What is a Vector Database (AI)?

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The vector database is a new type of database that is becoming popular in machine learning and AI. A vector database is a type of database that stores data in the form of vectors or mathematical representations of data points. They can index and retrieve data using vectors, similar to how a traditional database uses keys or text to find items using an index. A vector database uses a vector index to allow fast retrieval and insertion using a vector, and also it offers typical database features such as CRUD operations, filtering, and scalability. This gives us the best of both worlds: we get the CRUDness of traditional databases, along with the ability to store complex, unstructured data like images, videos, and 3D meshes.

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AI and machine learning are enabling this transformation of unstructured data into numerical representations (vectors) that capture meaning and context, benefiting from advances in natural language processing and computer vision. Vector databases power a new generation of search engines based on neural networks instead of keywords.

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What are vector databases for?

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They are used to store data as high-dimensional vectors, which are mathematical representations of features or attributes. Each vector has a certain number of dimensions, ranging from tens to thousands, depending on the complexity and granularity of the data. Vectors are usually generated by applying some sort of transformation or embedding function to raw data such as text, images, audio, video, and others. The embedding function can be based on various methods, such as machine learning models, word embedding, and feature extraction algorithms.

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For example, we can use a vector database to:

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• Find images similar to a given image based on its visual content and style.
• Find documents that are similar to a given document based on its topic and opinion.
• Find products similar to a given product based on its features and ratings.

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What are the advantages of vector databases?

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The main advantage of a vector database is that it enables fast and accurate similarity search and retrieval of data based on their distance or vector similarity. Instead of using traditional database query methods based on exact matches or predefined criteria, you can use a vector database to find the most similar or relevant data based on its semantic or contextual meaning.

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The attributes mentioned below are also essential qualities for a vector database:

• High-Dimensional Search: they can efficiently perform similarity searches on high-dimensional vectors, commonly used in machine learning and artificial intelligence applications such as image recognition, natural language processing, and recommender systems.
• Scalability: they can scale horizontally, efficiently storing and retrieving large amounts of vector data. Scalability is essential for applications that require real-time search and retrieval of large amounts of data.
• Flexibility: they can handle various vector data types, including sparse and dense vectors. They can also handle multiple data types, including numeric, text, and binary.
• Performance: perform similarity searches efficiently, providing faster search times than traditional databases.
• ‍Machine learning integration: Vector databases are designed to work seamlessly with machine learning algorithms, which can be used to analyze the data stored in the database. This integration enables more sophisticated and accurate forecasts and real-time data processing.

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Current uses for vector databases

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Vector databases have several.

They are used in different domains and applications involving natural language processing (NLP), computer vision (CV), recommender systems (RS), and other areas that require semantic understanding and data matching.

Recommendation engines: Recommender systems are everywhere. Vector databases are excellent for building recommender systems, and their similarity search feature makes vector databases a good choice for recommending relevant items to users.

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• We could use vector databases to create a recommendation system that recommends movies a user might like based on the user's historical movie rating data.
• We could also build a system that recommends products based on previous purchases made by a customer.

Semantic search: Semantic search is a data search technique that allows us to find keywords and seeks to understand natural language as a person would. It puts our search query in context.

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• We can use vector databases to index vector embeddings of NLP models to understand the text's context, which provides more accurate search results. If you remember everything you Google, you'll realize that we often use natural language in our queries.
•  Semantic search is then necessary for a search engine to provide relevant results.

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Similarity search: Unstructured data, such as images, video, and audio, is difficult to classify in relational databases. Vector databases can analyze large-scale data in real-time.

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• They are behind image search technologies such as self-driving cars that can recognize objects and phones that identify a user's face.
• They are also present in the audio search, which helps our devices identify the name of a song or a user's voice.

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And today, vector database-based AI applications can quickly and accurately analyze extensive, unstructured data for marketing, sales, research, and security purposes. Bringing great benefits to companies.

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Among the benefits we must mention

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• Recommender systems, including the recommendation of user-generated content.
• The personalized e-commerce search.
• Analysis of images and videos.
• Targeted advertising.
• Security Threat Assessments and Detection of Fraud.
• Chatbots with enhanced language skills. 

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The best vector databases

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• Weaviate: An open-source vector database that allows you to store vector data objects and embeddings of your favorite ML models and seamlessly scale into billions of data objects.

• The Kite: An open-source vector database that can manage trillions of vector data sets and supports multiple vector search indexes and built-in filtering. It was built on the modern cloud and can offer “millisecond searches across trillions of vector data sets.”

• Pinecone: A vector database designed for machine learning applications. It is fast, scalable, and supports a variety of machine learning algorithms. Pinecone is based on Faiss, a library for efficient dense vector similarity searching.‍
• Quadrant: A vector similarity search engine and vector database. It provides a production-ready service with a convenient API for storing, searching, and managing point vectors with an additional payload. Quadrant is designed for extended filtering support, making it useful for all neural networks, semantic-based matching, faceted search, and other applications.
• ‍Vespa: A fully featured search engine and vector database. Supports vector search (ANN), lexical search, and search on structured data, all in the same query. Built-in machine learning model inference lets you apply AI to make sense of your data in real-time. 

Conclusion

Vector databases offer an incredible new level of ability to search data. They provide consistent performance and user experience across different deployment environments.

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It is not just a technology for tech giants like Alphabet, Meta, or Microsoft, with their vast R&D operations and custom AI tools, but also for the average company and even SMEs. An example would be companies like Home Depot, which dramatically improved the search experience using this technology.

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Today, vector databases are finding a new role in helping organizations implement chatbots and other applications based on large language models.

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In general, vector databases offer significant advantages for applications involving similarity searching and machine learning, providing fast and efficient search and retrieval of high-dimensional vector data.

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