Vector databases have the capabilities of a traditional database that are absent in standalone vector indexes and the specialization of dealing with vector embeddings, which traditional scalar-based databases lack. Vector databases like Pinecone fulfill this requirement by offering optimized storage and querying capabilities for embeddings. That is why we need a specialized database designed specifically for handling this data type. In the context of AI and machine learning, these features represent different dimensions of the data that are essential for understanding patterns, relationships, and underlying structures. Efficient data processing has become more crucial than ever for applications that involve large language models, generative AI, and semantic search.Īll of these new applications rely on vector embeddings, a type of vector data representation that carries within it semantic information that’s critical for the AI to gain understanding and maintain a long-term memory they can draw upon when executing complex tasks.Įmbeddings are generated by AI models (such as Large Language Models) and have many attributes or features, making their representation challenging to manage.

It’s upending any industry it touches, promising great innovations - but it also introduces new challenges. A vector database indexes and stores vector embeddings for fast retrieval and similarity search, with capabilities like CRUD operations, metadata filtering, horizontal scaling, and serverless.

Matlab vectorize matrix