wagtailmeili 0.4b0

A Meilisearch backend for Wagtail CMS

Wagtailmeili

A search backend for Wagtail using MeiliSearch.

[!CAUTION] This package is still in development and until version 1.0.0, I will not maintain a DeprecationWarning pattern. I built the integration with meilisearch about 2 years ago for a project and decided to make it a public package to improve it and integrate more features.

[!TIP]
If you need support or require help with a Wagtail project, you can hire me 😊

Introduction

en - https://softquantum.com/resources/wagtailmeili-integrating-a-blazing-fast-search-engine-with-wagtail

fr - https://softquantum.com/fr/ressources/wagtailmeili-integrer-un-moteur-de-recherche-rapide-avec-wagtail/

Requirements

Wagtailmeili requires the following:

• Python >= 3.11
• Wagtail >= 5.2

Installation

In your Wagtail project

Configure your MeiliSearch instance in your settings.py file.

Install Meilisearch python client e.g., using pip

  pip install meilisearch

Add wagtailmeili to your INSTALLED_APPS

INSTALLED_APPS = [
    # ...
    "wagtailmeili",
    # ...
]

add the search backend 'meilisearch' to your WAGTAILSEARCH_BACKENDS

[!CAUTION] Leave the 'default' backend for the admin as you don't want to depend only on what was indexed in meilisearch Different use cases to consider so still work in progress.

import os

WAGTAILSEARCH_BACKENDS = {
    "meilisearch": {
        "BACKEND": "wagtailmeili.backend",
        "HOST":  os.environ.get("MEILISEARCH_HOST", "http://127.0.0.1"),
        "PORT": os.environ.get("MEILISEARCH_PORT", "7700"),
        "MASTER_KEY": os.environ.get("MEILISEARCH_MASTER_KEY", "your-master-key"),
        # "STOP_WORDS": ...
        # "RANKING_RULES: ...
        # "SKIP_MODELS": ...
        # "SKIP_MODELS_BY_FIELD_VALUE": ...
    },
    "default": {
        "BACKEND": "wagtail.search.backends.database",
    }
}

Features

Default search configs

• STOP_WORDS: see defaults in settings.py
• RANKING_RULES: see defaults in settings.py
• SKIP_MODELS: "skip_models" is a list of models that you want to skip from indexing no matter the model setup.

WAGTAILSEARCH_BACKENDS = {
    "meilisearch": {
        "SKIP_MODELS": ["app_label.Model1", "app_label.Model2",],
        # ...
    }
}

• SKIP_MODELS_BY_FIELD_VALUE: A convenient way to skip instances based on attributes

WAGTAILSEARCH_BACKENDS = {
    "meilisearch": {
        # add this to not index pages that are not published for example
        "SKIP_MODELS_BY_FIELD_VALUE": {
            "wagtailmeili_testapp.MoviePage": {
                "field": "live",
                "value": False,
            },
        },
        # ...
    }
}

Model fields

In any model you will be doing a search on with Meilisearch, add the page or model manager.
It will use the correct backend when doing something like MySuperPage.objects.search().

from wagtail.models import Page
from django.db import models
from wagtailmeili.manager import MeilisearchPageManager, MeilisearchModelManager

class MySuperPage(Page):
    """A Super Page to do incredible things indexed in Meilisearch."""
 
    objects = MeilisearchPageManager()

class MySuperModel(models.Model):
    """A Super Model to do incredible things indexed in Meilisearch."""
 
    objects = MeilisearchModelManager()

To declare sortable attributes or add ranking rules for the model, just add, for example:

from wagtail.models import Page


class MySuperPage(Page):
    """A Super Page to do incredible things indexed in Meilisearch."""
    
    sortable_attributes = [
        "published_last_timestamp", 
        # ...
    ]
    ranking_rules = [
        "published_last_timestamp:desc",
        # ...
    ]

Template Tag filter

{% load meilisearch %}

{% get_matches_position result %}

Roadmap before 1.0.0 (unsorted)

• -[x] Adding tests (v0.3.3)
• -[x] Cleaning up index if pages are unpublished or models deleted (v0.4.0)
• -[ ] Refactoring index.py to be with easier testing
• -[ ] Exploring Meilisearch and bringing more of its features for Wagtail
• -[ ] Getting a leaner implementation (looking at Autocomplete and rebuilder)
• -[ ] Giving more love to the Sample project with a frontend
• -[ ] official documentation

Sample Project: WMDB

The Wagtail Movie Database (WMDB) is a sample project for testing purposes. To run the project, follow these steps:

1. start the local meilisearch instance

meilisearch --master-key=<your masterKey>

2. copy the directory wagtail_moviedb wherever you want
3. create a virtualenv and activate it (instructions on linux/macOS)

python -m venv .venv
source .venv/bin/activate

4. install the dependencies

pip install -r requirements.txt

5. add an .env file

MEILISEARCH_MASTER_KEY="your masterKey"

6. apply migrations

python manage.py migrate

7. Create a superuser (optional)

python manage.py createsuperuser

8. load movies & start local web server

python manage.py load_movies
python manage.py runserver

9. visit your meilisearch local instance: https://127.0.0.1:7700, give it your master-key. You should see some movies loaded.
10. update index (optional):

python manage.py update_index

Contributions

Welcome to all contributions!

Prerequisites

• Install Meilisearch locally following their documentation
• Start Meilisearch instance in your favorite terminal

meilisearch --master-key correctmasterkey

Install

To make changes to this project, first clone this repository:

git clone [e-mail]:softquantum/wagtailmeili.git
cd wagtailmeili

With your preferred virtualenv activated, install testing dependencies:

Using pip

pip install "pip>=21.3"
pip install -e '.[dev]' -U

How to run tests

You can run tests as shown below:

pytest 

or with tox

tox

License

This project is released under the 3-Clause BSD License.