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topk-js / schema

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Functions

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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
ParameterType
optionsVectorOptions
Returns FieldSpec

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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

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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

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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

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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
ParameterType
optionsVectorOptions
Returns FieldSpec

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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

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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
ParameterType
optionsVectorOptions
Returns FieldSpec

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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

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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

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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
ParameterType
optionsListOptions
Returns FieldSpec

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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
ParameterType
optionsMatrixOptions
Returns FieldSpec

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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
ParameterType
optionsMultiVectorIndexOptions
Returns FieldIndex

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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
ParameterType
options?SemanticIndexOptions
Returns FieldIndex

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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

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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
ParameterType
optionsVectorOptions
Returns FieldSpec

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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
ParameterType
optionsVectorIndexOptions
Returns FieldIndex

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Classes

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FieldIndex

Internal

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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
ParameterType
indexFieldIndex
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

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Interfaces

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ListOptions

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

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MatrixOptions

Options for matrix field specifications. This struct contains configuration options for matrix fields, including the dimension and value type. Properties
PropertyTypeDescription
dimensionnumberThe dimension (number of columns) of the matrix
valueTypeMatrixValueTypeThe value type of the matrix elements

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MultiVectorIndexOptions

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

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SemanticIndexOptions

Options for semantic index specifications. This struct contains configuration options for semantic indexes, including the model and embedding type to use. Properties
PropertyTypeDescription
embeddingType?EmbeddingDataTypeThe type of embedding data
model?stringThe embedding model to use

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VectorIndexOptions

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

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VectorOptions

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

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Type Aliases

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EmbeddingDataType

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

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KeywordIndexType

type KeywordIndexType = "text";

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ListValueType

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

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MatrixValueType

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

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MultiVectorDistanceMetric

type MultiVectorDistanceMetric = "maxsim";

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VectorDistanceMetric

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