As a programmer, We do code in many languages. We create always a program named as Hello World.
This blog is all about for Hello World program for Salesforce Einstein. In this blog, I am not going to tell you how does Salesforce Einstein work. This is just about how to get started and write your first program in Salesforce Einstein. I will cover both Salesforce Einstein Vision and Salesforce Einstein Language.
A. Get the key: Follow Steps from here
And read until you get the einstein_platform.pem (predictive_services.pem) file.
Note: Keep this file very carefully. Don’t loose it.
B. Upload the key file to Salesforce: Now upload above .pem file into Salesforce org. Go to Files tab and upload this file here.
(https://ap5.salesforce.com/_ui/core/chatter/files/FileTabPage – URL from my org for the File tab)
C. Remote Site Setting: We need to add the remote site setting. See here
Note: As we will be using https://api.einstein.ai in EinsteinMaster class. We need to add https://api.einstein.ai in remote site instead of https://api.metamind.io
D. Copy Code: Git Repo is here. (Salesforce-Einstein-Custom-Code)
1. EinsteinMaster.apex: Download or Copy EinsteinMaster.apex from here. You don’t need to touch this file much. Just need to put the email id of yours. ( In the variable name as USER_EMAIL). Then save it.
Note: This apex code is combined and modified code of multiple Salesforce code files. I did some modification for the developers. So they don’t need to worry about multiple files. You can see these files here:(Original Resource)
- Vision API Example – Prediction From URL: It will do prediction from given image URL.
- Vision API Example – Prediction From Blob: It will do prediction from given Blob data ( I am using attachment’s body).
- Vision API Example – Prediction From Base64: It will do prediction from given Base64 data. You can generate base64 from this website.
Note: For the above 3 image vision prediction. I am using GeneralImageClassifier model. You can use your own model here. Just need to pass the Model ID in the function.
- Language API Example – Sentiment: It will return the sentiment of given text.
Note: For the above Sentiment prediction. I am using CommunitySentiment model. You can use your own model here. Just need to pass the Model ID in the function.
- Language API Example – Intent: It will return the intent of given text.
For intent, we don’t have any predefined model. So we need to create a model for this.
Read here to create an Intent model. After that pass the Model ID here.
If you want to see or get the access token. You can use EinsteinMaster.getAccessToken(); in the code.