(wagtailsearch_searching)=

# Searching

(wagtailsearch_searching_pages)=

## Searching QuerySets

Wagtail search is built on Django's [QuerySet API](django:ref/models/querysets). You should be able to search any Django QuerySet provided the model and the fields being filtered on have been added to the search index.

### Searching Pages

Wagtail provides a shortcut for searching pages: the `.search()` `QuerySet` method. You can call this on any `PageQuerySet`. For example:

```python
# Search future EventPages
>>> from wagtail.models import EventPage
>>> EventPage.objects.filter(date__gt=timezone.now()).search("Hello world!")
```

All other methods of `PageQuerySet` can be used with `search()`. For example:

```python
# Search all live EventPages that are under the events index
>>> EventPage.objects.live().descendant_of(events_index).search("Event")
[<EventPage: Event 1>, <EventPage: Event 2>]
```

```{note}
The `search()` method will convert your `QuerySet` into an instance of one of Wagtail's `SearchResults` classes (depending on backend). This means that you must perform filtering before calling `search()`.
```

Before the `autocomplete()` method was introduced, the search method also did partial matching. This behaviour is deprecated and you should either switch to the new `autocomplete()` method or pass `partial_match=False` into the search method to opt-in to the new behaviour.
The partial matching in `search()` will be completely removed in a future release.

### Autocomplete searches

Wagtail provides a separate method which performs partial matching on specific autocomplete fields. This is useful for suggesting pages to the user in real-time as they type their query.

```python
>>> EventPage.objects.live().autocomplete("Eve")
[<EventPage: Event 1>, <EventPage: Event 2>]
```

```{note}
This method should only be used for real-time autocomplete and actual search requests should always use the `search()` method.
```

(wagtailsearch_images_documents_custom_models)=

### Searching Images, Documents and custom models

Wagtail's document and image models provide a `search` method on their QuerySets, just as pages do:

```python
>>> from wagtail.images.models import Image

>>> Image.objects.filter(uploaded_by_user=user).search("Hello")
[<Image: Hello>, <Image: Hello world!>]
```

[Custom models](wagtailsearch_indexing_models) can be searched by using the `search` method on the search backend directly:

```python
>>> from myapp.models import Book
>>> from wagtail.search.backends import get_search_backend

# Search books
>>> s = get_search_backend()
>>> s.search("Great", Book)
[<Book: Great Expectations>, <Book: The Great Gatsby>]
```

You can also pass a QuerySet into the `search` method, which allows you to add filters to your search results:

```python
>>> from myapp.models import Book
>>> from wagtail.search.backends import get_search_backend

# Search books
>>> s = get_search_backend()
>>> s.search("Great", Book.objects.filter(published_date__year__lt=1900))
[<Book: Great Expectations>]
```

(wagtailsearch_specifying_fields)=

### Specifying the fields to search

By default, Wagtail will search all fields that have been indexed using `index.SearchField`.

This can be limited to a certain set of fields by using the `fields` keyword argument:

```python
# Search just the title field
>>> EventPage.objects.search("Event", fields=["title"])
[<EventPage: Event 1>, <EventPage: Event 2>]
```

(wagtailsearch_faceted_search)=

### Faceted search

Wagtail supports faceted search, which is a kind of filtering based on a taxonomy
field (such as category or page type).

The `.facet(field_name)` method returns an `OrderedDict`. The keys are
the IDs of the related objects that have been referenced by the specified field, and the
values are the number of references found for each ID. The results are ordered by the number
of references descending.

For example, to find the most common page types in the search results:

```python
>>> Page.objects.search("Test").facet("content_type_id")

# Note: The keys correspond to the ID of a ContentType object; the values are the
# number of pages returned for that type
OrderedDict([
    ('2', 4),  # 4 pages have content_type_id == 2
    ('1', 2),  # 2 pages have content_type_id == 1
])
```

## Changing search behaviour

### Search operator

The search operator specifies how the search should behave when the user has typed in multiple search terms. There are two possible values:

-   "or" - The results must match at least one term (default for Elasticsearch)
-   "and" - The results must match all terms (default for database search)

Both operators have benefits and drawbacks. The "or" operator will return many more results but will likely contain a lot of results that aren't relevant. The "and" operator only returns results that contain all search terms, but requires the user to be more precise with their query.

We recommend using the "or" operator when ordering by relevance and the "and" operator when ordering by anything else (note: the database backend doesn't currently support ordering by relevance).

Here's an example of using the `operator` keyword argument:

```python
# The database contains a "Thing" model with the following items:
# - Hello world
# - Hello
# - World


# Search with the "or" operator
>>> s = get_search_backend()
>>> s.search("Hello world", Things, operator="or")

# All records returned as they all contain either "hello" or "world"
[<Thing: Hello World>, <Thing: Hello>, <Thing: World>]


# Search with the "and" operator
>>> s = get_search_backend()
>>> s.search("Hello world", Things, operator="and")

# Only "hello world" returned as that's the only item that contains both terms
[<Thing: Hello world>]
```

For page, image and document models, the `operator` keyword argument is also supported on the QuerySet's `search` method:

```python
>>> Page.objects.search("Hello world", operator="or")

# All pages containing either "hello" or "world" are returned
[<Page: Hello World>, <Page: Hello>, <Page: World>]
```

### Phrase searching

Phrase searching is used for finding whole sentences or phrases rather than individual terms.
The terms must appear together and in the same order.

For example:

```python
>>> from wagtail.search.query import Phrase

>>> Page.objects.search(Phrase("Hello world"))
[<Page: Hello World>]

>>> Page.objects.search(Phrase("World hello"))
[<Page: World Hello day>]
```

If you are looking to implement phrase queries using the double-quote syntax, see [](wagtailsearch_query_string_parsing).

(fuzzy_matching)=

### Fuzzy matching

```{versionadded} 4.0

```

Fuzzy matching will return documents which contain terms similar to the search term, as measured by a [Levenshtein edit distance](https://en.wikipedia.org/wiki/Levenshtein_distance).

A maximum of one edit (transposition, insertion, or removal of a character) is permitted for three to five letter terms, two edits for longer terms, and shorter terms must match exactly.

For example:

```python
>>> from wagtail.search.query import Fuzzy

>>> Page.objects.search(Fuzzy("Hallo"))
[<Page: Hello World>]
```

Fuzzy matching is supported by the Elasticsearch search backend only.

(wagtailsearch_complex_queries)=

### Complex search queries

Through the use of search query classes, Wagtail also supports building search queries as Python
objects which can be wrapped by and combined with other search queries. The following classes are
available:

`PlainText(query_string, operator=None, boost=1.0)`

This class wraps a string of separate terms. This is the same as searching without query classes.

It takes a query string, operator and boost.

For example:

```python
>>> from wagtail.search.query import PlainText
>>> Page.objects.search(PlainText("Hello world"))

# Multiple plain text queries can be combined. This example will match both "hello world" and "Hello earth"
>>> Page.objects.search(PlainText("Hello") & (PlainText("world") | PlainText("earth")))
```

`Phrase(query_string)`

This class wraps a string containing a phrase. See the previous section for how this works.

For example:

```python
# This example will match both the phrases "hello world" and "Hello earth"
>>> Page.objects.search(Phrase("Hello world") | Phrase("Hello earth"))
```

`Boost(query, boost)`

This class boosts the score of another query.

For example:

```python
>>> from wagtail.search.query import PlainText, Boost

# This example will match both the phrases "hello world" and "Hello earth" but matches for "hello world" will be ranked higher
>>> Page.objects.search(Boost(Phrase("Hello world"), 10.0) | Phrase("Hello earth"))
```

Note that this isn't supported by the PostgreSQL or database search backends.

(wagtailsearch_query_string_parsing)=

### Query string parsing

The previous sections show how to construct a phrase search query manually, but a lot of search engines (Wagtail admin included, try it!)
support writing phrase queries by wrapping the phrase with double-quotes. In addition to phrases, you might also want to allow users to
add filters into the query using the colon syntax (`hello world published:yes`).

These two features can be implemented using the `parse_query_string` utility function. This function takes a query string that a user
typed and returns a query object and dictionary of filters:

For example:

```python
>>> from wagtail.search.utils import parse_query_string
>>> filters, query = parse_query_string('my query string "this is a phrase" this-is-a:filter', operator='and')

>>> filters
{
    'this-is-a': 'filter',
}

>>> query
And([
    PlainText("my query string", operator='and'),
    Phrase("this is a phrase"),
])
```

Here's an example of how this function can be used in a search view:

```python
from wagtail.search.utils import parse_query_string

def search(request):
    query_string = request.GET['query']

    # Parse query
    filters, query = parse_query_string(query_string, operator='and')

    # Published filter
    # An example filter that accepts either `published:yes` or `published:no` and filters the pages accordingly
    published_filter = filters.get('published')
    published_filter = published_filter and published_filter.lower()
    if published_filter in ['yes', 'true']:
        pages = pages.filter(live=True)
    elif published_filter in ['no', 'false']:
        pages = pages.filter(live=False)

    # Search
    pages = pages.search(query)

    return render(request, 'search_results.html', {'pages': pages})
```

### Custom ordering

By default, search results are ordered by relevance if the backend supports it. To preserve the QuerySet's existing ordering, the `order_by_relevance` keyword argument needs to be set to `False` on the `search()` method.

For example:

```python
# Get a list of events ordered by date
>>> EventPage.objects.order_by('date').search("Event", order_by_relevance=False)

# Events ordered by date
[<EventPage: Easter>, <EventPage: Halloween>, <EventPage: Christmas>]
```

(wagtailsearch_annotating_results_with_score)=

### Annotating results with score

For each matched result, Elasticsearch calculates a "score", which is a number
that represents how relevant the result is based on the user's query. The
results are usually ordered based on the score.

There are some cases where having access to the score is useful (such as
programmatically combining two queries for different models). You can add the
score to each result by calling the `.annotate_score(field)` method on the
`SearchQuerySet`.

For example:

```python
>>> events = EventPage.objects.search("Event").annotate_score("_score")
>>> for event in events:
...    print(event.title, event._score)
...
("Easter", 2.5),
("Halloween", 1.7),
("Christmas", 1.5),
```

Note that the score itself is arbitrary and it is only useful for comparison
of results for the same query.

(wagtailsearch_frontend_views)=

## An example page search view

Here's an example Django view that could be used to add a "search" page to your site:

```python
# views.py

from django.shortcuts import render

from wagtail.models import Page
from wagtail.search.models import Query


def search(request):
    # Search
    search_query = request.GET.get('query', None)
    if search_query:
        search_results = Page.objects.live().search(search_query)

        # Log the query so Wagtail can suggest promoted results
        Query.get(search_query).add_hit()
    else:
        search_results = Page.objects.none()

    # Render template
    return render(request, 'search_results.html', {
        'search_query': search_query,
        'search_results': search_results,
    })
```

And here's a template to go with it:

```html+django
{% extends "base.html" %}
{% load wagtailcore_tags %}

{% block title %}Search{% endblock %}

{% block content %}
    <form action="{% url 'search' %}" method="get">
        <input type="text" name="query" value="{{ search_query }}">
        <input type="submit" value="Search">
    </form>

    {% if search_results %}
        <ul>
            {% for result in search_results %}
                <li>
                    <h4><a href="{% pageurl result %}">{{ result }}</a></h4>
                    {% if result.search_description %}
                        {{ result.search_description|safe }}
                    {% endif %}
                </li>
            {% endfor %}
        </ul>
    {% elif search_query %}
        No results found
    {% else %}
        Please type something into the search box
    {% endif %}
{% endblock %}
```

## Promoted search results

"Promoted search results" allow editors to explicitly link relevant content to search terms, so results pages can contain curated content in addition to results from the search engine.

This functionality is provided by the {mod}`~wagtail.contrib.search_promotions` contrib module.
