Pencarian teks penuh¶

Pencarian 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>]

Ini membuat sebuah to_tsvector dalam basisdata dari bidang body_text dan plainto_tsquery dari istilah pencarian 'Cheese', kedua menggunakan konfigurasi pencarian basisdata awalan. Hasil didapatkan dengan mencocokkan permintaan dan vektor.

Untuk menggunakan pencarian search, 'django.contrib.postgres' harus berada dalam INSTALLED_APPS anda.

SearchVector¶

class SearchVector(*expressions, config=None, weight=None)¶

Pencarian terhadap bidang tunggal lebih hebat tetapi agak terbatas. Contoh-contoh Entry kami sedang cari milik sebuah Blog, yang mempunyai bidang tagline. Untuk meminta terhadap kedua bidang, gunakan 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>]

Argumen-argumen pada SearchVector dapat berupa Expression apapun atau nama dari sebuah bidang. Banyak argumen akan dihubungkan bersama menggunakan sebuah ruang sehingga pencarian dokumen menyertakan mereka semua.

Vektor SearchVector dapat dipadukan bersama-sama, mengizinkan anda menggunakan kembali mereka. Sebagai contoh:

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

Lihat Merubah konfigurasi pencarian dan Meminta pembobotan untuk sebuah penjelasan dari parameter config dan weight.

SearchQuery¶

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

SearchQuery menterjemahkan istilah pengguna sediakan kedalam obyek permintaan pencarian yang basisdata bandingkan pada pencarian vektor. Secara awalan, semua kata pengguna sediakan dilewatkan melalui algoritma berasal, dan kemudian itu mencari kecocokan untuk semua istilah yang dihasilkan.

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:

>>> 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

Istilah SearchQuery dapat dipadukan secara logika untuk menyediakan keluwesan lebih:

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

Lihat Merubah konfigurasi pencarian untuk sebuah penjelasan dari parameter config.

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>]

Lihat Meminta pembobotan untuk penjelasan dari parameter weights.

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.

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.

Contoh penggunaan:

>>> 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.

Lihat Merubah konfigurasi pencarian untuk sebuah penjelasan dari parameter config.

Merubah konfigurasi pencarian¶

Anda dapat menentukan atribut config pada SearchVector dan SearchQuery untuk menggunakan konfigurasi pencarian berbeda. Ini mengizinkan menggunakan pengurai bahasa berbeda dan dictionary sebagai ditentukan oleh basisdata:

>>> 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>]

Nilai dari config dapat juga disimpan dalam kolom lain:

>>> 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>]

Meminta pembobotan¶

Setiap bidang mungkin tidak memiliki hubungan dalam sebuah permintaan, jadi anda dapat mensetel berat dari beragam vektor sebelum anda mamadukan mereka:

>>> 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')

Berat harus beupa satu dari huruf berikut: D, C, B, A. Secara awalan, berat ini mengacu pada angka 0.1, 0.2, 0.4, dan 1.0, masing-masing. Jika anda berharap pada berat mereka berbeda, lewatkan daftar dari float emapt kami ke SearchRank sebagai weights dalam urutan sama diatas:

>>> 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')

Penampilan¶

Konfigurasi basisdata khusus tidak diperlukan untuk menggunakan fungsi ini apapun, bagaimapun, jika anda sedang mencari lebih dari sedikit ratusan rekaman, anda mungkin berjalan kedalam masalah penampilan. Pencarian teks penuh adalah pengolahan lebih intensif daripada membandingkan ukuran dari integer, sebagai contoh.

In the event that all the fields you're querying on are contained within one particular model, you can create a functional GIN or GiST index which matches the search vector you wish to use. For example:

GinIndex(
    SearchVector('body_text', 'headline', config='english'),
    name='search_vector_idx',
)

The PostgreSQL documentation has details on creating indexes for full text search.

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

Kemiripan trigram¶

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

Untuk menggunakan mereka, anda butuh mengaktifkan pg_trgm extension pada PostgreSQL. Anda dapat memasang itu menggunakan tindakan perpindahan TrigramExtension.

>>> 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>]

>>> 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>]

>>> 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>]

>>> 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>]