How to Get the Right Vector Embeddings

This post provides a comprehensive introduction to vector embeddings, explaining what they are, how they are created by removing the last layer of a deep learning model, and their dimensionality. It details how to generate embeddings for images (using ResNet-50), text (using Sentence Transformers like MiniLM-L6-v2 and MPNet-Base-V2), multimodal data (using CLIP VIT), audio (using Whisper), and video (using multimodal perceiver). Code examples are provided for each data type. The post also briefly mentions the use of these embeddings with vector databases like Milvus.