The growing popularity of large language models (LLMs) has sparked interest in embedding models, which are deep learning systems that compress data into numerical representations. Embedding models are crucial components of retrieval augmented generation (RAG) applications, expanding the possibilities of LLMs for enterprises. However, the potential of embedding models goes beyond current RAG applications, with impressive advancements seen in the past year and even more expected in 2024.
Transforming Data into Numerical Representations
The fundamental idea behind embeddings is to convert various data types, such as images or text documents, into lists of numerical values representing their most important features. Embedding models are trained on large datasets to learn relevant features that differentiate different types of data. In computer vision, embeddings can capture important aspects including objects, shapes, colors, and visual patterns. In text applications, embeddings encode semantic information like concepts, geographical locations, persons, companies, and objects.
