Comments (1)
Let me outline the process in the way I understand it, feel free to correct me.
For this task potentially you can either re-build an index based on the documents to re-use it, or de-serialize it from external service.
After that, one option is to write a python model and utilize a cuVS library. The latter one has apis to build an index, please check with their docs to see if it fits to your needs. This library also provides a variety of vector search algorithms to choose from as well as specifying k
for top-k.
Then, the last step for this model is to combine initial request with retrieved top-k embedding and prepare a response, which will be passed to the next stage of your ensemble.
Let me know how this sound to you, happy to discuss further.
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