114 lines
4 KiB
JavaScript
114 lines
4 KiB
JavaScript
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"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.HuggingFaceTransformersEmbeddings = void 0;
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const embeddings_1 = require("@langchain/core/embeddings");
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const chunk_array_1 = require("@langchain/core/utils/chunk_array");
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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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class HuggingFaceTransformersEmbeddings extends embeddings_1.Embeddings {
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constructor(fields) {
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super(fields ?? {});
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Object.defineProperty(this, "modelName", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: "Xenova/all-MiniLM-L6-v2"
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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: "Xenova/all-MiniLM-L6-v2"
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});
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Object.defineProperty(this, "batchSize", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: 512
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});
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Object.defineProperty(this, "stripNewLines", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: true
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});
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Object.defineProperty(this, "timeout", {
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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, "pretrainedOptions", {
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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, "pipelineOptions", {
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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, "pipelinePromise", {
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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.modelName = fields?.model ?? fields?.modelName ?? this.model;
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this.model = this.modelName;
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this.stripNewLines = fields?.stripNewLines ?? this.stripNewLines;
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this.timeout = fields?.timeout;
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this.pretrainedOptions = fields?.pretrainedOptions ?? {};
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this.pipelineOptions = {
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pooling: "mean",
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normalize: true,
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...fields?.pipelineOptions,
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};
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}
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async embedDocuments(texts) {
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const batches = (0, chunk_array_1.chunkArray)(this.stripNewLines ? texts.map((t) => t.replace(/\n/g, " ")) : texts, this.batchSize);
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const batchRequests = batches.map((batch) => this.runEmbedding(batch));
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const batchResponses = await Promise.all(batchRequests);
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const embeddings = [];
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for (let i = 0; i < batchResponses.length; i += 1) {
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const batchResponse = batchResponses[i];
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for (let j = 0; j < batchResponse.length; j += 1) {
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embeddings.push(batchResponse[j]);
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}
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}
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return embeddings;
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}
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async embedQuery(text) {
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const data = await this.runEmbedding([
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this.stripNewLines ? text.replace(/\n/g, " ") : text,
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]);
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return data[0];
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}
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async runEmbedding(texts) {
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const pipe = await (this.pipelinePromise ??= (await import("@xenova/transformers")).pipeline("feature-extraction", this.model, this.pretrainedOptions));
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return this.caller.call(async () => {
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const output = await pipe(texts, this.pipelineOptions);
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return output.tolist();
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});
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}
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}
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exports.HuggingFaceTransformersEmbeddings = HuggingFaceTransformersEmbeddings;
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