The vector embedding is inserted into the vector database, with some reference to the original content the embedding was created from. The diagram below gives us a better understanding of the role of vector databases in this type of application:įirst, we use the embedding model to create vector embeddings for the content we want to index. With a vector database, we can add advanced features to our AIs, like semantic information retrieval, long-term memory, and more.

That’s where vector databases come into play – they are intentionally designed to handle this type of data and offer the performance, scalability, and flexibility you need to make the most out of your data. The challenge of working with vector embeddings is that traditional scalar-based databases can’t keep up with the complexity and scale of such data, making it difficult to extract insights and perform real-time analysis. 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 type of data.

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 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 a large number of 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.

Matrix absence management locations