PluginBase is the plugin class that the simplest plugin inherits.

Inheriting class should implement:

• run() that implements the logic of the plugin
• add_to_schema() for plugin specific item types

## Creating a plugin

The memri pod uses a plugin system to add features to the backend memri backend. Plugins can import your data (importers), change your data (indexers), or call other serivces. Users can define their own plugins to add new behaviour to their memri app. Let's use the following plugin as an example of how we can start plugins.

## classExamplePlugin[source]

ExamplePlugin(**kwargs) :: PluginBase

Memri plugins need to define at least 2 methods: .run() and .add_to_schema(). .run() defines the logic of the plugin. .add_to_schema() defines the schema for the plugin in the pod. Note that currently, add_to_schema requires all item to have all properties defined that are used in the plugin. In the future, we might replace add_to_schema, to be done automatically, based on a declarative schema defined in the plugin.

### Authentication

Many plugins use authentication. For examples, see OAuthAuthenticator or PasswordAuthenticator.

## Running your plugin using the CLI

Plugins can be started using the pymemri run_plugin or simulate_run_plugin_from_frontend CLI. With run_plugin the plugin is invoked directly by spawning a new python process, while simulate_run_plugin_from_frontend requests the pod to spawn a new process, docker container, or kubernetes container, which in calls run_plugin (for more info see simulate_run_plugin_from_frontend. When using run_plugin, you can either pass your run arguments as parameters, or set them as environment variables. If both are set, the CLI will use the passed arguments.

#### run_plugin[source]

run_plugin(pod_full_address:"The pod full address"='http://localhost:3030', plugin_run_id:"Run id of the plugin to be executed"=None, database_key:"Database key of the pod"=None, owner_key:"Owner key of the pod"=None, read_args_from_env:"Read the args from the environment"=False, config_file:"config file for the PluginRun"=None)

To start a plugin on your local machine, you can use the CLI. This will create a client for you, and run the code defined in <myplugin>.run()

run_plugin(config_file="../example_config.json")

reading database_key from /Users/koen/.pymemri/pod_keys/keys.json
owner_key=6973887593419866958592645577399040666193244966403234969395379182

running


## Run from pod

In production, we start plugins by making an API call to the pod, which in turn creates an environment for the plugin and starts it using docker containers, kubernetes containers or a shell script. We can start this process using the simulate_run_plugin_from_frontend CLI. Note that when using docker, provided container name should be "installed" within the Pod environemnt (e.g. docker build -t pymemri . for this repo) in order to start it.

#### simulate_run_plugin_from_frontend[source]

simulate_run_plugin_from_frontend(pod_full_address:"The pod full address"='http://localhost:3030', database_key:"Database key of the pod"=None, owner_key:"Owner key of the pod"=None, container:"Pod container to run frod"=None, plugin_path:"Plugin path"=None, settings_file:"Plugin settings (json)"=None, config_file:"config file for the PluginRun"=None, account_id:"Account id to be used inside the plugin"=None)

client = PodClient()

!simulate_run_plugin_from_frontend --config_file="../example_config.json"

reading database_key from /Users/koen/.pymemri/pod_keys/keys.json
owner_key=6973887593419866958592645577399040666193244966403234969395379182

calling the create api on http://localhost:3030 to make your Pod start a plugin with id ab07B7dC297f9C1Fe7624bcFB28f73C7.
*Check the pod log/console for debug output.*