How do you build the prefixes when multiple words share the same prefix?
If my understanding is correct, the method is:
1. Find common search terms
2. For each search term (eg test, compute it's vector [1.23, 4.56...] and it's prefixes [t, te, tes...])
3. Store these in Qdrant as t->[1.23, 4.56...] , te->[1.23, 4.56...] , tes->[1.23, 4.56...] and so on. Here, each of the prefixes are used as point_ids
4. When a search query comes in, call /recommend and pass in the partial query as the point id
Hey Nikita! I was just looking at the docs but I was a bit confused about what the various compute instances were doing. Do they all serve reads and writes? If so, is there data partitioning or does this support distributed transactions?
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I was searching for research around the infection risk associated with various activities (outdoor vs indoor dining / gyms etc). Was hoping to see arguments motivated by contact-tracing data. Wasn't able to find much. Did anyone else have better luck?
The Wikipedia page describes what happens when someone does call. I wonder if that was a priori knowledge or if the writer called to see what would happen and then shared his findings :P
How do you build the prefixes when multiple words share the same prefix?
If my understanding is correct, the method is:
1. Find common search terms
2. For each search term (eg test, compute it's vector [1.23, 4.56...] and it's prefixes [t, te, tes...])
3. Store these in Qdrant as t->[1.23, 4.56...] , te->[1.23, 4.56...] , tes->[1.23, 4.56...] and so on. Here, each of the prefixes are used as point_ids
4. When a search query comes in, call /recommend and pass in the partial query as the point id