embed

Vector embeddings, similarity search, and distance calculations.

use embed

Functions

Function	Signature	Description
dot_product	(vec_a, vec_b) -> Number	Dot product of two vectors
magnitude	(vec) -> Number	Vector magnitude (L2 norm)
cosine_similarity	(vec_a, vec_b) -> Number	Cosine similarity (-1 to 1)
normalize	(vec) -> [Number]	Normalize to unit vector
euclidean_distance	(vec_a, vec_b) -> Number	Euclidean distance
centroid	(vectors) -> [Number]	Average vector (centroid)
most_similar	(query_vec, candidates, top_k) -> [{id, score}]	Top-k most similar vectors
chunk_and_embed	(text, chunk_size) -> [{chunk, index}]	Split text into chunks

Example

use embed

let a = [1.0, 0.0, 0.0]
let b = [0.0, 1.0, 0.0]
embed.cosine_similarity(a, b)  # => 0.0 (orthogonal)

let candidates = [
  {id: "doc1", vector: [0.9, 0.1, 0.0]},
  {id: "doc2", vector: [0.0, 0.8, 0.2]}
]
embed.most_similar(a, candidates, 1)  # => [{id: "doc1", score: ~0.99}]