ScaNN supports several vector search techniques, each with their own tuning parameters. This page explains the basics of the various techniques.

ScaNN performs vector search in three phases. They are described below:

1. Partitioning (optional): ScaNN partitions the dataset during training time, and at query time selects the top partitions to pass onto the scoring stage.
2. Scoring: ScaNN computes the distances from the query to all datapoints in the dataset (if partitioning isn't enabled) or all datapoints in a partition to search (if partitioning is enabled). These distances aren't necessarily exact.
3. Rescoring (optional): ScaNN takes the best k' distances from scoring and re-computes these distances more accurately. From these k' re-computed distances the top k are selected.

All three phases can be configured through the ScaNNBuilder class. Before going into details, below are some general guidelines for ScaNN configuration.

• For a small dataset (fewer than 20k points), use brute-force.
• For a dataset with less than 100k points, score with AH, then rescore.
• For datasets larger than 100k points, partition, score with AH, then rescore.
• When scoring with AH, dimensions_per_block should be set to 2.
• When partitioning, num_leaves should be roughly the square root of the number of datapoints.

Partitioning is configured through .tree(...). The most important parameters are num_leaves and num_leaves_to_search. The higher num_leaves, the higher-quality the partitioning will be; however, raising this parameter also makes partitioning take longer. num_leaves_to_search / num_leaves determines the proportion of the dataset that is pruned. Raising this proportion increases accuracy but leads to more points being scored and therefore less speed.

If a dataset has n points, the number of partitions should generally be the same order of magnitude as sqrt(n) for a good balance of partitioning quality and speed. num_leaves_to_search should be tuned based on recall target.

Scoring can either be done with brute-force or asymmetric hashing (AH). The corresponding functions in ScannBuilder are score_brute_force and score_ah. Unless near-perfect accuracy is required, AH gives better speed/accuracy tradeoffs. See Product quantization for nearest neighbor search and Gersho and Gray's Vector Quantization and Signal Compression for background on AH, and Accelerating Large-Scale Inference with Anisotropic Quantization for an academic descrition of anisotropic_quantization_threshold. We recommend setting dimensions_per_block to 2.

The only parameter for brute-force scoring is quantize. If enabled, this quantizes each dimension of each datapoint into an 8-bit integer. This is a 4x compression ratio over 32-bit floats and can therefore quarter the latency in memory-bandwidth bound scenarios. However, quantized brute force is slower than full brute force in non-memory bandwidth bound scenarios, which occur when the batch size is sufficiently large or when the dataset is small enough to fit into cache. Quantized brute-force generally leads to negligible accuracy losses over full brute force.

Rescoring is highly recommended if AH scoring is used. reordering_num_neighbors should be greater than k, the final number of neighbors. Raising reordering_num_neighbors increases accuracy at the cost of speed. quantize has the same meaning as in brute-force scoring.