const { toChunks } = require("../../helpers"); class AzureOpenAiEmbedder { constructor() { const { OpenAIClient, AzureKeyCredential } = require("@azure/openai"); if (!process.env.AZURE_OPENAI_ENDPOINT) throw new Error("No Azure API endpoint was set."); if (!process.env.AZURE_OPENAI_KEY) throw new Error("No Azure API key was set."); const openai = new OpenAIClient( process.env.AZURE_OPENAI_ENDPOINT, new AzureKeyCredential(process.env.AZURE_OPENAI_KEY) ); this.openai = openai; // The maximum amount of "inputs" that OpenAI API can process in a single call. // https://learn.microsoft.com/en-us/azure/ai-services/openai/faq#i-am-trying-to-use-embeddings-and-received-the-error--invalidrequesterror--too-many-inputs--the-max-number-of-inputs-is-1---how-do-i-fix-this-:~:text=consisting%20of%20up%20to%2016%20inputs%20per%20API%20request this.embeddingMaxChunkLength = 16; } async embedTextInput(textInput) { const result = await this.embedChunks(textInput); return result?.[0] || []; } async embedChunks(textChunks = []) { const textEmbeddingModel = process.env.EMBEDDING_MODEL_PREF || "text-embedding-ada-002"; if (!textEmbeddingModel) throw new Error( "No EMBEDDING_MODEL_PREF ENV defined. This must the name of a deployment on your Azure account for an embedding model." ); // Because there is a limit on how many chunks can be sent at once to Azure OpenAI // we concurrently execute each max batch of text chunks possible. // Refer to constructor embeddingMaxChunkLength for more info. const embeddingRequests = []; for (const chunk of toChunks(textChunks, this.embeddingMaxChunkLength)) { embeddingRequests.push( new Promise((resolve) => { this.openai .getEmbeddings(textEmbeddingModel, chunk) .then((res) => { resolve({ data: res.data, error: null }); }) .catch((e) => { resolve({ data: [], error: e?.error }); }); }) ); } const { data = [], error = null } = await Promise.all( embeddingRequests ).then((results) => { // If any errors were returned from Azure abort the entire sequence because the embeddings // will be incomplete. const errors = results .filter((res) => !!res.error) .map((res) => res.error) .flat(); if (errors.length > 0) { return { data: [], error: `(${errors.length}) Embedding Errors! ${errors .map((error) => `[${error.type}]: ${error.message}`) .join(", ")}`, }; } return { data: results.map((res) => res?.data || []).flat(), error: null, }; }); if (!!error) throw new Error(`Azure OpenAI Failed to embed: ${error}`); return data.length > 0 && data.every((embd) => embd.hasOwnProperty("embedding")) ? data.map((embd) => embd.embedding) : null; } } module.exports = { AzureOpenAiEmbedder, };