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Pinterest engineered Manas, an embedding retrieval system handling billions of vectors with approximate nearest neighbor search, hybrid queries, and real-time updates. They optimized it through quantization techniques that cut computing resources by 10-15% while maintaining search accuracy.
- SIMD intrinsics on 8-bit integers reduced per-query compute by 10-15% without sacrificing performance
- Product quantization on disk storage paired with full-precision centroids improved both accuracy and throughput, diverging from the original SPANN approach
- The system supports hybrid queries combining token and embedding search with sub-second indexing of new content