73 lines
2.8 KiB
JavaScript
73 lines
2.8 KiB
JavaScript
import { HfInference } from "@huggingface/inference";
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import { Embeddings } from "@langchain/core/embeddings";
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import { getEnvironmentVariable } from "@langchain/core/utils/env";
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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 class HuggingFaceInferenceEmbeddings extends Embeddings {
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constructor(fields) {
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super(fields ?? {});
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Object.defineProperty(this, "apiKey", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "model", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "endpointUrl", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "client", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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this.model = fields?.model ?? "BAAI/bge-base-en-v1.5";
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this.apiKey =
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fields?.apiKey ?? getEnvironmentVariable("HUGGINGFACEHUB_API_KEY");
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this.endpointUrl = fields?.endpointUrl;
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this.client = this.endpointUrl
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? new HfInference(this.apiKey).endpoint(this.endpointUrl)
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: new HfInference(this.apiKey);
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}
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async _embed(texts) {
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// replace newlines, which can negatively affect performance.
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const clean = texts.map((text) => text.replace(/\n/g, " "));
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return this.caller.call(() => this.client.featureExtraction({
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model: this.model,
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inputs: clean,
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}));
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}
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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) {
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return this._embed([document]).then((embeddings) => embeddings[0]);
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}
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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) {
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return this._embed(documents);
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}
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}
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