django/docs/ref/contrib/postgres/search.txt

================
Full text search
================

The database functions in the ``django.contrib.postgres.search`` module ease
the use of PostgreSQL's `full text search engine
<https://www.postgresql.org/docs/current/textsearch.html>`_.

For the examples in this document, we'll use the models defined in
:doc:`/topics/db/queries`.

.. seealso::

    For a high-level overview of searching, see the :doc:`topic documentation
    </topics/db/search>`.

.. currentmodule:: django.contrib.postgres.search

The ``search`` lookup
=====================

.. fieldlookup:: search

A common way to use full text search is to search a single term against a
single column in the database. For example::

    >>> Entry.objects.filter(body_text__search='Cheese')
    [<Entry: Cheese on Toast recipes>, <Entry: Pizza Recipes>]

This creates a ``to_tsvector`` in the database from the ``body_text`` field
and a ``plainto_tsquery`` from the search term ``'Cheese'``, both using the
default database search configuration. The results are obtained by matching the
query and the vector.

To use the ``search`` lookup, ``'django.contrib.postgres'`` must be in your
:setting:`INSTALLED_APPS`.

``SearchVector``
================

.. class:: SearchVector(*expressions, config=None, weight=None)

Searching against a single field is great but rather limiting. The ``Entry``
instances we're searching belong to a ``Blog``, which has a ``tagline`` field.
To query against both fields, use a ``SearchVector``::

    >>> from django.contrib.postgres.search import SearchVector
    >>> Entry.objects.annotate(
    ...     search=SearchVector('body_text', 'blog__tagline'),
    ... ).filter(search='Cheese')
    [<Entry: Cheese on Toast recipes>, <Entry: Pizza Recipes>]

The arguments to ``SearchVector`` can be any
:class:`~django.db.models.Expression` or the name of a field. Multiple
arguments will be concatenated together using a space so that the search
document includes them all.

``SearchVector`` objects can be combined together, allowing you to reuse them.
For example::

    >>> Entry.objects.annotate(
    ...     search=SearchVector('body_text') + SearchVector('blog__tagline'),
    ... ).filter(search='Cheese')
    [<Entry: Cheese on Toast recipes>, <Entry: Pizza Recipes>]

See :ref:`postgresql-fts-search-configuration` and
:ref:`postgresql-fts-weighting-queries` for an explanation of the ``config``
and ``weight`` parameters.

``SearchQuery``
===============

.. class:: SearchQuery(value, config=None, search_type='plain')

``SearchQuery`` translates the terms the user provides into a search query
object that the database compares to a search vector. By default, all the words
the user provides are passed through the stemming algorithms, and then it
looks for matches for all of the resulting terms.

If ``search_type`` is ``'plain'``, which is the default, the terms are treated
as separate keywords. If ``search_type`` is ``'phrase'``, the terms are treated
as a single phrase. If ``search_type`` is ``'raw'``, then you can provide a
formatted search query with terms and operators. If ``search_type`` is
``'websearch'``, then you can provide a formatted search query, similar to the
one used by web search engines. ``'websearch'`` requires PostgreSQL ≥ 11. Read
PostgreSQL's `Full Text Search docs`_ to learn about differences and syntax.
Examples:

.. _Full Text Search docs: https://www.postgresql.org/docs/current/textsearch-controls.html#TEXTSEARCH-PARSING-QUERIES

    >>> from django.contrib.postgres.search import SearchQuery
    >>> SearchQuery('red tomato')  # two keywords
    >>> SearchQuery('tomato red')  # same results as above
    >>> SearchQuery('red tomato', search_type='phrase')  # a phrase
    >>> SearchQuery('tomato red', search_type='phrase')  # a different phrase
    >>> SearchQuery("'tomato' & ('red' | 'green')", search_type='raw')  # boolean operators
    >>> SearchQuery("'tomato' ('red' OR 'green')", search_type='websearch')  # websearch operators

``SearchQuery`` terms can be combined logically to provide more flexibility::

    >>> from django.contrib.postgres.search import SearchQuery
    >>> SearchQuery('meat') & SearchQuery('cheese')  # AND
    >>> SearchQuery('meat') | SearchQuery('cheese')  # OR
    >>> ~SearchQuery('meat')  # NOT

See :ref:`postgresql-fts-search-configuration` for an explanation of the
``config`` parameter.

``SearchRank``
==============

.. class:: SearchRank(vector, query, weights=None, normalization=None, cover_density=False)

So far, we've returned the results for which any match between the vector and
the query are possible. It's likely you may wish to order the results by some
sort of relevancy. PostgreSQL provides a ranking function which takes into
account how often the query terms appear in the document, how close together
the terms are in the document, and how important the part of the document is
where they occur. The better the match, the higher the value of the rank. To
order by relevancy::

    >>> from django.contrib.postgres.search import SearchQuery, SearchRank, SearchVector
    >>> vector = SearchVector('body_text')
    >>> query = SearchQuery('cheese')
    >>> Entry.objects.annotate(rank=SearchRank(vector, query)).order_by('-rank')
    [<Entry: Cheese on Toast recipes>, <Entry: Pizza recipes>]

See :ref:`postgresql-fts-weighting-queries` for an explanation of the
``weights`` parameter.

Set the ``cover_density`` parameter to ``True`` to enable the cover density
ranking, which means that the proximity of matching query terms is taken into
account.

Provide an integer to the ``normalization`` parameter to control rank
normalization. This integer is a bit mask, so you can combine multiple
behaviors::

    >>> from django.db.models import Value
    >>> Entry.objects.annotate(
    ...     rank=SearchRank(
    ...         vector,
    ...         query,
    ...         normalization=Value(2).bitor(Value(4)),
    ...     )
    ... )

The PostgreSQL documentation has more details about `different rank
normalization options`_.

.. _different rank normalization options: https://www.postgresql.org/docs/current/textsearch-controls.html#TEXTSEARCH-RANKING

``SearchHeadline``
==================

.. class:: SearchHeadline(expression, query, config=None, start_sel=None, stop_sel=None, max_words=None, min_words=None, short_word=None, highlight_all=None, max_fragments=None, fragment_delimiter=None)

Accepts a single text field or an expression, a query, a config, and a set of
options. Returns highlighted search results.

Set the ``start_sel`` and ``stop_sel`` parameters to the string values to be
used to wrap highlighted query terms in the document. PostgreSQL's defaults are
``<b>`` and ``</b>``.

Provide integer values to the ``max_words`` and ``min_words`` parameters to
determine the longest and shortest headlines. PostgreSQL's defaults are 35 and
15.

Provide an integer value to the ``short_word`` parameter to discard words of
this length or less in each headline. PostgreSQL's default is 3.

Set the ``highlight_all`` parameter to ``True`` to use the whole document in
place of a fragment and ignore ``max_words``, ``min_words``, and ``short_word``
parameters. That's disabled by default in PostgreSQL.

Provide a non-zero integer value to the ``max_fragments`` to set the maximum
number of fragments to display. That's disabled by default in PostgreSQL.

Set the ``fragment_delimiter`` string parameter to configure the delimiter
between fragments. PostgreSQL's default is ``" ... "``.

The PostgreSQL documentation has more details on `highlighting search
results`_.

Usage example::

    >>> from django.contrib.postgres.search import SearchHeadline, SearchQuery
    >>> query = SearchQuery('red tomato')
    >>> entry = Entry.objects.annotate(
    ...     headline=SearchHeadline(
    ...         'body_text',
    ...         query,
    ...         start_sel='<span>',
    ...         stop_sel='</span>',
    ...     ),
    ... ).get()
    >>> print(entry.headline)
    Sandwich with <span>tomato</span> and <span>red</span> cheese.

See :ref:`postgresql-fts-search-configuration` for an explanation of the
``config`` parameter.

.. _highlighting search results: https://www.postgresql.org/docs/current/textsearch-controls.html#TEXTSEARCH-HEADLINE

.. _postgresql-fts-search-configuration:

Changing the search configuration
=================================

You can specify the ``config`` attribute to a :class:`SearchVector` and
:class:`SearchQuery` to use a different search configuration. This allows using
different language parsers and dictionaries as defined by the database::

    >>> from django.contrib.postgres.search import SearchQuery, SearchVector
    >>> Entry.objects.annotate(
    ...     search=SearchVector('body_text', config='french'),
    ... ).filter(search=SearchQuery('œuf', config='french'))
    [<Entry: Pain perdu>]

The value of ``config`` could also be stored in another column::

    >>> from django.db.models import F
    >>> Entry.objects.annotate(
    ...     search=SearchVector('body_text', config=F('blog__language')),
    ... ).filter(search=SearchQuery('œuf', config=F('blog__language')))
    [<Entry: Pain perdu>]

.. _postgresql-fts-weighting-queries:

Weighting queries
=================

Every field may not have the same relevance in a query, so you can set weights
of various vectors before you combine them::

    >>> from django.contrib.postgres.search import SearchQuery, SearchRank, SearchVector
    >>> vector = SearchVector('body_text', weight='A') + SearchVector('blog__tagline', weight='B')
    >>> query = SearchQuery('cheese')
    >>> Entry.objects.annotate(rank=SearchRank(vector, query)).filter(rank__gte=0.3).order_by('rank')

The weight should be one of the following letters: D, C, B, A. By default,
these weights refer to the numbers ``0.1``, ``0.2``, ``0.4``, and ``1.0``,
respectively. If you wish to weight them differently, pass a list of four
floats to :class:`SearchRank` as ``weights`` in the same order above::

    >>> rank = SearchRank(vector, query, weights=[0.2, 0.4, 0.6, 0.8])
    >>> Entry.objects.annotate(rank=rank).filter(rank__gte=0.3).order_by('-rank')

Performance
===========

Special database configuration isn't necessary to use any of these functions,
however, if you're searching more than a few hundred records, you're likely to
run into performance problems. Full text search is a more intensive process
than comparing the size of an integer, for example.

In the event that all the fields you're querying on are contained within one
particular model, you can create a functional index which matches the search
vector you wish to use. The PostgreSQL documentation has details on
`creating indexes for full text search
<https://www.postgresql.org/docs/current/textsearch-tables.html#TEXTSEARCH-TABLES-INDEX>`_.

``SearchVectorField``
---------------------

.. class:: SearchVectorField

If this approach becomes too slow, you can add a ``SearchVectorField`` to your
model. You'll need to keep it populated with triggers, for example, as
described in the `PostgreSQL documentation`_. You can then query the field as
if it were an annotated ``SearchVector``::

    >>> Entry.objects.update(search_vector=SearchVector('body_text'))
    >>> Entry.objects.filter(search_vector='cheese')
    [<Entry: Cheese on Toast recipes>, <Entry: Pizza recipes>]

.. _PostgreSQL documentation: https://www.postgresql.org/docs/current/textsearch-features.html#TEXTSEARCH-UPDATE-TRIGGERS

Trigram similarity
==================

Another approach to searching is trigram similarity. A trigram is a group of
three consecutive characters. In addition to the :lookup:`trigram_similar` and
:lookup:`trigram_word_similar` lookups, you can use a couple of other
expressions.

To use them, you need to activate the `pg_trgm extension
<https://www.postgresql.org/docs/current/pgtrgm.html>`_ on PostgreSQL. You can
install it using the
:class:`~django.contrib.postgres.operations.TrigramExtension` migration
operation.

``TrigramSimilarity``
---------------------

.. class:: TrigramSimilarity(expression, string, **extra)

Accepts a field name or expression, and a string or expression. Returns the
trigram similarity between the two arguments.

Usage example::

    >>> from django.contrib.postgres.search import TrigramSimilarity
    >>> Author.objects.create(name='Katy Stevens')
    >>> Author.objects.create(name='Stephen Keats')
    >>> test = 'Katie Stephens'
    >>> Author.objects.annotate(
    ...     similarity=TrigramSimilarity('name', test),
    ... ).filter(similarity__gt=0.3).order_by('-similarity')
    [<Author: Katy Stevens>, <Author: Stephen Keats>]

``TrigramWordSimilarity``
-------------------------

.. versionadded:: 4.0

.. class:: TrigramWordSimilarity(string, expression, **extra)

Accepts a string or expression, and a field name or expression. Returns the
trigram word similarity between the two arguments.

Usage example::

    >>> from django.contrib.postgres.search import TrigramWordSimilarity
    >>> Author.objects.create(name='Katy Stevens')
    >>> Author.objects.create(name='Stephen Keats')
    >>> test = 'Kat'
    >>> Author.objects.annotate(
    ...     similarity=TrigramWordSimilarity(test, 'name'),
    ... ).filter(similarity__gt=0.3).order_by('-similarity')
    [<Author: Katy Stevens>]

``TrigramDistance``
-------------------

.. class:: TrigramDistance(expression, string, **extra)

Accepts a field name or expression, and a string or expression. Returns the
trigram distance between the two arguments.

Usage example::

    >>> from django.contrib.postgres.search import TrigramDistance
    >>> Author.objects.create(name='Katy Stevens')
    >>> Author.objects.create(name='Stephen Keats')
    >>> test = 'Katie Stephens'
    >>> Author.objects.annotate(
    ...     distance=TrigramDistance('name', test),
    ... ).filter(distance__lte=0.7).order_by('distance')
    [<Author: Katy Stevens>, <Author: Stephen Keats>]

``TrigramWordDistance``
-----------------------

.. versionadded:: 4.0

.. class:: TrigramWordDistance(string, expression, **extra)

Accepts a string or expression, and a field name or expression. Returns the
trigram word distance between the two arguments.

Usage example::

    >>> from django.contrib.postgres.search import TrigramWordDistance
    >>> Author.objects.create(name='Katy Stevens')
    >>> Author.objects.create(name='Stephen Keats')
    >>> test = 'Kat'
    >>> Author.objects.annotate(
    ...     distance=TrigramWordDistance(test, 'name'),
    ... ).filter(distance__lte=0.7).order_by('distance')
    [<Author: Katy Stevens>]