Prerequisites and Environment Setup
- Ensure you have Node.js installed on your machine. This is essential for running any JavaScript-based server-side application like those built with Express.js.
- Install npm (Node Package Manager) if it's not already installed, as it will be required to install the necessary libraries for integrating the Salesforce Einstein Vision API.
- Make sure you have the necessary API keys and tokens. These will allow your application to authenticate with the Salesforce APIs.
Install Required Libraries
- You will need the 'axios' library for making HTTP requests from Node.js to the Salesforce API. You can install it using the following command:
npm install axios
- Additionally, you may need 'dotenv' for managing environment variables safely, which can be useful for handling your Salesforce API credentials.
npm install dotenv
Authenticate with Salesforce API
- Use OAuth 2.0 for secure authentication. First, create a function that can handle the authentication process:
require('dotenv').config();
const axios = require('axios');
async function getAuthToken() {
try {
const response = await axios.post('https://login.salesforce.com/services/oauth2/token', null, {
params: {
grant_type: 'password',
client_id: process.env.CLIENT_ID,
client_secret: process.env.CLIENT_SECRET,
username: process.env.USERNAME,
password: process.env.PASSWORD + process.env.SECURITY_TOKEN
}
});
return response.data.access_token;
} catch (error) {
console.error('Error fetching auth token:', error);
}
}
- Do not forget to substitute the environment variable placeholders with your actual credentials in a `.env` file.
Integrate Einstein Vision API
- After authentication, you can use the API to process images. Create a function to analyze an image with the Einstein Vision API:
async function analyzeImage(imagePath) {
const authToken = await getAuthToken();
const imageData = fs.readFileSync(imagePath, { encoding: 'base64' });
try {
const response = await axios.post('https://api.einstein.ai/v2/vision/classify', {
modelId: 'GeneralImageClassifier',
sampleBase64Content: imageData
}, {
headers: {
'Authorization': `Bearer ${authToken}`,
'Content-Type': 'application/json'
}
});
console.log('Classification result:', response.data);
} catch (error) {
console.error('Einstein Vision API error:', error);
}
}
- In this function, you read the image file and convert it into a Base64 string, which is then sent to the API with the necessary parameters.
Execute the Image Analysis Function
- Create a simple command-line interface or trigger point in your application to run this code. This could be as simple as:
// Make sure to replace 'path/to/your/image.jpg' with the actual image path
analyzeImage('path/to/your/image.jpg');
- This setup should allow you to pass the images you want to analyze directly, assuming the rest of your application is structured to support this function.
Handle and Store API Responses
- Once you receive a response, you'll typically want to process or store the results somehow. Consider adding functionality to save the results to a database or file for further analysis.
- For example, using a JSON database or even a text file can be a simple way of keeping track of classification results if a full database is overkill for your application.
Best Practices and Considerations
- Ensure that your application handles any API errors gracefully. Consider implementing retries or fallback logic if particular requests fail.
- Security of API Keys: Always store API keys and secrets securely. Using environment variables and tools like 'dotenv' is a good practice.
- Optimize your application for asynchronous execution as the Node.js event loop thrives on non-blocking I/O tasks. Adequate use of async/await will enhance performance.
