Documentation
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Overview ¶
Package similarity provides vector distance functions for semantic search.
Index ¶
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
func CosineDistance ¶
CosineDistance computes the cosine distance between two vectors. Returns a value in [0, 2] where 0 means identical direction and 2 means opposite direction. Lower values indicate more similar vectors.
func CosineSimilarity ¶
CosineSimilarity computes the cosine similarity between two vectors. Returns a value in [-1, 1] where 1 means identical direction, 0 means orthogonal, and -1 means opposite direction. Both vectors must have the same length.
Types ¶
type FakeEmbeddingClient ¶
type FakeEmbeddingClient struct {
// contains filtered or unexported fields
}
FakeEmbeddingClient is a deterministic embedding client for testing. It hashes input text with SHA-256 and uses the hash as a seed to generate reproducible float32 vectors. The vectors are L2-normalized to unit length.
func NewFakeEmbeddingClient ¶
func NewFakeEmbeddingClient(dimension int) *FakeEmbeddingClient
NewFakeEmbeddingClient creates a FakeEmbeddingClient that produces vectors of the given dimension.
func (*FakeEmbeddingClient) Close ¶
func (*FakeEmbeddingClient) Close() error
Close is a no-op for the fake client.
func (*FakeEmbeddingClient) Embed ¶
Embed returns a deterministic, unit-normalized vector for the given text.
func (*FakeEmbeddingClient) EmbedBatch ¶
EmbedBatch returns deterministic embeddings for each input text.
Source Files