Vector Search is based on vector search technology developed by Google research.
With Vector Search you can leverage the same infrastructure that provides a foundation for Google products such Google Search, YouTube, and Play..
A vector database can process text embeddings, which enables a computer to "understand" human — or natural — language.
Image recognition and retrieval applications: Vector databases transform images into image embeddings.
With similarity search, they are able to retrieve similar images or identify matching images..
Gather information through research, interviews, casual conversations with contacts from the other companies, and with formal interviews or questionnaires.
You can also collect secondary information from websites, reports, marketing materials, and news articles.
However, secondary information may not be as reliable..
For example, a vector database could be used to convert an image of a dog into a series of numbers (such as the dog's size, the color of its fur, etc.), which can easily be stored and analyzed..
You can use Redis Stack as a vector database.
It allows you to: Store vectors and the associated metadata within hashes or JSON documents.
Retrieve vectors..
A good vector database provides applications with a foundation through features like data management, fault tolerance, critical security features, and a query engine.
These capabilities allow users to operationalize their workloads to simplify scaling, maintain high scalability, and support security requirements..
Vector databases are typically used to power vector search use cases like visual, semantic, and multimodal search.
More recently, they're paired with generative artificial intelligence (AI) text models to create intelligent agents that provide conversational search experiences..
Database benchmarking is a well-defined, proven technique for comparing and analyzing how databases or database management systems (DBMS) perform..
For example, a vector database could be used to convert an image of a dog into a series of numbers (such as the dog's size, the color of its fur, etc.), which can easily be stored and analyzed..
Database benchmarking is a well-defined, proven technique for comparing and analyzing how databases or database management systems (DBMS) perform..
Indexing in vector databases is a complex but essential process, enabling efficient storage, search, and retrieval of vector data..
Database benchmarking is a well-defined, proven technique for comparing and analyzing how databases or database management systems (DBMS) perform..
