topk-js/schema

Functions

binaryVector()

function binaryVector(options: VectorOptions): FieldSpec;

Creates a FieldSpec type for binary_vector values. Example:

import { binaryVector } from "topk-js/schema";

await client.collections().create("books", {
  title_embedding: binaryVector({ dimension: 128 })
});

Parameters

Parameter	Type
`options`	`VectorOptions`

Returns FieldSpec

bool()

function bool(): FieldSpec;

Creates a FieldSpec type for bool values. Example:

import { bool } from "topk-js/schema";

await client.collections().create("books", {
  is_published: bool()
});

Returns FieldSpec

bytes()

function bytes(): FieldSpec;

Creates a FieldSpec type for bytes values. Example:

import { bytes } from "topk-js/schema";

await client.collections().create("books", {
  image: bytes()
});

Returns FieldSpec

f16Vector()

function f16Vector(options: VectorOptions): FieldSpec;

Creates a FieldSpec type for f16_vector values. Example:

import { f16Vector } from "topk-js/schema";

await client.collections().create("books", {
  title_embedding: f16Vector({ dimension: 1536 })
});

Parameters

Parameter	Type
`options`	`VectorOptions`

Returns FieldSpec

f32SparseVector()

function f32SparseVector(): FieldSpec;

Creates a FieldSpec type for f32_sparse_vector values. Note: Sparse vectors use u32 dimension indices to support dictionaries of up to 2^32 - 1 terms. Example:

import { f32SparseVector } from "topk-js/schema";

await client.collections().create("books", {
  sparse_field: f32SparseVector()
});

Returns FieldSpec

f32Vector()

function f32Vector(options: VectorOptions): FieldSpec;

Creates a FieldSpec type for f32_vector values. Example:

import { f32Vector } from "topk-js/schema";

await client.collections().create("books", {
  title_embedding: f32Vector({ dimension: 1536 })
});

Parameters

Parameter	Type
`options`	`VectorOptions`

Returns FieldSpec

f8Vector()

function f8Vector(options: VectorOptions): FieldSpec;

Creates a FieldSpec type for f8_vector values. Example:

import { f8Vector } from "topk-js/schema";

await client.collections().create("books", {
  title_embedding: f8Vector({ dimension: 1536 })
});

Parameters

Parameter	Type
`options`	`VectorOptions`

Returns FieldSpec

float()

function float(): FieldSpec;

Creates a FieldSpec type for float values. Example:

import { float } from "topk-js/schema";

await client.collections().create("books", {
  price: float()
});

Returns FieldSpec

i8Vector()

function i8Vector(options: VectorOptions): FieldSpec;

Creates a FieldSpec type for i8_vector values. Example:

import { i8Vector } from "topk-js/schema";

await client.collections().create("books", {
  title_embedding: i8Vector({ dimension: 1536 })
});

Parameters

Parameter	Type
`options`	`VectorOptions`

Returns FieldSpec

int()

function int(): FieldSpec;

Creates a FieldSpec type for int values. Example:

import { int } from "topk-js/schema";

await client.collections().create("books", {
  published_year: int()
});

Returns FieldSpec

keywordIndex()

function keywordIndex(indexType?: KeywordIndexType): FieldIndex;

Creates a FieldIndex type for keyword_index values. Supported index_types:

text (default) - text is tokenized before indexing
exact - text is indexed as a single term

Example:

import { text, keywordIndex } from "topk-js/schema";

await client.collections().create("books", {
  title: text().index(keywordIndex())
});

Adding a keyword index allows you to perform keyword search on this field. Parameters

Parameter	Type
`indexType?`	`KeywordIndexType`

Returns FieldIndex

list()

function list(options: ListOptions): FieldSpec;

Creates a FieldSpec type for list values. Example:

import { list } from "topk-js/schema";

await client.collections().create("books", {
  tags: list({ valueType: "text" })
});

Parameters

Parameter	Type
`options`	`ListOptions`

Returns FieldSpec

matrix()

function matrix(options: MatrixOptions): FieldSpec;

Creates a FieldSpec type for matrix values. Example:

import { matrix } from "topk-js/schema";

await client.collections().create("books", {
  token_embeddings: matrix({ dimension: 7, valueType: "f32" })
});

Parameters

Parameter	Type
`options`	`MatrixOptions`

Returns FieldSpec

multiVectorIndex()

function multiVectorIndex(options: MultiVectorIndexOptions): FieldIndex;

Creates a FieldIndex type for multi_vector_index values. Example:

import { matrix, multiVectorIndex } from "topk-js/schema";

await client.collections().create("books", {
  token_embeddings: matrix({ dimension: 7, valueType: "f32" }).index(
    multiVectorIndex({ metric: "max_sim" })
  )
});

Parameters

Parameter	Type
`options`	`MultiVectorIndexOptions`

Returns FieldIndex

ngramIndex()

function ngramIndex(): FieldIndex;

Creates a FieldIndex type for ngram_index values. Example:

import { text, ngramIndex } from "topk-js/schema";

await client.collections().create("books", {
  title: text().index(ngramIndex())
});

Returns FieldIndex

semanticIndex()

function semanticIndex(): FieldIndex;

Creates a FieldIndex type for semantic_index values. Example:

import { text, semanticIndex } from "topk-js/schema";

await client.collections().create("books", {
  title: text().index(semanticIndex())
});

Returns FieldIndex

struct()

function struct(fields: Record<string, SchemaFieldSpec>): FieldSpec;

Creates a FieldSpec type for struct values. Example:

import { struct, text, int } from "topk-js/schema";

await client.collections().create("books", {
  meta: struct({ author: text(), year: int() })
});

Parameters

Parameter	Type
`fields`	`Record`<`string`, `SchemaFieldSpec`>

Returns FieldSpec

text()

function text(): FieldSpec;

Creates a FieldSpec type for text values. Example:

import { text } from "topk-js/schema";

await client.collections().create("books", {
  title: text()
});

Returns FieldSpec

timestamp()

function timestamp(): FieldSpec;

Creates a FieldSpec type for timestamp values. Timestamps are stored as milliseconds since UNIX epoch. When upserting timestamps, use:

Date objects
number — epoch milliseconds

Example:

import { timestamp } from "topk-js/schema";

await client.collections().create("books", {
  published_ts: timestamp()
});

Returns FieldSpec

u8SparseVector()

function u8SparseVector(): FieldSpec;

Creates a FieldSpec type for u8_sparse_vector values. Note: Sparse vectors use u32 dimension indices to support dictionaries of up to 2^32 - 1 terms. Example:

import { u8SparseVector } from "topk-js/schema";

await client.collections().create("books", {
  sparse_field: u8SparseVector()
});

Returns FieldSpec

u8Vector()

function u8Vector(options: VectorOptions): FieldSpec;

Creates a FieldSpec type for u8_vector values. Example:

import { u8Vector } from "topk-js/schema";

await client.collections().create("books", {
  title_embedding: u8Vector({ dimension: 1536 })
});

Parameters

Parameter	Type
`options`	`VectorOptions`

Returns FieldSpec

vectorIndex()

function vectorIndex(options: VectorIndexOptions): FieldIndex;

Creates a FieldIndex type for vector_index values. Supported metrics:

euclidean (not supported for sparse vectors)
cosine (not supported for sparse vectors)
dot_product (supported for dense and sparse vectors)
hamming (only supported for binary_vector type)

Example:

import { f32Vector, vectorIndex } from "topk-js/schema";

await client.collections().create("books", {
  title_embedding: f32Vector({ dimension: 1536 }).index(
    vectorIndex({ metric: "cosine" })
  )
});

Parameters

Parameter	Type
`options`	`VectorIndexOptions`

Returns FieldIndex

Classes

FieldIndex

Internal

FieldSpec

Internal Methods

index()

index(index: FieldIndex): FieldSpec;

Creates an index on a field. Example:

import { text, keywordIndex } from "topk-js/schema";

await client.collections().create("books", {
  title: text().index(keywordIndex())
});

Parameters

Parameter	Type
`index`	`FieldIndex`

Returns FieldSpec

required()

required(): FieldSpec;

Marks the field as required. All fields are optional by default. Example:

import { text } from "topk-js/schema";

await client.collections().create("books", {
  title: text().required()
});

Returns FieldSpec

Interfaces

ListOptions

Options for list field specifications. This struct contains configuration options for list fields, including the type of values the list can contain. Properties

Property	Type	Description
`valueType`	`ListValueType`	The type of values the list can contain

MatrixOptions

Options for matrix field specifications. This struct contains configuration options for matrix fields, including the dimension and value type. Properties

Property	Type	Description
`dimension`	`number`	The dimension (number of columns) of the matrix
`valueType`	`MatrixValueType`	The value type of the matrix elements

MultiVectorIndexOptions

Options for multi-vector index specifications. This struct contains configuration options for multi-vector indexes, including the distance metric to use. Properties

Property	Type	Description
`metric`	`"maxsim"`	The distance metric to use for multi-vector similarity
`quantization?`	`MultiVectorQuantization`	The quantization to use for multi-vector values
`topK?`	`number`	Top-k projected values to keep
`width?`	`number`	Width of the sparse projection

VectorIndexOptions

Options for vector index specifications. This struct contains configuration options for vector indexes, including the distance metric to use. Properties

Property	Type	Description
`metric`	`VectorDistanceMetric`	The distance metric to use for vector similarity

VectorOptions

Options for vector field specifications. This struct contains configuration options for vector fields, including the required dimension parameter. Properties

Property	Type	Description
`dimension`	`number`	The dimension of the vector

Type Aliases

KeywordIndexType

type KeywordIndexType = "text" | "exact";

ListValueType

type ListValueType = "text" | "integer" | "float";

MatrixValueType

type MatrixValueType = "f32" | "f16" | "f8" | "u8" | "i8";

MultiVectorDistanceMetric

type MultiVectorDistanceMetric = "maxsim";

MultiVectorQuantization

type MultiVectorQuantization = "1bit" | "2bit" | "scalar";

VectorDistanceMetric

type VectorDistanceMetric = "cosine" | "euclidean" | "dot_product" | "hamming";

​Functions

​binaryVector()

​bool()

​bytes()

​f16Vector()

​f32SparseVector()

​f32Vector()

​f8Vector()

​float()

​i8Vector()

​int()

​keywordIndex()

​list()

​matrix()

​multiVectorIndex()

​ngramIndex()

​semanticIndex()

​struct()

​text()

​timestamp()

​u8SparseVector()

​u8Vector()

​vectorIndex()

​Classes

​FieldIndex

​FieldSpec

index()

required()

​Interfaces

​ListOptions

​MatrixOptions

​MultiVectorIndexOptions

​VectorIndexOptions

​VectorOptions

​Type Aliases

​KeywordIndexType

​ListValueType

​MatrixValueType

​MultiVectorDistanceMetric

​MultiVectorQuantization

​VectorDistanceMetric

Functions

binaryVector()

bool()

bytes()

f16Vector()

f32SparseVector()

f32Vector()

f8Vector()

float()

i8Vector()

int()

keywordIndex()

list()

matrix()

multiVectorIndex()

ngramIndex()

semanticIndex()

struct()

text()

timestamp()

u8SparseVector()

u8Vector()

vectorIndex()

Classes

FieldIndex

FieldSpec

Interfaces

ListOptions

MatrixOptions

MultiVectorIndexOptions

VectorIndexOptions

VectorOptions

Type Aliases

KeywordIndexType

ListValueType

MatrixValueType

MultiVectorDistanceMetric

MultiVectorQuantization

VectorDistanceMetric