@langchain/community/dist/embeddings/hf.d.cts.map

Third-party integrations for LangChain.js

{"version":3,"file":"hf.d.cts","names":["InferenceClient","InferenceProviderOrPolicy","Embeddings","EmbeddingsParams","HuggingFaceInferenceEmbeddingsParams","HuggingFaceInferenceEmbeddings","Promise"],"sources":["../../src/embeddings/hf.d.ts"],"sourcesContent":["import { InferenceClient, InferenceProviderOrPolicy } from \"@huggingface/inference\";\nimport { Embeddings, type EmbeddingsParams } from \"@langchain/core/embeddings\";\n/**\n * Interface that extends EmbeddingsParams and defines additional\n * parameters specific to the HuggingFaceInferenceEmbeddings class.\n */\nexport interface HuggingFaceInferenceEmbeddingsParams extends EmbeddingsParams {\n    apiKey?: string;\n    model?: string;\n    endpointUrl?: string;\n    provider?: InferenceProviderOrPolicy;\n}\n/**\n * Class that extends the Embeddings class and provides methods for\n * generating embeddings using Hugging Face models through the\n * HuggingFaceInference API.\n */\nexport declare class HuggingFaceInferenceEmbeddings extends Embeddings implements HuggingFaceInferenceEmbeddingsParams {\n    apiKey?: string;\n    model: string;\n    endpointUrl?: string;\n    provider?: InferenceProviderOrPolicy;\n    client: InferenceClient;\n    constructor(fields?: HuggingFaceInferenceEmbeddingsParams);\n    _embed(texts: string[]): Promise<number[][]>;\n    /**\n     * Method that takes a document as input and returns a promise that\n     * resolves to an embedding for the document. It calls the _embed method\n     * with the document as the input and returns the first embedding in the\n     * resulting array.\n     * @param document Document to generate an embedding for.\n     * @returns Promise that resolves to an embedding for the document.\n     */\n    embedQuery(document: string): Promise<number[]>;\n    /**\n     * Method that takes an array of documents as input and returns a promise\n     * that resolves to a 2D array of embeddings for each document. It calls\n     * the _embed method with the documents as the input.\n     * @param documents Array of documents to generate embeddings for.\n     * @returns Promise that resolves to a 2D array of embeddings for each document.\n     */\n    embedDocuments(documents: string[]): Promise<number[][]>;\n}\n//# sourceMappingURL=hf.d.ts.map"],"mappings":";;;;;;;AAMA;AAWA;AAIeC,UAfEG,oCAAAA,SAA6CD,gBAe/CF,CAAAA;EACHD,MAAAA,CAAAA,EAAAA,MAAAA;EACaI,KAAAA,CAAAA,EAAAA,MAAAA;EACIE,WAAAA,CAAAA,EAAAA,MAAAA;EASKA,QAAAA,CAAAA,EAvBnBL,yBAuBmBK;;;;AAhBoF;;;cAAjGD,8BAAAA,SAAuCH,UAAAA,YAAsBE;;;;aAInEH;UACHD;uBACaI;2BACIE;;;;;;;;;gCASKA;;;;;;;;uCAQOA"}