How to Build an AI-Powered Video Search App

How to Build an AI-Powered Video Search App

In this article, we will explore how to build a natural language processing (NLP)-powered intelligent search for video using YouTube as an example. YouTube revolutionized the way we consume video content, allowing normal people to share their lives and experiences. We will learn how to create a search tool that enables us to search through engaging YouTube content using NLP techniques.

The Evolution of YouTube and Video Search

YouTube started in 2005 with a simple 19-second video titled "I'm at the zoo," featuring YouTube's co-founder at a zoo with elephants. This video marked the beginning of a new era where normal people could share their daily lives. YouTube grew into a platform where users could find a wide range of content, making it more than just a glimpse into someone's life.

Today, YouTube offers a vast collection of engaging content covering various topics. While we can use standard search options provided by YouTube or Google, building a customized search tool can provide a more targeted and efficient search experience.

Building an Intelligent Search Tool

Before diving into the technical details, let's take a look at the features we can build in our video search app.

Using Streamlit, a Python library for building simple web interfaces, we can create a user interface that allows users to enter a search query like "What is deep learning?" The app will present a list of relevant videos based on the query. Clicking on a video will take the user directly to the specific timestamp in the video where the query is addressed.

One of the exciting aspects of building this search app is that we can leverage existing NLP models and develop it quickly. We'll be using a dataset consisting of transcript text files from YouTube videos. These files contain subtitles for different segments of the videos. By utilizing off-the-shelf models and pre-processed data, we can create an intelligent and fast search tool.

Acquiring and Processing the Dataset

To obtain the dataset, we can download it from Kaggle, a platform for data science and machine learning. The dataset contains text and audio files, but we will only focus on the text files. The dataset consists of around 116,000 text files, each representing a small segment of a video.

To download the dataset, you need to create a Kaggle account, obtain an API token, and authenticate the Kaggle Python client. Once downloaded, we can explore the dataset, which includes directories representing video IDs, timestamps, audio files, and subtitles.

While the dataset provides the necessary information for the search app, it lacks additional metadata like video thumbnails, titles, and descriptions. To gather this information, we need to scrape YouTube using web scraping techniques and tools like Beautiful Soup. With web scraping, we can capture video metadata such as titles and thumbnails.

Indexing the Data

Once we have the cleaned and enriched dataset, we can begin indexing the documents into a vector database. For this, we will use a service called Pinecone, which allows us to efficiently perform similarity searches.

First, we initialize the SentenceTransformer, which is a pre-trained model that converts text into high-dimensional vectors. These vectors capture semantic information and enable us to perform efficient similarity searches. We set the embedding dimensionality based on the model's specifications.

Next, we create an index in Pinecone, connecting it to our project and defining the index's properties—namely, cosine similarity and the embedding dimensionality. We encode the text, generate unique IDs for each document, and include metadata such as titles and start timestamps.

Finally, we describe the size of the index and see how many documents we have indexed so far.

Building the Search App

To build the search app, we can use Streamlit. With Streamlit, we create a simple interface where users can enter their search queries. When the search bar is populated, the app performs a search using the query and retrieves relevant video results. The app dynamically generates cards displaying video information, including titles and thumbnails. Users can click on a video to be directed to the relevant timestamp in the video.

Keyword

• NLP-powered search app
• YouTube video search
• Video metadata scraping
• Pinecone vector database
• Streamlit for web interfaces

FAQ

1. Can I use a different dataset for video search?

   • Yes, as long as you have a dataset with the necessary information and can represent it in a question and answer format, you can build a search app for any video content.

2. Do I need web scraping skills to gather video metadata?

   • Basic knowledge of web scraping using libraries like Beautiful Soup can be helpful, but you can also find pre-scraped metadata or use APIs provided by platforms like YouTube.

3. Can I customize the search app with additional features?

   • Absolutely! You can enhance the search app by adding filters, sorting options, user profiles, or even integrating recommendation systems to provide personalized video suggestions.

4. Is Pinecone the only option for a vector database?

   • No, there are other vector database solutions available. Pinecone was used in this tutorial for its efficient similarity search capabilities, but you can explore other options based on your specific requirements.

5. Can I deploy the search app on a website or a mobile app?

   • Yes, you can deploy the search app on various platforms, including websites and mobile apps. Streamlit allows you to create user-friendly interfaces that can be integrated into different applications.