Interface: IvfPqOptions

Options to create an IVF_PQ index

Properties

distanceType?

optional distanceType: "l2" | "cosine" | "dot"

Distance type to use to build the index.

Default value is "l2".

This is used when training the index to calculate the IVF partitions (vectors are grouped in partitions with similar vectors according to this distance type) and to calculate a subvector's code during quantization.

The distance type used to train an index MUST match the distance type used to search the index. Failure to do so will yield inaccurate results.

The following distance types are available:

"l2" - Euclidean distance. This is a very common distance metric that accounts for both magnitude and direction when determining the distance between vectors. L2 distance has a range of [0, ∞).

"cosine" - Cosine distance. Cosine distance is a distance metric calculated from the cosine similarity between two vectors. Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space. It is defined to equal the cosine of the angle between them. Unlike L2, the cosine distance is not affected by the magnitude of the vectors. Cosine distance has a range of [0, 2].

Note: the cosine distance is undefined when one (or both) of the vectors are all zeros (there is no direction). These vectors are invalid and may never be returned from a vector search.

"dot" - Dot product. Dot distance is the dot product of two vectors. Dot distance has a range of (-∞, ∞). If the vectors are normalized (i.e. their L2 norm is 1), then dot distance is equivalent to the cosine distance.

maxIterations?

optional maxIterations: number

Max iteration to train IVF kmeans.

When training an IVF PQ index we use kmeans to calculate the partitions. This parameter controls how many iterations of kmeans to run.

Increasing this might improve the quality of the index but in most cases these extra iterations have diminishing returns.

The default value is 50.

numPartitions?

optional numPartitions: number

The number of IVF partitions to create.

This value should generally scale with the number of rows in the dataset. By default the number of partitions is the square root of the number of rows.

If this value is too large then the first part of the search (picking the right partition) will be slow. If this value is too small then the second part of the search (searching within a partition) will be slow.

numSubVectors?

optional numSubVectors: number

Number of sub-vectors of PQ.

This value controls how much the vector is compressed during the quantization step. The more sub vectors there are the less the vector is compressed. The default is the dimension of the vector divided by 16. If the dimension is not evenly divisible by 16 we use the dimension divded by 8.

The above two cases are highly preferred. Having 8 or 16 values per subvector allows us to use efficient SIMD instructions.

If the dimension is not visible by 8 then we use 1 subvector. This is not ideal and will likely result in poor performance.

sampleRate?

optional sampleRate: number

The number of vectors, per partition, to sample when training IVF kmeans.

When an IVF PQ index is trained, we need to calculate partitions. These are groups of vectors that are similar to each other. To do this we use an algorithm called kmeans.

Running kmeans on a large dataset can be slow. To speed this up we run kmeans on a random sample of the data. This parameter controls the size of the sample. The total number of vectors used to train the index is sample_rate * num_partitions.

Increasing this value might improve the quality of the index but in most cases the default should be sufficient.

The default value is 256.