Vector Databases for RAG: Indexing and Similarity Methods

Learn about Vector Databases (Vector DBs) and their crucial role in Retrieval Augmented Generation (RAG) pipelines through this comprehensive video tutorial. Explore the fundamentals of Vector DBs, including their purpose in storing embeddings for efficient data retrieval, and dive deep into various indexing methods such as Flat Indexing, Local Sensitivity Hashing (LSH), Hierarchical Navigable Small World (HNSW), Inverted File Indexing (IFI), Product Quantization (PQ), Approximate Nearest Neighbour Oh Yeah (Annoy), and Random Projection (RP). Master the criteria for selecting appropriate indexing methods and understand the querying process in Vector DBs, building upon concepts from related tutorials on PDF parsing, chunking methods, and embedding in RAG systems.