42 lines
1.8 KiB
TypeScript
42 lines
1.8 KiB
TypeScript
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import { HfInference, HfInferenceEndpoint } from "@huggingface/inference";
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import { Embeddings, type EmbeddingsParams } from "@langchain/core/embeddings";
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/**
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* Interface that extends EmbeddingsParams and defines additional
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* parameters specific to the HuggingFaceInferenceEmbeddings class.
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*/
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export interface HuggingFaceInferenceEmbeddingsParams extends EmbeddingsParams {
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apiKey?: string;
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model?: string;
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endpointUrl?: string;
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}
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/**
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* Class that extends the Embeddings class and provides methods for
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* generating embeddings using Hugging Face models through the
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* HuggingFaceInference API.
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*/
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export declare class HuggingFaceInferenceEmbeddings extends Embeddings implements HuggingFaceInferenceEmbeddingsParams {
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apiKey?: string;
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model: string;
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endpointUrl?: string;
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client: HfInference | HfInferenceEndpoint;
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constructor(fields?: HuggingFaceInferenceEmbeddingsParams);
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_embed(texts: string[]): Promise<number[][]>;
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/**
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* Method that takes a document as input and returns a promise that
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* resolves to an embedding for the document. It calls the _embed method
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* with the document as the input and returns the first embedding in the
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* resulting array.
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* @param document Document to generate an embedding for.
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* @returns Promise that resolves to an embedding for the document.
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*/
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embedQuery(document: string): Promise<number[]>;
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/**
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* Method that takes an array of documents as input and returns a promise
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* that resolves to a 2D array of embeddings for each document. It calls
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* the _embed method with the documents as the input.
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* @param documents Array of documents to generate embeddings for.
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* @returns Promise that resolves to a 2D array of embeddings for each document.
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*/
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embedDocuments(documents: string[]): Promise<number[][]>;
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}
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