In this video, we explore the powerful capabilities of PostgreSQL as a vector database for handling billion-scale vector similarity searches with approximate nearest neighbor using the pgvector extension. We dive into the integration of deep learning with databases by leveraging the DeepFace library in Python, specifically utilizing the FaceNet model to convert facial images into vector embeddings.
Watch as we demonstrate step-by-step how to set up and use pgvector to efficiently store and query large volumes of high-dimensional vector data. Learn about the practical applications of this technology in facial recognition systems and other domains that require fast and accurate similarity searches.
We provide a detailed walkthrough of the setup process for pgvector, showcasing a real-world example of performing billion-scale vector similarity searches. Additionally, we discuss performance benchmarks and offer optimization tips for handling large datasets.
Tutorial: sefiks.com/202...
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21 окт 2024
