Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

to_tsvector support for saving VectorField #42

Open
john-parton opened this issue Oct 21, 2014 · 0 comments
Open

to_tsvector support for saving VectorField #42

john-parton opened this issue Oct 21, 2014 · 0 comments

Comments

@john-parton
Copy link

Right now the only way to update a VectorField is to write out a literal tsvector, e.g. 'a:1 fat:2 cat:3 sat:4 on:5 a:6 mat:7 and:8 ate:9 a:10 fat:11 rat:12' or 'a:1A fat:2B,4C cat:5D'. While this is obviously very flexible, most of the time I just want to call to_tsvector.

Here's a quick overview of the problem:

# search/models.py
from django.db import models

from djorm_pgfulltext.fields import VectorField
from djorm_pgfulltext.models import SearchManager 

class SearchTest(models.Model):    
    search_index = VectorField()
    objects = SearchManager()
In [1]: from search.models import SearchTest

In [2]: search_test = SearchTest()

In [3]: search_test.search_index = 'swim swimming swam'

In [4]: search_test.save()

In [5]: search_test = SearchTest.objects.get(id=search_test.id) # Reload model instance

In [6]: search_test.search_index
Out[6]: "'swam' 'swim' 'swimming'" 
# The string was literally inserted as a ts vector
# I would rather it be converted and stemmed: "'swam':3 'swim':1,2"

I think that inserting a string as a literal tsvector is a fine default, but I still needed to be able to call to_tsvector. I got around that by creating a special python object and registering an adapter with psycopg2.

# search/tsvector.py
from psycopg2.extensions import adapt, AsIs

class TsVector(object):
    """ Represents a call to to_tsvector at the database level.

        Use:        
            TsVector('swim swimming swam'),
            TsVector('simple', 'swim swimming swam')
            TsVector('english', 'swim swimming swam')
    """
    def __init__(self, *args):
        assert len(args) in (1, 2), "Arguments should be TsVector([ config regconfig, ] document text)"

        if len(args) == 1:
            self.config = None
            self.document = args[0]
        else:
            self.config = args[0]
            self.document = args[1]

def adapt_tsvector(tsvector):
    """ Adapts TsVector object for use in DB.
    """
    if tsvector.config is None:
        return AsIs("to_tsvector(%s)" % adapt(tsvector.document))
    else:
        return AsIs("to_tsvector(%s, %s)" % (adapt(tsvector.config), adapt(tsvector.document)))

Put this somewhere that it'll only get executed once. With Django 1.7, an AppConfig is a pretty natural place to put it.

# search/apps.py
from django.apps import AppConfig

from psycopg2.extensions import register_adapter

from search.tsvector import TsVector, adapt_tsvector

class SearchConfig(AppConfig):
    name = 'search'
    verbose_name = "Search"

    def ready(self):
        # Register the TsVector class
        register_adapter(TsVector, adapt_tsvector)

Example use:

In [1]: from search.models import SearchTest

In [2]: from search.tsvector import TsVector

In [3]: search_test = SearchTest()

In [4]: search_test.search_index = TsVector('english', 'swim swimming swam')

In [5]: search_test.save()

In [6]: search_test = SearchTest.objects.get(id=search_test.id)

In [7]: search_test.search_index
Out[7]: "'swam':3 'swim':1,2"

Thoughts?

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

1 participant