Choosing Indexes for Similarity Search - Faiss in Python

Explore the world of Facebook AI Similarity Search (Faiss) in this comprehensive video tutorial. Learn about the various search indexes available in Faiss and how to choose the right one for your specific use case. Dive into the pros and cons of key indexes such as Flat, LSH, HNSW, and IVF, and understand how to optimize their parameters for semantic search applications. Follow along with practical coding examples and performance comparisons to build efficient indexes for billion-scale datasets. Gain insights into the flexibility and power of Faiss for searching diverse media types with sub-second response times and high accuracy.

Intro
Getting the data
Flat indexes
Highlevel indexes
Coding
Performance
Inverted File Index
Implementation
Summary