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@lancedb/lancedb / embedding / EmbeddingFunction

Class: abstract EmbeddingFunction<T, M>

An embedding function that automatically creates vector representation for a given column.

It's important subclasses pass the original options to the super constructor and then pass those options to resolveVariables to resolve any variables before using them.

Example

class MyEmbeddingFunction extends EmbeddingFunction {
  constructor(options: {model: string, timeout: number}) {
    super(optionsRaw);
    const options = this.resolveVariables(optionsRaw);
    this.model = options.model;
    this.timeout = options.timeout;
  }
}

Extended by

Type Parameters

• T = any

• M extends FunctionOptions = FunctionOptions

Constructors

new EmbeddingFunction()

new EmbeddingFunction<T, M>(): EmbeddingFunction<T, M>

Returns

EmbeddingFunction<T, M>

Methods

computeQueryEmbeddings()

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

Compute the embeddings for a single query

Parameters

  • data: T

Returns

Promise<number[] | Float32Array | Float64Array>

computeSourceEmbeddings()

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

Creates a vector representation for the given values.

Parameters

  • data: T[]

Returns

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

embeddingDataType()

abstract embeddingDataType(): Float<Floats>

The datatype of the embeddings

Returns

Float<Floats>

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[]

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>

ndims()

ndims(): undefined | number

The number of dimensions of the embeddings

Returns

undefined | number

resolveVariables()

protected resolveVariables(config): Partial<M>

Apply variables to the config.

Parameters

  • config: Partial<M>

Returns

Partial<M>

sourceField()

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

sourceField 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 or the datatype

Returns

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

See

LanceSchema

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>

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