# Pymemri
> Pymemri is a python library for creating <b>Plugins</b> for the Memri Personal online datastore <a href='https://gitlab.memri.io/memri/pod'>(pod)</a>. Pymemri has a PodClient to communicate with the pod, and tools to build and test plugins.
[](https://gitlab.memri.io/memri/pymemri/-/pipelines/latest)
[](https://discord.gg/BcRfajJk4k)
[](https://twitter.com/YourMemri)
<a href="https://pypi.org/project/pymemri/"><img src="https://pepy.tech/badge/pymemri" /></a>
Plugins connect and add the information to your Pod. Plugins that <b>import your data from external services</b> are called **Importers** (Gmail, WhatsApp, etc.). Plugins that <b>connect new data to the existing data</b> are called **indexers** (face recognition, spam detection, object detection, etc.). Lastly there are plugins that <b>execute actions</b> (sending messages, uploading files).
## Installing
### As a package
```bash
pip install pymemri
```
### Development
To install the Python package for development, run:
```bash
pip install -e .
```
## Contributing
If you would like to contribute to Pymemri, have a look at our [contibuting guidelines](CONTRIBUTING.md) to get started and join our Discord if you have any questions about using or contributing to the library.
## Quickstart: Pod Client
All interaction between plugins and the pod goes via the Pymemri `PodClient`. To use this client in development, we first need to have a pod running locally. The quickest way to do this is to install from the [pod repo](https://gitlab.memri.io/memri/pod), and run `./examples/run_development.sh`.
If you have a running pod, you can define and add your own item definitions:
```python
from pymemri.data.schema import Item
from pymemri.pod.client import PodClient
class Dog(Item):
name: Optional[str] = None
age: Optional[int] = None
# Connect to the pod and add the Dog item definition
client = PodClient()
client.add_to_schema(Dog)
# Add a Dog to the pod
dog = Dog(name="Rita", age=3)
client.create(dog)
```
## Quickstart: Running a plugin
After installation, users can use the plugin CLI to manually run a plugin. For more information on how to build a plugin, see `run_plugin`.
<b>With the pod running, run in your terminal: </b>
```bash
store_keys
run_plugin --metadata "example_plugin.json"
```
This stores a random owner key and database key on your disk for future use, and runs the pymemri example plugin. If everything works correctly, the output should read `Plugin run success.`
## Docs
[pymemri docs](https://docs.memri.io/component-architectures/plugins/readme/)
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"description": "# Pymemri\n> Pymemri is a python library for creating <b>Plugins</b> for the Memri Personal online datastore <a href='https://gitlab.memri.io/memri/pod'>(pod)</a>. Pymemri has a PodClient to communicate with the pod, and tools to build and test plugins.\n\n\n[](https://gitlab.memri.io/memri/pymemri/-/pipelines/latest)\n[](https://discord.gg/BcRfajJk4k)\n[](https://twitter.com/YourMemri)\n<a href=\"https://pypi.org/project/pymemri/\"><img src=\"https://pepy.tech/badge/pymemri\" /></a>\n\nPlugins connect and add the information to your Pod. Plugins that <b>import your data from external services</b> are called **Importers** (Gmail, WhatsApp, etc.). Plugins that <b>connect new data to the existing data</b> are called **indexers** (face recognition, spam detection, object detection, etc.). Lastly there are plugins that <b>execute actions</b> (sending messages, uploading files).\n\n## Installing\n\n### As a package\n```bash\npip install pymemri\n```\n\n### Development\nTo install the Python package for development, run:\n```bash\npip install -e .\n```\n\n## Contributing\nIf you would like to contribute to Pymemri, have a look at our [contibuting guidelines](CONTRIBUTING.md) to get started and join our Discord if you have any questions about using or contributing to the library.\n## Quickstart: Pod Client\n\nAll interaction between plugins and the pod goes via the Pymemri `PodClient`. To use this client in development, we first need to have a pod running locally. The quickest way to do this is to install from the [pod repo](https://gitlab.memri.io/memri/pod), and run `./examples/run_development.sh`.\n\nIf you have a running pod, you can define and add your own item definitions:\n\n```python\nfrom pymemri.data.schema import Item\nfrom pymemri.pod.client import PodClient\n\nclass Dog(Item):\n name: Optional[str] = None\n age: Optional[int] = None\n\n# Connect to the pod and add the Dog item definition\nclient = PodClient()\nclient.add_to_schema(Dog)\n\n# Add a Dog to the pod\ndog = Dog(name=\"Rita\", age=3)\nclient.create(dog)\n```\n\n## Quickstart: Running a plugin\n\nAfter installation, users can use the plugin CLI to manually run a plugin. For more information on how to build a plugin, see `run_plugin`.\n\n<b>With the pod running, run in your terminal: </b>\n\n```bash\nstore_keys\nrun_plugin --metadata \"example_plugin.json\"\n```\n\nThis stores a random owner key and database key on your disk for future use, and runs the pymemri example plugin. If everything works correctly, the output should read `Plugin run success.`\n\n## Docs\n\n[pymemri docs](https://docs.memri.io/component-architectures/plugins/readme/)\n",
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