April 2026
Intermediate
292 pages
7h
Spanish
Content preview from Bases de datos vectoriales (Spanish Edition)
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Capítulo 3. Búsquedapor similitud con FAISS
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FAISS, o Facebook AI Similarity Search, es una biblioteca de código abierto desarrollada en Meta (antes Facebook) para realizar de manera eficiente búsquedas por similitud y agrupamiento de vectores densos. Su evolución refleja la creciente necesidad de Meta de soluciones escalables para manejar los enormes conjuntos de datos que impulsan sus recomendaciones personalizadas, el filtrado de contenido y las funcionalidades de búsqueda de imágenes y videos.
Este capítulo está dirigido a desarrolladores que se sienten cómodos con los fundamentos matemáticos y están interesados en el funcionamiento interno de la búsqueda vectorial. Puedes leerlo por encima si tu interés principal radica en crear aplicaciones en lugar de escribir tu propia base de datos vectorial. Si quieres experimentar con la creación de tu propia base de datos vectorial, FAISS es un excelente punto de partida. Es tanto un robusto motor de búsqueda por similitud listo para usar como un conjunto de herramientas potente y flexible para crear una base de datos vectorial y un motor de búsqueda por similitud personalizados. También te resultará útil para ampliar las capacidades de sqlite-vss, la extensión de búsqueda vectorial para SQLite3, que exploraremos en detalle en el próximo capítulo. Por ahora, ten en cuenta que FAISS es una herramienta poderosa que debes ...
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