> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cyborg.co/llms.txt
> Use this file to discover all available pages before exploring further.

# Types

## VectorItem

Represents a single vector with its associated data.

```go theme={null}
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.

```go theme={null}
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.

```go theme={null}
type IndexModel interface {
    ToIndexConfig() *internal.IndexConfig
}
```

### IVF Configuration

Create an IVF (Inverted File) index configuration.

```go theme={null}
func IndexIVF(dimension int32) *indexIVF
```

### IVFFlat Configuration

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

```go theme={null}
func IndexIVFFlat(dimension int32) *indexIVFFlat
```

### IVFPQ Configuration

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

```go theme={null}
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.

```go theme={null}
type QueryResponse = internal.QueryResponse
```

Contains search results with similar vectors and their metadata.

### QueryResultItem

A single result from a similarity search query.

```go theme={null}
type QueryResultItem = internal.QueryResultItem
```

Represents one matching vector with its similarity score and metadata.

### GetResponse

Response from Get operations containing retrieved vectors.

```go theme={null}
type GetResponse = internal.GetResponseModel
```

### ListIDsResponse

Response from ListIDs operations.

```go theme={null}
type ListIDsResponse = internal.ListIDsResponse  
```

Contains the list of vector IDs in the index.

***

## TrainParams

Parameters for training an encrypted vector index.

```go theme={null}
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)        |