Evolving Pinterest’s Embedding Retrieval Platform

This post details the evolution of Pinterest's Manas embedding retrieval platform. Key technical contributions include: 1. Quantization: Implementation and evaluation of Scalar Quantization (SQ) and Product Quantization (PQ), demonstrating trade-offs in compression vs. recall, and the use of SIMD intrinsics for Linear Scaling SQ to improve compute efficiency. 2. SSD-based Serving: Adoption of SPANN with PQ quantization for disk-based ANN serving, achieving significant memory reduction and improved QPS/latency compared to in-memory solutions like HNSW. 3. Multi-embedding Retrieval: Introduction of late interaction models like ColBERT, using "Sum of MaxSim" scoring, and updates to the Manas query parser to handle multiple query embeddings for richer similarity calculations.