Types

VectorItem

Represents a single vector with its associated data.

type VectorItem struct {
    Id       string                 `json:"id"`                // Unique identifier
    Vector   []float32              `json:"vector,omitempty"`  // Vector data
    Contents NullableContents       `json:"contents,omitempty"` // Optional text content  
    Metadata map[string]interface{} `json:"metadata,omitempty"` // Optional metadata
}

Fields

Field	Type	Description
`Id`	`string`	Unique identifier for the vector (required)
`Vector`	`[]float32`	The vector data as float32 slice
`Contents`	`NullableContents`	Optional text content associated with the vector
`Metadata`	`map[string]interface{}`	Optional key-value pairs for filtering and retrieval

CreateIndexParams

Parameters for creating a new encrypted vector index.

type CreateIndexParams struct {
    IndexName      string     `json:"index_name"`            // Unique identifier for the index
    IndexKey       []byte     `json:"index_key"`             // 32-byte encryption key
    IndexConfig    IndexModel `json:"index_config,omitempty"` // Index configuration
    Metric         *string    `json:"metric,omitempty"`       // Distance metric
    EmbeddingModel *string    `json:"embedding_model,omitempty"` // Associated embedding model
}

Fields

Field	Type	Required	Description
`IndexName`	`string`	Yes	Unique identifier for the index
`IndexKey`	`[]byte`	Yes	32-byte encryption key (use GenerateKey() to create)
`IndexConfig`	`IndexModel`	No	Index configuration (IVFFlat, IVFPQ, or IVFSQ)
`Metric`	`*string`	No	Distance metric (“euclidean”, “squared_euclidean”, “cosine”)
`EmbeddingModel`	`*string`	No	Name of embedding model to associate

Index Configuration Types

IndexModel Interface

All index configuration types implement this interface.

type IndexModel interface {
    ToIndexConfig() *internal.IndexConfig
}

IVFSQ Configuration

Create an IVFSQ (Scalar Quantization) index configuration that compresses each dimension independently.

func IndexIVFSQ(dimension int32, sqBits int32) *indexIVFSQ

Parameters

Parameter	Type	Description
`dimension`	`int32`	The dimensionality of vectors that will be stored
`sqBits`	`int32`	Number of bits per dimension for scalar quantization (typically 16)

IVFFlat Configuration

Create an IVFFlat (Inverted File Flat) index configuration for higher accuracy.

func IndexIVFFlat(dimension int32) *indexIVFFlat

Parameters

Parameter	Type	Description
`dimension`	`int32`	The dimensionality of vectors that will be stored

IVFPQ Configuration

Create an IVFPQ (Inverted File with Product Quantization) index configuration for memory efficiency.

func IndexIVFPQ(dimension int32, pqDim int32, pqBits int32) *indexIVFPQ

Parameters

Parameter	Type	Description
`dimension`	`int32`	The dimensionality of vectors that will be stored
`pqDim`	`int32`	Product quantization dimension (typically dimension/8 or dimension/16)
`pqBits`	`int32`	Bits per PQ code (typically 8, higher = more accurate but larger)

Response Types

QueryResponse

Response from similarity search operations.

type QueryResponse = internal.QueryResponse

Contains search results with similar vectors and their metadata.

QueryResultItem

A single result from a similarity search query.

type QueryResultItem = internal.QueryResultItem

Represents one matching vector with its similarity score and metadata.

GetResponse

Response from Get operations containing retrieved vectors.

type GetResponse = internal.GetResponseModel

ListIDsResponse

Response from ListIDs operations.

type ListIDsResponse = internal.ListIDsResponse

Contains the list of vector IDs in the index.

Fields

Field	Type	Description
`Ids`	`[]string`	List of all vector ID strings in the index
`Count`	`int32`	Total number of IDs in the index

TrainParams

Parameters for training an encrypted vector index.

type TrainParams struct {
    BatchSize  *int32   `json:"batch_size,omitempty"`  // Number of vectors per training batch
    MaxIters   *int32   `json:"max_iters,omitempty"`   // Maximum training iterations  
    Tolerance  *float64 `json:"tolerance,omitempty"`   // Convergence tolerance
    MaxMemory  *int32   `json:"max_memory,omitempty"`  // Maximum memory usage in MB
    NLists     *int32   `json:"n_lists,omitempty"`     // Number of IVF clusters
}

Fields

Field	Type	Description
`BatchSize`	`*int32`	Number of vectors processed per training batch (default: 2048)
`MaxIters`	`*int32`	Maximum training iterations (default: 100)
`Tolerance`	`*float64`	Convergence tolerance for training (default: 1e-6)
`MaxMemory`	`*int32`	Maximum memory usage in MB, 0 = no limit (default: 0)
`NLists`	`*int32`	Number of IVF clusters, 0 = auto-determine (default: 0)

UpsertInput Interface

Sealed interface implemented by types that can be passed to Upsert.

type UpsertInput interface {
    isUpsertInput()
}

Valid implementations: VectorItems, BinaryUpsertParams

QueryInput Interface

Sealed interface implemented by types that can be passed to Query.

type QueryInput interface {
    isQueryInput()
}

Valid implementations: QueryParams, BinaryQueryParams

VectorItems

A slice of VectorItem that implements UpsertInput for type-safe upsert operations.

type VectorItems []VectorItem

BinaryUpsertParams

Parameters for binary format vector upserts. More efficient than VectorItems for large batches.

type BinaryUpsertParams struct {
    IDs      []string
    Vectors  [][]float32
    Metadata []map[string]interface{}
    Contents []string
}

Fields

Field	Type	Required	Description
`IDs`	`[]string`	Yes	Unique identifiers for each vector
`Vectors`	`[][]float32`	Yes	Vector data, shape [n_vectors][dimension]
`Metadata`	`[]map[string]interface{}`	No	Optional metadata for each vector (length must match IDs if provided)
`Contents`	`[]string`	No	Optional content strings for each vector (length must match IDs if provided)

BinaryQueryParams

Parameters for binary format similarity search. More efficient than QueryParams for large batch queries.

type BinaryQueryParams struct {
    QueryVectors [][]float32
    TopK         int32
    NProbes      *int32
    Greedy       *bool
    Filters      map[string]interface{}
    Include      []string
}

Fields

Field	Type	Required	Description
`QueryVectors`	`[][]float32`	Yes	Query vectors, shape [n_queries][dimension]
`TopK`	`int32`	No	Number of nearest neighbors to return (default: 100)
`NProbes`	`*int32`	No	Number of IVF lists to probe
`Greedy`	`*bool`	No	Enable greedy search mode
`Filters`	`map[string]interface{}`	No	Metadata filters to apply
`Include`	`[]string`	No	Fields to include in response

QueryParams

Parameters for similarity search queries. Supports single vector, batch, and content-based queries.

type QueryParams struct {
    QueryVector       []float32              `json:"query_vector,omitempty"`
    BatchQueryVectors [][]float32            `json:"query_vectors,omitempty"`
    QueryContents     *string                `json:"query_contents,omitempty"`
    TopK              int32                  `json:"top_k"`
    NProbes           *int32                 `json:"n_probes,omitempty"`
    Greedy            *bool                  `json:"greedy,omitempty"`
    Filters           map[string]interface{} `json:"filters,omitempty"`
    Include           []string               `json:"include"`
}

Fields

Field	Type	Required	Description
`QueryVector`	`[]float32`	No	Single query vector for similarity search
`BatchQueryVectors`	`[][]float32`	No	Multiple query vectors for batch search
`QueryContents`	`*string`	No	Text content for semantic search
`TopK`	`int32`	No	Number of nearest neighbors to return
`NProbes`	`*int32`	No	Number of clusters to probe for search
`Greedy`	`*bool`	No	Use greedy search algorithm
`Filters`	`map[string]interface{}`	No	Metadata filters to apply
`Include`	`[]string`	Yes	Fields to include in results: `"metadata"`, `"distance"`, `"vector"`, `"contents"`

Introduction

Client

Encrypted Index

Types & Helpers

VectorItem

Fields

CreateIndexParams

Fields

Index Configuration Types

IndexModel Interface

IVFSQ Configuration

Parameters

IVFFlat Configuration

Parameters

IVFPQ Configuration

Parameters

Response Types

QueryResponse

QueryResultItem

GetResponse

ListIDsResponse

Fields

TrainParams

Fields

UpsertInput Interface

QueryInput Interface

VectorItems

BinaryUpsertParams

Fields

BinaryQueryParams

Fields

QueryParams

Fields

Introduction

Client

Encrypted Index

Types & Helpers

​VectorItem

​Fields

​CreateIndexParams

​Fields

​Index Configuration Types

​IndexModel Interface

​IVFSQ Configuration

​Parameters

​IVFFlat Configuration

​Parameters

​IVFPQ Configuration

​Parameters

​Response Types

​QueryResponse

​QueryResultItem

​GetResponse

​ListIDsResponse

​Fields

​TrainParams

​Fields

​UpsertInput Interface

​QueryInput Interface

​VectorItems

​BinaryUpsertParams

​Fields

​BinaryQueryParams

​Fields

​QueryParams

​Fields

VectorItem

Fields

CreateIndexParams

Fields

Index Configuration Types

IndexModel Interface

IVFSQ Configuration

Parameters

IVFFlat Configuration

Parameters

IVFPQ Configuration

Parameters

Response Types

QueryResponse

QueryResultItem

GetResponse

ListIDsResponse

Fields

TrainParams

Fields

UpsertInput Interface

QueryInput Interface

VectorItems

BinaryUpsertParams

Fields

BinaryQueryParams

Fields

QueryParams

Fields