Plugins connect and add the information to your Pod. Plugins that import your data from external services are called Importers (Gmail, WhatsApp, etc.). Plugins that connect new data to the existing data are called indexers (face recognition, spam detection, object detection, etc.). Lastly there are plugins that execute actions (sending messages, uploading files). This repository is built with nbdev, which means that the repo structure has a few differences compared to a standard python repo.
from pymemri.data.schema import * from pymemri.pod.client import * class Dog(Item): def __init__(self, name, age, id=None, deleted=None): super().__init__(id=id, deleted=deleted) self.name = name self.age = age @classmethod def from_json(cls, json): id = json.get("id", None) name = json.get("name", None) age = json.get("age", None) return cls(id=id,name=name,age=age) client = PodClient() example_dog = Dog("max", 2) client.add_to_schema(example_dog) dog = Dog("bob", 3) client.create(dog)
After installation, users can use the plugin CLI to manually run a plugin. For more information on how to build a plugin, see
With the pod running, run :
store_keys run_plugin --config_file="example_config.json"
The Python integrators are written in nbdev (video). With nbdev, it is encouraged to write code in
Jupyter Notebooks. Nbdev syncs all the notebooks in
/nbs with the python code in
/pymemri. Tests are written side by side with the code in the notebooks, and documentation is automatically generated from the code and markdown in the notebooks and exported into the
/docs folder. Check out the nbdev quickstart for an introduction, watch the video linked above, or see the nbdev documentation for a all functionalities and tutorials.