Vector database 101: What is it, and how does it work?
Explore how vector databases enhance AI ecosystems by efficiently managing complex data for generative AI applications, and learn about their benefits, applications, and impact on businesses.
Data is one of the most transformative resources driving innovation today. Nearly every cutting-edge technology relies on the data that shapes it. For instance, the quality and effectiveness of any artificial intelligence (AI) application depend heavily on the underlying data.
However, data today is more complex than ever before. Businesses are striving to find scalable and economical ways to leverage complex data, especially when dealing with generative AI (GenAI) applications. This is where vector databases can come in. In this article, I will introduce vector databases and explain how they enhance AI ecosystems. We'll address these questions:
What is a vector database?
How do vector databases work?
What are the practical applications of vector databases?
How do you choose the right vector database?
Why should you choose the Aerospike vector database?
Let’s begin.
What is a vector database?
A vector database stores and indexes data as vectors, numerical representations of various data types. Traditional relational databases use rows and columns optimized for storing structured data; a NoSQL database, on the other hand, can handle unstructured data, as it can support various data models, such as graphs, key-value stores, and vectors. Businesses could benefit from vector databases for use cases demanding both speed and accuracy.
These databases store vector embeddings in a multi-dimensional space, enabling similarity calculations through vector computations. Previously, search methods relied on exact matches or fuzzy logic. Now, vector databases allow businesses to leverage both structured and unstructured data more effectively. As demand for generative AI applications grows, managing unstructured data through vector databases is a game-changer.
What are the benefits of vector databases?
As data demands evolve, companies need new ways to scale and optimize AI and machine learning (ML) applications using large amounts of unstructured data. The rise of generative AI has expanded these use cases, boosting interest in vector databases.
While other technologies like data lakes and document databases also handle unstructured data, vector databases stand out by translating complex data points into a format optimized for similarity searches. They’re particularly effective for advanced, compute-heavy AI processes, which were previously difficult for both machines and humans to manage.
The increased interest in vector databases across sectors can be seen in the growth of its global market, which is expected to reach a whopping $4.3 billion by 2028.
According to Gartner, 45% of executive leaders are increasing AI investments based on the excitement around GenAI chatbots like ChatGPT.
BARC's March 2024 report Optimizing Your Architecture for AI Innovation, revealed the following about enterprise adoption of vector databases:
20% of companies are using vector databases in production.
26% are testing them.
29% are researching their potential.
24% have no plans to adopt them.
To remain competitive, businesses need to accelerate their understanding and adoption of vector databases.
What are embeddings?
Vector databases store, search, and retrieve embeddings, also called vector embeddings. An embedding is a numerical representation of data, such as text, images, or videos. To create these vectors, unstructured data must undergo an embedding process, where it is converted into lower-dimensional vector data using machine learning models.
The goal is to preserve semantic relationships within the data. For instance, using models like Word2Vec or BERT, similar items are placed closer together in vector space. This can be achieved through either supervised or unsupervised learning.
The dimensionality of these embedding vectors can vary—some have a few dozen dimensions, while others have thousands. Typically, vectors consist of around 300 dimensions. There’s always a trade-off between retaining more information with high-dimensional vector data and improving efficiency with lower-dimensional vectors.
An example of this concept would be transforming words like orange, yellow, and banana into numbers and mapping their relationships. By placing them in a high-dimensional vector space, you can see how these items relate based on attributes such as color or size.
If two vectors are close in this space, they share similarities—whether that’s for simple words or more complex datasets like full documents, images, or videos.
How do vector databases work?
Now that you understand embeddings, let’s explore how vector databases function. The process starts with creating vector embeddings from unstructured data. Key elements in this process include indexing and metadata.
Indexing
Transforming unstructured data into vector embeddings is only the first step. Optimized indexing is crucial for performing similarity searches, often called vector similarity search. Vector databases use indexes—such as hash tables, trees, or graphs—to calculate the distance between vectors, enabling fast, efficient retrieval.
Metadata
In addition to storing vector embeddings, vector databases include metadata—information about the vectors, like their source or context. Efficient metadata filtering can enhance search accuracy and streamline AI processes. While organizations recognize the importance of metadata, many struggle to manage and leverage it effectively.
Online embedding search
To fully understand advanced embedding search techniques, let's explore key concepts: machine learning models, similarity search algorithms, and distance metrics.
Machine learning models
The speed and accuracy of vector databases depend heavily on machine learning models that convert raw unstructured data into high-dimensional vector data. These models are responsible for creating the embedding vectors that allow for efficient search and analysis.
Similarity search algorithms
Similarity search algorithms are fundamental to vector search. They identify the vectors closest to the content of a query. Two primary algorithms include:
K-Nearest Neighbors (KNN): A simple yet effective method.
Approximate Nearest Neighbors (ANN): A faster, more scalable approach, often used in vector databases.
Common ANN techniques include Hierarchical Navigable Small World (HNSW) and Locality-Sensitive Hashing (LSH).
Distance metrics
In vector databases, similarity is measured using distance metrics, which improve the accuracy of tasks like clustering and classification. By calculating how close vectors are in vector space, these metrics enable fast and precise retrieval of similar data points.
Vector databases and scalability
As businesses scale their AI applications to handle larger datasets and more users, vector databases must provide high performance and low latency. Scalable vector databases ensure that AI applications maintain efficiency even with growing data volumes and query loads.
Data density and dimensionality
Vector databases handle high-dimensional data by optimizing the balance between data density (how compactly data is represented) and dimensionality (the number of attributes in a vector). This ensures that the database can process complex vector data without compromising performance.
Real-time updates
The ability to manage real-time updates is essential for many AI use cases, such as fraud detection or personalized recommendations. Vector databases must handle large volumes of incoming vector data in real time, ensuring accuracy and relevance in time-sensitive applications.
Multiple user interactions
Scalable vector databases support high concurrency, allowing multiple users to interact with the database simultaneously without performance degradation. This is critical for real-time applications like e-commerce platforms, where user traffic is high.
What are the practical applications for vector databases?
Now that we've covered how vector databases work, let's explore their real-world applications. Each of these use cases involves vector search and similarity search.
Enhancing personalized recommendation systems
McKinsey reports that 71% of customers expect personalized services and interactions and that 76% become frustrated if companies don’t deliver. Businesses that don’t personalize services will fall behind in competitive markets.
Vector databases are a powerful backend support for recommendation engines. Typically, recommendation engines rely on user data such as browsing histories and demographic information, as well as domain-specific data like product information and historical customer metrics.
With vector databases, recommendation systems can benefit from a real-time influx of user data, the option to weave in domain-specific data sets, the ability to handle petabyte-scale data workloads, and semantic search.
Furthermore, vector databases can enable multi-modal recommendations, meaning that product recommendations will be based on a combination of user interactions and multi-format product details like photos and text descriptions. This is ideal for use cases like media content personalization, e-commerce recommendations, and investment recommendations.
Optimizing real-time analytics at scale
Many businesses handle petabytes of data, but the value lies in how effectively it’s analyzed. Vector databases enhance AI and machine learning applications by enabling real-time analysis of complex data like IoT sensor data. This is vital for industries such as manufacturing and healthcare, where fast, accurate insights are critical.
Implementing retrieval augmented generation (RAG) in chatbots
Using the RAG architecture, businesses can integrate vector databases into their AI systems to provide faster, more accurate responses in chatbots. By accessing vector data, these chatbots reduce retrieval time and minimize AI hallucinations, resulting in more coherent and relevant answers.
What to look for in the right vector database
Choosing the right vector database is crucial for optimizing your AI and machine learning workloads. Here are five key attributes to consider:
1. Performance and real-time capabilities
Assess your performance and speed requirements. Determine the data throughput you need and the level of latency your applications can tolerate. Look for a vector database that offers:
Low latency and high throughput
Real-time indexing and updates
Efficient data ingestion to handle large volumes of vector data
2. Scalability and storage
Not all vector databases ensure scalability, so businesses must plan for future growth. Look for a database that:
Maintains speed and accuracy as data volumes and user loads increase
Optimizes storage to manage costs as the system scales
3. Efficiency
Efficiency in querying, updating, and scaling should minimize costs while maximizing speed and accuracy. A low total cost of ownership (TCO) is essential when handling large, high-dimensional vector data.
4. Developer-friendliness and ease of use
The right vector database should integrate seamlessly with your existing tech stack and provide developer-friendly tools and APIs for working with different data types. This makes deployment and scaling easier without needing to overhaul your infrastructure.
5. Robust security and compliance
The world of GenAI can be a cybersecurity and compliance minefield. According to Gartner, 57% of organizations worry about leaked secrets via AI-generated code, and 58% worry about biased GenAI outputs.
These types of GenAI challenges can result in compliance failures and legal penalties to the tune of millions. Businesses must thus ensure that potential vector databases comply with frameworks such as HIPAA, GDPR, and CCPA.
Why should you choose Aerospike Vector Search?
To conclude this blog post, let’s look at five reasons you should choose Aerospike Vector Search:
Optimal for AI: With its ability to ingest, manage, and leverage key-value stores, graph, document, and vector data, Aerospike is a powerful solution for real-time AI use cases.
Performance at scale: Aerospike uniquely builds and assembles the HNSW index in parallel across devices, enabling horizontal scale-out ingestion.
Low TCO: Via a Hybrid Memory Architecture and a partitioned index strategy, Aerospike ensures reliably low latency and accuracy without wasting resources.
Dev-friendly: By offering several sample Python apps and LangChain integrations, Aerospike can help your developers get started with a myriad of vector use cases.
RBAC-powered security: With strong role-based access controls, Aerospike provides robust security by ensuring the optimized management of users and access privileges.
There’s no better solution than Aerospike for achieving accuracy and speed at scale for even the most demanding and ambitious projects.
Get access to Aerospike Vector Search and try out the transformative capabilities of our robust vector database solution.
