This repo describes how to use Milvus vector database indexing and search framework in combination with NVIDIA Merlin, an open-source framework for developing recommenders systems at any scale.
There are two notebooks provided for guidance. Notebook 01 demonstrates how to use Merlin Models library to train a model and export embeddings vectors for users and items based on interaction data from an e-commerce system. Notebook 02 shows how to use a customer Merlin Systems operator as an interface between Milvus server and NVIDIA Triton Inference Server, for making inference queries on user-item similarity.
The results folder provides information on several performance benchmark conducted with Milvus framework using the user and item embeddings calculated in Notebook 01. Milvus is GPU-accelerated and shows improved and much needed performance for real-life RecSys usecases and datasets, so be sure to take a look at the benchmark performance results.
A blog post about all the work conducted is available here once published.