3 ms·

Yep that's true! I'd probably do something like that if I were starting again, but the ease of calling a few APIs is pretty nice. I feel like that alone will dr

• rvnx 4y ago
  Great result!
• freediver 4y ago
  I'd argue that using something like SBERT + Faiss is easier and would take less time (you do not have two account creations + one billing setup), plus a working example of SBERT + Faiss is probably total less than 10 lines of code.
  • rattray 4y ago
    What does it look like? I've never heard of either.
    • deleted 4y ago
      [deleted]
    • pbourke 4y ago
      # pip install faiss-cpu sentence-transformers from sentence_transformers import SentenceTransformer import faiss # replace with own texts - this is a bad example since it contains only single words with open("/usr/share/dict/words", mode="r") as infile: corpus = { num: s.strip() for num, s in enumerate(infile.readlines()) } # encode the corpus using a good sentence transformer model - will be slow if no GPU model = SentenceTransformer("all-mpnet-base-v2") corpus_vectors = model.encode(sentences=list(corpus.values())) # construct a faiss kNN index num_vectors, num_dimensions = corpus_vectors.shape index = faiss.index_factory(num_dimensions, "L2norm,Flat") index.add(corpus_vectors) # optional: save index for reuse faiss.write_index(index, "/tmp/corpus_index.bin") # index = faiss.read_index("/tmp/corpus_index.bin") # encode target text and find 10 nearest neighbors in index target_vector = model.encode(sentences=["apples"]) distances, nearest_indexes = index.search(target_vector, 10) print(list(zip([corpus[i] for i in nearest_indexes[0]], distances[0]))) # [('apples', 4.382169e-13), ('fruits', 0.47413948), ('fruit', 0.57227534), ...
      • vimota 4y ago
        This is great, thanks!
  • KRAKRISMOTT 4y ago
    Perhaps the Faiss dependency can be dropped? Supabase supports the pg_vector extension now.