Annoy mentions (35)

• Do we think about vector dbs wrong?

  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 / 9 months ago

• Vector Databases 101

  If you want to go larger you could still use some simple setup in conjunction with faiss, annoy or hnsw. Source: 11 months ago

• Calculating document similarity in a special domain

  I then use annoy to compare them. Annoy can use different measures for distance, like cosine, euclidean and more. Source: 12 months ago

• Can Parquet file format index string columns?

  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: 12 months ago

• [D]: Best nearest neighbour search for high dimensions

  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: about 1 year ago

Vector Vault mentions (0)