@lancedb/lancedb • Docs

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@lancedb/lancedb / embedding / TextEmbeddingFunction

Class: abstract TextEmbeddingFunction<M>

an abstract class for implementing embedding functions that take text as input

Extends

• EmbeddingFunction<string, M>

Type Parameters

• M extends FunctionOptions = FunctionOptions

Constructors

new TextEmbeddingFunction()

new TextEmbeddingFunction<M>(): TextEmbeddingFunction<M>

Returns

TextEmbeddingFunction<M>

Inherited from

EmbeddingFunction.constructor

Methods

computeQueryEmbeddings()

computeQueryEmbeddings(data): Promise<number[] | Float32Array | Float64Array>

Compute the embeddings for a single query

Parameters

• data: string

Returns

Promise<number[] | Float32Array | Float64Array>

Overrides

EmbeddingFunction.computeQueryEmbeddings

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computeSourceEmbeddings()

computeSourceEmbeddings(data): Promise<number[][] | Float32Array[] | Float64Array[]>

Creates a vector representation for the given values.

Parameters

• data: string[]

Returns

Promise<number[][] | Float32Array[] | Float64Array[]>

Overrides

EmbeddingFunction.computeSourceEmbeddings

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embeddingDataType()

embeddingDataType(): Float<Floats>

The datatype of the embeddings

Returns

Float<Floats>

Overrides

EmbeddingFunction.embeddingDataType

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generateEmbeddings()

abstract generateEmbeddings(texts, ...args): Promise<number[][] | Float32Array[] | Float64Array[]>

Parameters

• texts: string[]

• ...args: any[]

Returns

Promise<number[][] | Float32Array[] | Float64Array[]>

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getSensitiveKeys()

protected getSensitiveKeys(): string[]

Provide a list of keys in the function options that should be treated as sensitive. If users pass raw values for these keys, they will be rejected.

Returns

string[]

Inherited from

EmbeddingFunction.getSensitiveKeys

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init()?

optional init(): Promise<void>

Optionally load any resources needed for the embedding function.

This method is called after the embedding function has been initialized but before any embeddings are computed. It is useful for loading local models or other resources that are needed for the embedding function to work.

Returns

Promise<void>

Inherited from

EmbeddingFunction.init

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ndims()

ndims(): undefined | number

The number of dimensions of the embeddings

Returns

undefined | number

Inherited from

EmbeddingFunction.ndims

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resolveVariables()

protected resolveVariables(config): Partial<M>

Apply variables to the config.

Parameters

• config: Partial<M>

Returns

Partial<M>

Inherited from

EmbeddingFunction.resolveVariables

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sourceField()

sourceField(): [DataType<Type, any>, Map<string, EmbeddingFunction<any, FunctionOptions>>]

sourceField is used in combination with LanceSchema to provide a declarative data model

Returns

[DataType<Type, any>, Map<string, EmbeddingFunction<any, FunctionOptions>>]

See

LanceSchema

Overrides

EmbeddingFunction.sourceField

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toJSON()

toJSON(): Record<string, any>

Get the original arguments to the constructor, to serialize them so they can be used to recreate the embedding function later.

Returns

Record<string, any>

Inherited from

EmbeddingFunction.toJSON

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vectorField()

vectorField(optionsOrDatatype?): [DataType<Type, any>, Map<string, EmbeddingFunction<any, FunctionOptions>>]

vectorField is used in combination with LanceSchema to provide a declarative data model

Parameters

• optionsOrDatatype?: DataType<Type, any> | Partial<FieldOptions<DataType<Type, any>>> The options for the field

Returns

[DataType<Type, any>, Map<string, EmbeddingFunction<any, FunctionOptions>>]

See

LanceSchema

Inherited from

EmbeddingFunction.vectorField