> ## 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 (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. ```go theme={null} type IndexModel interface { ToIndexConfig() *internal.IndexConfig } ``` ### IVFSQ Configuration Create an IVFSQ (Scalar Quantization) index configuration that compresses each dimension independently. ```go theme={null} 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 (default: 16) | ### IVFFlat Configuration Create an IVFFlat (Inverted File Flat) index configuration for higher accuracy. ```go theme={null} 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. ```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. #### 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. ```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) | *** ## UpsertInput Interface Sealed interface implemented by types that can be passed to `Upsert`. ```go theme={null} type UpsertInput interface { isUpsertInput() } ``` Valid implementations: [`VectorItems`](#vectoritems), [`BinaryUpsertParams`](#binaryupsertparams) *** ## QueryInput Interface Sealed interface implemented by types that can be passed to `Query`. ```go theme={null} type QueryInput interface { isQueryInput() } ``` Valid implementations: [`QueryParams`](#queryparams), [`BinaryQueryParams`](#binaryqueryparams) *** ## VectorItems A slice of `VectorItem` that implements `UpsertInput` for type-safe upsert operations. ```go theme={null} type VectorItems []VectorItem ``` *** ## BinaryUpsertParams Parameters for binary format vector upserts. More efficient than `VectorItems` for large batches. ```go theme={null} 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. ```go theme={null} 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. Defaults to `[]` (only `id` is returned) | *** ## QueryParams Parameters for similarity search queries. Supports single vector, batch, and content-based queries. ```go theme={null} 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` | No | Fields to include in results: `"metadata"`, `"distance"`, `"vector"`, `"contents"`. Defaults to `[]` (only `id` is returned) |