Data Integration

Vector Databases: A Primer

Read the first part of our series of blogs on Vector Databases
Arti Gupta
4 min to read

Since the launch of ChatGPT by OpenAI, there has been an explosive surge in organizations eager to dive into Artificial Intelligence (AI). Beyond the simplistic chatbots, the rise of Generative AI and the development of Small Language Models (SLMs) signal that 2024 is a pivotal year for AI innovation. This year promises to be a landmark period, marked by groundbreaking advancements that will redefine the boundaries of what AI can achieve.

Thanks to the ongoing excitement around artificial intelligence, many people are becoming familiar with its key terms. One such term is vector databases, which are intricately connected to the world of Large Language Models (LLMs), Machine Learning (ML), and AI, including ChatGPT. These databases play a crucial role in managing and optimizing the vast amounts of data that power these advanced systems. As AI continues to evolve, understanding the function and importance of vector databases becomes increasingly essential. They are the silent engines driving the efficiency and accuracy of AI applications, making our interactions with technology more seamless and intelligent. Whether you’re a tech enthusiast or just curious, diving into the world of vector databases unveils the sophisticated machinery behind the magic of AI.

In our in-depth series on Vector Databases, we'll explore everything you need to know about these cutting-edge systems. Discover the 'What, Why, and How' behind vector databases, understand their distinctions from traditional databases, and learn how they integrate with Large Language Models (LLMs). We'll also delve into their diverse use cases, showcasing their practical applications, and highlight the best vector databases available in the market. Whether you're a seasoned data professional or just curious about the future of database technology, this series promises to be both informative and engaging.

Let’s dive right in!

What are Vector Databases?

A vector database is a database that stores information as vectors, which are numerical representations of data objects, also known as vector embeddings. 

Vector embeddings are numerical representations of data objects that capture the semantic meaning or characteristics of the data in a multi-dimensional space. Essentially, they transform complex data types, like text, images, or audio, into fixed-size vectors of numbers. This transformation enables various machine learning models to process and analyze the data more effectively.

For example:

Text embeddings represent words, sentences, or documents in a way that similar meanings are placed closer together in the vector space. Techniques like Word2Vec, GloVe, and BERT are commonly used for generating text embeddings.

Image embeddings convert images into vectors based on their visual features, allowing for tasks like image classification or similarity searches. Convolutional neural networks (CNNs) are often used for this purpose.

Audio embeddings transform audio signals into vectors that capture characteristics like tone, pitch, and rhythm, facilitating tasks such as speech recognition or music recommendation.

Vector embeddings simplify complex data, making it easier to perform tasks like clustering, searching, and comparing within large datasets.

It leverages the power of these vector embeddings to index and search across a massive dataset of unstructured data and semi-structured data, such as images, text, or sensor data. Vector databases are built to manage vector embeddings offering a complete solution for managing unstructured and semi-structured data.

A vector database organizes data through high-dimensional vectors. High-dimensional vectors contain hundreds of dimensions each corresponding to a specific feature or property of the data object it represents.

Why a Vector Database?

A vector database becomes important for businesses dealing regularly with unstructured data to power machine learning models and who need to be frequently involved in the search and retrieval of required data out of huge volumes of datasets. Let’s understand in a bit detail as to why vector databases are important and the capabilities that they offer better than the traditional ones will be dealt with later on in this blog.

Vector databases provide a method to operationalize embedding models. Application development is more productive with database capabilities like security controls, scalability, fault tolerance, and efficient information retrieval through sophisticated query languages.

AI and machine learning (ML) require vector databases for several key reasons:

Efficient Data Retrieval: AI and ML often work with large datasets containing unstructured or semi-structured data such as text, images, and audio. Vector databases enable fast and efficient retrieval of relevant data using vector embeddings, that allow for rapid similarity searches.

Handling High-Dimensional Data: Machine learning models, especially those used in deep learning, often generate high-dimensional vector embeddings. Vector databases are specifically designed to store and manage these high-dimensional vectors, ensuring efficient data processing and storage.

Scalability: AI and ML applications frequently involve scaling up to handle massive amounts of data. Vector databases are optimized for scalability, enabling them to manage and process large datasets without significant performance degradation.

Similarity Searches: Many AI applications, such as recommendation systems, natural language processing, and image recognition, rely on finding similar items within a dataset. Vector databases use techniques like nearest neighbor search to quickly find and retrieve similar vectors, enhancing the performance of these applications.

Improved Performance: By leveraging vector embeddings, vector databases can perform complex queries and analysis tasks more efficiently than traditional databases. This improved performance is crucial for real-time AI applications where speed and accuracy are essential.

Versatility: Vector databases can handle various types of data, making them versatile tools for different AI and ML tasks. Whether it's text, images, or audio, vector databases provide a unified solution for managing and querying diverse datasets.

Overall, vector databases are essential for AI and ML because they offer specialized capabilities that enhance data retrieval, handling, and processing, leading to more efficient and effective AI applications.

Vector databases ultimately empower developers to create unique application experiences. For example, users could snap photographs on their smartphones to search for similar images. Innovations in generative artificial intelligence (AI) have introduced new types of models like ChatGPT that can generate text and manage complex conversations with humans. Some can operate on multiple modalities; for instance, some models allow users to describe a landscape and generate an image that fits the description. Generative models are, however, prone to hallucinations, which could, for instance, cause a chatbot to mislead users. Vector databases can complement generative AI models. They can provide an external knowledge base for generative AI chatbots and help ensure they provide trustworthy information. 

How does a Vector Database Work?

A vector database works by using algorithms to index and query vector embeddings. The algorithms enable approximate nearest neighbor (ANN) search through hashing, quantization, or graph-based search.

To retrieve information, an ANN search finds a query’s nearest vector neighbor. Less computationally intensive than a kNN search (known nearest neighbor, or true k nearest neighbor algorithm), an approximate nearest neighbor search is also less accurate. However, it works efficiently and at scale for large datasets of high-dimensional vectors.

The secret sauce of a successful vector database lies in its vector embeddings, broken-down bits of stored content. First, embeddings are generated from content — text, images, audio, or video.

In this “vectorization” process, with words, for instance, the relationships between the words are captured. This ensures that the ones with similar meanings or contexts — similar vectors — will be placed physically near each other in the vector space.

As you might expect with a traditional database, the next step is vector indexing. Using algorithms (for example, product quantization or hierarchical navigable small world, HNSW), the embeddings are mapped to a data structure that facilitates quick search and duly stored in the database for easy retrieval.

Third is the querying stage. User queries are sent through the vector embedding model used to generate the data storage. When a query is submitted, the indexed query vector is compared with the indexed vectors, and the best retrieved information is pushed to the front.

How do Vector Databases differ from Traditional Databases?

A traditional database stores information in tabular form, and indexes data by assigning values to data points. When queried, a traditional database will return results that exactly match the query. A vector database stores vectors in the form of embeddings and enables vector search, which returns query results based on similarity metrics (rather than exact matches). A vector database "steps up" where a traditional database cannot: It is intentionally designed to operate with vector embeddings. A vector database is also better suited than a traditional database in certain applications, such as similarity search, artificial intelligence, and machine learning applications, because it enables high-dimensional search and customized indexing, and because it is scalable, flexible, and efficient.

Vector Databases are inherently suited for tasks involving similarity search where the goal is to find the closest data points in a high-dimensional space, a common requirement in AI applications like image and voice recognition, recommendation systems and natural language processing. By leveraging indexing and search algorithms optimized for high-dimensional vector spaces, vector databases offer a more efficient and effective way to handle the kind of data that is increasingly prevalent in the age of advanced AI and machine learning.

We hope you were able to understand a little bit more about vector databases, why we need one in the first place, how do they differ from traditional databases and a quick walkthrough of their working. In the next upcoming blogs of our series we'll talk in detail about their working, their use cases, and how they will be an essential part of any modern tech stack. Stay tuned for more. Meanwhile, if you want to start your data journey with DataChannel, book a call with us to know all about it.

Try DataChannel Free for 14 days

No contracts, no credit card.
Get started now
Write to us at info@datachannel.co
The first 14 days are on us
Free hands-on onboarding & support
Simple usage based pricing