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TopK introduces semantic_index, a single schema annotation that abstracts multi-vector retrieval complexity for production systems, achieving state-of-the-art performance with sub-second latency and high throughput.
The author shares their work on reducing the cost of multi-vector retrieval by using k-means as top-1 sparse coding. Omar Khattab adds that late-interaction sparse retrieval with neuron-level inverted indexing on unsupervised sparse autoencoders works well.
This paper presents a single-stage sparse coding method using unsupervised sparse autoencoders and natural inverted indexing to accelerate multi-vector retrieval, outperforming traditional k-means based approaches.
This paper proposes Single-stage Sparse Retrieval (SSR), which replaces K-means clustering with sparse autoencoders and inverted indexing, achieving 15x faster indexing and halved retrieval latency while improving accuracy on the BEIR benchmark.