In this episode of "The Deep Dive," the hosts explore embeddings and vector stores, explaining how they function as low-dimensional numerical representations that capture the underlying meaning of data for AI. They discuss the efficiency and importance of embeddings in processing various data types, highlighting applications like retrieval and recommendation systems. The hosts also cover joint embeddings for multimodal data, methods for measuring effectiveness such as precision and recall, and practical considerations like model size and latency. They delve into Retrieval Augmented Generation (RAG), different types of embeddings (text, image, structured data, and graph embeddings), training processes, vector search techniques, and the use of vector databases. The discussion emphasizes the evolving nature of embedding models and their increasing accessibility, encouraging listeners to experiment with these tools for innovative projects.
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