How prefetch work?

Prefetching “mean_pooling_columns” and “mean_pooling_rows” is not just an extra step—it provides a way to optimize search efficiency while maintaining accuracy. The key reasons are:

🚀 1. Speed Optimization: HNSW Indexing on Compressed Vectors

• The "original" vectors are larger and computationally expensive to search directly.

• The "mean_pooling_columns" and "mean_pooling_rows" vectors are compressed representations of the original embeddings.

• HNSW (Hierarchical Navigable Small World) graph-based indexing is typically used on these compressed representations, allowing fast nearest-neighbor retrieval.

✅ Benefit: Instead of searching on large "original" vectors, we quickly retrieve candidates from the compressed vectors and then refine them.

🎯 2. Reduced Search Space → Faster Ranking

• Directly searching "original" requires comparing against all vectors in the collection.

• Instead, first searching on “mean_pooling_columns” and “mean_pooling_rows” quickly narrows down to 200 candidates (instead of searching through millions).

• Then, reranking is performed on these fewer candidates using "original", which is computationally cheaper.

✅ Benefit: Instead of a full database search, we reduce the search space to a manageable number before performing more expensive computations.

🏗 3. Precomputed Mean-Pooling Preserves Structural Similarity

• "mean_pooling_columns" and "mean_pooling_rows" provide alternative views of the image embeddings that capture different levels of abstraction:

• “mean_pooling_columns” preserves features across columns, meaning it can capture horizontal structures in an image.

• “mean_pooling_rows” captures vertical structures.

• This helps approximate the similarity of "original" without needing a full search.

✅ Benefit: Precomputed mean-pooling embeddings speed up search while preserving useful similarity information.

📈 4. HNSW Does Not Work Well on Large Dimensional Vectors

• "original" vectors are high-dimensional (e.g., 128D or more).

• HNSW struggles with high-dimensional data—it performs best on lower-dimensional embeddings.

• "mean_pooling_columns" and "mean_pooling_rows" are low-dimensional summaries of "original", making them more suitable for efficient nearest-neighbor search.

✅ Benefit: HNSW indexing on mean-pooled embeddings is much faster and more memory efficient.

🔥 Conclusion: Prefetching Optimizes Search Without Sacrificing Accuracy

  1. 🔹 Fast first-pass retrieval → Using "mean_pooling_columns" and "mean_pooling_rows" with HNSW is much faster than searching "original".

  2. 🔹 Reduced ranking computation → Instead of ranking against all images, we only rank against a small pool of 200.

  3. 🔹 Efficient memory usage → HNSW is more efficient with "mean_pooling_columns" and "mean_pooling_rows", leading to faster lookups.

✅ Prefetching optimizes search by enabling a fast, approximate lookup before performing the final, expensive reranking. 🚀