Submitted by silverstone1903 t3_10iucs0 in MachineLearning
Original_Rip_8182 t1_j5i0ol5 wrote
For top-k product search you could also follow this: Index all product embeddings through faiss. To get a top match for a given product, take it's embedding & query it with built faiss index, you'll get top-k matches from it. This is way faster than brute force comparision between each pair.
silverstone1903 OP t1_j5ivdmx wrote
Thank you for your answer. What is the difference from using annoy? I'm experimenting with annoy, faiss, and hsnw. The performance is not the thing just because I can't measure the quality of retrievals 🤷🏻♂️
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