Ah, vector databases! Surely you’ve heard of them. Considering that the vector database market is projected to reach $1.7 Billion by 2027, data professionals REALLY need to make sure they’re staying ahead of the curve with respect to this tech. In this blog post, I’m sharing my favorite vector database tutorials.
But first, an easy intro to vector DBs
Vector databases are a fundamental component of generative AI infrastructure. With the massive generative AI explosion we’ve seen over the last year, the need for vector databases has also taken off at breakneck speed.
But, what is a vector database exactly?
Simply put, a vector database is a database management system that’s designed to efficiently store, manage, and retrieve multidimensional data, often used for similarity searches in machine learning and generative AI applications.
While traditional relational databases store, manage, and retrieve data from structured tables and defined columns, vector databases think a bit outside that box.
Their vectorized data format (aka; “vectors”) is especially handy when dealing with large-scale and multifaceted datasets.
I’ll borrow this handy-dandy diagram from Jatin Solanski to illustrate:
What’s this got to do with the price of tea in China?
With generative AI breaking the forefront of every single aspect of business and digital work, technologies that directly support generative AI are of course getting their day in the sun. Case in point, vector databases.
With respect to large language models (LLMs), vector databases are significant because they allow for efficient storage and retrieval of high-dimensional data. They enable quick similarity searches while also enhancing the model’s ability to reference vast amounts of information. In this way, vector databases improve the response accuracy and context understanding of LLMs.
Source: Pinecone
Here are some of the ways that vector databases are helpful when working with LLMs:
- Encoding information: LLMs can produce feature vectors, which are compact representations of longer text inputs or other data types. Vector databases store these feature vectors.
- Efficient similarity searches: When a new query comes in, an LLM generates a corresponding vector for it. The vector database can then quickly identify “nearest neighbors” or vectors that are most similar to the query vector. This is much more efficient than scanning through raw data.
- Scalability: As the amount of data and knowledge grows, it becomes impractical to directly search raw data every time. Vector databases allow LLMs to scale by providing a means by which to efficiently search through vast amounts of data using compact vector representations.
- Handling contextual information: Vector representations capture semantic meaning and context. When searching for relevant information, vector databases can identify vectors (and thus data points) that not only match the direct query but also align with the contextual or semantic intent behind it.
At the most basic level you could say that vector databases act as bridges that allow LLMs to tap into vast reservoirs of data in an optimized manner, thus ensuring rapid and contextually accurate responses.
Now that you understand what vector databases are and why they’ve become so important so fast, I wanted to let you in on 3 of my favorite vector database tutorials that you can use to get up-to-speed in record time.
Vector database tutorial #1: A Beginner’s Guide to Vector Databases
🔍 Discover the Future of Data Management: A Beginner’s Guide to Vector Databases.
📅 TRAINING TIME: On-demand
The world of data is evolving, and with it, so are the tools and technologies we use to harness its full potential. Because the vector database market is projected to reach a staggering USD $1.7 Billion by 2027, data professionals REALLY need to make sure they’re staying ahead of the curve with respect to this tech.
Join us for an eye-opening session where we explore the burgeoning world of vector databases. This training is a practical guide into why and how vector databases are becoming indispensable in our data-driven era, with a specific focus on how to use them to enhance AI capabilities and manage complex spatial data.
What you’ll gain from this 1-hour training:
- Learn the fundamentals of vector databases and their pivotal role in modern data management.
- See how 73% of AI and machine learning projects are being revolutionized by vector databases.
- Overview the best practices for integrating vector databases into your existing data infrastructure.
- Experience a live demonstration replay, including a hands-on code-sharing session, to see vector databases in action.
Whether you’re a seasoned data professional or just starting, this training is custom-tailored to provide you a comprehensive understanding of vector databases and their practical applications, all in 60 sweet minutes!
Don’t miss out on your chance to stay at the forefront of data management innovation. Click here to register now and secure your spot in this transformative journey into the world of vector databases.
Vector database tutorial #2: Vector Embedding Training
In this vector database tutorial, you’ll learn how to build an LLM app on inventory, product & reviews data!
Vector database tutorial #3: Real-time AI threat detection using Vector DBs & Kafka
Learn to achieve real-time AI threat detection using vector DBs & Kafka in this free vector database tutorial.
I hope you find these three vector database tutorials as valuable as I have! A word of thanks to the training host, SingleStore, for making these trainings freely available and for sponsoring The Convergence.
Pro-tip: If you like this training on AI implementation in business, consider checking out other free AI app development trainings we are offering here, here, here, here, here, here, here, here, here, here, here, here, here,here, and here.
