The article delves into Retrieval-Augmented Generation (RAG), which integrates retrieval and generative models to enhance GenAI applications efficiently. It highlights the architecture of RAG, utilizing vector databases for data retrieval and response generation.
In this article, we delve into Vector Databases by using Pinecone and explore the fundamentals of vector embeddings, indexes, and essential components of these databases. Furthermore, it will provide a guide on setting up a Vector Database on Pinecone, walking through the installation process, obtaining API keys, and initializing client connections.
