Question: I'm looking for an open-source library for building recommender systems, do you know of any good ones?

If you're looking for an open-source library for building recommender systems, Milvus is a fantastic option. It's optimized for high-dimensional vector search and offers features like extremely fast search with its Global Index, reusable code, and elastic scaling to support tens of billions of vectors. This makes it well-suited for GenAI use cases like image search and recommender systems.

Another great choice is Qdrant, which is built for fast and scalable vector similarity searches. This Rust-powered database supports cloud-native scalability and is designed for high-availability, ease of use, and cost efficiency. It integrates with leading embeddings and frameworks, making it ideal for recommendation systems and advanced search applications.

For a fully managed vector database service, consider Zilliz. Based on open-source Milvus, Zilliz offers high performance, reliability, and scalability, with features like fast vector retrieval, high availability, and robust security. It supports a broad range of use cases, including recommender systems, and offers flexible pricing plans with a free account sign-up option.

Lastly, Vespa is an online platform that combines a unified search engine and vector database, supporting vector search, lexical search, and search in structured data. It's designed to build production-ready search applications at any scale, making it suitable for recommendation and personalization tasks.