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Couple of follow up questions:
Can you please provide a concrete example to what it is you're trying to achieve ? |
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Hi everyone,
I am currently trying to implement a system that takes for arguments two embedding vectors and returns the K paths that have the highest combines similarities. Currently, the only thing i manage to do is find K nodes for start and K nodes for end, and then try to find matches between them.
However, this seems to be quite different from what i want to do in the end, and not very optimized, since the path that has the best "combined similaraty" is not necessarily a path that has its start in top 5 similarities and end in top 5 similarities.
And with only the queryNodes procedure, it does not seem possible to get the K nearest neighbors given a condition, all that is possible is to get the K nearest neighbors, then filter so the only way seems to go wide with the queryNodes and then hope that enough items match the condition.
Would it be possible, also, for a given fixed node in the graph to have a procedure that only searches among nodes accessible by paths coming from that start node?
I don't know if i am asking for impossible things, or what is the roadmap for the vector search features, or maybe I am just missing something in how the procedure works. Does someone have an idea on how i could solve this problem?
Thank you very much
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