Getting Started with Reveal SDK AI - HTML/JavaScript

This guide will walk you through creating your first AI-powered analytics application using vanilla HTML and JavaScript. You'll build a simple application that generates AI insights from dashboard data.

Time to complete: 15-20 minutes

What You'll Build

A web application that:

• Displays a Reveal dashboard
• Adds AI-powered context menu items to the dashboard
• Generates AI insights (Summary, Analysis, and Forecast)

Prerequisites

Before you begin, ensure you meet the System Requirements and have:

1. Reveal SDK Server installed and configured
2. Reveal SDK AI Server installed
3. LLM Provider API Key from OpenAI, Anthropic, or Google Cloud

Other server platforms

This guide uses ASP.NET Core for the server. The Reveal SDK AI Server is also available for Node.js and Java — see Install Server SDK for the equivalent installation and configuration steps. The HTML/JavaScript client below is identical regardless of which server you choose.

Step 1: Create the ASP.NET Core Server

1.1 Create a New ASP.NET Core Web API Project

Open a terminal and run:

dotnet new webapi -n RevealAiServer
cd RevealAiServer

1.2 Install the AI NuGet Package

Install the Reveal AI package (this automatically includes Reveal.Sdk.AspNetCore):

dotnet add package Reveal.Sdk.AI.AspNetCore

1.3 Configure the Server

Open Program.cs and replace its contents with:

Program.cs

using Reveal.Sdk;
using Reveal.Sdk.AI;
using RevealAiServer.Reveal;

var builder = WebApplication.CreateBuilder(args);

// Add CORS for local development
builder.Services.AddCors(options =>
{
    options.AddPolicy("AllowAll",
        policy => policy.AllowAnyOrigin()
                       .AllowAnyHeader()
                       .AllowAnyMethod());
});

// Add Reveal SDK with data source provider
builder.Services.AddControllers().AddReveal(builder =>
{
    builder.AddDataSourceProvider<DataSourceProvider>();
});

// Add Reveal AI with OpenAI provider
builder.Services.AddRevealAI()
    .AddOpenAI(options =>
    {
        options.ApiKey = builder.Configuration["RevealAI:OpenAI:ApiKey"];
        options.ModelId = "gpt-4.1";
    });

var app = builder.Build();

app.UseCors("AllowAll");
app.MapControllers();

app.Run();

1.4 Configure Your API Key

Option A: Using appsettings.json (not recommended for production)

Create or modify appsettings.json:

appsettings.json

{
  "Logging": {
    "LogLevel": {
      "Default": "Information",
      "Microsoft.AspNetCore": "Warning"
    }
  },
  "AllowedHosts": "*",
  "RevealAI": {
    "OpenAI": {
      "ApiKey": "sk-your-openai-api-key-here"
    }
  }
}

Option B: Using User Secrets (recommended for development)

dotnet user-secrets init
dotnet user-secrets set "RevealAI:OpenAI:ApiKey" "sk-your-openai-api-key-here"

Never Commit API Keys

Never commit API keys to source control. Always use User Secrets, environment variables, or a secure key management service.

1.5 Create a DataSource Provider

The Reveal SDK requires a data source provider. For this sample, we'll create a minimal one that simply returns data sources unchanged.

Create a new folder Reveal and add DataSourceProvider.cs:

Reveal/DataSourceProvider.cs

using Reveal.Sdk.Data;

namespace RevealAiServer.Reveal;

public class DataSourceProvider : IRVDataSourceProvider
{
    public Task<RVDataSourceItem> ChangeDataSourceItemAsync(
        IRVUserContext userContext,
        string dashboardId,
        RVDataSourceItem dataSourceItem)
    {
        return Task.FromResult(dataSourceItem);
    }

    public Task<RVDashboardDataSource> ChangeDataSourceAsync(
        IRVUserContext userContext,
        RVDashboardDataSource dataSource)
    {
        return Task.FromResult(dataSource);
    }
}

1.6 Add the Sample Dashboard and Data

Create the necessary folders in your project root:

mkdir Dashboards
mkdir Data

Download the required files and place them in the correct folders:

1. Dashboard file → Save to Dashboards/ folder:

• Download: Accounts.rdash
• Location: Dashboards/Accounts.rdash

2. Data file → Save to Data/ folder:

• Download: NorthwindInvoices.xlsx
• Location: Data/NorthwindInvoices.xlsx

Your project structure should look like:

RevealAiServer/
├── Dashboards/
│   └── Accounts.rdash
├── Data/
│   └── NorthwindInvoices.xlsx
├── Reveal/
│   └── DataSourceProvider.cs
└── Program.cs

1.7 Run the Server

dotnet run

Your server should start at https://localhost:5111 (or similar). Note the URL - you'll need it for the client.

Step 2: Create the HTML Client

2.1 Create index.html

Create a new file index.html in your project root (or a separate client folder):

index.html

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Reveal AI - Insights Demo</title>
    <style>
        body { margin: 0; font-family: Arial, sans-serif; }
        .container { display: flex; height: 100vh; gap: 10px; padding: 10px; box-sizing: border-box; }
        #revealView { flex: 1; }
        #output { flex: 0 0 400px; padding: 20px; overflow-y: auto; border: 1px solid #ddd;
                  border-radius: 4px; white-space: pre-wrap; font-size: 14px; line-height: 1.6; }
    </style>
</head>
<body>
    <div class="container">
        <div id="revealView"></div>
        <div id="output">Use the dashboard overflow menu and select an AI insight option.</div>
    </div>

    <script src="https://cdn.jsdelivr.net/npm/jquery@3.6.0/dist/jquery.min.js"></script>
    <script src="https://dl.revealbi.io/reveal/libs/1.8.3/infragistics.reveal.js"></script>
    <script src="https://cdn.jsdelivr.net/npm/@revealbi/api@0.0.1-preview.2/dist/index.umd.min.js"></script>

    <script>
        const SERVER_URL = 'https://localhost:5111/';

        // Initialize Reveal SDK and AI Client
        RevealSdkSettings.setBaseUrl(SERVER_URL);
        rv.RevealSdkClient.initialize({ hostUrl: SERVER_URL });
        const client = rv.RevealSdkClient.getInstance();

        // Load dashboard
        RVDashboard.loadDashboard("Accounts").then(dashboard => {
            const revealView = new RevealView("#revealView");
            revealView.dashboard = dashboard;

            // Add AI insight options to the dashboard context menu
            revealView.onMenuOpening = function (visualization, args) {
                if (args.menuLocation === RVMenuLocation.Dashboard) {
                    args.menuItems.push(new RVMenuItem("Summary", null, async () => {
                        document.getElementById('output').textContent = 'Generating summary...';
                        const result = await client.ai.insights.get({
                            dashboard: dashboard,
                            insightType: rv.InsightType.Summary,
                        });
                        document.getElementById('output').textContent = result.explanation;
                    }));

                    args.menuItems.push(new RVMenuItem("Analysis", null, async () => {
                        document.getElementById('output').textContent = 'Generating analysis...';
                        const result = await client.ai.insights.get({
                            dashboard: dashboard,
                            insightType: rv.InsightType.Analysis,
                        });
                        document.getElementById('output').textContent = result.explanation;
                    }));

                    args.menuItems.push(new RVMenuItem("Forecast", null, async () => {
                        document.getElementById('output').textContent = 'Generating forecast...';
                        const result = await client.ai.insights.get({
                            dashboard: dashboard,
                            insightType: rv.InsightType.Forecast,
                        });
                        document.getElementById('output').textContent = result.explanation;
                    }));
                }
            };
        });
    </script>
</body>
</html>

Step 3: Run the Application

3.1 Start the Server

If not already running:

dotnet run

3.2 Open the Client

Open index.html in your web browser. You can:

• Use VS Code Live Server extension
• Simply double-click the file

3.3 Test the AI Insights

1. Wait for the dashboard to load
2. Click the dashboard overflow menu (kebab icon)
3. Select Summary, Analysis, or Forecast
4. The AI-generated insight appears in the right panel

From here, explore the Using the SDK section to learn about streaming responses, visualization-level insights, chat, and more.