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GetIndexName

Returns the unique name of the index. 
func (e *EncryptedIndex) GetIndexName() string
Returns: string - The index name as specified during creation Example: 
indexName := index.GetIndexName()
fmt.Printf("Working with index: %s\n", indexName)

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GetIndexType

Returns the algorithm type of the index. 
func (e *EncryptedIndex) GetIndexType() string
Returns: string - Index type (“ivfflat”, “ivfpq”, or “ivfsq”) Example: 
indexType := index.GetIndexType()
fmt.Printf("Index type: %s\n", indexType)

// Use type for conditional logic
switch indexType {
case "ivfflat":
    fmt.Println("Using IVFFlat (IVF with flat search) index")
case "ivfpq":
    fmt.Println("Using IVFPQ (IVF with Product Quantization) index")
case "ivfsq":
    fmt.Println("Using IVFSQ (IVF with Scalar Quantization) index")
}

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IsTrained

Checks whether the index has been optimized through training. This method makes an API call to get the current training status from the server. 
func (e *EncryptedIndex) IsTrained(ctx context.Context) (bool, error)
Parameters:
  • ctx: context.Context - Context for cancellation and timeouts
Returns:
  • bool - true if the index has been trained, false otherwise
  • error - Any error encountered during the status check
Example: 
ctx := context.Background()
trained, err := index.IsTrained(ctx)
if err != nil {
    log.Printf("Failed to check training status: %v", err)
    return
}

if trained {
    fmt.Println("Index is trained and optimized")
} else {
    fmt.Println("Index may benefit from training")
    // Optionally trigger training
    params := cyborgdb.TrainParams{}
    err := index.Train(ctx, params)
    if err != nil {
        log.Printf("Training failed: %v", err)
    }
}

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GetIndexConfig

Returns the detailed configuration of the index. 
func (e *EncryptedIndex) GetIndexConfig() internal.IndexConfig
Returns: internal.IndexConfig - The index configuration, or empty if not available Note: For indexes loaded via LoadIndex(), the configuration may be incomplete. Example: 
config := index.GetIndexConfig()

// Access configuration based on index type
switch index.GetIndexType() {
case "ivfsq":
    if config.IndexIVFSQModel != nil {
        fmt.Printf("IVFSQ Dimension: %d\n", config.IndexIVFSQModel.GetDimension())
    }
case "ivfflat":
    if config.IndexIVFFlatModel != nil {
        fmt.Printf("IVFFlat Dimension: %d\n", config.IndexIVFFlatModel.GetDimension())
    }
case "ivfpq":
    if config.IndexIVFPQModel != nil {
        fmt.Printf("IVFPQ Dimension: %d\n", config.IndexIVFPQModel.GetDimension())
        fmt.Printf("PQ Dimension: %d\n", config.IndexIVFPQModel.GetPqDim())
        fmt.Printf("PQ Bits: %d\n", config.IndexIVFPQModel.GetPqBits())
    }
}

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CheckTrainingStatus

Queries the server to check if the index is currently being trained and updates the cached training status. 
func (e *EncryptedIndex) CheckTrainingStatus(ctx context.Context) (bool, error)
Parameters:
  • ctx: context.Context - Context for cancellation and timeouts
Returns:
  • bool - true if the index is currently being trained, false otherwise
  • error - Any error encountered during the status check
Note: This method makes an API call and updates the cached IsTrained() value if training has completed. Example: 
isTraining, err := index.CheckTrainingStatus(context.Background())
if err != nil {
    log.Printf("Failed to check training status: %v", err)
    return
}

if isTraining {
    fmt.Println("Index is currently being trained")
    // Wait for training to complete
    for isTraining {
        time.Sleep(10 * time.Second)
        isTraining, err = index.CheckTrainingStatus(context.Background())
        if err != nil {
            log.Printf("Error checking training status: %v", err)
            break
        }
        fmt.Println("Still training...")
    }
    fmt.Println("Training completed!")
} else {
    trained, err := index.IsTrained(context.Background())
    if err != nil {
        log.Printf("Failed to check training status: %v", err)
        return
    }
    if trained {
        fmt.Println("Index is trained")
    } else {
        fmt.Println("Index is not trained and not currently training")
    }
}