topk-js/schema

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

---

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

---

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(): FieldIndex;

Creates a FieldIndex type for keyword_index values.

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.

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

---

semanticIndex()

function semanticIndex(options?: SemanticIndexOptions): 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({ model: "cohere/embed-v4" }))
});

Parameters:

• model: Embedding model to use for semantic search. Currently supported:
  • cohere/embed-english-v3
  • cohere/embed-multilingual-v3
  • cohere/embed-v4 (default)
• embeddingType: TopK supports the following embedding types for Cohere models:
  • float32
  • uint8
  • binary

Parameters

Parameter	Type
options?	SemanticIndexOptions

Returns

FieldIndex

---

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

---

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

---

SemanticIndexOptions

Options for semantic index specifications.

This struct contains configuration options for semantic indexes, including the model and embedding type to use.

Properties

Property	Type	Description
embeddingType?	EmbeddingDataType	The type of embedding data
model?	string	The embedding model to use

---

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

EmbeddingDataType

type EmbeddingDataType = "float32" | "uint8" | "binary";

---

KeywordIndexType

type KeywordIndexType = "text";

---

ListValueType

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

---

VectorDistanceMetric

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