AutoGPTFlow.py

import sys
from typing import Dict, Any

from flows.base_flows import CircularFlow
from flows.utils import logging

logging.set_verbosity_debug()

log = logging.get_logger(__name__)

from flow_modules.aiflows.ControllerExecutorFlowModule import ControllerAtomicFlow
from flow_modules.aiflows.VectorStoreFlowModule import ChromaDBFlow

class AutoGPTFlow(CircularFlow):
    """ 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 _on_reach_max_round(self):
        """ This method is called when the flow reaches the max_rounds."""
        self._state_update_dict({
            "answer": "The maximum amount of rounds was reached before the model found an answer.",
            "status": "unfinished"
        })

    @staticmethod
    def _get_memory_key(flow_state):
        """ 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 = flow_state.get("goal")
        last_command = flow_state.get("command")
        last_command_args = flow_state.get("command_args")
        last_observation = flow_state.get("observation")
        last_human_feedback = 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

    @CircularFlow.input_msg_payload_builder
    def prepare_memory_read_input(self, flow_state: Dict[str, Any], dst_flow: ChromaDBFlow) -> Dict[str, Any]:
        """ This method prepares the input for the Memory Flow. It is called before the Memory Flow is called.
            A (very) basic example implementation of how the memory retrieval could be constructed.
            
        :param flow_state: The state of the flow
        :type flow_state: Dict[str, Any]
        :param dst_flow: The destination flow
        :type dst_flow: Flow
        :return: The input message for the Memory Flow
        :rtype: Dict[str, Any]
        """
        query = self._get_memory_key(flow_state)

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

    @CircularFlow.output_msg_payload_processor
    def prepare_memory_read_output(self, output_payload: Dict[str, Any], src_flow: ControllerAtomicFlow):
        """ This method processes the output of the Memory Flow. It is called after the Memory Flow is called.
        
        :param output_payload: The output payload of the Memory Flow
        :type output_payload: Dict[str, Any]
        :param src_flow: The source flow
        :type src_flow: Flow
        :return: The processed output payload
        :rtype: Dict[str, Any]
        """
        retrieved_memories = output_payload["retrieved"][0][1:]
        return {"memory": "\n".join(retrieved_memories)}

    @CircularFlow.input_msg_payload_builder
    def prepare_memory_write_input(self, flow_state: Dict[str, Any], dst_flow: ChromaDBFlow) -> Dict[str, Any]:
        """ This method prepares the input for the Memory Flow. It is called before the Memory Flow is called.
            A (very) basic example implementation of how the memory population could be constructed.
            
        :param flow_state: The state of the flow
        :type flow_state: Dict[str, Any]
        :param dst_flow: The destination flow
        :type dst_flow: Flow
        :return: The input message to write the Memory Flow
        :rtype: Dict[str, Any]
        """""
        query = self._get_memory_key(flow_state)

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

    @CircularFlow.output_msg_payload_processor
    def detect_finish_or_continue(self, output_payload: Dict[str, Any], src_flow: ControllerAtomicFlow) -> Dict[
        str, Any]:
        """ This method detects whether the Controller flow has generated a "finish" command or not to terminate the flow. . It is called after the Controller Flow is called.
        
        :param output_payload: The output payload of the Controller Flow
        :type output_payload: Dict[str, Any]
        :param src_flow: The source flow
        :type src_flow: Flow
        :return: The processed output payload
        :rtype: Dict[str, Any]
        """
        command = output_payload["command"]
        if command == "finish":
            return {
                "EARLY_EXIT": True,
                "answer": output_payload["command_args"]["answer"],
                "status": "finished"
            }
        else:
            return output_payload

    @CircularFlow.output_msg_payload_processor
    def detect_finish_in_human_input(self, output_payload: Dict[str, Any], src_flow: ControllerAtomicFlow) -> Dict[
        str, Any]:
        """ This method detects whether the HumanFeedback (the human/user) flow has generated a "finish" command or not to terminate the flow. It is called after the HumanFeedback Flow is called.
        
        :param output_payload: The output payload of the HumanFeedback Flow
        :type output_payload: Dict[str, Any]
        :param src_flow: The source flow
        :type src_flow: Flow
        :return: The processed output payload
        :rtype: Dict[str, Any]
        """
        
        human_feedback = output_payload["human_input"]
        if human_feedback.strip().lower() == "q":
            return {
                "EARLY_EXIT": True,
                "answer": "The user has chosen to exit before a final answer was generated.",
                "status": "unfinished"
            }

        return {"human_feedback": human_feedback}