137 lines
5 KiB
JavaScript
137 lines
5 KiB
JavaScript
"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.FireworksEmbeddings = void 0;
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const env_1 = require("@langchain/core/utils/env");
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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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* A class for generating embeddings using the Fireworks AI API.
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*/
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class FireworksEmbeddings extends embeddings_1.Embeddings {
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/**
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* Constructor for the FireworksEmbeddings class.
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* @param fields - An optional object with properties to configure the instance.
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*/
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constructor(fields) {
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const fieldsWithDefaults = { ...fields };
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super(fieldsWithDefaults);
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/**
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* @deprecated Use `model` instead.
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*/
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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: "nomic-ai/nomic-embed-text-v1.5"
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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: "nomic-ai/nomic-embed-text-v1.5"
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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: 8
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});
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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, "basePath", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: "https://api.fireworks.ai/inference/v1"
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});
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Object.defineProperty(this, "apiUrl", {
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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, "headers", {
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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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const apiKey = fieldsWithDefaults?.apiKey || (0, env_1.getEnvironmentVariable)("FIREWORKS_API_KEY");
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if (!apiKey) {
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throw new Error("Fireworks AI API key not found");
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}
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this.model = fieldsWithDefaults?.model ?? this.model;
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this.modelName = this.model;
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this.batchSize = fieldsWithDefaults?.batchSize ?? this.batchSize;
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this.apiKey = apiKey;
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this.apiUrl = `${this.basePath}/embeddings`;
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}
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/**
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* Generates embeddings for an array of texts.
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* @param texts - An array of strings to generate embeddings for.
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* @returns A Promise that resolves to an array of embeddings.
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*/
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async embedDocuments(texts) {
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const batches = (0, chunk_array_1.chunkArray)(texts, this.batchSize);
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const batchRequests = batches.map((batch) => this.embeddingWithRetry({
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model: this.model,
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input: batch,
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}));
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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 batch = batches[i];
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const { data: batchResponse } = batchResponses[i];
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for (let j = 0; j < batch.length; j += 1) {
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embeddings.push(batchResponse[j].embedding);
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}
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}
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return embeddings;
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}
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/**
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* Generates an embedding for a single text.
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* @param text - A string to generate an embedding for.
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* @returns A Promise that resolves to an array of numbers representing the embedding.
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*/
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async embedQuery(text) {
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const { data } = await this.embeddingWithRetry({
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model: this.model,
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input: text,
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});
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return data[0].embedding;
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}
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/**
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* Makes a request to the Fireworks AI API to generate embeddings for an array of texts.
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* @param request - An object with properties to configure the request.
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* @returns A Promise that resolves to the response from the Fireworks AI API.
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*/
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async embeddingWithRetry(request) {
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const makeCompletionRequest = async () => {
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const url = `${this.apiUrl}`;
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const response = await fetch(url, {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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Authorization: `Bearer ${this.apiKey}`,
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...this.headers,
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},
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body: JSON.stringify(request),
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});
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if (!response.ok) {
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const { error: message } = await response.json();
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const error = new Error(`Error ${response.status}: ${message ?? "Unspecified error"}`);
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// eslint-disable-next-line @typescript-eslint/no-explicit-any
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error.response = response;
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throw error;
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}
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const json = await response.json();
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return json;
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};
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return this.caller.call(makeCompletionRequest);
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}
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}
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exports.FireworksEmbeddings = FireworksEmbeddings;
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