Embeddings 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. 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.
That is why we need a specialized database designed specifically for handling this type of data. Vector databases like Pinecone fulfill this requirement by offering optimized storage and querying capabilities for embeddings. 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.
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. 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.
With a vector database, we can add advanced features to our AIs, like semantic information retrieval, long-term memory, and more.