'Navigating the Vector Database Landscape' by karllhughes vectordatabase databases
In a machine learning model that deals with vector embeddings, you need to not only store the vectors efficiently but also search and perform mathematical operations on them. However, the most commonly used relational databases are not suitable for dealing with vector embeddings because of the fundamental difference between relational data and vector data. Vector databases are made specifically for working with vector embeddings.
Imagine you're building a recommendation system for an e-commerce site. With a traditional SQL database, you can store each item and its information . If a user shows interest in an item, you can now search for items that are of the same color or same price as that item, and recommend them to the user. However, a similar item may not necessarily have the same color or price. Because SQL databases have no concept of similarity, your selections can be inaccurate.
If you represent the items as vectors, you can leverage a vector database like Embeddinghub to easily filter similar items to the ones the user has previously shown interest in. For example, you can perform alookup to find the most relevant item, or you can calculate the average vector difference between a black item and a red item and use that difference to recommend the closest color for items that don’t have a red version.
Since vector databases are critical to the success of machine learning projects, they need to be easy to set up, easy to learn, and use. Features like SDK support for multiple languages, data cluster management, deployment capabilities, and administrative add-ons should be considered when reviewing vector database options.The ability to blend with the needs and ecosystem of the organization should be one of your top criteria.
A vector database should not only efficiently store and retrieve vectors, they should also support common vector operations such as
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