This package is unmaintained, and we have no plans to maintain it.
We advise you to use it as an example, maybe copy the code into your own project, but don't install the package.
This package is for replacing Wagtail's Document class with one that allows searching in Document file contents using textract.
Textract can extract text from (among others) PDF, Excel and Word files.
The package was inspired by the "Search: Extract text from documents" issue in Wagtail.
Documents will work as before, except that Document search in Wagtail's admin interface will also find search terms in the files' contents.
Some screenshots to illustrate.
In our fresh Wagtail site with wagtail_textract installed,
we uploaded a file called test_document.pdf with handwritten text in it.
It is listed in the admin interface under Documents:
If we now search in Documents for the word correct, which is one of the handwritten words,
the live search finds it:
The assumption is that this search should not only be available in Wagtail's admin interface, but also in a public-facing search view, for which we provide a code example.
- Wagtail 2 (see tox.ini)
- The Textract dependencies
We have been using this package in production since August 2018 on https://nuffic.nl.
- Install the Textract dependencies
- Add
wagtail_textractto your requirements and/orpip install wagtail_textract - Add to your Django
INSTALLED_APPS. - Put
WAGTAILDOCS_DOCUMENT_MODEL = "wagtail_textract.document"in your Django settings.
Note: You'll get an incompatibility warning during installation of wagtail_textract (Wagtail 2.0.1 installed):
requests 2.18.4 has requirement chardet<3.1.0,>=3.0.2, but you'll have chardet 2.3.0 which is incompatible.
textract 1.6.1 has requirement beautifulsoup4==4.5.3, but you'll have beautifulsoup4 4.6.0 which is incompatible.
We haven't seen this leading to problems, but it's something to keep in mind.
In order to make textract use Tesseract, which happens if regular
textract finds no text, you need to add the data files that Tesseract can
base its word matching on.
Create a tessdata directory in your project directory, and download the
languages you want.
Transcription is done automatically after Document save,
in an asyncio executor to prevent blocking the response during processing.
To transcribe all existing Documents, run the management command::
./manage.py transcribe_documents
This may take a long time, obviously.
Here is a code example for a search view (outside Wagtail's admin interface) that shows both Page and Document results.
from itertools import chain
from wagtail.core.models import Page
from wagtail.documents.models import get_document_model
def search(request):
# Search
search_query = request.GET.get('query', None)
if search_query:
page_results = Page.objects.live().search(search_query)
document_results = Document.objects.search(search_query)
search_results = list(chain(page_results, document_results))
# Log the query so Wagtail can suggest promoted results
Query.get(search_query).add_hit()
else:
search_results = Page.objects.none()
# Render template
return render(request, 'website/search_results.html', {
'search_query': search_query,
'search_results': search_results,
})Your template should allow for handling Documents differently than Pages,
because you can't do pageurl result on a Document:
{% if result.file %}
<a href="{{ result.url }}">{{ result }}</a>
{% else %}
<a href="{% pageurl result %}">{{ result }}</a>
{% endif %}In order to use wagtail_textract, your CustomizedDocument model should do
the same as wagtail_textract's Document:
- subclass
TranscriptionMixin - alter
search_fields
from wagtail_textract.models import TranscriptionMixin
class CustomizedDocument(TranscriptionMixin, ...):
"""Extra fields and methods for Document model."""
search_fields = ... + [
index.SearchField(
'transcription',
partial_match=False,
),
]Note that the first class to subclass should be TranscriptionMixin,
so its save() takes precedence over that of the other parent classes.
To run tests, checkout this repository and:
make test
A coverage report will be generated in ./coverage_html_report/.
- Karl Hobley
- Bertrand Bordage
- Kees Hink
- Tom Hendrikx
- Coen van der Kamp
- Mike Overkamp
- Thibaud Colas
- Dan Braghis
- Dan Swain
React
Typescript
Django
Laravel
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Microsoft
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Alibaba
D3
Tencent