Comparing Different Vector Embeddings

This article discusses the differences between vector embeddings generated by different neural networks and how to evaluate them in Jupyter Notebook. Vector embeddings are numerical representations of unstructured data, such as images, videos, audio, text, and molecular images. They are generated by running input data through a pre-trained neural network and taking the output of the second-to-last layer. The article provides an example of comparing vector embeddings from three different multilingual models based on MiniLM from Hugging Face using L2 distance metric and an inverted file index as the vector index. It also demonstrates how to compare vector embeddings directly in a Jupyter Notebook with Milvus Lite, a lightweight version of Milvus.