Vector Databases: from Embeddings to Applications

Vector databases play a pivotal role across various fields, such as natural language processing, image recognition, recommender systems and semantic search, and have gained more importance with the growing adoption of LLMs. These databases are exceptionally valuable as they provide LLMs with access to real-time proprietary data, enabling the development of Retrieval Augmented Generation (RAG) applications. At their core, vector databases rely on the use of embeddings to capture the meaning of data and gauge the similarity between different pairs of vectors and sift through extensive dataset…