vector

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v0.4.1 Latest Latest Go to latest Published: Jun 4, 2026 License: MIT Imports: 5 Imported by: 0

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Repository

github.com/dd0wney/graphdb

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Documentation ¶

Index ¶

Variables
func CosineDistance(a, b []float32) (float32, error)
func CosineSimilarity(a, b []float32) (float32, error)
func Distance(a, b []float32, metric DistanceMetric) (float32, error)
func DotProduct(a, b []float32) (float32, error)
func EuclideanDistance(a, b []float32) (float32, error)
func Magnitude(v []float32) float32
func Normalize(v []float32) []float32
type DistanceMetric
type HNSWIndex
- func NewHNSWIndex(dimensions, m, efConstruction int, metric DistanceMetric) (*HNSWIndex, error)
type SearchResult

Constants ¶

This section is empty.

Variables ¶

View Source

var ErrDimensionMismatch = fmt.Errorf("vector dimensions mismatch")

ErrDimensionMismatch is returned when vector dimensions don't match

Functions ¶

func CosineDistance ¶

func CosineDistance(a, b []float32) (float32, error)

CosineDistance calculates the cosine distance between two vectors Returns a value between 0 (identical) and 2 (opposite) Formula: 1 - cosine_similarity(a, b) Returns error if vector dimensions don't match

func CosineSimilarity ¶

func CosineSimilarity(a, b []float32) (float32, error)

CosineSimilarity calculates the cosine similarity between two vectors Returns a value between -1 (opposite) and 1 (identical) Formula: (a · b) / (||a|| * ||b||) Returns error if vector dimensions don't match

func Distance ¶

func Distance(a, b []float32, metric DistanceMetric) (float32, error)

Distance calculates the distance between two vectors using the specified metric Returns error if vector dimensions don't match

func DotProduct ¶

func DotProduct(a, b []float32) (float32, error)

DotProduct calculates the dot product of two vectors Formula: sum(a[i] * b[i]) Returns error if vector dimensions don't match

func EuclideanDistance ¶

func EuclideanDistance(a, b []float32) (float32, error)

EuclideanDistance calculates the Euclidean (L2) distance between two vectors Formula: sqrt(sum((a[i] - b[i])^2)) Returns error if vector dimensions don't match

func Magnitude ¶

func Magnitude(v []float32) float32

Magnitude calculates the magnitude (L2 norm) of a vector

func Normalize ¶

func Normalize(v []float32) []float32

Normalize normalizes a vector to unit length Formula: v / ||v||

Types ¶

type DistanceMetric ¶

type DistanceMetric string

DistanceMetric represents the type of distance calculation

const (
	MetricCosine     DistanceMetric = "cosine"
	MetricEuclidean  DistanceMetric = "euclidean"
	MetricDotProduct DistanceMetric = "dot_product"
)

type HNSWIndex ¶

type HNSWIndex struct {
	// contains filtered or unexported fields
}

HNSWIndex implements Hierarchical Navigable Small World graph for approximate nearest neighbor search

func NewHNSWIndex ¶

func NewHNSWIndex(dimensions, m, efConstruction int, metric DistanceMetric) (*HNSWIndex, error)

NewHNSWIndex creates a new HNSW index

func (*HNSWIndex) Delete ¶

func (h *HNSWIndex) Delete(id uint64) error

Delete removes a vector from the index

func (*HNSWIndex) Dimensions ¶

func (h *HNSWIndex) Dimensions() int

Dimensions returns the vector dimensions

func (*HNSWIndex) EfConstruction ¶

func (h *HNSWIndex) EfConstruction() int

EfConstruction returns the construction-time candidate-list size. See M.

func (*HNSWIndex) Insert ¶

func (h *HNSWIndex) Insert(id uint64, vector []float32) error

Insert adds a vector to the index

func (*HNSWIndex) Len ¶

func (h *HNSWIndex) Len() int

Len returns the number of vectors in the index

func (*HNSWIndex) M ¶

func (h *HNSWIndex) M() int

M returns the construction parameter m (bi-directional links per node). Exposed so the index definition can be persisted and recreated identically on restart (the HNSW graph itself is not serialized; it is rebuilt from the node set on load).

func (*HNSWIndex) Metric ¶

func (h *HNSWIndex) Metric() DistanceMetric

Metric returns the distance metric used by this index

func (*HNSWIndex) Search ¶

func (h *HNSWIndex) Search(query []float32, k int, ef int) ([]SearchResult, error)

Search finds k nearest neighbors

type SearchResult ¶

type SearchResult struct {
	ID       uint64
	Distance float32
}

SearchResult represents a search result with ID and distance

Source Files ¶

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