What Are Vector Databases? Why They Are So Important?

Introduction to Vector Databases

Imagine a database that doesn’t just store data but also understands it. In recent years, AI applications have been transforming nearly every industry and reshaping the future of computing.

Vector databases are transforming how we handle unstructured data by allowing us to store knowledge in a way that captures relationships, similarities, and context. Unlike traditional databases, which primarily rely on structured data stored in tables and focus on exact matching, vector databases enable storing unstructured data—such as images, text, and audio—in a format that machine learning models can understand and compare.

Instead of relying on exact matches, vector databases can find the “closest” matches, facilitating the efficient retrieval of contextually or semantically similar items. In today’s era, where AI powers everything, vector databases have become foundational to applications involving large language models and machine learning models that generate and process embeddings.

So, what is an embedding? We’ll discuss that soon in this article.
Whether used for recommendation systems or powering conversational AI, vector databases have become a powerful data storage solution, enabling us to access and interact with data in exciting, new ways.

Now let us take a look at what databases that are most commonly used:

• SQL: Stores structured data and uses tables to store the data with a defined schema. The most common ones are MySQL, Oracle Database, and PostgreSQL.
• NoSQL: It is pretty flexible and a schema-less database. It is also known to handle unstructured or semi-structured data. This has been great for many real-time web applications and big data. The most common ones include MongoDB and Cassandra.
• Graph: Then came Graph, which stores data as nodes and edges and was designed to handle interconnected data. Example: Neo4j, ArangoDB.
• Vector: Databases built to store and query high-dimensional vectors, allowing similarity search and powering AI/ML tasks. The most common ones are Pinecone, Weaviate, and Chroma.

Prerequisites

• Knowledge of Similarity Metrics: Understanding metrics like cosine similarity, Euclidean distance, or dot product for comparing vector data.
• Basic ML and AI Concepts: Awareness of machine learning models and applications, especially those producing embeddings (e.g., NLP, computer vision).
• Familiarity with Database Concepts: General database knowledge, including indexing, querying, and data storage principles.
• Programming Skills: Proficiency in Python or similar languages commonly used in ML and vector database libraries.

Why use vector databases, and how are they different?

Let’s say we are storing data in a traditional SQL database, where each data point has been converted to an embedding and stored. When a search query is made, it is also converted to an embedding, and we then attempt to find the most relevant matches by comparing this query embedding to the stored embeddings using cosine similarity.

However, this approach can become inefficient for several reasons:

• High Dimensionality: Embeddings are typically high-dimensional. This can result in slow query times, as each comparison might require a full-scan search through all stored embeddings.
• Scalability Issues: The computational cost of calculating cosine similarity across millions of embeddings becomes too high with large datasets. Traditional SQL databases are not optimized for this, making it challenging to achieve real-time retrieval.

Therefore, a traditional database may struggle with efficient, large-scale similarity searches. Furthermore, a significant amount of data generated daily is unstructured and cannot be stored in traditional databases.

Well, to tackle this problem, we use a vector database. In a vector database, there is a concept of Index, which enables efficient similarity search for high-dimensional data. It plays a crucial role in speeding up queries by organizing vector embeddings, allowing the database to quickly retrieve vectors similar to a given query vector, even in large datasets. Vector Indexes reduce the search space, making it possible to scale up to millions or billions of vectors. This allows for fast query responses even on huge datasets.

In traditional databases, we search for rows matching our query. We use similarity metrics in vector databases to find the most similar vector to our query.

Vector databases use a mix of algorithms for Approximate Nearest Neighbor (ANN) search, which optimizes search through hashing, quantization, or graph-based methods. These algorithms work together in a pipeline to deliver fast and accurate results. Since vector databases provide approximate matches, there’s a trade-off between accuracy and speed—higher accuracy may slow down the query.

Fundamentals of Vector Representations

What are Vectors?

Vectors can be understood as arrays of numbers stored in a database. Any type of data—such as images, text, PDFs, and audio—can be converted into numerical values and stored in a vector database as an array. This numeric representation of the data allows for something called a similarity search.

Before understanding vectors, we will try to understand Semantic Search and embeddings.

What is a Semantic Search?

A semantic search is a way of searching for the meaning of the words and the context rather than just matching the exact terms. Instead of focusing on the keyword, semantic search tries to understand the intent. For example, the word “python.” In a traditional search, the word “python” might give results for both Python programming and pythons, the snakes, because it only recognizes the word itself. With semantic search, the engine looks for context. If the recent searches were about “coding languages” or “machine learning,” they would likely show results about Python programming. But if the searches had been about “exotic animals” or “reptiles,” it would assume Pythons were snakes and adjust results accordingly.

By recognizing context, semantic search helps surface the most relevant information based on the actual intent.

What are Embeddings?

Embeddings are a way to represent words as numerical vectors ( as of now, let us consider vectors to be a list of numbers; for example, the word “cat” might become [.1,.8,.75,.85]) in a high-dimensional space. Computers quickly process this numerical representation of a word.

Words have different meanings and relationships. For example, in word embeddings, the words “king” and “queen” would have vectors similar to “king” and “car.”

Embeddings can capture a word’s context based on its usage in sentences. For instance, “bank” can mean a financial institution or the side of a river, and embeddings help distinguish these meanings based on surrounding words. Embeddings are a smarter way to make computers understand words, meanings, and relationships.

One way to think about embedding is different features or properties of that word and then assigning values to each of these properties. This provides a sequence of numbers, and that is called a vector. There are a variety of techniques that can be used to generate these word embeddings. Hence, vector embedding is a way to represent a word sentence or document into numbers that can capture the meaning and relationships. Vector embeddings allow these words to be represented as points in a space where similar words are close to each other.

These vector embeddings allow for mathematical operations like addition and subtraction, which can be used to capture relationships. For example, the famous vector operation “king - man + woman” can yield a vector close to “queen.”

Similarity Measures in Vector Spaces

Now, to measure how similar each vector is some mathematical tools are used to quantify the similarity or dissimilarity. A few of them are listed below:

• Cosine Similarity: Measures the cosine of the angle between two vectors, ranging from -1 to 1. Where -1 means exactly opposite, 1 means identical vectors, 0 means orthogonal or no similarity.
• Euclidean Distance: Measures the straight-line distance between two points in a vector space. Smaller values indicate higher similarity.
• Manhattan Distance (L1 Norm): Measures the distance between two points by summing the absolute differences of their corresponding components.
• Minkowski Distance: A generalization of Euclidean and Manhattan distances.

These are the few most common distance or similarity measures used in Machine Learning algorithms.

Popular vector databases

Here are some of the most popular vector databases widely used today:

• Pinecone: A fully managed vector database known for its ease of use, scalability, and fast Approximate Nearest Neighbor (ANN) search. Pinecone is famous for integrating with machine learning workflows, particularly semantic search and recommendation systems.
• FAISS (Facebook AI Similarity Search): Developed by Meta (formerly Facebook), FAISS is a highly optimized library for similarity search and clustering of dense vectors. It’s open-source, efficient, and commonly used in academic and industry research, especially for large-scale similarity searches.
• Weaviate: A cloud-native, open-source vector database that supports both vector and hybrid search capabilities. Weaviate is known for its integrations with models from Hugging Face, OpenAI, and Cohere, making it a strong choice for semantic search and NLP applications.
• Milvus: An open-source, highly scalable vector database optimized for large-scale AI applications. Milvus supports various indexing methods and has a broad ecosystem of integrations, making it popular for real-time recommendation systems and computer vision tasks.
• Qdrant: A high-performance vector database focused on user-friendliness, Qdrant provides features like real-time indexing and distributed support. It’s designed to handle high-dimensional data, making it suitable for recommendation engines, personalization, and NLP tasks.
• Chroma: Open-source and explicitly designed for LLM applications, Chroma provides an embedding store for LLMs and supports similarity searches. It’s often used with LangChain for conversational AI and other LLM-driven applications.

Use cases

Now, let us review some of the use cases of vector databases.

• Vector databases can be used for conversational agents that require long-term memory storage. This can be easily implemented with Langchain, enabling the conversational agent to query and store conversation history in a vector database. When users interact, the bot pulls contextually relevant snippets from past conversations, enhancing user experience.
• Vector databases can be used for Semantic Search and Information Retrieval by retrieving semantically similar documents or passages. Instead of exact keyword matches, they find content contextually related to the query.
• Platforms like e-commerce, music streaming, or social media use vector databases to generate recommendations. By representing items and user preferences as vectors, the system can find products, songs, or content similar to the user’s past interests.
• Image and video platforms use vector databases to find visually similar content.

Challenges for Vector Databases

• Scalability and Performance: As the data volume continues to grow, keeping vector databases fast and scalable while maintaining accuracy can become a challenge. Balancing speed and accuracy can also be a potential challenge when generating accurate search results.
• Cost and Resource Intensity: High-dimensional vector operations can be resource-intensive, requiring powerful hardware and efficient indexing, which can increase storage and computation costs.
• Accuracy vs. Approximation Trade-Off: Vector databases use Approximate Nearest Neighbor (ANN) techniques to achieve faster searches, but this may lead to approximate, rather than exact, matches.
• Integration with Traditional Systems: Integrating vector databases with existing traditional databases can be challenging, as they use different data structures and retrieval methods.

Conclusion

Vector databases change how we store and search complex data like images, audio, text, and recommendations by allowing similarity-based searches in high-dimensional spaces. Unlike traditional databases that need exact matches, vector databases use embeddings and similarity scores to find “close enough” results, making them perfect for applications like personalized recommendations, semantic search, and anomaly detection.

The main benefits of vector databases include:

• Faster Searches: Quickly finds similar data without searching the entire database.
• Efficient Data Storage: Uses embeddings, which reduces the space needed for complex data.
• Supports AI Applications: This is essential for natural language processing, computer vision, and recommendation systems.
• Handling Unstructured Data: Works well with non-tabular data, like images and audio, making it adaptable for modern applications.

Vector databases are becoming crucial for AI and machine learning tasks. They offer better performance and flexibility than traditional databases.

References

• BART Model for Text Summarization
• What is Retrieval Augmented Generation (RAG)? The Key to Smarter, More Accurate AI
• Learn How to Build a RAG Application using GPU Droplets
• Vector Databases: A Beginner’s Guide!
• What is a Vector Database & How Does it Work? Use Cases + Examples