mirror of https://github.com/django/django.git
251 lines
9.8 KiB
Plaintext
251 lines
9.8 KiB
Plaintext
================
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Full text search
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================
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The database functions in the ``django.contrib.postgres.search`` module ease
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the use of PostgreSQL's `full text search engine
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<https://www.postgresql.org/docs/current/textsearch.html>`_.
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For the examples in this document, we'll use the models defined in
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:doc:`/topics/db/queries`.
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.. seealso::
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For a high-level overview of searching, see the :doc:`topic documentation
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</topics/db/search>`.
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.. currentmodule:: django.contrib.postgres.search
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The ``search`` lookup
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=====================
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.. fieldlookup:: search
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A common way to use full text search is to search a single term against a
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single column in the database. For example::
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>>> Entry.objects.filter(body_text__search='Cheese')
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[<Entry: Cheese on Toast recipes>, <Entry: Pizza Recipes>]
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This creates a ``to_tsvector`` in the database from the ``body_text`` field
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and a ``plainto_tsquery`` from the search term ``'Cheese'``, both using the
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default database search configuration. The results are obtained by matching the
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query and the vector.
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To use the ``search`` lookup, ``'django.contrib.postgres'`` must be in your
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:setting:`INSTALLED_APPS`.
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``SearchVector``
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================
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.. class:: SearchVector(*expressions, config=None, weight=None)
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Searching against a single field is great but rather limiting. The ``Entry``
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instances we're searching belong to a ``Blog``, which has a ``tagline`` field.
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To query against both fields, use a ``SearchVector``::
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>>> from django.contrib.postgres.search import SearchVector
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>>> Entry.objects.annotate(
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... search=SearchVector('body_text', 'blog__tagline'),
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... ).filter(search='Cheese')
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[<Entry: Cheese on Toast recipes>, <Entry: Pizza Recipes>]
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The arguments to ``SearchVector`` can be any
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:class:`~django.db.models.Expression` or the name of a field. Multiple
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arguments will be concatenated together using a space so that the search
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document includes them all.
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``SearchVector`` objects can be combined together, allowing you to reuse them.
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For example::
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>>> Entry.objects.annotate(
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... search=SearchVector('body_text') + SearchVector('blog__tagline'),
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... ).filter(search='Cheese')
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[<Entry: Cheese on Toast recipes>, <Entry: Pizza Recipes>]
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See :ref:`postgresql-fts-search-configuration` and
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:ref:`postgresql-fts-weighting-queries` for an explanation of the ``config``
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and ``weight`` parameters.
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``SearchQuery``
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===============
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.. class:: SearchQuery(value, config=None, search_type='plain')
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``SearchQuery`` translates the terms the user provides into a search query
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object that the database compares to a search vector. By default, all the words
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the user provides are passed through the stemming algorithms, and then it
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looks for matches for all of the resulting terms.
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If ``search_type`` is ``'plain'``, which is the default, the terms are treated
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as separate keywords. If ``search_type`` is ``'phrase'``, the terms are treated
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as a single phrase. If ``search_type`` is ``'raw'``, then you can provide a
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formatted search query with terms and operators. Read PostgreSQL's `Full Text
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Search docs`_ to learn about differences and syntax. Examples:
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.. _Full Text Search docs: https://www.postgresql.org/docs/current/textsearch-controls.html#TEXTSEARCH-PARSING-QUERIES
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>>> from django.contrib.postgres.search import SearchQuery
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>>> SearchQuery('red tomato') # two keywords
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>>> SearchQuery('tomato red') # same results as above
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>>> SearchQuery('red tomato', search_type='phrase') # a phrase
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>>> SearchQuery('tomato red', search_type='phrase') # a different phrase
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>>> SearchQuery("'tomato' & ('red' | 'green')", search_type='raw') # boolean operators
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``SearchQuery`` terms can be combined logically to provide more flexibility::
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>>> from django.contrib.postgres.search import SearchQuery
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>>> SearchQuery('meat') & SearchQuery('cheese') # AND
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>>> SearchQuery('meat') | SearchQuery('cheese') # OR
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>>> ~SearchQuery('meat') # NOT
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See :ref:`postgresql-fts-search-configuration` for an explanation of the
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``config`` parameter.
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``SearchRank``
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==============
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.. class:: SearchRank(vector, query, weights=None)
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So far, we've returned the results for which any match between the vector and
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the query are possible. It's likely you may wish to order the results by some
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sort of relevancy. PostgreSQL provides a ranking function which takes into
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account how often the query terms appear in the document, how close together
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the terms are in the document, and how important the part of the document is
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where they occur. The better the match, the higher the value of the rank. To
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order by relevancy::
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>>> from django.contrib.postgres.search import SearchQuery, SearchRank, SearchVector
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>>> vector = SearchVector('body_text')
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>>> query = SearchQuery('cheese')
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>>> Entry.objects.annotate(rank=SearchRank(vector, query)).order_by('-rank')
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[<Entry: Cheese on Toast recipes>, <Entry: Pizza recipes>]
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See :ref:`postgresql-fts-weighting-queries` for an explanation of the
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``weights`` parameter.
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.. _postgresql-fts-search-configuration:
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Changing the search configuration
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=================================
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You can specify the ``config`` attribute to a :class:`SearchVector` and
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:class:`SearchQuery` to use a different search configuration. This allows using
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different language parsers and dictionaries as defined by the database::
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>>> from django.contrib.postgres.search import SearchQuery, SearchVector
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>>> Entry.objects.annotate(
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... search=SearchVector('body_text', config='french'),
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... ).filter(search=SearchQuery('œuf', config='french'))
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[<Entry: Pain perdu>]
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The value of ``config`` could also be stored in another column::
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>>> from django.db.models import F
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>>> Entry.objects.annotate(
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... search=SearchVector('body_text', config=F('blog__language')),
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... ).filter(search=SearchQuery('œuf', config=F('blog__language')))
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[<Entry: Pain perdu>]
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.. _postgresql-fts-weighting-queries:
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Weighting queries
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=================
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Every field may not have the same relevance in a query, so you can set weights
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of various vectors before you combine them::
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>>> from django.contrib.postgres.search import SearchQuery, SearchRank, SearchVector
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>>> vector = SearchVector('body_text', weight='A') + SearchVector('blog__tagline', weight='B')
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>>> query = SearchQuery('cheese')
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>>> Entry.objects.annotate(rank=SearchRank(vector, query)).filter(rank__gte=0.3).order_by('rank')
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The weight should be one of the following letters: D, C, B, A. By default,
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these weights refer to the numbers ``0.1``, ``0.2``, ``0.4``, and ``1.0``,
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respectively. If you wish to weight them differently, pass a list of four
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floats to :class:`SearchRank` as ``weights`` in the same order above::
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>>> rank = SearchRank(vector, query, weights=[0.2, 0.4, 0.6, 0.8])
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>>> Entry.objects.annotate(rank=rank).filter(rank__gte=0.3).order_by('-rank')
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Performance
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===========
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Special database configuration isn't necessary to use any of these functions,
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however, if you're searching more than a few hundred records, you're likely to
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run into performance problems. Full text search is a more intensive process
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than comparing the size of an integer, for example.
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In the event that all the fields you're querying on are contained within one
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particular model, you can create a functional index which matches the search
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vector you wish to use. The PostgreSQL documentation has details on
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`creating indexes for full text search
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<https://www.postgresql.org/docs/current/textsearch-tables.html#TEXTSEARCH-TABLES-INDEX>`_.
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``SearchVectorField``
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---------------------
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.. class:: SearchVectorField
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If this approach becomes too slow, you can add a ``SearchVectorField`` to your
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model. You'll need to keep it populated with triggers, for example, as
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described in the `PostgreSQL documentation`_. You can then query the field as
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if it were an annotated ``SearchVector``::
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>>> Entry.objects.update(search_vector=SearchVector('body_text'))
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>>> Entry.objects.filter(search_vector='cheese')
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[<Entry: Cheese on Toast recipes>, <Entry: Pizza recipes>]
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.. _PostgreSQL documentation: https://www.postgresql.org/docs/current/textsearch-features.html#TEXTSEARCH-UPDATE-TRIGGERS
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Trigram similarity
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==================
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Another approach to searching is trigram similarity. A trigram is a group of
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three consecutive characters. In addition to the :lookup:`trigram_similar`
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lookup, you can use a couple of other expressions.
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To use them, you need to activate the `pg_trgm extension
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<https://www.postgresql.org/docs/current/pgtrgm.html>`_ on PostgreSQL. You can
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install it using the
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:class:`~django.contrib.postgres.operations.TrigramExtension` migration
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operation.
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``TrigramSimilarity``
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---------------------
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.. class:: TrigramSimilarity(expression, string, **extra)
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Accepts a field name or expression, and a string or expression. Returns the
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trigram similarity between the two arguments.
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Usage example::
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>>> from django.contrib.postgres.search import TrigramSimilarity
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>>> Author.objects.create(name='Katy Stevens')
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>>> Author.objects.create(name='Stephen Keats')
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>>> test = 'Katie Stephens'
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>>> Author.objects.annotate(
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... similarity=TrigramSimilarity('name', test),
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... ).filter(similarity__gt=0.3).order_by('-similarity')
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[<Author: Katy Stevens>, <Author: Stephen Keats>]
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``TrigramDistance``
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-------------------
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.. class:: TrigramDistance(expression, string, **extra)
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Accepts a field name or expression, and a string or expression. Returns the
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trigram distance between the two arguments.
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Usage example::
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>>> from django.contrib.postgres.search import TrigramDistance
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>>> Author.objects.create(name='Katy Stevens')
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>>> Author.objects.create(name='Stephen Keats')
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>>> test = 'Katie Stephens'
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>>> Author.objects.annotate(
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... distance=TrigramDistance('name', test),
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... ).filter(distance__lte=0.7).order_by('distance')
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[<Author: Katy Stevens>, <Author: Stephen Keats>]
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