Inside a Vector Database: Embeddings, HNSW, and Hybrid Search

How does a vector database find relevant information among millions of embeddings without checking every single one? In this animated explainer, we open the box and explore how vector stores work, from storing embeddings to finding similar items quickly. You’ll learn: • What a vector store keeps: IDs, embedding vectors, and metadata • How embeddings help search find related meanings • How cosine similarity, Euclidean distance, and dot product compare vectors • Why exact search becomes expensive at scale • How approximate nearest neighbor search trades some recall for speed • How IVF searches promising clusters and HNSW navigates a layered graph • How quantization reduces memory requirements • How metadata filters and hybrid search refine results We also introduce tools such as FAISS and show a short Python example of building and searching an HNSW index. If you’re learning about embeddings, semantic search, or retrieval-augmented generation (RAG), this video explains what happens inside the vector store. 💬 Which topic would you like to explore next: embeddings, HNSW, or hybrid search? 📩 Get more practical AI and machine learning explainers in the ML Pills newsletter: https://mlpills.substack.com/ Subscribe to Machine Learning Pills for clear, visual explanations of machine learning, statistics, and AI. #VectorDatabase #SemanticSearch #RAG