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Vector database: Difference between revisions
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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 effectively store and analyze complex data, 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 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 effectively store and analyze complex data, 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 AI-driven applications. | ||
==Vector | ==What is a Vector Database?== | ||
[[ | In a [[relational database]], data is organized in rows and columns, while in a [[document database]], it is organized in documents and collections. In contrast, a vector database stores arrays of numbers that are clustered based on [[similarity]]. These databases can be queried with [[ultra-low latency]], making them ideal for AI-driven applications. | ||
A vector database is a type of database that indexes and stores vector embeddings for efficient retrieval and similarity search. In addition to traditional CRUD (create, read, update, and delete) operations and metadata filtering, vector databases enable the organization and comparison of any vector to one another or to the vector of a search query. This capability allows vector databases to excel at similarity search or "vector search," providing more comprehensive search results that would not be possible with traditional search technology. | |||
==Vector Database Products== | ==Vector Database Products== | ||