_id = agent_id
self.llm_model_name = llm_model_name
self.max_token_size = max_token_size
self.llm_learn_token_limit = llm_learn_token_limit
def get_id(self) -> str:
return self.agent_id
def get_llm_model_name(self) -> str:
return self.llm_model_name
def get_max_token_size(self) -> int:
return self.max_token_size
def get_llm_learn_token_limit(self) -> int:
return self.llm_learn_token_limit
class BaseAIAgent:
def __init__(self) -> None:
pass
@classmethod
def get_inner_functions(cls, env:Environment) -> (dict,int):
if env is None:
@@ -621,12 +589,9 @@ class BaseAIAgent:
return result_func,result_len
@classmethod
async def do_llm_complection(
cls,
self,
prompt:AgentPrompt,
llm_model_name:str,
max_token_size:int,
org_msg:AgentMsg=None,
env:Environment=None,
inner_functions=None,
@@ -635,11 +600,11 @@ class BaseAIAgent:
from .compute_kernel import ComputeKernel
#logger.debug(f"Agent {self.agent_id} do llm token static system:{system_prompt_len},function:{function_token_len},history:{history_token_len},input:{input_len}, totoal prompt:{system_prompt_len + function_token_len + history_token_len} ")
if inner_functions is None and env is not None:
inner_functions,_ = cls.get_inner_functions(env)
inner_functions,_ = BaseAIAgent.get_inner_functions(env)
if is_json_resp:
task_result:ComputeTaskResult = await ComputeKernel.get_instance().do_llm_completion(prompt,"json",llm_model_name,max_token_size,inner_functions,timeout=None)
task_result:ComputeTaskResult = await ComputeKernel.get_instance().do_llm_completion(prompt,resp_mode="json",mode_name=self.get_llm_model_name(),max_token=self.get_max_token_size(),inner_functions=inner_functions,timeout=None)
else:
task_result:ComputeTaskResult = await ComputeKernel.get_instance().do_llm_completion(prompt,"text",llm_model_name,max_token_size,inner_functions,timeout=None)
task_result:ComputeTaskResult = await ComputeKernel.get_instance().do_llm_completion(prompt,resp_mode="text",mode_name=self.get_llm_model_name(),max_token=self.get_max_token_size(),inner_functions=inner_functions,timeout=None)
if task_result.result_code != ComputeTaskResultCode.OK:
logger.error(f"_do_llm_complection llm compute error:{task_result.error_str}")
#error_resp = msg.create_error_resp(task_result.error_str)
@@ -652,20 +617,17 @@ class BaseAIAgent:
if inner_func_call_node:
call_prompt : AgentPrompt = copy.deepcopy(prompt)
task_result = await cls._execute_func(env,inner_func_call_node,call_prompt,inner_functions,org_msg,llm_model_name,max_token_size)
task_result = await self._execute_func(env,inner_func_call_node,call_prompt,inner_functions,org_msg)
return task_result
@classmethod
async def _execute_func(
cls,
self,
env: Environment,
inner_func_call_node: dict,
prompt: AgentPrompt,
inner_functions: dict,
org_msg:AgentMsg,
llm_model_name:str,
max_token_size:int,
stack_limit = 5
) -> ComputeTaskResult:
from .compute_kernel import ComputeKernel
@@ -677,9 +639,6 @@ class BaseAIAgent:
if func_node is None:
result_str = f"execute {func_name} error,function not found"
else:
if org_msg:
ineternal_call_record = AgentMsg.create_internal_call_msg(func_name,arguments,org_msg.get_msg_id(),org_msg.target)
try:
result_str:str = await func_node.execute(**arguments)
except Exception as e:
@@ -690,15 +649,16 @@ class BaseAIAgent:
logger.info("llm execute inner func result:" + result_str)
prompt.messages.append({"role":"function","content":result_str,"name":func_name})
task_result:ComputeTaskResult = await ComputeKernel.get_instance().do_llm_completion(prompt,llm_model_name,max_token_size,inner_functions)
task_result:ComputeTaskResult = await ComputeKernel.get_instance().do_llm_completion(prompt,mode_name=self.get_llm_model_name(),max_token=self.get_max_token_size(),inner_functions=inner_functions)
if task_result.result_code != ComputeTaskResultCode.OK:
logger.error(f"llm compute error:{task_result.error_str}")
return task_result
ineternal_call_record.result_str = task_result.result_str
ineternal_call_record.done_time = time.time()
if org_msg:
org_msg.inner_call_chain.append(ineternal_call_record)
internal_call_record = AgentMsg.create_internal_call_msg(func_name,arguments,org_msg.get_msg_id(),org_msg.target)
internal_call_record.result_str = task_result.result_str
internal_call_record.done_time = time.time()
org_msg.inner_call_chain.append(internal_call_record)
inner_func_call_node = None
if stack_limit > 0:
@@ -707,7 +667,22 @@ class BaseAIAgent:
inner_func_call_node = result_message.get("function_call")
if inner_func_call_node:
return await cls._execute_func(env,inner_func_call_node,prompt,inner_functions,org_msg,llm_model_name,max_token_size,stack_limit-1)
return await self._execute_func(env,inner_func_call_node,prompt,inner_functions,org_msg,stack_limit-1)
else:
return task_result
>>>>>>> 2f9cee9 (a issue parser of email)
class CustomAIAgent(BaseAIAgent):
def __init__(self, agent_id: str, llm_model_name: str, max_token_size: int) -> None:
self.agent_id = agent_id
self.llm_model_name = llm_model_name
self.max_token_size = max_token_size
def get_id(self) -> str:
return self.agent_id
def get_llm_model_name(self) -> str:
return self.llm_model_name
def get_max_token_size(self) -> int:
return self.max_token_size