metadata
license: mit
Table of Contents
AutoGPTFlow
AutoGPTFlow Objects
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".
Arguments:
flow_config(Dict[str,Any]): The configuration of the flow. Contains the parameters described above and the parameters required by the parent class (CircularFlow).subflows(List[Flow]): A list of subflows constituating the circular flow. Required when instantiating the subflow programmatically (it replaces subflows_config from flow_config).
prepare_memory_read_input
@CircularFlow.input_msg_payload_builder
def prepare_memory_read_input(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.
Arguments:
flow_state(Dict[str, Any]): The state of the flowdst_flow(Flow): The destination flow
Returns:
Dict[str, Any]: The input message for the Memory Flow
prepare_memory_read_output
@CircularFlow.output_msg_payload_processor
def prepare_memory_read_output(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.
Arguments:
output_payload(Dict[str, Any]): The output payload of the Memory Flowsrc_flow(Flow): The source flow
Returns:
Dict[str, Any]: The processed output payload
detect_finish_or_continue
@CircularFlow.output_msg_payload_processor
def detect_finish_or_continue(
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.
Arguments:
output_payload(Dict[str, Any]): The output payload of the Controller Flowsrc_flow(Flow): The source flow
Returns:
Dict[str, Any]: The processed output payload
detect_finish_in_human_input
@CircularFlow.output_msg_payload_processor
def detect_finish_in_human_input(
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.
Arguments:
output_payload(Dict[str, Any]): The output payload of the HumanFeedback Flowsrc_flow(Flow): The source flow
Returns:
Dict[str, Any]: The processed output payload