"use strict"; Object.defineProperty(exports, "__esModule", { value: true }); exports.HuggingFaceTransformersEmbeddings = void 0; const embeddings_1 = require("@langchain/core/embeddings"); const chunk_array_1 = require("@langchain/core/utils/chunk_array"); /** * @example * ```typescript * const model = new HuggingFaceTransformersEmbeddings({ * model: "Xenova/all-MiniLM-L6-v2", * }); * * // Embed a single query * const res = await model.embedQuery( * "What would be a good company name for a company that makes colorful socks?" * ); * console.log({ res }); * * // Embed multiple documents * const documentRes = await model.embedDocuments(["Hello world", "Bye bye"]); * console.log({ documentRes }); * ``` */ class HuggingFaceTransformersEmbeddings extends embeddings_1.Embeddings { constructor(fields) { super(fields ?? {}); Object.defineProperty(this, "modelName", { enumerable: true, configurable: true, writable: true, value: "Xenova/all-MiniLM-L6-v2" }); Object.defineProperty(this, "model", { enumerable: true, configurable: true, writable: true, value: "Xenova/all-MiniLM-L6-v2" }); Object.defineProperty(this, "batchSize", { enumerable: true, configurable: true, writable: true, value: 512 }); Object.defineProperty(this, "stripNewLines", { enumerable: true, configurable: true, writable: true, value: true }); Object.defineProperty(this, "timeout", { enumerable: true, configurable: true, writable: true, value: void 0 }); Object.defineProperty(this, "pretrainedOptions", { enumerable: true, configurable: true, writable: true, value: void 0 }); Object.defineProperty(this, "pipelineOptions", { enumerable: true, configurable: true, writable: true, value: void 0 }); Object.defineProperty(this, "pipelinePromise", { enumerable: true, configurable: true, writable: true, value: void 0 }); this.modelName = fields?.model ?? fields?.modelName ?? this.model; this.model = this.modelName; this.stripNewLines = fields?.stripNewLines ?? this.stripNewLines; this.timeout = fields?.timeout; this.pretrainedOptions = fields?.pretrainedOptions ?? {}; this.pipelineOptions = { pooling: "mean", normalize: true, ...fields?.pipelineOptions, }; } async embedDocuments(texts) { const batches = (0, chunk_array_1.chunkArray)(this.stripNewLines ? texts.map((t) => t.replace(/\n/g, " ")) : texts, this.batchSize); const batchRequests = batches.map((batch) => this.runEmbedding(batch)); const batchResponses = await Promise.all(batchRequests); const embeddings = []; for (let i = 0; i < batchResponses.length; i += 1) { const batchResponse = batchResponses[i]; for (let j = 0; j < batchResponse.length; j += 1) { embeddings.push(batchResponse[j]); } } return embeddings; } async embedQuery(text) { const data = await this.runEmbedding([ this.stripNewLines ? text.replace(/\n/g, " ") : text, ]); return data[0]; } async runEmbedding(texts) { const pipe = await (this.pipelinePromise ??= (await import("@xenova/transformers")).pipeline("feature-extraction", this.model, this.pretrainedOptions)); return this.caller.call(async () => { const output = await pipe(texts, this.pipelineOptions); return output.tolist(); }); } } exports.HuggingFaceTransformersEmbeddings = HuggingFaceTransformersEmbeddings;