AutoGPTFlow.py

import sys
from typing import Dict, Any

from aiflows.utils import logging

logging.set_verbosity_debug()

log = logging.get_logger(__name__)
from aiflows.interfaces import KeyInterface
from flow_modules.aiflows.ControllerExecutorFlowModule import ControllerExecutorFlow

class AutoGPTFlow(ControllerExecutorFlow):
    """ This class implements a (very basic) AutoGPT flow. It is a flow that consists of multiple sub-flows that are executed circularly. It Contains the following subflows:
    
    - A Controller Flow: A Flow that controls which subflow of the Executor Flow to execute next.
    - A Memory Flow: A Flow used to save and retrieve messages or memories which might be useful for the Controller Flow.
    - A HumanFeedback Flow: A flow use to get feedback from the user/human.
    - A Executor Flow: A Flow that executes commands generated by the Controller Flow. Typically it's a branching flow (see BranchingFlow) and the commands are which branch to execute next.

    An illustration of the flow is as follows:
    
        | -------> Memory Flow -------> Controller Flow ------->|
        ^                                                       |      
        |                                                       |
        |                                                       v
        | <----- HumanFeedback Flow <------- Executor Flow <----|
    
    *Configuration Parameters*:
    
    - `name` (str): The name of the flow. Default is "AutoGPTFlow".
    - `description` (str): A description of the flow.  Default is "An example implementation of AutoGPT with Flows."
    - `max_rounds` (int): The maximum number of rounds the circular flow can run for. Default is 30.
    - `early_exit_key` (str): The key that is used to terminate the flow early. Default is "EARLY_EXIT".
    - `subflows_config` (Dict[str,Any]): A dictionary of subflows configurations. Default:
        - `Controller` (Dict[str,Any]): The configuration of the Controller Flow. By default the controller flow is a ControllerAtomicFlow (see ControllerExecutorFlowModule). It's default values are
        defined in ControllerAtomicFlow.yaml of the ControllerExecutorFlowModule. Except for the following parameters who are overwritten by the AutoGPTFlow in AutoGPTFlow.yaml:
            - `finish` (Dict[str,Any]): The configuration of the finish command (used to terminate the flow early when the controller has accomplished its goal).
                - `description` (str):  The description of the command. Default is "The finish command is used to terminate the flow early when the controller has accomplished its goal."
                - `input_args` (List[str]): The list of expected keys to run the finish command. Default is ["answer"].
                - `human_message_prompt_template`(Dict[str,Any]): The prompt template used to generate the message that is shown to the user/human when the finish command is executed. Default is:
                    - `template` (str): The template of the humand message prompt (see AutoGPTFlow.yaml for default template)
                    - `input_variables` (List[str]): The list of variables to be included in the template. Default is ["observation", "human_feedback", "memory"].
            - `ìnput_interface_initialized` (List[str]): The input interface that Controller Flow expects except for the first time in the flow. Default is ["observation", "human_feedback", "memory"].
        - `Executor` (Dict[str,Any]): The configuration of the Executor Flow. By default the executor flow is a Branching Flow (see BranchingFlow). It's default values are the default values of the BranchingFlow. Fields to define:
            - `subflows_config` (Dict[str,Any]):  A Dictionary of subflows configurations.The keys are the names of the subflows and the values are the configurations of the subflows. Each subflow is a branch of the branching flow.
        - `HumanFeedback` (Dict[str,Any]): The configuration of the HumanFeedback Flow. By default the human feedback flow is a HumanStandardInputFlow (see HumanStandardInputFlowModule ).
        It's default values are specified in the REAMDE.md of HumanStandardInputFlowModule. Except for the following parameters who are overwritten by the AutoGPTFlow in AutoGPTFlow.yaml:
            - `request_multi_line_input_flag` (bool): Flag to request multi-line input. Default is False.
            - `query_message_prompt_template` (Dict[str,Any]): The prompt template presented to the user/human to request input. Default is:
                - `template` (str): The template of the query message prompt (see AutoGPTFlow.yaml for default template)
                - `input_variables` (List[str]): The list of variables to be included in the template. Default is ["goal","command","command_args",observation"]
            - input_interface_initialized (List[str]):  The input interface that HumanFeeback Flow expects except for the first time in the flow. Default is ["goal","command","command_args",observation"]
        - `Memory` (Dict[str,Any]): The configuration of the Memory Flow. By default the memory flow is a ChromaDBFlow (see VectorStoreFlowModule). It's default values are defined in ChromaDBFlow.yaml of the VectorStoreFlowModule. Except for the following parameters who are overwritten by the AutoGPTFlow in AutoGPTFlow.yaml:
            - `n_results`: The number of results to retrieve from the memory. Default is 2.
    - `topology` (List[Dict[str,Any]]): The topology of the flow which is "circular". By default, the topology is the one shown in the illustration above (the topology is also described in AutoGPTFlow.yaml).

        
    *Input Interface*:
            
    - `goal` (str): The goal of the flow.
        
    *Output Interface*:

    - `answer` (str): The answer of the flow.
    - `status` (str): The status of the flow. It can be "finished" or "unfinished".
            
    :param flow_config: The configuration of the flow. Contains the parameters described above and the parameters required by the parent class (CircularFlow).
    :type flow_config: Dict[str,Any]
    :param subflows: A list of subflows constituating the circular flow. Required when instantiating the subflow programmatically (it replaces subflows_config from flow_config).
    :type subflows: List[Flow]
    """
    
    
    def __init__(self, **kwargs):
        super().__init__( **kwargs)
        self.rename_human_output_interface = KeyInterface(
            keys_to_rename={"human_input": "human_feedback"}
        )
        
        self.input_interface_controller = KeyInterface(
            keys_to_select = ["goal","observation","human_feedback", "memory"],
        )
        
        self.input_interface_human_feedback = KeyInterface(
            keys_to_select = ["goal","command","command_args","observation"],
        )
        
        self.memory_read_ouput_interface = KeyInterface(
            additional_transformations = [self.prepare_memory_read_output],
            keys_to_select = ["memory"],
        )
        
        self.human_feedback_ouput_interface = KeyInterface(
            keys_to_rename={"human_input": "human_feedback"},
            keys_to_select = ["human_feedback"],
        )
            
        self.next_flow_to_call = {
            None: "MemoryRead",
            "MemoryRead": "Controller",
            "Controller": "Executor",
            "Executor": "HumanFeedback",
            "HumanFeedback": "MemoryWrite",
            "MemoryWrite": "MemoryRead",
        }
            
    def set_up_flow_state(self):
        super().set_up_flow_state()
        self.flow_state["early_exit_flag"] = False
        
    def prepare_memory_read_output(self,data_dict: Dict[str, Any],**kwargs):
        
        retrieved_memories = data_dict["retrieved"][0][1:]
        return {"memory": "\n".join(retrieved_memories)}
    
    def _get_memory_key(self):
        """ This method returns the memory key that is used to retrieve memories from the ChromaDB model.
        
        :param flow_state: The state of the flow
        :type flow_state: Dict[str, Any]
        :return: The current context
        :rtype: str
        """
        goal = self.flow_state.get("goal")
        last_command = self.flow_state.get("command")
        last_command_args = self.flow_state.get("command_args")
        last_observation = self.flow_state.get("observation")
        last_human_feedback = self.flow_state.get("human_feedback")

        if last_command is None:
            return ""

        assert goal is not None, goal
        assert last_command_args is not None, last_command_args
        assert last_observation is not None, last_observation

        current_context = \
            f"""
            == Goal == 
            {goal}
            
            == Command ==
            {last_command}
            == Args
            {last_command_args}
            == Result
            {last_observation}

            == Human Feedback ==
            {last_human_feedback}
            """

        return current_context

    def prepare_memory_read_input(self) -> Dict[str, Any]:
    
        query = self._get_memory_key()

        return {
            "operation": "read",
            "content": query
        }

    def prepare_memory_write_input(self) -> Dict[str, Any]:

        query = self._get_memory_key()

        return {
            "operation": "write",
            "content": str(query)
        }

    
    def call_memory_read(self):
        memory_read_input = self.prepare_memory_read_input()
        
        message = self.package_input_message(
            data = memory_read_input,
            dst_flow = "Memory",
        )
        
        self.subflows["Memory"].get_reply(
            message,
            self.get_instance_id(),
        )

    def call_memory_write(self):
        memory_write_input = self.prepare_memory_write_input()
        
        message = self.package_input_message(
            data = memory_write_input,
            dst_flow = "Memory",
        )
        
        self.subflows["Memory"].get_reply(
            message,
            self.get_instance_id(),
        )
    
    def call_human_feedback(self):
            
        message = self.package_input_message(
            data = self.input_interface_human_feedback(self.flow_state),
            dst_flow = "HumanFeedback",
        )
        
        self.subflows["HumanFeedback"].get_reply(
            message,
            self.get_instance_id(),
        )
             
    def register_data_to_state(self, input_message):
        
        #Making this explicit so it's easier to understand
        #I'm also showing different ways of writing to the state
        # either programmatically or using the _state_update_dict and 
        # input and ouput interface methods
        
        last_called = self.flow_state["last_called"]
        
        if last_called is None:            
            self.flow_state["input_message"] = input_message
            self.flow_state["goal"] = input_message.data["goal"]
        
        elif last_called == "Executor":
            self.flow_state["observation"] = input_message.data
        
        elif last_called == "Controller":
            self._state_update_dict(
                {
                    "command": input_message.data["command"],
                    "command_args": input_message.data["command_args"]
                }
            )
            
            #detect and early exit
            if self.flow_state["command"] == "finish":
                
                self._state_update_dict(
                    {
                        "EARLY_EXIT": True,
                        "answer": self.flow_state["command_args"]["answer"],
                        "status": "finished"
                    }
                )
                self.flow_state["early_exit_flag"] = True
                
                
        elif last_called == "MemoryRead":
            self._state_update_dict(
                self.memory_read_ouput_interface(input_message).data
            )
            
        elif last_called == "HumanFeedback":
            self._state_update_dict(
                self.human_feedback_ouput_interface(input_message).data
            )
            
            #detect early exit
            if self.flow_state["human_feedback"].strip().lower() == "q":
                
                self._state_update_dict(
                    {
                        "EARLY_EXIT": True,
                        "answer": "The user has chosen to exit before a final answer was generated.",
                        "status": "unfinished",
                    }
                )
                
                self.flow_state["early_exit_flag"] = True
        
    def run(self,input_message):
        
        self.register_data_to_state(input_message)
        
        flow_to_call = self.get_next_flow_to_call()

        if self.flow_state.get("early_exit_flag",False):
            self.generate_reply()
            
        elif flow_to_call == "MemoryRead":
            self.call_memory_read()
        
        elif flow_to_call == "Controller":
            self.call_controller()
        
        elif flow_to_call == "Executor":
            self.call_executor()
        
        elif flow_to_call == "HumanFeedback":
            self.call_human_feedback()
        
        elif flow_to_call == "MemoryWrite":
            self.call_memory_write()
            self.flow_state["current_round"] += 1
            
        else:
            self._on_reach_max_round()
            self.generate_reply()
            
        self.flow_state["last_called"] = self.get_next_flow_to_call()