Vector database: Difference between revisions

==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 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.

===REST APIs and Language Clients===

Vector databases often offer REST APIs, which add flexibility by allowing the database to be accessed from any environment capable of making HTTPS calls. Developers can also interact with the vector database using clients in various programming languages, such as Python, Java, and Go. These APIs enable actions such as upserting vectors, retrieving query results, or deleting vectors to be performed seamlessly within the context of an application.