Vector Databases and How to Pick the Right One for Your AI Project

Choosing Your AI Project’s Vector Database

Published in

Level Up Coding

5 min readJul 30, 2023

In the wild world of artificial intelligence (AI) and machine learning, there’s always something new on the horizon. Today, we’re talking about vector databases — the pillars supporting your AI projects. But before we get into how to pick the right one, let’s understand what these things really are.

The Basics of Vectors

To better understand vector databases, we need to start at the beginning: vectors. In essence, a vector is a row of numbers that represents some form of data. For instance, imagine having a photograph. To a machine, this photograph isn’t a cute picture of your dog but rather a complex array of pixel values. Each pixel value can be considered as a number, and when all these numbers are arranged in a certain order, they form a vector that represents the image. In other words, vectors are essentially numerical summaries of our data.

Vector Embeddings: Turning Complexity Into Simple Numbers

With the concept of vectors in hand, let’s take a detour to grasp the idea of vector embeddings. Think about giving a machine a piece of text — say, “chocolate cake”. The machine converts this text into a list of numbers, capturing the essence of the term “chocolate cake”. This number list, known as a vector embedding, is a form of high-dimensional representation.

Think about your favorite streaming platform that hosts thousands of films, each falling under one or multiple genres, such as sci-fi, drama, or comedy. Each movie can be represented by its dominant genre traits.

Let’s consider three films: ‘Interstellar’, ‘The Social Network’, and ‘Guardians of the Galaxy’. ‘Interstellar’ is heavily sci-fi, ‘The Social Network’ is primarily a drama, and ‘Guardians of the Galaxy’ merges sci-fi with a good dose of comedy. From the machine’s point of view, ‘Interstellar’ and ‘The Social Network’ would sit farther apart on our ‘map’ due to their differing genres, whereas ‘Guardians of the Galaxy’ might land somewhere in between due to its blend of sci-fi and comedy.

This is a basic illustration of vector embeddings: each movie gets a spot (or a ‘number’) based on its genre mix, in a landscape where similar movies cluster together. While our example uses a simple two-dimensional space (sci-fi and drama), in reality, the data usually involves more dimensions, forming what we call ‘high-dimensional spaces’.

*Visual representation of a vector space — image from Weaviate*

Vector Databases: Handling Complex Data with Ease

Now, let’s get to the heart of the matter — vector databases. Unlike traditional databases that organize data in rows, columns, or documents, vector databases arrange arrays of numbers (remember our vectors and vector embeddings?) based on similarity.

When we generate vector embeddings for data, we need a place to store them for later retrieval. This is where a vector database comes in handy. The database clusters these vectors based on their closeness, allowing for fast and efficient queries. Similar vectors huddle together in the same corner of this high-dimensional space.

Applications such as online shopping recommendations, image search engines, and video streaming suggestions all leverage the capabilities of vector databases. Additionally, they’re making waves in fields like biology, finance, and IoT, assisting in the identification of similar genetic sequences, fraud detection, and sensor data analysis.

Top Contenders in 2023

Several standouts are making their presence felt in the vector database world:

Chroma: The open-source sensation, Chroma, is adept at handling audio data. Its flexible, supports multiple data types, and scales well, making it perfect for large language model applications and powering audio-based search engines.
Pinecone: Pinecone, a cloud-based managed vector database, is a developer’s best friend. It focuses on managing infrastructure, allowing developers to concentrate on creating kickass applications. Its real-time data analysis capability makes it ideal for cybersecurity threat detection.
Weaviate: Weaviate ups the ante by storing both vectors and objects. Its flexibility and data management prowess make it versatile for various data types and applications.
Milvus: The open-source maverick, Milvus, is popular in data science and machine learning circles due to its robust vector indexing and querying. Its compatibility with popular frameworks like PyTorch and TensorFlow makes it an excellent fit for existing machine learning workflows.
Faiss: Faiss shines when dealing with large collections of high-dimensional vectors. Its optimization of memory usage and query time makes it ideal for storing and retrieving vectors, perfect for large-scale image search engines or semantic search systems.

Guidelines to Choose Your Vector Database

Choosing a vector database isn’t just about picking the popular kid. Here’s what you should consider:

Scalability: Can the database handle large volumes of high-dimension data and keep up as your data needs grow?
Performance: You want a speedy database that excels in data retrieval, search operations, and smoothly performing operations on vectors.
Flexibility: Pick a database that plays nice with a wide range of data types and formats and can adapt to various use cases.
Ease of Use: Who wants a headache? A user-friendly setup, intuitive APIs, and comprehensive documentation make life much easier.
Reliability: Opt for databases with a strong track record of being reliable and robust.

In the end, the perfect vector database for your AI project will depend on its specific requirements. Your project’s objectives should guide your selection process.

Wrapping Up

Vector databases are changing the game in data indexing and similarity search in the context of AI. As AI projects become more complex, the demand for specialized tools like these also grows. By understanding vector databases and their functionalities, you’re enabling yourself to make well-informed decisions that will help your AI project succeed.

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