Google Custom Search with NodeJS

Goo

Google provides a custom search API by which you can use the power of google search engine in your own application. The base URL for the REST version of custom search API is:

https://www.googleapis.com/customsearch/v1

Before moving on to integration part, we need two things.

  1. API KEY
  2. Search Engine ID

The API KEY can be created by tapping the GET A KEY button and creating a new project at the following link:

https://developers.google.com/custom-search/json-api/v1/introduction

The next step is the search engine ID. You need to create an instance of a search engine at:

https://cse.google.com/

  1. Here you need to add a new instance, give the name of the engine, any website and just create it for now.
  2. Now go to its control panel and in the basic options > Sites to search, you have the option to search the entire web (with emphasis on some websites if you want to include some) or search only for some websites (should be included in list, patterns can also be included).
  3. Configure it as per your choice and in Details section you will find the search engine ID by clicking on the Search Engine ID button.

Now you are ready to integrate. The code to integrate the API using Express and Node JS is:

var express = require(‘express’);

var path = require(‘path’);

var app = express();

var request = require(‘request’);

// The main search function

var google_web_search = function(search, callback) {

    console.log(‘Searching the web for: ‘, search);

    var options = {

        method: ‘GET’,

        url: ‘https://www.googleapis.com/customsearch/v1’,

        qs: {

            q: search,

            key: ‘<API_KEY>’,

            cx: ‘<SEARCH_ENGINE_ID>’,

        }

    };

    request(options, function (error, response, body) {

        callback(error, body);

    });

};

app.get(‘/’, function (req, res) {

   google_web_search(‘<YOUR_SEARCH_QUERY>’, function(error, body) {

     if (!error) {

        res.send(body);

     } else {

        throw new Error(error);

     }

   });

});

app.listen(3000, function () {

    console.log(‘Example app listening on port 3000!’);

});

If you want more results, then you can use pagination by adding two query params:

  1. num: specifies the number of results to return in the response
  2. start: specifies the number of results to skip

gle provides a custom search API by which you can use the power of google search engine in your own application. The base URL for the REST version of custom search API is:

https://www.googleapis.com/customsearch/v1

Before moving on to integration part, we need two things.
1. API KEY
2. Search Engine ID

The API KEY can be created by tapping the GET A KEY button and creating a new project at the following link:

https://developers.google.com/custom-search/json-api/v1/introduction

The next step is the search engine ID. You need to create an instance of a search engine at:

https://cse.google.com/

1. Here you need to add a new instance, give the name of the engine, any website and just create it for now.
2. Now go to its control panel and in the basic options > Sites to search, you have the option to search the entire web (with emphasis on some websites if you want to include some) or search only for some websites (should be included in list, patterns can also be included).
3. Configure it as per your choice and in Details section you will find the search engine ID by clicking on the Search Engine ID button.

Now you are ready to integrate. The code to integrate the API using Express and Node JS is:

var express = require('express');
var path = require('path');
var app = express();

var request = require('request');

// The main search function
var google_web_search = function(search, callback) {
    console.log('Searching the web for: ', search);
    var options = {
        method: 'GET',
        url: 'https://www.googleapis.com/customsearch/v1',
        qs: {
            q: search,
            key: '<API_KEY>',
            cx: '<SEARCH_ENGINE_ID>',
        }
    };

    request(options, function (error, response, body) {
        callback(error, body);
    });
};
app.get('/', function (req, res) {
   google_web_search('<YOUR_SEARCH_QUERY>', function(error, body) {
     if (!error) {
        res.send(body);
     } else {
        throw new Error(error);
     }
   });
});

app.listen(3000, function () {
    console.log('Example app listening on port 3000!');
});

If you want more results, then you can use pagination by adding two query params:
1. num: specifies the number of results to return in the response
2. start: specifies the number of results to skip

Comments

One response to “Google Custom Search with NodeJS”

  1. Thanks for sharing. I read many of your blog posts, cool, your blog is very good.

Recent Post

  • What Is Synthetic Data? Benefits, Techniques & Applications in AI & ML

    In today’s data-driven era, information is the cornerstone of technological advancement and business innovation. However, real-world data often presents challenges—such as scarcity, sensitivity, and high costs—especially when it comes to specific or restricted datasets. Synthetic data offers a transformative solution, providing businesses and researchers with a way to generate realistic and usable data without the […]

  • Federated vs Centralized Learning: The Battle for Privacy, Efficiency, and Scalability in AI

    The ever-expanding field of Artificial Intelligence (AI) and Machine Learning (ML) relies heavily on data to train models. Traditionally, this data is centralized, aggregated, and processed in one location. However, with the emergence of privacy concerns, the need for decentralized systems has grown significantly. This is where Federated Learning (FL) steps in as a compelling […]

  • Federated Learning’s Growing Role in Natural Language Processing (NLP)

    Federated learning is gaining traction in one of the most exciting areas: Natural Language Processing (NLP). Predictive text models on your phone and virtual assistants like Google Assistant and Siri constantly learn from how you interact with them. Traditionally, your interactions (i.e., your text messages or voice commands) would need to be sent back to […]

  • What is Knowledge Distillation? Simplifying Complex Models for Faster Inference

    As AI models grow increasingly complex, deploying them in real-time applications becomes challenging due to their computational demands. Knowledge Distillation (KD) offers a solution by transferring knowledge from a large, complex model (the “teacher”) to a smaller, more efficient model (the “student”). This technique allows for significant reductions in model size and computational load without […]

  • Priority Queue in Data Structures: Characteristics, Types, and C Implementation Guide

    In the realm of data structures, a priority queue stands as an advanced extension of the conventional queue. It is an abstract data type that holds a collection of items, each with an associated priority. Unlike a regular queue that dequeues elements in the order of their insertion (following the first-in, first-out principle), a priority […]

  • SRE vs. DevOps: Key Differences and How They Work Together

    In the evolving landscape of software development, businesses are increasingly focusing on speed, reliability, and efficiency. Two methodologies, Site Reliability Engineering (SRE) and DevOps, have gained prominence for their ability to accelerate product releases while improving system stability. While both methodologies share common goals, they differ in focus, responsibilities, and execution. Rather than being seen […]

Click to Copy