@langchain/community/dist/embeddings/tensorflow.cjs

Third-party integrations for LangChain.js

Object.defineProperty(exports, Symbol.toStringTag, { value: "Module" });
const require_runtime = require("../_virtual/_rolldown/runtime.cjs");
let _langchain_core_embeddings = require("@langchain/core/embeddings");
let _tensorflow_models_universal_sentence_encoder = require("@tensorflow-models/universal-sentence-encoder");
let _tensorflow_tfjs_core = require("@tensorflow/tfjs-core");
_tensorflow_tfjs_core = require_runtime.__toESM(_tensorflow_tfjs_core);
//#region src/embeddings/tensorflow.ts
var tensorflow_exports = /* @__PURE__ */ require_runtime.__exportAll({ TensorFlowEmbeddings: () => TensorFlowEmbeddings });
/**
* Class that extends the Embeddings class and provides methods for
* generating embeddings using the Universal Sentence Encoder model from
* TensorFlow.js.
* @example
* ```typescript
* const embeddings = new TensorFlowEmbeddings();
* const store = new MemoryVectorStore(embeddings);
*
* const documents = [
*   "A document",
*   "Some other piece of text",
*   "One more",
*   "And another",
* ];
*
* await store.addDocuments(
*   documents.map((pageContent) => new Document({ pageContent }))
* );
* ```
*/
var TensorFlowEmbeddings = class extends _langchain_core_embeddings.Embeddings {
	constructor(fields) {
		super(fields ?? {});
		try {
			_tensorflow_tfjs_core.backend();
		} catch {
			throw new Error("No TensorFlow backend found, see instructions at ...");
		}
	}
	_cached;
	/**
	* Private method that loads the Universal Sentence Encoder model if it
	* hasn't been loaded already. It returns a promise that resolves to the
	* loaded model.
	* @returns Promise that resolves to the loaded Universal Sentence Encoder model.
	*/
	async load() {
		if (this._cached === void 0) this._cached = (0, _tensorflow_models_universal_sentence_encoder.load)();
		return this._cached;
	}
	_embed(texts) {
		return this.caller.call(async () => {
			return (await this.load()).embed(texts);
		});
	}
	/**
	* Method that takes a document as input and returns a promise that
	* resolves to an embedding for the document. It calls the _embed method
	* with the document as the input and processes the result to return a
	* single embedding.
	* @param document Document to generate an embedding for.
	* @returns Promise that resolves to an embedding for the input document.
	*/
	embedQuery(document) {
		return this._embed([document]).then((embeddings) => embeddings.array()).then((embeddings) => embeddings[0]);
	}
	/**
	* Method that takes an array of documents as input and returns a promise
	* that resolves to a 2D array of embeddings for each document. It calls
	* the _embed method with the documents as the input and processes the
	* result to return the embeddings.
	* @param documents Array of documents to generate embeddings for.
	* @returns Promise that resolves to a 2D array of embeddings for each input document.
	*/
	embedDocuments(documents) {
		return this._embed(documents).then((embeddings) => embeddings.array());
	}
};
//#endregion
exports.TensorFlowEmbeddings = TensorFlowEmbeddings;
Object.defineProperty(exports, "tensorflow_exports", {
	enumerable: true,
	get: function() {
		return tensorflow_exports;
	}
});

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