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Create a Vector Database With Open-Source

BeginnerGuided Project

Vector databases are especially important in AI, but they are used for many other applications as well. Allowing for very quick and accurate indexing using AI embeddings that have been trained on contextual data, vector databases have a great role in our modern day tech. Learn how to create a vector-store database using open-source AI embeddings for absolutely free!

4.6 (21 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Enrollment Count

  • 220

Skills You Will Learn

  • Artificial Intelligence, Data Science, Python

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 2 hours

Platform

  • SkillsNetwork

Last Update

  • November 19, 2025
About this Guided Project

A Look at the Project Ahead

Today, AI relies heavily on vector databases because they provide a way for computers to understand and organize complex information. AI algorithms often need to process large amounts of data and make sense of it in order to perform tasks like image recognition, natural language processing, or recommendation systems. Vector databases help in this process by representing data as vectors, which are numerical representations of the characteristics or features of that data.

So what exactly are vector databases?

Vector databases are like smart organizers for information. They are important because they help us find things quickly and accurately. Imagine you have a big collection of pictures, and you want to find all the pictures of dogs. Instead of going through each picture one by one, a vector database can analyze the unique features of the images, like the shape of the ears or the color of the fur. This is possible through what we call "Embeddings", which are a way to measure those features. It then creates special codes called vectors that represent those features. So when you search for "dog," the database compares your search with all the vectors it has and finds the pictures that are most like dogs. This saves a lot of time and effort! 

But that's not all! Vector databases can also help us with other things, like finding recommendations. Let's say you're listening to music on a streaming platform and you really like a song. The platform can use a vector database to analyze the characteristics of that song, like the rhythm or the melody. Then, it can compare it to other songs in its library and find similar ones that you might enjoy. So, vector databases help us organize and discover things in a clever and efficient way, whether it's finding pictures or suggesting awesome songs!

In this project you will learn how to create your own vector database using open-source tools. By the end of this guided project, you will know:
  • What vector databases are and how they are constructed
  • What embeddings are, and their role in vector databases
  • How to choose the right embeddings for your project
  • How to build and use your own vector database

What You'll Need

This guided project requires basic knowledge of Python and database queries.

However, don't worry if you don't have much experience with the above. This project will explain each step along the way, and you may learn as-you-need along the way.

IBM Skills Network Labs environment also comes with many technical requisites pre-installed to save the hassle of setting everything up, so we won't be dealing with any environment issues.

Instructors

Efkan Serhat Goktepe

Developer | Architect

Efkan is a 4th year student in Computer Science at University of Toronto. Efkan is currently working at IBM as a Software Architect. Contact: efkan@ibm.com.

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Contributors

Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Vicky Kuo

Data Scientist

I believe that success isn't just about individual milestones, but also about uplifting and encouraging others to reach their potential. This is why I'm passionate about combining my technical background with my eagerness to help people overcome technological hurdles and accelerate growth. When I’m not on the job, I love hiking with my two dogs or relaxing in a coffee shop. There's nothing better than having an insightful conversation over coffee, or even better, some volunteer work! Please feel free to reach out to me on LinkedIn.

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