Refactor the Action/Function components, and refactor the basic architecture of Agent Task/Todo.
This commit is contained in:
Liu Zhicong
+230
-108
@@ -1,34 +1,31 @@
|
||||
# Old name is behavior, I belive new name "llm_process" is better
|
||||
# pylint:disable=E0402
|
||||
from ..utils import video_utils,image_utils
|
||||
|
||||
from ..proto.compute_task import LLMPrompt,LLMResult,ComputeTaskResult,ComputeTaskResultCode
|
||||
from ..proto.ai_function import AIFunction,AIAction,ActionNode
|
||||
from ..proto.agent_msg import AgentMsg,AgentMsgType
|
||||
|
||||
from .agent_memory import AgentMemory
|
||||
from .workspace import AgentWorkspace
|
||||
from .llm_context import LLMProcessContext,GlobaToolsLibrary, SimpleLLMContext
|
||||
|
||||
from ..frame.compute_kernel import ComputeKernel
|
||||
|
||||
from abc import ABC,abstractmethod
|
||||
import copy
|
||||
import json
|
||||
import shlex
|
||||
import datetime
|
||||
from datetime import datetime
|
||||
from typing import Any, Callable, Coroutine, Optional,Dict,Awaitable,List
|
||||
from enum import Enum
|
||||
|
||||
from aios.agent.chatsession import AIChatSession
|
||||
|
||||
from ..utils import video_utils
|
||||
|
||||
from ..proto.compute_task import *
|
||||
from ..proto.ai_function import *
|
||||
|
||||
from .agent_base import *
|
||||
from .agent_memory import *
|
||||
from .workspace import *
|
||||
|
||||
from ..frame.compute_kernel import *
|
||||
from ..environment.environment import *
|
||||
from ..environment.workspace_env import *
|
||||
|
||||
import logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
MIN_PREDICT_TOKEN_LEN = 32
|
||||
|
||||
class LLMProcessContext:
|
||||
def __init__(self) -> None:
|
||||
pass
|
||||
|
||||
|
||||
class BaseLLMProcess(ABC):
|
||||
def __init__(self) -> None:
|
||||
@@ -38,37 +35,23 @@ class BaseLLMProcess(ABC):
|
||||
self.result_example:str = None #llm_result样例
|
||||
|
||||
self.enable_json_resp = False
|
||||
self.model_name = "gpt-4"
|
||||
#None means system default,
|
||||
# TODO: support abcstract model name like: local-hight,local-low,local-medium,remote-hight,remote-low,remote-medium
|
||||
self.model_name = None
|
||||
self.max_token = 1000 # result_token
|
||||
self.max_prompt_token = 1000 # not include input prompt
|
||||
self.timeout = 1800 # 30 min
|
||||
|
||||
self.envs : Dict[str,BaseEnvironment] = []
|
||||
self.env : CompositeEnvironment = None
|
||||
|
||||
def aifunction_to_inner_function(self,all_inner_function:List[AIFunction]) -> List[Dict]:
|
||||
result_func = []
|
||||
result_len = 0
|
||||
for inner_func in all_inner_function:
|
||||
func_name = inner_func.get_name()
|
||||
this_func = {}
|
||||
this_func["name"] = func_name
|
||||
this_func["description"] = inner_func.get_description()
|
||||
this_func["parameters"] = inner_func.get_parameters()
|
||||
result_len += len(json.dumps(this_func)) / 4
|
||||
result_func.append(this_func)
|
||||
return result_func
|
||||
|
||||
@abstractmethod
|
||||
async def prepare_prompt(self,input:Dict) -> LLMPrompt:
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def get_inner_function(self,func_name:str) -> AIFunction:
|
||||
pass
|
||||
|
||||
async def get_inner_function_for_exec(self,func_name:str) -> AIFunction:
|
||||
return GlobaToolsLibrary.get_instance().get_tool_function(func_name)
|
||||
|
||||
@abstractmethod
|
||||
async def post_llm_process(self,actions:List[ActionItem],input:Dict,llm_result:LLMResult) -> bool:
|
||||
async def post_llm_process(self,actions:List[ActionNode],input:Dict,llm_result:LLMResult) -> bool:
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
@@ -93,15 +76,11 @@ class BaseLLMProcess(ABC):
|
||||
@abstractmethod
|
||||
async def initial(self,params:Dict = None) -> bool:
|
||||
pass
|
||||
|
||||
def append_envs(self,envs:Dict[str,BaseEnvironment]):
|
||||
self.envs.update(envs)
|
||||
self.env = CompositeEnvironment(self.envs)
|
||||
|
||||
def _format_content_by_env_value(self,content:str,env)->str:
|
||||
return content.format_map(env)
|
||||
|
||||
async def _execute_inner_func(self,inner_func_call_node,prompt: LLMPrompt,stack_limit = 1) -> ComputeTaskResult:
|
||||
async def _execute_inner_func(self,inner_func_call_node:Dict,prompt: LLMPrompt,stack_limit = 1) -> ComputeTaskResult:
|
||||
arguments = None
|
||||
stack_limit = stack_limit - 1
|
||||
try:
|
||||
@@ -109,7 +88,7 @@ class BaseLLMProcess(ABC):
|
||||
arguments = json.loads(inner_func_call_node.get("arguments"))
|
||||
logger.info(f"LLMProcess execute inner func:{func_name} :\n\t {json.dumps(arguments)}")
|
||||
|
||||
func_node : AIFunction = await self.get_inner_function(func_name)
|
||||
func_node : AIFunction = await self.get_inner_function_for_exec(func_name)
|
||||
if func_node is None:
|
||||
result_str:str = f"execute {func_name} error,function not found"
|
||||
else:
|
||||
@@ -172,6 +151,7 @@ class BaseLLMProcess(ABC):
|
||||
else:
|
||||
resp_mode = "text"
|
||||
|
||||
# Action define in prompt, will be execute after llm compute
|
||||
prompt = await self.prepare_prompt(input)
|
||||
max_result_token = self.max_token - ComputeKernel.llm_num_tokens(prompt,self.model_name)
|
||||
if max_result_token < MIN_PREDICT_TOKEN_LEN:
|
||||
@@ -209,11 +189,61 @@ class BaseLLMProcess(ABC):
|
||||
llm_result = LLMResult.from_str(task_result.result_str)
|
||||
|
||||
# use action to save history?
|
||||
if llm_result.action_list or len(llm_result.action_list) > 0:
|
||||
await self.post_llm_process(llm_result.action_list,input,llm_result)
|
||||
await self.post_llm_process(llm_result.action_list,input,llm_result)
|
||||
|
||||
return llm_result
|
||||
|
||||
class LLMAgentBaseProcess(BaseLLMProcess):
|
||||
def __init__(self) -> None:
|
||||
super().__init__()
|
||||
|
||||
self.role_description:str = None
|
||||
self.process_description:str = None
|
||||
self.reply_format:str = None
|
||||
self.context : str = None
|
||||
|
||||
self.known_info_tips :str = None
|
||||
self.tools_tips:str = None
|
||||
|
||||
self.workspace : AgentWorkspace = None # If Workspace is not none , enable Agent Tasklist
|
||||
self.memory : AgentMemory = None
|
||||
self.kb = None
|
||||
|
||||
async def load_default_config(self) -> bool:
|
||||
return True
|
||||
|
||||
|
||||
async def load_from_config(self, config: dict,is_load_default=True) -> Coroutine[Any, Any, bool]:
|
||||
if is_load_default:
|
||||
await self.load_default_config()
|
||||
|
||||
if await super().load_from_config(config) is False:
|
||||
return False
|
||||
|
||||
self.role_description = config.get("role_desc")
|
||||
if self.role_description is None:
|
||||
logger.error(f"role_description not found in config")
|
||||
return False
|
||||
|
||||
if config.get("process_description"):
|
||||
self.process_description = config.get("process_description")
|
||||
|
||||
if config.get("reply_format"):
|
||||
self.reply_format = config.get("reply_format")
|
||||
|
||||
if config.get("context"):
|
||||
self.context = config.get("context")
|
||||
|
||||
if config.get("known_info_tips"):
|
||||
self.known_info_tips = config.get("known_info_tips")
|
||||
|
||||
if config.get("tools_tips"):
|
||||
self.tools_tips = config.get("tools_tips")
|
||||
|
||||
if config.get("knowledge_base"):
|
||||
self.kb = config.get("knowledge_base")
|
||||
|
||||
|
||||
class LLMAgentMessageProcess(BaseLLMProcess):
|
||||
def __init__(self) -> None:
|
||||
super().__init__()
|
||||
@@ -226,27 +256,13 @@ class LLMAgentMessageProcess(BaseLLMProcess):
|
||||
self.known_info_tips :str = None
|
||||
self.tools_tips:str = None
|
||||
|
||||
self.enable_inner_functions : Dict[str,bool] = None
|
||||
self.enable_actions : Dict[str,AIOperation] = None
|
||||
self.actions_desc : Dict[str,Dict] = None
|
||||
self.workspace : AgentWorkspace = None
|
||||
|
||||
self.workspace : AgentWorkspace = None # If Workspace is not none , enable Agent Tasklist
|
||||
self.memory : AgentMemory = None
|
||||
self.enable_kb = False
|
||||
self.kb = None
|
||||
|
||||
def init_actions(self):
|
||||
self.enable_actions = {}
|
||||
self.actions_desc = {}
|
||||
self.enable_actions.update(self.memory.get_actions())
|
||||
if self.workspace:
|
||||
self.enable_actions.update(self.workspace.get_actions())
|
||||
if self.enable_kb:
|
||||
self.enable_actions.update(self.kb.get_actions())
|
||||
self.llm_context : LLMProcessContext = None
|
||||
|
||||
for name,op in self.enable_actions.items():
|
||||
self.actions_desc[name] = op.get_description()
|
||||
|
||||
async def initial(self,params:Dict = None) -> bool:
|
||||
self.memory = params.get("memory")
|
||||
if self.memory is None:
|
||||
@@ -254,7 +270,7 @@ class LLMAgentMessageProcess(BaseLLMProcess):
|
||||
return False
|
||||
self.workspace = params.get("workspace")
|
||||
|
||||
self.init_actions()
|
||||
|
||||
return True
|
||||
|
||||
async def load_default_config(self) -> bool:
|
||||
@@ -290,14 +306,18 @@ class LLMAgentMessageProcess(BaseLLMProcess):
|
||||
|
||||
if config.get("enable_kb"):
|
||||
self.enable_kb = config.get("enable_kb") == "true"
|
||||
|
||||
if config.get("enable_function"):
|
||||
self.enable_inner_functions = config.get("enable_function")
|
||||
|
||||
if config.get("enable_actions"):
|
||||
self.enable_actions = config.get("enable_actions")
|
||||
|
||||
self.llm_context = SimpleLLMContext()
|
||||
if config.get("llm_context"):
|
||||
self.llm_context.load_from_config(config.get("llm_context"))
|
||||
|
||||
|
||||
|
||||
def check_and_to_base64(self, image_path: str) -> str:
|
||||
if image_utils.is_file(image_path):
|
||||
return image_utils.to_base64(image_path, (1024, 1024))
|
||||
else:
|
||||
return image_path
|
||||
|
||||
|
||||
async def get_prompt_from_msg(self,msg:AgentMsg) -> LLMPrompt:
|
||||
msg_prompt = LLMPrompt()
|
||||
@@ -334,8 +354,9 @@ class LLMAgentMessageProcess(BaseLLMProcess):
|
||||
|
||||
async def get_action_desc(self) -> Dict:
|
||||
result = {}
|
||||
for name,op in self.enable_actions.items():
|
||||
result[name] = op.get_description()
|
||||
actions_list = self.llm_context.get_all_ai_action()
|
||||
for action in actions_list:
|
||||
result[action.get_name()] = action.get_description()
|
||||
return result
|
||||
|
||||
async def sender_info(self,msg:AgentMsg)->str:
|
||||
@@ -420,14 +441,16 @@ class LLMAgentMessageProcess(BaseLLMProcess):
|
||||
if self.tools_tips:
|
||||
system_prompt_dict["tools_tips"] = self.tools_tips
|
||||
#prompt.append_system_message(self.tools_tips)
|
||||
prompt.inner_functions.extend(self.get_inner_function_desc_from_env())
|
||||
#self.llm_context.
|
||||
|
||||
if self.workspace:
|
||||
prompt.inner_functions.extend(self.aifunction_to_inner_function(self.workspace.get_inner_function_desc()))
|
||||
#TODO eanble workspace functions?
|
||||
logger.info(f"workspace is not none,enable workspace functions")
|
||||
|
||||
## 给予查询KB的权限
|
||||
if self.enable_kb:
|
||||
prompt.inner_functions.extend(self.get_inner_function_desc_from_kb())
|
||||
logger.info(f"enable kb")
|
||||
|
||||
|
||||
prompt.append_system_message(json.dumps(system_prompt_dict))
|
||||
## 扩展已知信息 (这可能是一个LLM过程)
|
||||
@@ -436,35 +459,41 @@ class LLMAgentMessageProcess(BaseLLMProcess):
|
||||
return prompt
|
||||
|
||||
|
||||
async def get_inner_function(self,func_name:str) -> AIFunction:
|
||||
return self.workspace.inner_functions.get(func_name)
|
||||
async def get_inner_function_for_exec(self,func_name:str) -> AIFunction:
|
||||
return self.llm_context.get_ai_function(func_name)
|
||||
|
||||
async def post_llm_process(self,actions:List[ActionItem],input:Dict,llm_result:LLMResult) -> bool:
|
||||
msg = input.get("msg")
|
||||
async def post_llm_process(self,actions:List[ActionNode],input:Dict,llm_result:LLMResult) -> bool:
|
||||
msg:AgentMsg = input.get("msg")
|
||||
if msg.msg_type == AgentMsgType.TYPE_GROUPMSG:
|
||||
resp_msg = msg.create_group_resp_msg(self.memory.agent_id,llm_result.resp)
|
||||
else:
|
||||
resp_msg = msg.create_resp_msg(llm_result.resp)
|
||||
|
||||
llm_result.raw_result["resp_msg"] = resp_msg
|
||||
llm_result.raw_result["_resp_msg"] = resp_msg
|
||||
|
||||
for action_item in actions:
|
||||
op : AIOperation = self.enable_actions.get(action_item.name)
|
||||
op : AIAction = self.llm_context.get_ai_action(action_item.name)
|
||||
if op:
|
||||
if action_item.parms is None:
|
||||
action_item.parms = {}
|
||||
|
||||
action_item.parms["input"] = input
|
||||
action_item.parms["resp_msg"] = resp_msg
|
||||
action_item.parms["llm_result"] = llm_result
|
||||
action_item.parms["start_at"] = datetime.now()
|
||||
action_item.parms["creator"] = self.memory.agent_id
|
||||
action_item.parms["result"] = await op.execute(action_item.parms)
|
||||
action_item.parms["end_at"] = datetime.now()
|
||||
action_item.parms["_input"] = input
|
||||
action_item.parms["_memory"] = self.memory
|
||||
action_item.parms["_workspace"] = self.workspace
|
||||
action_item.parms["_resp_msg"] = resp_msg
|
||||
action_item.parms["_llm_result"] = llm_result
|
||||
action_item.parms["_start_at"] = datetime.now()
|
||||
action_item.parms["_agentid"] = self.memory.agent_id
|
||||
|
||||
action_item.parms["_result"] = await op.execute(action_item.parms)
|
||||
action_item.parms["_end_at"] = datetime.now()
|
||||
else:
|
||||
logger.warn(f"action {action_item.name} not found")
|
||||
return False
|
||||
|
||||
|
||||
chatsession = self.memory.get_session_from_msg(msg)
|
||||
chatsession.append(msg)
|
||||
chatsession.append(resp_msg)
|
||||
return True
|
||||
|
||||
|
||||
@@ -473,25 +502,50 @@ class ReviewTaskProcess(BaseLLMProcess):
|
||||
def __init__(self) -> None:
|
||||
super().__init__()
|
||||
|
||||
async def load_from_config(self, config: dict) -> Coroutine[Any, Any, bool]:
|
||||
self.role_description:str = None
|
||||
self.process_description:str = None
|
||||
self.reply_format = None
|
||||
|
||||
# 虽然在架构上LLM Process可以很容易的去Call另一个Process，但实际应用中还是应该慎重的保持LLM Process的简单性
|
||||
#self.do_task_llm_process : BaseLLMProcess = None
|
||||
|
||||
async def initial(self,params:Dict = None) -> bool:
|
||||
self.memory = params.get("memory")
|
||||
if self.memory is None:
|
||||
logger.error(f"LLMAgeMessageProcess initial failed! memory not found")
|
||||
return False
|
||||
self.workspace = params.get("workspace")
|
||||
|
||||
|
||||
return True
|
||||
async def load_from_config(self, config: dict):
|
||||
if await super().load_from_config(config) is False:
|
||||
return False
|
||||
|
||||
async def prepare_prompt(self) -> LLMPrompt:
|
||||
prompt = LLMPrompt()
|
||||
pass
|
||||
system_prompt_dict = {}
|
||||
system_prompt_dict["role_description"] = self.role_description
|
||||
system_prompt_dict["process_rule"] = self.process_description
|
||||
system_prompt_dict["reply_format"] = self.reply_format
|
||||
|
||||
return prompt
|
||||
|
||||
|
||||
async def get_inner_function(self,func_name:str) -> AIFunction:
|
||||
async def get_review_task_actions(self) -> Dict[str,Dict]:
|
||||
pass
|
||||
|
||||
async def post_llm_process(self,actions:List[ActionItem]) -> bool:
|
||||
async def get_inner_function_for_exec(self,func_name:str) -> AIFunction:
|
||||
pass
|
||||
|
||||
async def post_llm_process(self,actions:List[ActionNode]) -> bool:
|
||||
pass
|
||||
|
||||
class QuickReviewTaskProcess(BaseLLMProcess):
|
||||
def __init__(self) -> None:
|
||||
super().__init__()
|
||||
|
||||
async def load_from_config(self, config: dict) -> Coroutine[Any, Any, bool]:
|
||||
async def load_from_config(self, config: dict):
|
||||
if await super().load_from_config(config) is False:
|
||||
return False
|
||||
|
||||
@@ -499,17 +553,17 @@ class QuickReviewTaskProcess(BaseLLMProcess):
|
||||
prompt = LLMPrompt()
|
||||
pass
|
||||
|
||||
async def get_inner_function(self,func_name:str) -> AIFunction:
|
||||
async def get_inner_function_for_exec(self,func_name:str) -> AIFunction:
|
||||
pass
|
||||
|
||||
async def post_llm_process(self,actions:List[ActionItem]) -> bool:
|
||||
async def post_llm_process(self,actions:List[ActionNode]) -> bool:
|
||||
pass
|
||||
|
||||
class DoTodoProcess(BaseLLMProcess):
|
||||
def __init__(self) -> None:
|
||||
super().__init__()
|
||||
|
||||
async def load_from_config(self, config: dict) -> Coroutine[Any, Any, bool]:
|
||||
async def load_from_config(self, config: dict):
|
||||
if await super().load_from_config(config) is False:
|
||||
return False
|
||||
|
||||
@@ -517,10 +571,10 @@ class DoTodoProcess(BaseLLMProcess):
|
||||
prompt = LLMPrompt()
|
||||
pass
|
||||
|
||||
async def get_inner_function(self,func_name:str) -> AIFunction:
|
||||
async def get_inner_function_for_exec(self,func_name:str) -> AIFunction:
|
||||
pass
|
||||
|
||||
async def post_llm_process(self,actions:List[ActionItem]) -> bool:
|
||||
async def post_llm_process(self,actions:List[ActionNode]) -> bool:
|
||||
pass
|
||||
|
||||
|
||||
@@ -536,10 +590,10 @@ class CheckTodoProcess(BaseLLMProcess):
|
||||
prompt = LLMPrompt()
|
||||
pass
|
||||
|
||||
async def get_inner_function(self,func_name:str) -> AIFunction:
|
||||
async def get_inner_function_for_exec(self,func_name:str) -> AIFunction:
|
||||
pass
|
||||
|
||||
async def post_llm_process(self,actions:List[ActionItem]) -> bool:
|
||||
async def post_llm_process(self,actions:List[ActionNode]) -> bool:
|
||||
pass
|
||||
|
||||
class SelfLearningProcess(BaseLLMProcess):
|
||||
@@ -554,10 +608,10 @@ class SelfLearningProcess(BaseLLMProcess):
|
||||
prompt = LLMPrompt()
|
||||
pass
|
||||
|
||||
async def get_inner_function(self,func_name:str) -> AIFunction:
|
||||
async def get_inner_function_for_exec(self,func_name:str) -> AIFunction:
|
||||
pass
|
||||
|
||||
async def post_llm_process(self,actions:List[ActionItem]) -> bool:
|
||||
async def post_llm_process(self,actions:List[ActionNode]) -> bool:
|
||||
pass
|
||||
|
||||
class SelfThinkingProcess(BaseLLMProcess):
|
||||
@@ -568,14 +622,82 @@ class SelfThinkingProcess(BaseLLMProcess):
|
||||
if await super().load_from_config(config) is False:
|
||||
return False
|
||||
|
||||
|
||||
async def _get_history_prompt_for_think(self,chatsession,summary:str,system_token_len:int,pos:int)->(LLMPrompt,int):
|
||||
history_len = (self.max_token_size * 0.7) - system_token_len
|
||||
|
||||
messages = chatsession.read_history(self.history_len,pos,"natural") # read
|
||||
result_token_len = 0
|
||||
result_prompt = LLMPrompt()
|
||||
have_summary = False
|
||||
if summary is not None:
|
||||
if len(summary) > 1:
|
||||
have_summary = True
|
||||
|
||||
if have_summary:
|
||||
result_prompt.messages.append({"role":"user","content":summary})
|
||||
result_token_len -= len(summary)
|
||||
else:
|
||||
result_prompt.messages.append({"role":"user","content":"There is no summary yet."})
|
||||
result_token_len -= 6
|
||||
|
||||
read_history_msg = 0
|
||||
history_str : str = ""
|
||||
for msg in messages:
|
||||
read_history_msg += 1
|
||||
dt = datetime.datetime.fromtimestamp(float(msg.create_time))
|
||||
formatted_time = dt.strftime('%y-%m-%d %H:%M:%S')
|
||||
record_str = f"{msg.sender},[{formatted_time}]\n{msg.body}\n"
|
||||
history_str = history_str + record_str
|
||||
|
||||
history_len -= len(msg.body)
|
||||
result_token_len += len(msg.body)
|
||||
if history_len < 0:
|
||||
logger.warning(f"_get_prompt_from_session reach limit of token,just read {read_history_msg} history message.")
|
||||
break
|
||||
|
||||
result_prompt.messages.append({"role":"user","content":history_str})
|
||||
return result_prompt,pos+read_history_msg
|
||||
|
||||
async def _think_chatsession(self,session_id):
|
||||
if self.agent_think_prompt is None:
|
||||
return
|
||||
logger.info(f"agent {self.agent_id} think session {session_id}")
|
||||
chatsession = AIChatSession.get_session_by_id(session_id,self.chat_db)
|
||||
|
||||
while True:
|
||||
cur_pos = chatsession.summarize_pos
|
||||
summary = chatsession.summary
|
||||
prompt:LLMPrompt = LLMPrompt()
|
||||
#prompt.append(self._get_agent_prompt())
|
||||
prompt.append(await self._get_agent_think_prompt())
|
||||
system_prompt_len = ComputeKernel.llm_num_tokens(prompt)
|
||||
#think env?
|
||||
history_prompt,next_pos = await self._get_history_prompt_for_think(chatsession,summary,system_prompt_len,cur_pos)
|
||||
prompt.append(history_prompt)
|
||||
is_finish = next_pos - cur_pos < 2
|
||||
if is_finish:
|
||||
logger.info(f"agent {self.agent_id} think session {session_id} is finished!,no more history")
|
||||
break
|
||||
#3) llm summarize chat history
|
||||
task_result:ComputeTaskResult = await self.do_llm_complection(prompt)
|
||||
if task_result.result_code != ComputeTaskResultCode.OK:
|
||||
logger.error(f"think_chatsession llm compute error:{task_result.error_str}")
|
||||
break
|
||||
else:
|
||||
new_summary= task_result.result_str
|
||||
logger.info(f"agent {self.agent_id} think session {session_id} from {cur_pos} to {next_pos} summary:{new_summary}")
|
||||
chatsession.update_think_progress(next_pos,new_summary)
|
||||
return
|
||||
|
||||
async def prepare_prompt(self) -> LLMPrompt:
|
||||
prompt = LLMPrompt()
|
||||
pass
|
||||
|
||||
async def get_inner_function(self,func_name:str) -> AIFunction:
|
||||
async def get_inner_function_for_exec(self,func_name:str) -> AIFunction:
|
||||
pass
|
||||
|
||||
async def post_llm_process(self,actions:List[ActionItem]) -> bool:
|
||||
async def post_llm_process(self,actions:List[ActionNode]) -> bool:
|
||||
pass
|
||||
|
||||
class LLMProcessLoader:
|
||||
|
||||
Reference in New Issue
Block a user