Vector databases play a crucial role in managing vector embeddings, which are mathematical representations of data points generated by machine learning models like Large Language Models (LLMs). These databases enable the storage and retrieval of large volumes of data in a multi-dimensional space, facilitating advanced use cases such as Semantic Search, Multimodal Search, and Retrieval Augmented Generation (RAG). RAG enhances LLMs by providing them with up-to-date and domain-specific information, overcoming their limitations of outdated training data and generalization. This process involves organizing data into vectors, querying with an embedded model, retrieving relevant information, and generating informed responses. Semantic Search offers superior search capabilities by utilizing vectors instead of traditional keyword matching, while Multimodal Search allows for querying across diverse data types such as text, images, audio, and video. Clarifai offers integrated solutions for building RAG systems and performing vector-based searches, along with Compute Orchestration for deploying AI workloads across various environments.