These days, you can’t go anywhere without hearing about the latest breakthroughs in artificial intelligence (AI). The concept may feel overwhelming if you haven’t incorporated AI into your platform, but it’s much easier to understand than it seems. One of the main features driving innovation in this space is vector search due to its ability to understand what users are searching for without exact-match keywords. Because of the limitations of exact-match keywords, implementing this feature is crucial for delivering the best user experience and the results customers expect to see when they’re exploring a topic, looking for recommendations, or trying to find a particular image.
In this post, we’ll explore what vector databases are, how they work, and why vector search is critical in overcoming the limitations of traditional keyword-based search. We’ll also examine use cases for vector search, see examples of vector databases, and explore the misconceptions surrounding vector databases. Read on to learn more about how vector search is changing the game.
Vector Databases Explained
A vector database is a specialized type of database designed to store and manage high-dimensional vectors. These vectors are numerical representations that capture the essence of data points in a multi-dimensional space, including size and direction.
Developers can think of vectors as just an array of numbers.
[ [ 0.6, 0.3, 04 ….], [0.1, 0.5, 0.2….] ]
Unlike traditional databases that focus on structured data, vector databases excel in handling unstructured data, such as images, audio, and text, by encoding them into vectors. These databases are optimized for efficient vector operations and similarity searches, making them a powerful tool for applications like recommendation systems, content search, machine learning, and anomaly detection. These are often referred to as k-nearest neighbor (kNN) searches.
How Do Vector Databases Work?
Vector databases operate based on the principles of vector space indexing and search. When data is ingested into a vector database, it undergoes a process known as vectorization. This process involves transforming the raw data, such as text or images, into high-dimensional vectors using machine learning models like word embeddings or image encoders using what are called transformers. These vectors represent the data’s semantic and contextual information in a compact form across hundreds or even thousands of different dimensions.
🐊 = [0.1, 0.6, 0.3, … ] (100s of dimensions)
🐘 = [0.9, 0.3, 0.2, … ]
Vector Embeddings
Vector embeddings play a crucial role in vector databases. They result from the vectorization process, where data points are transformed into vectors. These embeddings preserve the relationships between data points, enabling efficient comparison and similarity calculations. By placing similar data points closer to each other in the vector space, vector embeddings facilitate fast and accurate similarity searches.
Why Vector Search is Crucial
Vector search, a fundamental feature of vector databases, addresses the limitations of traditional keyword-based search. While keyword search is effective for exact matches, vector search goes beyond keywords by considering the semantic meaning and context of data. This capability is crucial in applications like content recommendation, where understanding similarities between items is paramount.
For example, imagine our vectors were types of animals. The similarity between sea creatures could be quickly discovered by their vector similarities and distinguish them from land creatures.
Vector Search Use Cases
Vector search has a wide range of use cases across industries, and they are often seen in machine learning applications. One prominent application is recommendation systems. By analyzing vector distances, these systems can suggest products, movies, or content similar to users’ preferences. Image and content classification is another notable use case, where a system can search for existing images or text that closely matches new content. In healthcare, vector databases aid in genomic data analysis by identifying similar DNA sequences efficiently.
In the context of AI large language models (LLMs) like ChatGPT, utilizing a vector database for long-term storage introduces a crucial strategy to mitigate AI confusion and hallucination. By consolidating LLM embeddings within a vector database, developers ensure that the AI model’s knowledge remains coherent and grounded, minimizing the risk of generating unreliable or nonsensical responses.
Couchbase Server has the capability to serve prediction services as well, which we document in the blog 5 Use Cases for Real-Time Prediction Serving Systems with Couchbase [Part 1 of 2] and How to Use Couchbase as a Machine Learning Model Store [Part 2 of 2]. There are also capabilities in Couchbase Lite’s Predictive Query API for mobile use cases.
Vector Database Examples
Several vector databases have emerged to address specific use cases. Milvus, for instance, focuses on providing an open-source platform for similarity search and vector analysis. Pinecone offers a cloud-based vector database service that simplifies deployment and scaling. These examples highlight the diversity of vector databases, catering to various developer needs and preferences.
Vector Search: Database Category or Database Feature?
It’s important to note that vector search can be both a standalone database and a feature integrated into existing multi-model databases such as Couchbase Server and Capella. Several databases incorporate vector search capabilities to enhance their querying and retrieval functionalities while maintaining a common data storage and management interface. These additions highlight the increasing recognition of the importance of vector operations in modern data management.
Vector databases excel in similarity search tasks, but they might not be the best fit for all use cases. They’re tailored for scenarios where finding similar vectors is the primary goal, like recommendation systems or semantic search. For other database functions, the available services such as Key-Value, SQL++, Geospatial, and Full-Text Search capabilities of Couchbase Capella might be more suitable or at least complementary. By deploying vector search within a multimodal database, it can not only safeguard against confusion and hallucination but also counteract complexity’s detrimental impact on the reliability of real-time AI applications.
Conclusion
Due to their capabilities, vector databases are ideal for handling high-dimensional data and enabling similarity search tasks. As developers grapple with managing unstructured data and extracting meaningful insights, vector search offers a way to address these challenges. Their applications span industries from entertainment to healthcare, enabling organizations of all kinds to better cater to user search intent. By understanding the principles of vector search and its capabilities, developers can create smarter, more intuitive applications that get closer to the heart of what users are looking for, whether that’s identifying a similar type of tree or looking for a new movie to watch. As AI continues to evolve, vector databases are poised to play a significant role in shaping the future of data-driven applications.
Additional Resources:
👁️ Milvus: Open-Source Vector Database
🌲 Pinecone: Cloud-Based Vector Database
🤗 HuggingFace: Library of Transformers and AI Models
🚀 Couchbase Capella: The Managed Multi-Model Database
