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The focus on the top 10 in vector search is a product of wanting to prove value over keyword search. Keyword search is going to miss some conceptual matches. You can try to work around that with tokenization and complex queries with all variations but it's not easy. Vector search isn't all that new a concept. For example, the annoy library (https://github.com/spotify/annoy), an open source embeddings database. - Source: Hacker News / 8 months ago
If you want to go larger you could still use some simple setup in conjunction with faiss, annoy or hnsw. Source: 10 months ago
I then use annoy to compare them. Annoy can use different measures for distance, like cosine, euclidean and more. Source: 11 months ago
Yes you can do this for equality predicates if your row groups are sorted . This blog post (that I didn't write) might add more color. You can't do this for any kind of text searching. If you need to do this with file based storage I'd recommend using a vector based text search and utilize a ANN index library like Annoy. Source: 11 months ago
If you need large scale (1000+ dimension, millions+ source points, >1000 queries per second) and accept imperfect results / approximate nearest neighbors, then other people have already mentioned some of the best libraries (FAISS, Annoy). Source: 12 months ago
Vectara Neural Search - Neural search as a service API with breakthrough relevance
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.
machine-learning in Python - Do you want to do machine learning using Python, but you’re having trouble getting started? In this post, you will complete your first machine learning project using Python.
Amazon Forecast - Accurate time-series forecasting service, based on the same technology used at Amazon.com. No machine learning experience required.
txtai - AI-powered search engine
