Vector database

A vector database is a specialized database created to store and retrieve vector data efficiently. Similar to the complexity of memories in the human brain, storing and representing the intricate dimensions, patterns, and relationships in AI poses a challenge. This complexity requires a purpose-built database (a brain for AI) designed for highly scalable access and specialized in storing and retrieving vector embeddings.

In artificial intelligence (AI) and machine learning (ML), vectors serve as numerical representations of data points, such as embeddings or feature vectors. These vectors are multi-dimensional numeric representations.

Vector databases play a crucial role in this context by storing data in these multi-dimensional numeric vectors. They are designed to efficiently manage the storage, retrieval, and similarity search operations associated with vector data. The term "vector" here refers to a mathematical concept where data points are represented as arrays of numerical values. In the context of AI and ML, these vectors encapsulate essential information about the underlying data.

Modern vector databases, like Pinecone vector database, have been specifically optimized to effectively meet the demands of storing and retrieving vector representations of data. This optimization is particularly vital for the successful deployment of generative AI models in real-world, production applications. These databases are foundational for generative AI systems, facilitating the seamless and efficient handling of vector data, which is fundamental for various AI and ML applications.

Vector databases streamline development by indexing embeddings, allowing efficient retrieval of similar assets by querying for nearest neighbours. These databases operationalize embedding models, enhancing application productivity with resource management, security controls, scalability, and fault tolerance features. They empower developers to create unique experiences, like enabling users to find similar pictures with their phone camera or recommendation engines used in streaming platforms. Vector databases also prevent hallucinations in generative models by serving as an additional knowledge source. By integrating vector databases, chatbots can access external information to enhance reliability and provide more trustworthy responses.

The most common example of this concept is recommendation engines. These engines take a user's query and suggest other content they might find interesting. Suppose a user is watching a crime thriller show on a streaming platform. The recommendation engine can efficiently suggest other shows or movies that are closest in similarity through vector search, utilizing the entire media library. This eliminates the need to label each piece of media with a theme manually. Moreover, the engine might provide additional recommendations based on the users' viewing patterns, encompassing relevant themes that the user hasn't explicitly queried but aligns with his interests sparked by the initial show.

Here are few examples of vector database:

• Pinecone: Pinecone is a cloud-based vector DB designed for working with vector embeddings at scale.
• Chroma: Chroma is an open-source embedding database, that makes it easy to build LLM apps by making knowledge, facts, and skills pluggable for LLMs.
• Weaviate: Weaviate is an open-source vector DB, which allows you to store data objects and vector embeddings from your ML models and scale seamlessly into billions of data objects.
• Faiss: Developed by Meta AI, Faiss is an open-source library for efficient similarity search and clustering of dense vectors. It is widely used for tasks like image and text similarity.
• Qdrant: Qdrant is an open-source vector database designed for similarity search and storage of high-dimensional vectors.