Vector Databases: The Engine of the Generative AI Revolution

Today I learned about vector databases which is an important component in LLM where the data is stored as a vector. Let me share some important information about vector db here.

A vector database is a type of database that stores data as vectors. A vector is a list of numbers, and each number represents a different attribute of the data. For example, a vector database could store data about books, and each vector could represent a book. The numbers in the vector could represent the book's title, author, genre, publication date, and so on.

Vector databases are used for a variety of applications, including natural language processing (NLP), computer vision (CV), and recommendation systems. For example, a vector database could be used to store data about images, and each vector could represent an image. The numbers in the vector could represent the image's color, brightness, texture, and so on. A vector database could then be used to find images that are similar to a given image.

Vector databases offer a number of advantages over traditional relational databases. First, vector databases can store data in a more efficient way. This is because vectors can be compressed, and they can be stored in a way that makes it easy to find similar vectors. Second, vector databases can perform fast similarity search. This is because they can use specialized algorithms to find vectors that are similar to a given vector.

If you are working on an application that requires fast similarity search or efficient storage, then a vector database may be a good choice.

There are two main types of vector databases:

• Dense vector databases?store vectors as dense matrices. This means that each element of the vector is stored explicitly. Dense vector databases are typically faster than sparse vector databases, but they require more storage space.
• Sparse vector databases?store vectors as sparse matrices. This means that only the non-zero elements of the vector are stored. Sparse vector databases are typically more efficient in terms of storage space, but they can be slower than dense vector databases.

Some popular vector databases include:

• Milvus?is a vector database that is designed for large-scale similarity search. Milvus is open source and can be deployed on-premises or in the cloud.
• Weaviate?is a vector database that is designed for natural language processing (NLP) applications. Weaviate is open source and can be deployed on-premises or in the cloud.
• Elasticsearch?is a search engine that can be used to store and index vector data. Elasticsearch is not a dedicated vector database, but it can be used for vector data storage and search.

Happy learning!

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