---
title: "Pencarian teks penuh"
version: 5.1
locale: id
source: https://docs.djangoproject.com/id/5.1/ref/contrib/postgres/search/
canonical: https://djangodocs.dev/id/5.1/ref/contrib/postgres/search/
---
# Pencarian teks penuh

Fungsi-fungsi basisdata dalam modul `django.contrib.postgres.search` kemudahan penggunaan  [full text search engine](https://www.postgresql.org/docs/current/textsearch.html) PostgreSQL.

Sebagai contoh dalam dokumen ini, kami akan menggunakan model ditentukan dalam [Membuat query](/id/5.1/topics/db/queries/).

> **See also**
>
> Untuk tinjauan tingkat-tinggi dari pencarian, lihat :doc:topic documentation .

## 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:

```pycon
>>> 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`](/id/5.1/ref/settings/#std-setting-INSTALLED_APPS) anda.

## `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`:

```pycon
>>> 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`](/id/5.1/ref/models/expressions/#django.db.models.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.
For example:

```pycon
>>> 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](#postgresql-fts-search-configuration) dan [Meminta pembobotan](#postgresql-fts-weighting-queries) 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](https://www.postgresql.org/docs/current/textsearch-controls.html#TEXTSEARCH-PARSING-QUERIES) to learn about differences and syntax.
Examples:

```pycon
>>> 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:

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

Lihat [Merubah konfigurasi pencarian](#postgresql-fts-search-configuration) 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:

```pycon
>>> 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](#postgresql-fts-weighting-queries) 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:

```pycon
>>> 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](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](https://www.postgresql.org/docs/current/textsearch-controls.html#TEXTSEARCH-HEADLINE).

Usage example:

```pycon
>>> 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](#postgresql-fts-search-configuration) untuk sebuah penjelasan dari parameter `config`.

## Merubah konfigurasi pencarian

You can specify the `config` attribute to a [`SearchVector`](#django.contrib.postgres.search.SearchVector) and
[`SearchQuery`](#django.contrib.postgres.search.SearchQuery) to use a different search configuration. This allows using
different language parsers and dictionaries as defined by the database:

```pycon
>>> 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:

```pycon
>>> 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

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

```pycon
>>> 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 [`SearchRank`](#django.contrib.postgres.search.SearchRank) as `weights` in the same order above:

```pycon
>>> 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`](/id/5.1/ref/contrib/postgres/indexes/#django.contrib.postgres.indexes.GinIndex) or
[`GiST`](/id/5.1/ref/contrib/postgres/indexes/#django.contrib.postgres.indexes.GistIndex) 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](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](https://www.postgresql.org/docs/current/textsearch-features.html#TEXTSEARCH-UPDATE-TRIGGERS). You can then query the field as
if it were an annotated `SearchVector`:

```pycon
>>> 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`](/id/5.1/ref/contrib/postgres/lookups/#std-fieldlookup-trigram_similar),
[`trigram_word_similar`](/id/5.1/ref/contrib/postgres/lookups/#std-fieldlookup-trigram_word_similar), and [`trigram_strict_word_similar`](/id/5.1/ref/contrib/postgres/lookups/#std-fieldlookup-trigram_strict_word_similar)
lookups, you can use a couple of other expressions.

Untuk menggunakan mereka, anda butuh mengaktifkan [pg\_trgm extension](https://www.postgresql.org/docs/current/pgtrgm.html) pada PostgreSQL. Anda dapat memasang itu menggunakan tindakan perpindahan [`TrigramExtension`](/id/5.1/ref/contrib/postgres/operations/#django.contrib.postgres.operations.TrigramExtension).

### `TrigramSimilarity`

#### `class TrigramSimilarity(expression, string, **extra)`

Menerima nama bidang atau pernyataan, dan string atau pernyataan. Mengembalikan kemiripan trigram diantara dua argumen.

Usage example:

```pycon
>>> 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`

#### `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:

```pycon
>>> 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>]
```

### `TrigramStrictWordSimilarity`

#### `class TrigramStrictWordSimilarity(string, expression, **extra)`

Accepts a string or expression, and a field name or expression. Returns the
trigram strict word similarity between the two arguments. Similar to
[`TrigramWordSimilarity()`](#django.contrib.postgres.search.TrigramWordSimilarity), except that it forces
extent boundaries to match word boundaries.

### `TrigramDistance`

#### `class TrigramDistance(expression, string, **extra)`

Menerima nama bidang atau pernyataan, dan string atau pernyataan. mengembalikan jarak trigram diantara dua argumen.

Usage example:

```pycon
>>> 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`

#### `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:

```pycon
>>> 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>]
```

### `TrigramStrictWordDistance`

#### `class TrigramStrictWordDistance(string, expression, **extra)`

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