anything-llm/server/utils/EmbeddingEngines/azureOpenAi/index.js

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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;
// Limit of how many strings we can process in a single pass to stay with resource or network limits
// 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.maxConcurrentChunks = 16;
this.embeddingMaxChunkLength = 1_000;
}
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 maxConcurrentChunks for more info.
const embeddingRequests = [];
for (const chunk of toChunks(textChunks, this.maxConcurrentChunks)) {
embeddingRequests.push(
new Promise((resolve) => {
this.openai
.getEmbeddings(textEmbeddingModel, chunk)
.then((res) => {
resolve({ data: res.data, error: null });
})
.catch((e) => {
e.type =
e?.response?.data?.error?.code ||
e?.response?.status ||
"failed_to_embed";
e.message = e?.response?.data?.error?.message || e.message;
resolve({ data: [], error: e });
});
})
);
}
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) {
let uniqueErrors = new Set();
errors.map((error) =>
uniqueErrors.add(`[${error.type}]: ${error.message}`)
);
return {
data: [],
error: Array.from(uniqueErrors).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,
};