Django REST Framework (DRF) allows you to build powerful APIs quickly. Integrating search functionality into your DRF project using PostgreSQL’s full-text search capabilities can enhance your application’s usability. In this blog, we’ll adapt our previous Django example to use DRF, implementing basic and advanced search using SearchVector, SearchQuery, and SearchRank.
Before diving into the implementation, ensure you have the following prerequisites:
pip install django djangorestframework psycopg2Related read: Building Powerful REST APIs with Node.js, Prisma ORM, and PostgreSQL
1. Install PostgreSQL: Ensure you’re using PostgreSQL, as Django’s full-text search capabilities leverage its built-in features.
2. Install Required Packages: Ensure your Django project has the necessary dependencies.
pip install django psycopg23. Create a Model: Suppose you have a simple Article model for this example.
from django.db import models
class Article(models.Model):
title = models.CharField(max_length=200)
content = models.TextField()
created_at = models.DateTimeField(auto_now_add=True)
def __str__(self):
return self.title4. Set Up the Database: Configure your settings.py to connect to PostgreSQL.
Create a serializer for the Article model in serializers.py.
from rest_framework import serializers
from .models import Article
class ArticleSerializer(serializers.ModelSerializer):
class Meta:
model = Article
fields = '__all__'First, implement a basic search using Django’s contains filter, allowing case-insensitive substring matching.
from rest_framework.views import APIView
from rest_framework.response import Response
from .models import Article
from .serializers import ArticleSerializer
class BasicSearchView(APIView):
def get(self, request):
query = request.query_params.get('q', None)
if query:
articles = Article.objects.filter(title__icontains=query)
else:
articles = Article.objects.all()
serializer = ArticleSerializer(articles, many=True)
return Response(serializer.data)Next, we’ll enhance our search functionality using SearchVector, SearchQuery, and SearchRank.
from django.contrib.postgres.search import SearchVector, SearchQuery, SearchRank
class AdvancedSearchView(APIView):
def get(self, request):
query = request.query_params.get('q', None)
if query:
search_vector = SearchVector('title', 'content')
search_query = SearchQuery(query)
articles = Article.objects.annotate(
rank=SearchRank(search_vector, search_query)
).filter(rank__gte=0.3).order_by('-rank') # Adjust threshold as necessary
else:
articles = Article.objects.all()
serializer = ArticleSerializer(articles, many=True)
return Response(serializer.data)Define the URLs for your search views in urls.py.
from django.urls import path
from .views import BasicSearchView, AdvancedSearchView
urlpatterns = [
path('search/basic/', BasicSearchView.as_view(), name='basic-search'),
path('search/advanced/', AdvancedSearchView.as_view(), name='advanced-search'),
]You can now test the API endpoints using tools like Postman or curl.
Basic Search Example
To perform a basic search for articles, make a GET request to /search/basic/?q=django.
GET /search/basic/?q=djangoAdvanced Search Example
To perform an advanced search, make a GET request to /search/advanced/?q=django.
GET /search/advanced/?q=djangoExample Response
For both searches, you might get a response like this:
[
{
"id": 1,
"title": "Understanding Django",
"content": "Django is a powerful web framework...",
"created_at": "2023-09-23T12:00:00Z"
},
...
]
By following these steps, you have successfully implemented both basic and advanced search functionality in a Django REST Framework application. The basic search uses Django’s contains, while the advanced search leverages PostgreSQL’s full-text search capabilities.
Feel free to expand this implementation by adding features such as pagination, filtering, or integrating a frontend to display search results dynamically. Happy coding!
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