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This podcast explores vector databases, their architecture, and applications in AI. It explains how vectors represent high-dimensional data, detailing core database architecture, indexing techniques (like HNSW and FAISS), and distance metrics. The article then examines various applications including semantic search, recommendation systems, and anomaly detection, across diverse sectors. Finally, it discusses challenges like scalability and data privacy, outlining future directions in indexing, real-time processing, and ethical considerations, and provides an overview of popular vector databases such as Pinecone and Weaviate.

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