Fungsi-fungsi basisdata dalam modul django.contrib.postgres.search kemudahan penggunaan full text search engine PostgreSQL.
Sebagai contoh dalam dokumen ini, kami akan menggunakan model ditentukan dalam Membuat query.
\n\nsearchLink to this heading#A common way to use full text search is to search a single term against a\nsingle column in the database. For example:
\n>>> Entry.objects.filter(body_text__search="Cheese")\n<QuerySet [<Entry: Cheese on Toast recipes>, <Entry: Pizza Recipes>]>\nIni 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.
SearchVectorLink to this heading#Searching against a single field is great but rather limiting. The Entry\ninstances we're searching belong to a Blog, which has a tagline field.\nTo query against both fields, use a SearchVector:
>>> from django.contrib.postgres.search import SearchVector\n>>> Entry.objects.annotate(\n... search=SearchVector("body_text", "blog__tagline"),\n... ).filter(search="Cheese")\n<QuerySet [<Entry: Cheese on Toast recipes>, <Entry: Pizza Recipes>]>\nArgumen-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.
SearchVector objects can be combined together, allowing you to reuse them.\nFor example:
>>> Entry.objects.annotate(\n... search=SearchVector("body_text") + SearchVector("blog__tagline"),\n... ).filter(search="Cheese")\n<QuerySet [<Entry: Cheese on Toast recipes>, <Entry: Pizza Recipes>]>\nLihat Merubah konfigurasi pencarian dan Meminta pembobotan untuk sebuah penjelasan dari parameter config dan weight.
SearchQueryLink to this heading#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\nas separate keywords. If search_type is 'phrase', the terms are treated\nas a single phrase. If search_type is 'raw', then you can provide a\nformatted search query with terms and operators. If search_type is\n'websearch', then you can provide a formatted search query, similar to the\none used by web search engines. 'websearch' requires PostgreSQL ≥ 11. Read\nPostgreSQL's Full Text Search docs to learn about differences and syntax.\nExamples:
>>> from django.contrib.postgres.search import SearchQuery, Lexeme\n>>> SearchQuery("red tomato") # two keywords\n>>> SearchQuery("tomato red") # same results as above\n>>> SearchQuery("red tomato", search_type="phrase") # a phrase\n>>> SearchQuery("tomato red", search_type="phrase") # a different phrase\n>>> SearchQuery("'tomato' & ('red' | 'green')", search_type="raw") # boolean operators\n>>> SearchQuery(\n... "'tomato' ('red' OR 'green')", search_type="websearch"\n... ) # websearch operators\n>>> SearchQuery(Lexeme("tomato") & (Lexeme("red") | Lexeme("green"))) # Lexeme objects\nSearchQuery terms can be combined logically to provide more flexibility:
>>> from django.contrib.postgres.search import SearchQuery\n>>> SearchQuery("meat") & SearchQuery("cheese") # AND\n>>> SearchQuery("meat") | SearchQuery("cheese") # OR\n>>> ~SearchQuery("meat") # NOT\nLihat Merubah konfigurasi pencarian untuk sebuah penjelasan dari parameter config.
SearchRankLink to this heading#So far, we've returned the results for which any match between the vector and\nthe query are possible. It's likely you may wish to order the results by some\nsort of relevancy. PostgreSQL provides a ranking function which takes into\naccount how often the query terms appear in the document, how close together\nthe terms are in the document, and how important the part of the document is\nwhere they occur. The better the match, the higher the value of the rank. To\norder by relevancy:
\n>>> from django.contrib.postgres.search import SearchQuery, SearchRank, SearchVector\n>>> vector = SearchVector("body_text")\n>>> query = SearchQuery("cheese")\n>>> Entry.objects.annotate(rank=SearchRank(vector, query)).order_by("-rank")\n<QuerySet [<Entry: Cheese on Toast recipes>, <Entry: Pizza recipes>]>\nLihat Meminta pembobotan untuk penjelasan dari parameter weights.
Set the cover_density parameter to True to enable the cover density\nranking, which means that the proximity of matching query terms is taken into\naccount.
Provide an integer to the normalization parameter to control rank\nnormalization. This integer is a bit mask, so you can combine multiple\nbehaviors:
>>> from django.db.models import Value\n>>> Entry.objects.annotate(\n... rank=SearchRank(\n... vector,\n... query,\n... normalization=Value(2).bitor(Value(4)),\n... )\n... )\nThe PostgreSQL documentation has more details about different rank\nnormalization options.
\nSearchHeadlineLink to this heading#Accepts a single text field or an expression, a query, a config, and a set of\noptions. Returns highlighted search results.
\nSet the start_sel and stop_sel parameters to the string values to be\nused to wrap highlighted query terms in the document. PostgreSQL's defaults are\n<b> and </b>.
Provide integer values to the max_words and min_words parameters to\ndetermine the longest and shortest headlines. PostgreSQL's defaults are 35 and\n15.
Provide an integer value to the short_word parameter to discard words of\nthis length or less in each headline. PostgreSQL's default is 3.
Set the highlight_all parameter to True to use the whole document in\nplace of a fragment and ignore max_words, min_words, and short_word\nparameters. That's disabled by default in PostgreSQL.
Provide a non-zero integer value to the max_fragments to set the maximum\nnumber of fragments to display. That's disabled by default in PostgreSQL.
Set the fragment_delimiter string parameter to configure the delimiter\nbetween fragments. PostgreSQL's default is " ... ".
The PostgreSQL documentation has more details on highlighting search\nresults.
\nUsage example:
\n>>> from django.contrib.postgres.search import SearchHeadline, SearchQuery\n>>> query = SearchQuery("red tomato")\n>>> entry = Entry.objects.annotate(\n... headline=SearchHeadline(\n... "body_text",\n... query,\n... start_sel="<span>",\n... stop_sel="</span>",\n... ),\n... ).get()\n>>> print(entry.headline)\nSandwich with <span>tomato</span> and <span>red</span> cheese.\nLihat Merubah konfigurasi pencarian untuk sebuah penjelasan dari parameter config.
You can specify the config attribute to a SearchVector and\nSearchQuery to use a different search configuration. This allows using\ndifferent language parsers and dictionaries as defined by the database:
>>> from django.contrib.postgres.search import SearchQuery, SearchVector\n>>> Entry.objects.annotate(\n... search=SearchVector("body_text", config="french"),\n... ).filter(search=SearchQuery("œuf", config="french"))\n<QuerySet [<Entry: Pain perdu>]>\nThe value of config could also be stored in another column:
>>> from django.db.models import F\n>>> Entry.objects.annotate(\n... search=SearchVector("body_text", config=F("blog__language")),\n... ).filter(search=SearchQuery("œuf", config=F("blog__language")))\n<QuerySet [<Entry: Pain perdu>]>\nEvery field may not have the same relevance in a query, so you can set weights\nof various vectors before you combine them:
\n>>> from django.contrib.postgres.search import SearchQuery, SearchRank, SearchVector\n>>> vector = SearchVector("body_text", weight="A") + SearchVector(\n... "blog__tagline", weight="B"\n... )\n>>> query = SearchQuery("cheese")\n>>> Entry.objects.annotate(rank=SearchRank(vector, query)).filter(rank__gte=0.3).order_by(\n... "rank"\n... )\nThe weight should be one of the following letters: D, C, B, A. By default,\nthese weights refer to the numbers 0.1, 0.2, 0.4, and 1.0,\nrespectively. If you wish to weight them differently, pass a list of four\nfloats to SearchRank as weights in the same order above:
>>> rank = SearchRank(vector, query, weights=[0.2, 0.4, 0.6, 0.8])\n>>> Entry.objects.annotate(rank=rank).filter(rank__gte=0.3).order_by("-rank")\nLexemeLink to this heading#Lexeme objects allow search operators to be safely used with strings from\nan untrusted source. The content of each lexeme is escaped so that any\noperators that may exist in the string itself will not be interpreted.
You can combine lexemes with other lexemes using the & and | operators\nand also negate them with the ~ operator. For example:
>>> from django.contrib.postgres.search import SearchQuery, SearchVector, Lexeme\n>>> vector = SearchVector("body_text", "blog__tagline")\n>>> Entry.objects.annotate(search=vector).filter(\n... search=SearchQuery(Lexeme("fruit") & Lexeme("dessert"))\n... )\n<QuerySet [<Entry: Apple Crumble Recipes>, <Entry: Banana Split Recipes>]>\n>>> Entry.objects.annotate(search=vector).filter(\n... search=SearchQuery(Lexeme("fruit") & Lexeme("dessert") & ~Lexeme("banana"))\n... )\n<QuerySet [<Entry: Apple Crumble Recipes>]>\nLexeme objects also support term weighting and prefixes:
\n>>> Entry.objects.annotate(search=vector).filter(\n... search=SearchQuery(Lexeme("Pizza") | Lexeme("Cheese"))\n... )\n<QuerySet [<Entry: Cheese on Toast recipes>, <Entry: Pizza recipes>]>\n>>> Entry.objects.annotate(search=vector).filter(\n... search=SearchQuery(Lexeme("Pizza") | Lexeme("Cheese", weight="A"))\n... )\n<QuerySet [<Entry: Pizza recipes>]>\n>>> Entry.objects.annotate(search=vector).filter(\n... search=SearchQuery(Lexeme("za", prefix=True))\n... )\n<QuerySet []>\nKonfigurasi 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.
\nIn the event that all the fields you're querying on are contained within one\nparticular model, you can create a functional\nGinIndex or\nGistIndex which matches the search\nvector you wish to use. For example:
GinIndex(\n SearchVector("body_text", "headline", config="english"),\n name="search_vector_idx",\n)\nThe PostgreSQL docs has details on creating indexes for full text search.
\nSearchVectorFieldLink to this heading#If this approach becomes too slow, you can add a SearchVectorField to your\nmodel. You'll need to keep it populated with triggers, for example, as\ndescribed in the PostgreSQL documentation. You can then query the field as\nif it were an annotated SearchVector:
>>> Entry.objects.update(search_vector=SearchVector("body_text"))\n>>> Entry.objects.filter(search_vector="cheese")\n<QuerySet [<Entry: Cheese on Toast recipes>, <Entry: Pizza recipes>]>\nGeneratedFieldLink to this heading#An alternative method is to use a GeneratedField,\nwhich has PostgreSQL maintain the column rather than a trigger:
from django.contrib.postgres.indexes import GinIndex\nfrom django.contrib.postgres.search import SearchVector, SearchVectorField\nfrom django.db import models\n\n\nclass Entry(models.Model):\n headline = models.CharField(max_length=255)\n body_text = models.TextField()\n\n search_vector = models.GeneratedField(\n expression=SearchVector("headline", weight="A", config="english")\n + SearchVector("body_text", weight="B", config="english"),\n output_field=SearchVectorField(),\n db_persist=True,\n )\n\n class Meta:\n indexes = [GinIndex(fields=["search_vector"])]\nAnother approach to searching is trigram similarity. A trigram is a group of\nthree consecutive characters. In addition to the trigram_similar,\ntrigram_word_similar, and trigram_strict_word_similar\nlookups, 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.
TrigramSimilarityLink to this heading#Menerima nama bidang atau pernyataan, dan string atau pernyataan. Mengembalikan kemiripan trigram diantara dua argumen.
\nUsage example:
\n>>> from django.contrib.postgres.search import TrigramSimilarity\n>>> Author.objects.create(name="Katy Stevens")\n>>> Author.objects.create(name="Stephen Keats")\n>>> test = "Katie Stephens"\n>>> Author.objects.annotate(\n... similarity=TrigramSimilarity("name", test),\n... ).filter(\n... similarity__gt=0.3\n... ).order_by("-similarity")\n<QuerySet [<Author: Katy Stevens>, <Author: Stephen Keats>]>\nTrigramWordSimilarityLink to this heading#Accepts a string or expression, and a field name or expression. Returns the\ntrigram word similarity between the two arguments.
\nUsage example:
\n>>> from django.contrib.postgres.search import TrigramWordSimilarity\n>>> Author.objects.create(name="Katy Stevens")\n>>> Author.objects.create(name="Stephen Keats")\n>>> test = "Kat"\n>>> Author.objects.annotate(\n... similarity=TrigramWordSimilarity(test, "name"),\n... ).filter(\n... similarity__gt=0.3\n... ).order_by("-similarity")\n<QuerySet [<Author: Katy Stevens>]>\nTrigramStrictWordSimilarityLink to this heading#Accepts a string or expression, and a field name or expression. Returns the\ntrigram strict word similarity between the two arguments. Similar to\nTrigramWordSimilarity(), except that it forces\nextent boundaries to match word boundaries.
TrigramDistanceLink to this heading#Menerima nama bidang atau pernyataan, dan string atau pernyataan. mengembalikan jarak trigram diantara dua argumen.
\nUsage example:
\n>>> from django.contrib.postgres.search import TrigramDistance\n>>> Author.objects.create(name="Katy Stevens")\n>>> Author.objects.create(name="Stephen Keats")\n>>> test = "Katie Stephens"\n>>> Author.objects.annotate(\n... distance=TrigramDistance("name", test),\n... ).filter(\n... distance__lte=0.7\n... ).order_by("distance")\n<QuerySet [<Author: Katy Stevens>, <Author: Stephen Keats>]>\nTrigramWordDistanceLink to this heading#Accepts a string or expression, and a field name or expression. Returns the\ntrigram word distance between the two arguments.
\nUsage example:
\n>>> from django.contrib.postgres.search import TrigramWordDistance\n>>> Author.objects.create(name="Katy Stevens")\n>>> Author.objects.create(name="Stephen Keats")\n>>> test = "Kat"\n>>> Author.objects.annotate(\n... distance=TrigramWordDistance(test, "name"),\n... ).filter(\n... distance__lte=0.7\n... ).order_by("distance")\n<QuerySet [<Author: Katy Stevens>]>\nTrigramStrictWordDistanceLink to this heading#Accepts a string or expression, and a field name or expression. Returns the\ntrigram strict word distance between the two arguments.
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