Add backend and frontend

backend/AgentCoord/RehearsalEngine_V2/Action/baseAction.py

@@ -0,0 +1,95 @@
+from AgentCoord.LLMAPI.LLMAPI import LLM_Completion
+from AgentCoord.util.colorLog import print_colored
+import copy
+PROMPT_TEMPLATE_TAKE_ACTION_BASE = '''
+Your name is {agentName}. You will play the role as the Profile indicates.
+Profile: {agentProfile}
+
+You are within a multi-agent collaboration for the "Current Task". 
+Now it's your turn to take action. Read the "Context Information" and take your action following "Instruction for Your Current Action". 
+Note: Important Input for your action are marked with *Important Input*
+
+## Context Information
+
+### General Goal (The "Current Task" is indeed a substep of the general goal)
+{General_Goal}
+
+### Current Task
+{Current_Task_Description}
+
+### Input Objects (Input objects for the current task)
+{Input_Objects}
+
+### History Action
+{History_Action}
+
+## Instruction for Your Current Action 
+{Action_Description} 
+
+{Action_Custom_Note}
+
+'''
+
+PROMPT_TEMPLATE_INPUTOBJECT_RECORD = '''
+{{
+{Important_Mark}
+{Input_Objects_Name}:
+{Input_Objects_Content}
+}}
+
+'''
+
+PROMPT_TEMPLATE_ACTION_RECORD = '''
+{{
+{Important_Mark}
+{AgentName} ({Action_Description}):
+{Action_Result}
+}}
+
+'''
+
+class BaseAction():
+    def __init__(self, info, OutputName, KeyObjects) -> None:
+        self.KeyObjects = KeyObjects
+        self.OutputName = OutputName
+        self.Action_Result = None
+        self.info = info
+        self.Action_Custom_Note = ""
+
+    def postRun_Callback(self) -> None:
+        return
+
+    def run(self, General_Goal, TaskDescription, agentName, AgentProfile_Dict, InputName_List, OutputName, KeyObjects, ActionHistory):
+        # construct input record
+        inputObject_Record = ""
+        for InputName in InputName_List:
+            ImportantInput_Identifier = "InputObject:" + InputName
+            if ImportantInput_Identifier in self.info["ImportantInput"]:
+                Important_Mark = "*Important Input*"
+            else:
+                Important_Mark = ""
+            inputObject_Record += PROMPT_TEMPLATE_INPUTOBJECT_RECORD.format(Input_Objects_Name = InputName, Input_Objects_Content = KeyObjects[InputName], Important_Mark = Important_Mark)
+        
+        # construct history action record
+        action_Record = ""
+        for actionInfo in ActionHistory:
+            ImportantInput_Identifier = "ActionResult:" + actionInfo["ID"]
+            if ImportantInput_Identifier in self.info["ImportantInput"]:
+                Important_Mark = "*Important Input*"
+            else:
+                Important_Mark = ""
+            action_Record += PROMPT_TEMPLATE_ACTION_RECORD.format(AgentName = actionInfo["AgentName"], Action_Description = actionInfo["AgentName"], Action_Result = actionInfo["Action_Result"], Important_Mark = Important_Mark)
+
+        prompt = PROMPT_TEMPLATE_TAKE_ACTION_BASE.format(agentName = agentName, agentProfile = AgentProfile_Dict[agentName], General_Goal = General_Goal, Current_Task_Description = TaskDescription, Input_Objects = inputObject_Record, History_Action = action_Record, Action_Description = self.info["Description"], Action_Custom_Note = self.Action_Custom_Note)
+        print_colored(text = prompt, text_color="red")
+        messages = [{"role":"system", "content": prompt}]
+        ActionResult = LLM_Completion(messages,True,False)
+        ActionInfo_with_Result = copy.deepcopy(self.info)
+        ActionInfo_with_Result["Action_Result"] = ActionResult
+
+        # run customizable callback
+        self.Action_Result = ActionResult
+        self.postRun_Callback()
+        return ActionInfo_with_Result
+
+