Vector embeddings are a powerful tool in artificial intelligence. They are mathematical (numerical) representations of words or phrases in a vector space. Usually processed by embedding models, these vector representations capture semantic relationships between words, allowing algorithms to understand the context and meaning of text. By analyzing the context in which a word appears, embeddings can capture its meaning and semantic relationships with other words.
The Role of Embeddings and Vector Stores/Databases in Modern Applications
Embeddings are crucial for modern applications like recommendation systems, search engines, and natural language processing. They help understand user preferences, match search queries with relevant documents, and improve the accuracy of language-related tasks.