Documentation Index
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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 (IVF, IVFFlat, or IVFPQ) |
Metric | *string | No | Distance metric (“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
}
IVF Configuration
Create an IVF (Inverted File) index configuration.
func IndexIVF(dimension int32) *indexIVF
IVFFlat Configuration
Create an IVFFlat (Inverted File Flat) index configuration for higher accuracy.
func IndexIVFFlat(dimension int32) *indexIVFFlat
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
QueryResultItem
A single result from a similarity search query.
type QueryResultItem = internal.QueryResultItem
GetResponse
Response from Get operations containing retrieved vectors.
type GetResponse = internal.GetResponseModel
ListIDsResponse
Response from ListIDs operations.
type ListIDsResponse = internal.ListIDsResponse
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) |
