django-elasticsearch-dsl-drf | Integrate Elasticsearch DSL with Django REST framework | REST library

How to make OR query (using django-elasticsearch-dsl-drf) with multiple contains statements? Query in words: Search for objects where title contains "hello" OR description contains "world"

I can't find about it in documentation: https://django-elasticsearch-dsl-drf.readthedocs.io/en/0.3.12/advanced_usage_examples.html#usage-example

I used two packages (i.e. django-elasticsearch-dsl==7.1.4 and django-elasticsearch-dsl-drf==0.20.8) to add a search engine to my Django project. The model which I indexed in elastic is:

I’m building a Django web application to store documents and their associated metadata.

The bulk of the metadata will be stored in the underlying MySQL database, with the OCR’d document text indexed in Elasticsearch to enable full-text search. I’ve incorporated django-elasticsearch-dsl to connect and synchronize my data models, as I’m also indexing (and thus, double-storing) a few other fields found in my models. I had considered using Haystack, but it lacks support for the latest Elasticsearch versions.

When a document is uploaded via the applications’s admin interface, a post_save signal automatically triggers a Celery asynchronous background task to perform the OCR and will ultimately index the extracted text into Elasticsearch.

Seeing as how I don’t have a full-text field defined in my model (and hope to avoid doing so as I don’t want to store or search against CLOB’s in the database), I’m seeking the best practice for updating my Elasticsearch documents from my tasks.py file. There doesn’t seem to be a way to do so using django-elasticseach-dsl (but maybe I’m wrong?) and so I’m wondering if I should either:

1. Try to interface with Elasticsearch via REST using the sister django-elasticsearch-dsl-drf package.

2. More loosely integrate my application with Elasticsearch by using the more vanilla elasticsearch-dsl-py package (based on elasticsearch-py). I‘d lose some “luxury” with this approach as I’d have to write a bit more integration code, at least if I want to wire up my models with signals.

Is there a best practice? Or another approach I haven’t considered?

Update 1: In trying to implement the answer from @Nielk, I'm able to persist the OCR'd text (result = "test" in tasks.py below) into ElasticSearch, but it's also persisting in the MySQL database. I'm still confused about how to essentially configure Submission.rawtext as a passthru to ElasticSearch.

models.py:

The situation I'm currently trying to solve is a case where GeoSpatialOrderingFilterBackend is used from ElasticSearch DSL - DRF, which orders the documents based on a location (lat, long), this in return gives out a elasticsearch_dsl.search.Search queryset - ordered as it should; although I have a case where some are featured and therefore would like to move them to the top of the queryset.

Scenario (as is):

On my MacBook (version 10.14.6) I am succesfully running a Django application including TensorFlow and tf-sentencepiece (in particular to use the universal sentence encoder model). When I perform a pipenv lock -r > requirements.txt I get the following required packages: