Explore the fundamentals of Facebook AI Similarity Search (FAISS) in this comprehensive tutorial video. Learn about efficient similarity search implementation, understand what makes FAISS unique, and discover how to leverage this powerful tool for faster semantic search. Dive into various FAISS options, their functionalities, and practical applications. Follow along with code examples, datasets, and a provided notebook to gain hands-on experience. Covers topics such as Index Flat L2, Index Training, Adding Vectors, Query Time, Voronoi Cells, Index IVF, and Product Quantization Index. Gain insights from additional resources, including a Pinecone article and recommended books on mining massive datasets for similarity search.
Overview
Syllabus
Introduction
Code Overview
Index Flat L2
Index Training
Adding Vectors
Query Time
Voronoi Cells
Coding
Index IVF
Product Quantization Index
Implementing Product Quantization Index
Comparison
Taught by
James Briggs