ControllerAtomicFlow.py

import json
from copy import deepcopy
from typing import Any, Dict, List
from flow_modules.aiflows.OpenAIChatFlowModule import OpenAIChatAtomicFlow

from dataclasses import dataclass


@dataclass
class Command:
    name: str
    description: str
    input_args: List[str]


class ControllerAtomicFlow(OpenAIChatAtomicFlow):
    def __init__(self, commands: List[Command], **kwargs):
        super().__init__(**kwargs)
        self.system_message_prompt_template = self.system_message_prompt_template.partial(
            commands=self._build_commands_manual(commands)
        )

    @staticmethod
    def _build_commands_manual(commands: List[Command]) -> str:
        ret = ""
        for i, command in enumerate(commands):
            command_input_json_schema = json.dumps(
                {input_arg: f"YOUR_{input_arg.upper()}" for input_arg in command.input_args})
            ret += f"{i + 1}. {command.name}: {command.description} Input arguments (given in the JSON schema): {command_input_json_schema}\n"
        return ret

    @classmethod
    def instantiate_from_config(cls, config):
        flow_config = deepcopy(config)

        kwargs = {"flow_config": flow_config}

        # ~~~ Set up prompts ~~~
        kwargs.update(cls._set_up_prompts(flow_config))
        kwargs.update(cls._set_up_backend(flow_config))

        # ~~~ Set up commands ~~~
        commands = flow_config["commands"]
        commands = [
            Command(name, command_conf["description"], command_conf["input_args"]) for name, command_conf in
            commands.items()
        ]
        kwargs.update({"commands": commands})

        # ~~~ Instantiate flow ~~~
        return cls(**kwargs)

    def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        api_output = super().run(input_data)["api_output"].strip()
        response = json.loads(api_output)
        return response