The unsung Hero of vector database. And I am talking about a machine learning concept not one of the companies (because there are so many) 1. Metric learning: What is metric learning? 2. Problem without metric learning, mostly with an example of negation. Cosine similarity doesn't work with negation. 3. How to train metric learning embeddings. 4. Data, Model, and the loss function 5. Data, what is anchor, positive and negative 6. Model: Siamese networks, since we deal with different data, which needs different architecture. 7. Loss: the loss function is big, triple loss, and contrastive loss. 5. Demo of how it improved the overall experience of working with negations.