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Rough sketching out of a hello world using our refactored autogpt library. See the tracking issue here: #4770. # Run instructions There are two client applications for Auto-GPT included. ## CLI Application 🌟 **This is the reference application I'm working with for now** 🌟 The first app is a straight CLI application. I have not done anything yet to port all the friendly display stuff from the `logger.typewriter_log` logic. - [Entry Point](https://github.com/Significant-Gravitas/Auto-GPT/blob/re-arch/hello-world/autogpt/core/runner/cli_app/cli.py) - [Client Application](https://github.com/Significant-Gravitas/Auto-GPT/blob/re-arch/hello-world/autogpt/core/runner/cli_app/main.py) To run, you first need a settings file. Run ``` python REPOSITORY_ROOT/autogpt/core/runner/cli_app/cli.py make-settings ``` where `REPOSITORY_ROOT` is the root of the Auto-GPT repository on your machine. This will write a file called `default_agent_settings.yaml` with all the user-modifiable configuration keys to `~/auto-gpt/default_agent_settings.yml` and make the `auto-gpt` directory in your user directory if it doesn't exist). At a bare minimum, you'll need to set `openai.credentials.api_key` to your OpenAI API Key to run the model. You can then run Auto-GPT with ``` python REPOSITORY_ROOT/autogpt/core/runner/cli_app/cli.py make-settings ``` to launch the interaction loop. ## CLI Web App The second app is still a CLI, but it sets up a local webserver that the client application talks to rather than invoking calls to the Agent library code directly. This application is essentially a sketch at this point as the folks who were driving it have had less time (and likely not enough clarity) to proceed. - [Entry Point](https://github.com/Significant-Gravitas/Auto-GPT/blob/re-arch/hello-world/autogpt/core/runner/cli_web_app/cli.py) - [Client Application](https://github.com/Significant-Gravitas/Auto-GPT/blob/re-arch/hello-world/autogpt/core/runner/cli_web_app/client/client.py) - [Server API](https://github.com/Significant-Gravitas/Auto-GPT/blob/re-arch/hello-world/autogpt/core/runner/cli_web_app/server/api.py) To run, you still need to generate a default configuration. You can do ``` python REPOSITORY_ROOT/autogpt/core/runner/cli_web_app/cli.py make-settings ``` It invokes the same command as the bare CLI app, so follow the instructions above about setting your API key. To run, do ``` python REPOSITORY_ROOT/autogpt/core/runner/cli_web_app/cli.py client ``` This will launch a webserver and then start the client cli application to communicate with it. ⚠️ I am not actively developing this application. It is a very good place to get involved if you have web application design experience and are looking to get involved in the re-arch. --------- Co-authored-by: David Wurtz <davidjwurtz@gmail.com> Co-authored-by: Media <12145726+rihp@users.noreply.github.com> Co-authored-by: Richard Beales <rich@richbeales.net> Co-authored-by: Daryl Rodrigo <darylrodrigo@gmail.com> Co-authored-by: Daryl Rodrigo <daryl@orkestro.com> Co-authored-by: Swifty <craigswift13@gmail.com> Co-authored-by: Nicholas Tindle <nick@ntindle.com> Co-authored-by: Merwane Hamadi <merwanehamadi@gmail.com>
75 lines
3.0 KiB
Python
75 lines
3.0 KiB
Python
from importlib import import_module
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from typing import TYPE_CHECKING
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from autogpt.core.plugin.base import (
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PluginLocation,
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PluginService,
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PluginStorageFormat,
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PluginStorageRoute,
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)
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if TYPE_CHECKING:
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from autogpt.core.plugin.base import PluginType
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class SimplePluginService(PluginService):
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@staticmethod
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def get_plugin(plugin_location: dict | PluginLocation) -> "PluginType":
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"""Get a plugin from a plugin location."""
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if isinstance(plugin_location, dict):
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plugin_location = PluginLocation.parse_obj(plugin_location)
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if plugin_location.storage_format == PluginStorageFormat.WORKSPACE:
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return SimplePluginService.load_from_workspace(
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plugin_location.storage_route
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)
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elif plugin_location.storage_format == PluginStorageFormat.INSTALLED_PACKAGE:
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return SimplePluginService.load_from_installed_package(
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plugin_location.storage_route
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)
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else:
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raise NotImplementedError(
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f"Plugin storage format {plugin_location.storage_format} is not implemented."
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)
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####################################
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# Low-level storage format loaders #
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####################################
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@staticmethod
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def load_from_file_path(plugin_route: PluginStorageRoute) -> "PluginType":
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"""Load a plugin from a file path."""
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# TODO: Define an on disk storage format and implement this.
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# Can pull from existing zip file loading implementation
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raise NotImplemented("Loading from file path is not implemented.")
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@staticmethod
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def load_from_import_path(plugin_route: PluginStorageRoute) -> "PluginType":
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"""Load a plugin from an import path."""
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module_path, _, class_name = plugin_route.rpartition(".")
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return getattr(import_module(module_path), class_name)
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@staticmethod
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def resolve_name_to_path(
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plugin_route: PluginStorageRoute, path_type: str
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) -> PluginStorageRoute:
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"""Resolve a plugin name to a plugin path."""
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# TODO: Implement a discovery system for finding plugins by name from known
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# storage locations. E.g. if we know that path_type is a file path, we can
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# search the workspace for it. If it's an import path, we can check the core
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# system and the auto_gpt_plugins package.
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raise NotImplemented("Resolving plugin name to path is not implemented.")
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#####################################
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# High-level storage format loaders #
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#####################################
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@staticmethod
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def load_from_workspace(plugin_route: PluginStorageRoute) -> "PluginType":
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"""Load a plugin from the workspace."""
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plugin = SimplePluginService.load_from_file_path(plugin_route)
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return plugin
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@staticmethod
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def load_from_installed_package(plugin_route: PluginStorageRoute) -> "PluginType":
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plugin = SimplePluginService.load_from_import_path(plugin_route)
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return plugin
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