65 lines
2 KiB
TypeScript
65 lines
2 KiB
TypeScript
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import type { PretrainedOptions, FeatureExtractionPipelineOptions } from "@xenova/transformers";
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import { Embeddings, type EmbeddingsParams } from "@langchain/core/embeddings";
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export interface HuggingFaceTransformersEmbeddingsParams extends EmbeddingsParams {
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/**
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* Model name to use
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* Alias for `model`
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*/
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modelName: string;
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/** Model name to use */
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model: string;
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/**
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* Timeout to use when making requests to OpenAI.
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*/
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timeout?: number;
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/**
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* The maximum number of documents to embed in a single request.
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*/
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batchSize?: number;
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/**
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* Whether to strip new lines from the input text. This is recommended by
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* OpenAI, but may not be suitable for all use cases.
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*/
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stripNewLines?: boolean;
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/**
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* Optional parameters for the pretrained model.
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*/
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pretrainedOptions?: PretrainedOptions;
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/**
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* Optional parameters for the pipeline.
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*/
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pipelineOptions?: FeatureExtractionPipelineOptions;
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}
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/**
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* @example
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* ```typescript
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* const model = new HuggingFaceTransformersEmbeddings({
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* model: "Xenova/all-MiniLM-L6-v2",
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* });
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*
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* // Embed a single query
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* const res = await model.embedQuery(
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* "What would be a good company name for a company that makes colorful socks?"
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* );
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* console.log({ res });
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*
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* // Embed multiple documents
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* const documentRes = await model.embedDocuments(["Hello world", "Bye bye"]);
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* console.log({ documentRes });
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* ```
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*/
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export declare class HuggingFaceTransformersEmbeddings extends Embeddings implements HuggingFaceTransformersEmbeddingsParams {
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modelName: string;
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model: string;
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batchSize: number;
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stripNewLines: boolean;
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timeout?: number;
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pretrainedOptions?: PretrainedOptions;
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pipelineOptions?: FeatureExtractionPipelineOptions;
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private pipelinePromise;
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constructor(fields?: Partial<HuggingFaceTransformersEmbeddingsParams>);
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embedDocuments(texts: string[]): Promise<number[][]>;
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embedQuery(text: string): Promise<number[]>;
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private runEmbedding;
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}
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