AI Assist Node.js Server Setup in ASP.NET Core Spreadsheet

18 Jun 20267 minutes to read

AI Assist requires a backend service to process prompts and return AI-generated responses. This topic explains how to create a Node.js server with Azure OpenAI credentials.

Prerequisites

Ensure the following are available before you begin.

Azure OpenAI credentials

You must have an Azure OpenAI resource. Collect these values from the Azure Portal:

Credential Description
API Key Azure OpenAI service key
Endpoint Base URL of your Azure OpenAI resource (e.g., https://your-resource.openai.azure.com/)
API Version REST API version (e.g., 2024-02-01)
Deployment Name Model deployment name (e.g., gpt-4o)

These values correspond to the configuration used in the application:

const azureOpenAIApiKey     = 'Your_Azure_OpenAI_API_Key';
const azureOpenAIEndpoint   = 'Your_Azure_OpenAI_Endpoint';
const azureOpenAIApiVersion = 'Your_Azure_OpenAI_API_Version';
const azureDeploymentName   = 'Your_Deployment_Name';

Runtime environment

  • Node.js v18 or later
  • npm v9 or later

Install dependencies

Run the following command in your server project:

npm install express cors dotenv openai date-fns
Package Purpose
express HTTP server framework
cors Cross-Origin Resource Sharing middleware
dotenv Loads credentials from a .env file
openai Official Azure OpenAI client SDK
date-fns Date formatting for token-reset messages

Ensure your package.json includes "type": "module" to support ES module imports:

{
  "name": "service",
  "version": "1.0.0",
  "type": "module",
  "scripts": {
    "start": "node server.js"
  },
  "dependencies": {
    "cors": "^2.8.5",
    "date-fns": "^4.1.0",
    "dotenv": "^16.4.5",
    "express": "^4.21.0",
    "openai": "4.50.0"
  }
}

Configure credentials

Create a .env file in the project root and add your Azure OpenAI credentials:

apiKey      = Your_Azure_OpenAI_API_Key
endpoint    = https://your-resource.openai.azure.com/
deployment  = Your_Deployment_Name
apiVersion  = Your_Azure_OpenAI_API_Version

Important: Add .env to .gitignore to prevent exposing secrets.

Configure required modules

Create ai-model.js to initialize the Azure OpenAI client using the credentials from .env:

import { AzureOpenAI } from "openai";
import dotenv from 'dotenv';

dotenv.config();

const endpoint   = process.env.endpoint;
const apiKey     = process.env.apiKey;
const deployment = process.env.deployment;
const apiVersion = process.env.apiVersion;

const client = new AzureOpenAI({
    endpoint,
    apiKey,
    apiVersion,
    deployment
});

export async function getAzureChatAIRequest(options) {
    const result = await client.chat.completions.create({
        messages:          options.messages,
        model:             "",
        top_p:             options.topP,
        temperature:       options.temperature,
        max_tokens:        options.maxTokens,
        frequency_penalty: options.frequencyPenalty,
        presence_penalty:  options.presencePenalty,
        stop:              options.stopSequences
    });
    return result;
}

Create server.js to expose the AI Assist API:

import express from 'express';
import cors from 'cors';
import { getAzureChatAIRequest } from './ai-model.js';

const app  = express();
const PORT = process.env.PORT || 3000;

app.use(cors());
app.use(express.json());

app.post('/api/AIAssist/Chat', async (req, res) => {
    const { visitorId, ...chatData } = req.body;
    const responseText = await getAzureChatAIRequest(chatData);
    if (responseText) {
        return res.status(200).json({
            response: responseText.choices[0].message.content
        });
    }
    return res.status(500).json({ error: 'Failed to generate response' });
});

app.listen(PORT, () => {
    console.log(`Server is running on http://localhost:${PORT}`);
});

Run the server

Run the following command to start the server:

npm start

The server runs on http://localhost:3000. Update the AI Assist endpoint like below:

http://localhost:3000/api/AIAssist/Chat

Connect to the ASP.NET Core Spreadsheet

Once the server is listening, configure the requestUrl inside aiAssistSettings to point to the server endpoint:

```cshtml
<ejs-spreadsheet id="spreadsheet" enableAIAssist="true"
    openUrl="https://document.syncfusion.com/web-services/spreadsheet-editor/api/spreadsheet/open"
    saveUrl="https://document.syncfusion.com/web-services/spreadsheet-editor/api/spreadsheet/save">
    <e-spreadsheet-aiassistsettings requestUrl="http://localhost:3000/api/AIAssist/Chat">
    </e-spreadsheet-aiassistsettings>
</ejs-spreadsheet>
```

Reference

Environment variables (.env)

Variable Description
apiKey Your Azure OpenAI API key
endpoint Your Azure OpenAI resource URL
deployment Your model deployment name
apiVersion Azure OpenAI REST API version

Chat endpoint contract

The server accepts a POST request with the following JSON body:

{
  "messages": [
    { "role": "system",    "content": "You are a spreadsheet assistant." },
    { "role": "user",      "content": "Make the header row bold." }
  ]
}

And returns:

{
  "ok": true,
  "response": "..."
}

Sample

A Node.js server sample project is available for quick setup. Extract the archive, update the Azure OpenAI credentials in the .env file, and start the server using the following command

npm start

Download Node.js Server

See also