Vector Database Explained: How AI Systems Store and Search Embeddings

Key takeaways

Vector databases store information as high-dimensional vectors, which help machine learning (ML) models understand meaning and remember context.

• Vector databases work by first converting multimodal data into vectors, indexing them into new data structures for efficient search, and performing nearest neighbor searches to retrieve results most similar to the query.

• While traditional databases return results based on exact keyword match, vector databases use semantic similarity to return more contextually relevant results.

• You can use vector databases in a number of advanced applications, including large language models, retrieval-augmented generation, recommendation engines, and semantic search.

Learn more about vector databases and their importance in AI technology, including common use cases and how to choose the right vector database. If you’re ready to start learning more about artificial intelligence and data analytics, enroll in the Generative AI for Data Analysts Specialization. You’ll‌ learn about generative AI tools for data analytics, generative AI prompt engineering concepts, and applications of generative AI in data analytics in as little as eight weeks. Upon completion, you’ll have earned a career certificate for your resume.

What is a vector database, and why does it exist?

Vector databases store information as high-dimensional vectors, which are numerical representations of text, images, audio, and video locations along several dimensions. Machine learning (ML) models use these high-dimensional data points to understand meaning and remember context. 

When you query an ML model, instead of searching for exact keyword matches, it uses vector searches that rely on similarity metrics and nearest neighbor clustering to retrieve information from unstructured data sources such as documents and audio files. For example, vector database searches let you snap a photo on your smartphone and search for similar images. In this way, vector databases allow artificial intelligence (AI) systems to compare, understand, and retrieve information in more human-like ways while offering high processing speed and scalability.

Where vector databases fit in modern AI architectures

AI models in modern architectures are trained to convert all types of data, including text, audio, and images, into vectors (i.e., arrays of numbers) that AI models can understand. These numerical representations are called vector embeddings, which use several attributes and features to model different dimensions of data, capturing its meaning and context. 

Vector embeddings are crucial to an AI model’s ability to understand, remember, and identify patterns and relationships in data. Vector databases store and index these vector embeddings, serving as the memory layer in AI architecture that powers the model’s semantic search, contextual analysis, natural language processing (NLP), computer vision, and generative AI capabilities. 

How do vector databases work?

Vector databases use multiple nearest neighbor search algorithms organized in a pipeline to efficiently and accurately search for and retrieve vectors that most closely resemble the query. The vector database provides approximate results based on semantic similarity and often requires a tradeoff between accuracy and speed; that is, if you want your results to be highly accurate, you’ll have to compromise on the speed of your output. When you query a vector database, it initiates a sequence of processes, including vectorization, indexing, and query execution.

Vectorization

A pre-trained ML model generates vector embeddings from raw, multimodal data like text, images, and audio. Vector databases store these vector embeddings along with metadata, like data types, timestamps, and descriptions. To capture correlations within the data, the vector database stores semantically similar vectors close to each other in the vector space; for example, it will store words like “cat” and “dog” together but “car” separately. 

Indexing

Vector databases index vectors using ML algorithms to enable rapid search and retrieval of similar vectors. Indexing organizes vectors into new data structures, such as graphs or trees, to support efficient similarity or nearest-neighbor searches between vectors. Common indexing algorithms include Hierarchical Navigable Small World (HNSW) to create tree-like representations of vector sets, Product Quantization (PQ) to compress vectors into memory-efficient chunks, and Locality-Sensitive Hashing (LSH) to group similar vectors based on approximate nearest neighbor (ANN) search.

Read more: 10 Machine Learning Algorithms to Know

Querying

When you query an AI model, for example, by uploading an image and asking for similar images, the model first converts your query image into a vector embedding. Then, it calculates the distance between the query vector and vectors in the database using methods like nearest neighbor algorithms, cosine similarity, or ANN, and returns the closest vectors to your query vector based on similarity rankings. 

You can also choose to refine your search using metadata filtering, which combines traditional SQL-based database searches with vector searches to return more specific results. 

Vector databases vs. traditional databases

The primary difference between vector databases and traditional databases is that traditional databases return results based on exact keyword match, while vector databases use semantic similarity to return more contextual results. For example, if your search for “smartphone,” traditional databases will only return results containing the word smartphone, while vector databases will also return results containing words like “cellphone” or “mobile phone.”

How vector storage differs from relational and NoSQL databases

Relational databases excel at storing structured or semi-structured data, but handling unstructured data, such as images and text from emails and social media posts, poses a challenge. Non-relational databases can handle large amounts of unstructured data and offer more scalability than relational databases. Vector databases can deliver better results when used with AI applications because they store unstructured data as numerical representations, capturing the multiple features necessary for an AI model’s contextual understanding.

Compare the key differences between vector databases, relational databases, and NoSQL databases.

Is SQL a vector DB?

No, SQL (structured query language) is a type of programming language you can use to interact with and retrieve structured data from relational databases. Vector databases store unstructured data as high-dimensional vectors, a capacity not afforded by SQL databases. However, some traditional databases now offer extensions that add vector processing capabilities. For example, PostgreSQL has the pgvector extension, which enables it to store vector embeddings and perform nearest neighbor searches, combining the capabilities of relational databases with those of vector databases.

When vector databases complement existing data systems

Vector databases are part of the larger data ecosystem, offering application programming interfaces (APIs), extensions, and connectors that integrate with different analytics tools, software development kits (SDKs), and third-party applications. 

If you already have traditional database systems set up, vector databases won’t necessarily replace them but complement them instead. Using a vector database alongside your existing relational or NoSQL database can allow you to leverage a powerful hybrid search approach. A hybrid search approach combines the flexibility of NoSQL databases or the vertical scaling capabilities of relational databases with the semantic search features of vector databases, allowing you to scale your AI and traditional applications simultaneously. 

Common use cases for vector databases

You can build a wide range of intelligent tools and drive AI innovation with the help of vector databases. Explore some popular applications below. 

LLMs and NLP 

Large language models (LLMs) like GPTs and BERT generate vector embeddings of text that capture semantic similarities. These embeddings are stored in vector databases that enable rapid similarity searches to find contextually relevant data, combining the LLM’s NLP capabilities with the vector database’s nearest neighbor search to understand human language queries and even generate text. In this way, vector databases form the backbone of chatbots, question-answering systems, text classification, and sentiment analysis tools. 

Retrieval-augmented generation (RAG)

RAG relies on an LLM’s ability to retrieve information from an external knowledge base. RAG requires high-dimensional vector data to ensure AI models output contextually relevant, accurate, and up-to-date answers drawn directly from relevant sources, such as a company’s internal document base. Storing high-dimensional vectors of current factual data in vector databases increases the trustworthiness of the LLM’s responses by reducing hallucinations and offers speed and scalability, crucial for applications like customer support agents, legal document analyzers, and talent management systems.

Recommendation engines

Vector databases for e-commerce sites, media streaming platforms, and social media feeds may store user behavior like past purchases, as well as product and content features, as vector embeddings. When a user asks for recommendations, the system can query the vector database to retrieve items similar to the user’s preferences, embedded as vectors, allowing for personalization that extends beyond collaborative filtering. 

Semantic search 

Vector databases convert multimodal data into high-dimensional vectors that capture the semantic relationships between elements; that is, they capture the meaning, user intent, and context of the data, and group similar data points together. This allows them to understand natural language and return results that are most similar to your query, rather than only results that contain the keyword you searched for. For example, if you search for “best beach vacation spots,” you might also get results for “summer vacation spots” or “tropical island vacation ideas,” which aren’t an exact keyword match but are similar in context. 

Fraud and anomaly detection

When vector databases store normal behavioral data like transactions and login patterns as vectors, you can easily identify unusual patterns that don’t match established similarity clusters. This makes vector databases important for real-time fraud detection, network security monitoring, and manufacturing quality control.

How teams choose a vector database

Choosing the right vector database is crucial for teams that rely on AI technologies for their applications. Before deciding on your vector database, you first need to outline your project or software’s requirements, including the size and complexity of your data set and what use cases you need it for. Once you have that down, consider the following factors:

• Performance: Evaluate the speed and efficiency with which the database processes queries. Look at what indexing techniques it uses, whether it incorporates ANN for optimization, and features like distributed processing that ensure high throughput.

• Scalability: Check whether the database can handle massive data sets and workloads. Your vector database should support horizontal scaling, allowing you to add more capacity and maintain consistent performance across growing data volumes.

• Data types and search: Evaluate what data types you need and whether the vector database supports them. Check whether you’ll be storing metadata along with vectors. If you’ll be performing both keyword and vector searches, a database solution that offers hybrid search capabilities may be ideal. 

• Integrations: Ensure the database seamlessly integrates with your AI infrastructure and existing tools, and verify its compatibility with the programming languages you use. Find out if the vector database provides APIs or SDKs to ease integration.

• Cost: Evaluate licensing costs and vendor pricing models. You might opt for an open-source solution if your team already has the technical expertise to maintain the database, which will be more cost-effective. Commercial models will require larger budgets, although they offer significant customer support and require less maintenance.

What is the most popular vector database?

Milvus is one of the most popular open-source vector databases, while Pincecone is popular as a robust, easy-to-use solution. These database solutions are more popular if you use over 10 million vectors. In hybrid solutions, Weaviate is a popular choice for a database with both vector and traditional search capabilities.

Why vector databases are becoming more important

As AI adoption surges across industries, vector databases are becoming more important in driving AI development. Due to their ability to handle complex, unstructured data, vector databases serve as the underlying infrastructure that allows AI models to grasp context and generate responses. Combined with their high speed and performance, advanced contextual search capabilities, and scalability for handling growing data sets, vector databases support ML models for NLP, computer vision, and recommendation engines to perform efficient real-time analytics. 

Also notable, with rapid advancements in AI technologies and growth in the amount of unstructured data, vector databases will continue to increase in importance over the coming years. Data suggests that the vector database market will grow by 23.7 percent by 2030, reaching a projected value of $7.34 billion, up from $2.05 billion in 2024 [1].

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