vectorstore

package
v1.17.0 Latest Latest
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 Go to latest Published: Jul 8, 2026 License: Apache-2.0 Imports: 14 Imported by: 0

Documentation

Index

Constants

View Source 
const (
	ErrorCodeInvalidConfig        = "vectorstore.invalid_config"
	ErrorCodeInvalidInput         = "vectorstore.invalid_input"
	ErrorCodeInvalidVector        = "vectorstore.invalid_vector"
	ErrorCodeDimensionMismatch    = "vectorstore.dimension_mismatch"
	ErrorCodeNotFound             = "vectorstore.not_found"
	ErrorCodeUnsupportedOperation = "vectorstore.unsupported_operation"
	ErrorCodeEmbeddingFailed      = "vectorstore.embedding_failed"
)
View Source 
const (
	EnvVectorBucketName          = "APPTHEORY_VECTOR_BUCKET_NAME"
	EnvVectorIndexName           = "APPTHEORY_VECTOR_INDEX_NAME"
	EnvVectorIndexARN            = "APPTHEORY_VECTOR_INDEX_ARN"
	EnvVectorDimension           = "APPTHEORY_VECTOR_DIMENSION"
	EnvEmbeddingProvider         = "APPTHEORY_EMBEDDING_PROVIDER"
	EnvEmbeddingModelID          = "APPTHEORY_EMBEDDING_MODEL_ID"
	EnvEmbeddingDimensions       = "APPTHEORY_EMBEDDING_DIMENSIONS"
	EnvEmbeddingNormalize        = "APPTHEORY_EMBEDDING_NORMALIZE"
	DefaultTitanEmbedTextModelID = "amazon.titan-embed-text-v2:0"
)
View Source 
const (
	DefaultEmbeddingDimensions = 1024
	DefaultQueryTopK           = 12
	MaxQueryTopK               = 10000
	MaxPutDeleteBatchSize      = 500
)

Variables

View Source 
var (
	ErrInvalidConfig        = &Error{Code: ErrorCodeInvalidConfig, Message: "vectorstore: invalid config"}
	ErrInvalidInput         = &Error{Code: ErrorCodeInvalidInput, Message: "vectorstore: invalid input"}
	ErrInvalidVector        = &Error{Code: ErrorCodeInvalidVector, Message: "vectorstore: invalid vector"}
	ErrDimensionMismatch    = &Error{Code: ErrorCodeDimensionMismatch, Message: "vectorstore: vector dimension mismatch"}
	ErrNotFound             = &Error{Code: ErrorCodeNotFound, Message: "vectorstore: vector not found"}
	ErrUnsupportedOperation = &Error{Code: ErrorCodeUnsupportedOperation, Message: "vectorstore: unsupported operation"}
	ErrEmbeddingFailed      = &Error{Code: ErrorCodeEmbeddingFailed, Message: "vectorstore: embedding failed"}
)

Functions

func CloneMetadata 

func CloneMetadata(in map[string]any) map[string]any

func CloneVector 

func CloneVector(in []float32) []float32

func EmbeddingErrorCode 

func EmbeddingErrorCode(err error) string

func NormalizeTopK 

func NormalizeTopK(topK int) int

func ValidateDimension 

func ValidateDimension(dimension int) error

func ValidateKey 

func ValidateKey(key string) error

func ValidateRequiredMetadata 

func ValidateRequiredMetadata(metadata map[string]any, required []string) error

func ValidateVector 

func ValidateVector(vector []float32, dimension int) error

Types

type BedrockRuntimeAPI 

type BedrockRuntimeAPI interface {
	InvokeModel(context.Context, *bedrockruntime.InvokeModelInput, ...func(*bedrockruntime.Options)) (*bedrockruntime.InvokeModelOutput, error)
}

type Call 

type Call struct {
	Operation      string
	Keys           []string
	Records        []VectorRecord
	Vector         []float32
	TopK           int
	Filter         map[string]any
	ReturnMetadata bool
}

type DeleteInput 

type DeleteInput struct {
	Keys []string
}

type Embedder 

type Embedder interface {
	Embed(context.Context, string) ([]float32, error)
	EmbedBatch(context.Context, []string) ([][]float32, error)
}

type Error 

type Error struct {
	Code    string
	Message string
	Cause   error
}

func NewError 

func NewError(code, message string, cause error) *Error

func (*Error) Error 

func (e *Error) Error() string

func (*Error) Is 

func (e *Error) Is(target error) bool

func (*Error) Unwrap 

func (e *Error) Unwrap() error

type FakeEmbedder 

type FakeEmbedder struct {
	Embeddings map[string][]float32
	Default    []float32
	Calls      []string
}

func NewFakeEmbedder 

func NewFakeEmbedder(embeddings map[string][]float32) *FakeEmbedder

func (*FakeEmbedder) Embed 

func (e *FakeEmbedder) Embed(_ context.Context, text string) ([]float32, error)

func (*FakeEmbedder) EmbedBatch 

func (e *FakeEmbedder) EmbedBatch(ctx context.Context, texts []string) ([][]float32, error)

type FakeStore 

type FakeStore struct {
	Dimension            int
	RequiredMetadataKeys []string
	// contains filtered or unexported fields
}

func NewFakeStore 

func NewFakeStore(dimension int) *FakeStore

func (*FakeStore) Calls 

func (s *FakeStore) Calls() []Call

func (*FakeStore) DeleteVectors 

func (s *FakeStore) DeleteVectors(_ context.Context, input DeleteInput) error

func (*FakeStore) GetVectors 

func (s *FakeStore) GetVectors(_ context.Context, input GetInput) ([]VectorRecord, error)

func (*FakeStore) PutVectors 

func (s *FakeStore) PutVectors(_ context.Context, input PutInput) error

func (*FakeStore) QueryVectors 

func (s *FakeStore) QueryVectors(_ context.Context, input QueryInput) ([]QueryHit, error)

func (*FakeStore) SetError 

func (s *FakeStore) SetError(operation string, err error)

type GetInput 

type GetInput struct {
	Keys           []string
	ReturnMetadata bool
}

type PutInput 

type PutInput struct {
	Records []VectorRecord
}

type QueryHit 

type QueryHit struct {
	Key      string         `json:"key"`
	Distance float32        `json:"distance"`
	Metadata map[string]any `json:"metadata,omitempty"`
}

type QueryInput 

type QueryInput struct {
	Vector         []float32
	TopK           int
	Filter         map[string]any
	ReturnMetadata bool
}

type S3VectorStore 

type S3VectorStore struct {
	Client           S3VectorsAPI
	VectorBucketName string
	IndexName        string
	Dimension        int
	MaxBatchSize     int
}

func NewS3VectorStore 

func NewS3VectorStore(ctx context.Context, vectorBucketName, indexName string, dimension int) (*S3VectorStore, error)

func (*S3VectorStore) DeleteVectors 

func (s *S3VectorStore) DeleteVectors(ctx context.Context, input DeleteInput) error

func (*S3VectorStore) GetVectors 

func (s *S3VectorStore) GetVectors(ctx context.Context, input GetInput) ([]VectorRecord, error)

func (*S3VectorStore) PutVectors 

func (s *S3VectorStore) PutVectors(ctx context.Context, input PutInput) error

func (*S3VectorStore) QueryVectors 

func (s *S3VectorStore) QueryVectors(ctx context.Context, input QueryInput) ([]QueryHit, error)

type S3VectorsAPI 

type S3VectorsAPI interface {
	PutVectors(context.Context, *s3vectors.PutVectorsInput, ...func(*s3vectors.Options)) (*s3vectors.PutVectorsOutput, error)
	QueryVectors(context.Context, *s3vectors.QueryVectorsInput, ...func(*s3vectors.Options)) (*s3vectors.QueryVectorsOutput, error)
	GetVectors(context.Context, *s3vectors.GetVectorsInput, ...func(*s3vectors.Options)) (*s3vectors.GetVectorsOutput, error)
	DeleteVectors(context.Context, *s3vectors.DeleteVectorsInput, ...func(*s3vectors.Options)) (*s3vectors.DeleteVectorsOutput, error)
}

type SemanticIndex 

type SemanticIndex struct {
	Store                Store
	Embedder             Embedder
	Dimension            int
	RequiredMetadataKeys []string
}

func (*SemanticIndex) PutText 

func (i *SemanticIndex) PutText(ctx context.Context, records []SemanticRecord) error

func (*SemanticIndex) QueryText 

func (i *SemanticIndex) QueryText(ctx context.Context, text string, input QueryInput) ([]QueryHit, error)

type SemanticRecord 

type SemanticRecord struct {
	Key      string
	Text     string
	Metadata map[string]any
}

type Store 

type Store interface {
	PutVectors(context.Context, PutInput) error
	GetVectors(context.Context, GetInput) ([]VectorRecord, error)
	DeleteVectors(context.Context, DeleteInput) error
	QueryVectors(context.Context, QueryInput) ([]QueryHit, error)
}

type TitanEmbedder 

type TitanEmbedder struct {
	Runtime          BedrockRuntimeAPI
	ModelID          string
	Dimensions       int
	Normalize        bool
	BatchConcurrency int
}

func NewTitanEmbedder 

func NewTitanEmbedder(ctx context.Context) (*TitanEmbedder, error)

func (*TitanEmbedder) Embed 

func (e *TitanEmbedder) Embed(ctx context.Context, text string) ([]float32, error)

func (*TitanEmbedder) EmbedBatch 

func (e *TitanEmbedder) EmbedBatch(ctx context.Context, texts []string) ([][]float32, error)

type VectorRecord 

type VectorRecord struct {
	Key      string         `json:"key"`
	Data     []float32      `json:"data"`
	Metadata map[string]any `json:"metadata,omitempty"`
}

Source Files

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