What Are Vector Databases?

In the process of building out AI infrastructure for efficient AI models, vector databases have become an essential component. They enable common machine learning model functions like similarity searches.

So, what is a vector database, and how does it work?

In this introduction guide, we'll cover areas such as vector database definition, its applications, and examples of vector database tools.

Introduction to Vector Databases

Let's first explore their definition to help you understand how they help to enable AI models.

What is a vector database?

Vector databases are engines optimized for storing and querying vectors — sequences of numbers or embeddings. Vector databases excel with high-dimensional data, making data points searchable by similarity rather than exact matches.

These databases empower AI applications to perform similarity searches, catering to tasks that involve understanding complex patterns and relationships within the data. These systems facilitate nuanced searches and analytics, often surpassing traditional databases when dealing with AI and machine learning tasks.

(Related reading: database vs. data warehouse & database management systems.)

Core characteristics

Here are the core characteristics of vector databases:

• Vectors. As the name implies, vector databases store and handle vectors efficiently. Vector data are arrays of numbers representing various features of objects, like images, text, or audio clips.
• High-dimensional. They excel in working with high-dimensional data, making it possible to store and query vectors with hundreds or even thousands of dimensions.
• Fast querying. Vector databases are optimized for similarity searches, allowing for quick retrieval of similar data points.
• Embeddings. These databases use embeddings, which are numerical representations of objects or concepts, making it possible to compare and match data points based on their underlying patterns.

Comparison: traditional vs. vector DBs

A simple rule of thumb for defining these two DBs:

• Traditional relational databases do well with structured data, where schema and SQL queries are commonly used. This usually means ACID properties and transactional integrity are maintained.
• Vector databases are specialized for handling high-dimensional vector data, which is critical for AI and machine learning applications.

Relational DBs manage rows and columns of data within tables. Vector databases store and process data as vectors, enabling operations that understand the context and semantics of the data they contain, which is paramount for AI-driven searching and analytics.

Conventionally, SQL databases rely on indexing approaches like B-trees. Conversely, vector DBs leverage highly specialized indexing algorithms, such as Approximate Nearest Neighbor (ANN) search, to handle similarity-based queries efficiently.

While traditional databases are optimized for CRUD operations on structured data, vector databases are engineered to enable fast similarity searches, scaling to meet the needs of applications that center around high-dimensional data.

Scalability in traditional databases often involves sharding or partitioning. Vector databases, however, are innately handled by distributed systems and offer scalable infrastructure designed to accommodate the growing volume and complexity of data associated with vectors.

How vector databases work

Vector databases are powerful. Here’s a simple explanation of how they work:

Creating vectors

First, the data (like an image or piece of text) is transformed into a vector using a method such as deep learning. This vector captures the essential features of the data in a numeric form. For instance, in the context of images, this could include aspects like:

• Color
• Texture
• Shape

Storing vectors

These vectors are then stored in a vector database. Unlike traditional databases that store data as entries in tables, vector databases are optimized to store and handle these numeric arrays efficiently.

Indexing for quick search

To enable fast retrieval, vector databases use special indexing techniques like:

• Tree-based indexing
• Hashing-based indexing
• Graph-based indexing
• Quantization-based indexing

These indexes help in organizing the vectors in a way that similar vectors are stored close to each other. This is akin to organizing books in a library by topic so you can find related books grouped together.

Searching for a vector

When you want to find data that is similar to a query (like finding images similar to a given image), the query is also converted into a vector.

When a query is made, the vector database employs algorithms to assess the proximity between vectors, utilizing techniques like cosine similarity or Euclidean distance to measure how 'close' or 'similar' one vector is to another.

The database uses the index to quickly find vectors that are close or similar to the query vector. This process is much faster than comparing the query vector to every vector in the database.

This process is substantially accelerated with the help of Approximate Nearest Neighbor (ANN) search algorithms, which enable the system to swiftly find the best matches in a high-dimensional space without exhaustively comparing every possible pair of vectors.

Returning results

The database then retrieves the original data corresponding to the closest vectors found, such as images or documents, and presents them as search results. This makes for an excellent result response for AI applications like a chatbot reply or search engine results.

Practical applications

The applications of vector databases span several industries, with a few key uses in the following fields:

Enhancing search functionality

Vector databases bring precision and nuance to search capabilities beyond traditional text-based methods. Here are some reasons why a vector database is most suitable for search:

• Semantic understanding: Interpreting the meaning behind queries for more relevant results.
• Speed: Delivering faster searches by indexing vectors instead of text.
• Scalability: Managing large-scale data without sacrificing performance.
• Multimedia queries: Enabling searches for images, video, and audio content.
• Contextual relevance: Incorporating context to refine search accuracy.
• Language agnosticism: Functioning effectively across multiple languages and dialects.
• Personalization: Tailoring search results to individual user preferences and behaviors.

In a search functionality use case, a vector database is an ideal tool to find content similar to either a query or another reference item.

Real-time recommendations

Vector databases also enable dynamically curated content that adapts in real-time, offering highly personalized user experiences. They ensure recommendations are relevant and up-to-date.

Leveraging the speed and efficiency of vector databases, real-time recommendation engines can process and analyze large volumes of data at high speed, achieving a delicate balance between timeliness, relevancy, and personalization.

A powerful AI recommendation engine would require a database that could handle high-dimensional data, like vectors, and process it at fast speeds — something only vector databases can achieve.

Vector database examples

To have a better understanding of what vector databases are available, here are a few examples of vector database tools.

Chroma

Chroma is an AI-native open-source embedding vector database that's relatively new to the market. Their main use of embeddings is what sets them apart. Chroma provides lightweight wrappers around popular embedding providers, making it easy to use them in your apps.

Chroma works by creating and using collections, which will be used to load embeddings or documents. All queries to the database will be retrieved from the collection.

Pinecone

Pinecone is a serverless vector database made for GenAI applications. It stands out for using its hybrid search approach, combining both semantic and keyword search in one query to achieve the query result you need.

Moreover, Pinecone's intuitive API simplifies the integration process, allowing data scientists and engineers to seamlessly introduce vector search into existing systems. With support for common LLM frameworks like Hugging Face, OpenAI, LangChain, and Llama Index, it streamlines the implementation of complex vector operations.

(Related reading: LLMs vs. SLMs.)

Weaviate

Weaviate is an open-source AI-native vector database that supports multiple languages like Python, Javascript, and Go. It is also made for hybrid search queries, using both the vector and keyword search approach to find similarities in your database.

What sets them apart is their focus on ML tasks through their modular approach to using common machine learning models and frameworks. They also provide several options for deployment, such as:

• Weaviate Cloud Services: a paid serverless option for database deployment.
• Docker: For local deployment
• Kubernetes: For deployment in production environments.
• Embedded Weaviate: For running a Weaviate instance from your application code.

Final thoughts

Vector databases are powerful tools that enable the handling of high-dimensional data at lightning-fast speeds. As they can search and index unstructured data efficiently using vectors, vector databases are essential in building AI applications with search functionality and recommendation engines.

Therefore, building a vector database is essential to the development and deployment of any AI applications within your organization.