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Vector database: Difference between revisions
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{{see also|AI terms}} | {{see also|AI terms}} | ||
==Explain Like I'm 5 (ELI5)== | |||
A vector database is a special kind of computer storage that helps find things that are similar, like finding pictures that look like a cat or finding songs that sound happy. It's really good at helping computers understand what things mean, even if they are in different forms like words, pictures, or sounds. | |||
Vector databases help big computer brains called "large language models" remember things for a long time, so they can give better answers when you ask them questions. They can also help find things that are similar or different, which is useful for things like shopping websites and spotting unusual activities. | |||
These databases are like magic boxes that can find what you're looking for really fast, even when you have lots and lots of things inside. | |||
==Introduction== | ==Introduction== | ||
A [[vector database]] is a type of [[database]] specifically designed for storing and querying [[high-dimensional vector data]], which is often used in [[artificial intelligence applications]] ([[AI]] [[apps]]). Complex data, including unstructured forms like documents, images, videos, and plain text, is growing rapidly. Traditional databases designed for structured data struggle to store and analyze complex data effectively, often requiring extensive keyword and metadata classification. Vector databases address this issue by transforming complex data into [[vector embeddings]], which describe data objects in numerous dimensions. These databases are gaining popularity due to their ability to extend [[large language models]] ([[LLMs]]) with [[long-term memory]] and provide efficient [[querying]] for [[artificial intelligence applications|AI-driven applications]]. | A [[vector database]] is a type of [[database]] specifically designed for storing and querying [[high-dimensional vector data]], which is often used in [[artificial intelligence applications]] ([[AI]] [[apps]]). Complex data, including unstructured forms like documents, images, videos, and plain text, is growing rapidly. Traditional databases designed for structured data struggle to store and analyze complex data effectively, often requiring extensive keyword and metadata classification. Vector databases address this issue by transforming complex data into [[vector embeddings]], which describe data objects in numerous dimensions. These databases are gaining popularity due to their ability to extend [[large language models]] ([[LLMs]]) with [[long-term memory]] and provide efficient [[querying]] for [[artificial intelligence applications|AI-driven applications]]. | ||