--- title: Plugins keywords: fastai sidebar: home_sidebar nb_path: "nbs/plugin.pluginbase.ipynb" ---
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.
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.
MyPlugin()
class MyItem(Item):
properties = Item.properties + ["name", "age"]
edges = Item.edges
def __init__(self, name=None, age=None, **kwargs):
super().__init__(**kwargs)
self.name = name
self.age = age
class MyPlugin(PluginBase):
""""""
properties = PluginBase.properties
edges= PluginBase.edges
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.pluginPackage="pymemri.plugin.pluginbase"
def run(self, run, client):
print("running")
client.create(MyItem("some person", 20))
def add_to_schema(self, client):
client.add_to_schema(MyItem("my name", 10))
from pymemri.pod.client import PodClient
client = PodClient()
assert client.add_to_schema(MyPlugin(name="abc", data_query="abc"))
assert client.add_to_schema(PluginRun())
plugin = MyPlugin(name="abc", data_query="abc")
run = PluginRun()
run.add_edge("plugin", plugin)
client.create(run)
client.create(plugin)
client.create_edge(run.get_edges("plugin")[0]);
run = client.get(run.id)
Plugins can be started using the pymemri run_plugin
CLI. To use the CLI, you can either pass your run arguments as parameters, or set them as environment variables. If both are set, the CLI will prefer the passed arguments.
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 --pod_full_address=$DEFAULT_POD_ADDRESS --plugin_run_id=$run.id --owner_key=$client.owner_key \
--database_key=$client.database_key
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 (currently on docker is supported). We can start this process using the CLI by provding --from_pod==True
and providing a --container
(the docker container used by the pod). Note that the provided docker container should be installed within the Pod environemnt (e.g. docker build -t pymemri .
for this repo) in order to start it.
!run_plugin --pod_full_address=$DEFAULT_POD_ADDRESS --plugin_run_id=$run.id --owner_key=$client.owner_key \
--database_key=$client.database_key --from_pod=True, --container="pymemri"
{% include note.html content='The data that was created earlier (PluginRun, plugin) should be in the pod in order for this to work' %}