link_embeddings(embedding: list[number], {
    "param": value,
    ...
}) -> (targets: column, weights: column)

Uses Spotify’s Annoy to perform approximate nearest neighbour search.

link_embeddings(embedding: list[number], {
    "param": value,
    ...
}) -> (targets: column, weights: column)
​
n_nearest
integer
default:"15"

Number of nearest neighbours to connect to.

Values must be in the following range:

1 ≤ n_nearest < inf
​
similarity_min
number
default:"0"

Minimum similarity for connecting two nodes.

Values must be in the following range:

0 ≤ similarity_min ≤ 1
​
similarity_min_q
number
default:"0"

Minimum similarity for connecting two nodes, expressed as a quantile of the similarity distribution.

Values must be in the following range:

0 ≤ similarity_min_q ≤ 1
​
n_trees
integer
default:"30"

Number of trees. Affects the build time and the index size. A larger value will give more accurate results, but will take longer to create a larger index.

​
search_k_mult
integer
default:"2"

Accuracy multipler. A larger value will give more accurate results, but will take longer time to return.

​
metric
string
default:"angular"

Metric to use, only angular supported for now. Annoy’s angular metric is equivalent to sqrt(2*(1-cos(u,v))), whose max. is sqrt(2*2) = 2. I.e. the distance between (1,0) and (-1,0), at maximum angular separation, should be exactly 2 Note that for the weights of the resulting network links Annoy’s distances are converted to similarities in the interval [0,1].

Values must be one of the following:

  • angular
  • euclidean
  • manhattan
  • hamming
  • dot
​
seed
number

Used to seed the random number generator, creating deterministic results.

Was this page helpful?

Suggest edits