When we think we are getting good at something, new technology emerges, which makes us go back to studying. But this time it’s a little easier, since it’s a subject that I like a lot: Database! If you also like this theme and want to learn the basics of vector database… This article is for you!

What is a vector database?

A vector database is a category that indexes and stores embedding vectors, providing an efficient search. These databases have the ability to save, modify, delete, and recover data, offering an innovative approach to information management.

What is an Embedding?

Embeddings, in turn, are vectors of texts generated by AI models. These have several characteristics, making their representation a challenge due to their complexity. In contexts such as artificial intelligence and machine learning, these characteristics represent different dimensions of data, which are essential for discerning underlying patterns, relationships, and structures. In summary, it is a way of storing data and its meanings, giving it a unique semantic dimension.

Why is this important?

The representation of vector data, enriched with semantic information, is crucial for artificial intelligence to gain understanding and maintain a long-term memory. This proves to be fundamental in performing complex tasks, allowing AI to extract valuable insights from the vast set of data with which it interacts.

But where am I going to use it? Imagine the scenario in which you are developing artificial intelligence for your company, requiring it to keep crucial information in its memory, such as policies, product data, prices, customers, among others. Therefore, to give this AI an efficient memory, the choice for a vector database becomes essential.

Image generated by DALL-E, depicting a woman giving memory to a robot.

Where to start?

To take the first steps in this universe, choosing the right database is crucial. Among the available options, some stand out:

Pinecone: Developed by a startup, it is easy to learn, allowing free trials.

Qdrant: An open-source option, requiring the configuration of a docker for local execution, but it offers a full range of features.

Azure Search: An excellent choice for Azure users looking for an enterprise option.

Redis: Recognized for its undeniable capacity, Redis, in addition to its applications in other data structures, also offers a vector database.

In summary, vector databases represent a significant evolution in the field of information management. Their ability to represent data semantically and efficiently makes them an indispensable tool for developing robust artificial intelligence systems. By choosing the right vector database and understanding the importance of this approach, we are prepared to enter the future of data, empowering our technological creations to reach new heights of understanding and performance.

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Rafael Dourado is BR Operations Manager at Programmers. Its main role is to ensure end-to-end customer satisfaction to embrace this technological revolution. With 15 years of experience working in the software and analytics universe, he is passionate about AI because he believes in its transformative power. In his leisure time, Rafael likes to practice chess with his daughter.