@lancedb/lancedb • Docs @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 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 embeddingDataType() embeddingDataType(): Float<Floats> The datatype of the embeddings Returns Float<Floats> Overrides EmbeddingFunction.embeddingDataType generateEmbeddings() abstract generateEmbeddings(texts, ...args): Promise<number[][] | Float32Array[] | Float64Array[]> Parameters texts: string[] ...args: any[] Returns Promise<number[][] | Float32Array[] | Float64Array[]> 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 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 ndims() ndims(): undefined | number The number of dimensions of the embeddings Returns undefined | number Inherited from EmbeddingFunction.ndims resolveVariables() protected resolveVariables(config): Partial<M> Apply variables to the config. Parameters config: Partial<M> Returns Partial<M> Inherited from EmbeddingFunction.resolveVariables 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 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 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