This post explores the challenges and solutions of implementing 'collections' at daily.dev, focusing on content similarity detection using machine learning and vector embeddings. It discusses the technologies used, such as Redis and pgvector, and the process of creating and updating collections. The post also addresses the challenges of distributed content processing and the benefits of using embeddings.
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PrefaceSimilar posts searchCollection creation, update and publishResults and conclusionsRelated Reading67 Impressions