Vector Databases

This talk will explore the fundamentals of vector databases, their real-world applications, and their critical role in powering AI-driven systems. We will begin by understanding how vector databases store and retrieve high-dimensional data, highlighting their use in semantic search, recommendation systems, image retrieval, and cybersecurity. The discussion will then shift to Hybrid Retrieval-Augmented Generation (Hybrid RAG) and how it leverages sparse and dense embeddings to enhance retrieval efficiency in AI applications. We will compare sparse (TF-IDF, BM25) and dense (Transformer-based) embeddings, discussing their trade-offs in accuracy and scalability. The session will also cover evaluation methods for vector search, including recall, precision, and ranking metrics, ensuring optimal performance. A key focus will be on the efficiency of Approximate Nearest Neighbors (ANN) compared to K-Nearest Neighbors (KNN), explaining how ANN techniques like HNSW, PQ, and IVF enable faster, scalable retrieval in vector databases. Finally, we will discuss how ANN is implemented in modern vector search engines, optimizing AI workflows and paving the way for future advancements in AI-powered retrieval systems.