This library is currently in developer preview, any improvements & feedback welcome!
This is the official Node.js client for interacting with our powerful API. The Clarifai Node.js SDK offers a comprehensive set of tools to integrate Clarifai's AI platform to leverage computer vision capabilities like classification, detection, segmentation and natural language capabilities like classification, summarisation, generation, Q&A, etc into your applications. With just a few lines of code, you can leverage cutting-edge artificial intelligence to unlock valuable insights from visual and textual content.
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npm install clarifai-nodejs
To use Clarifai Node.js in Next.js App Directory with server components, you will need to add clarifai-nodejs-grpc package (which is one of the primary dependency of Clarifai Node.js SDK) to the serverComponentsExternalPackages config of next.config.js
/** @type {import('next').NextConfig} */
const nextConfig = {
experimental: {
serverComponentsExternalPackages: ['clarifai-nodejs-grpc'],
},
}
module.exports = nextConfig
Clarifai uses Personal Access Tokens(PATs) to validate requests. You can create and manage PATs under your Clarifai account security settings.
- 🔗 Create PAT: Log into Portal → Profile Icon → Security Settings → Create Personal Access Token → Set the scopes → Confirm
Export your PAT as an environment variable. Then, import and initialize the API Client.
Set PAT as environment variable through terminal:
export CLARIFAI_PAT={your personal access token}
or use dotenv to load environment variables via a .env
file
Using the celebrity face recognition model to predict the celebrity in a given picture. For list of all available models visit clarifai models page.
import { Input, Model } from "clarifai-nodejs";
const input = Input.getInputFromUrl({
inputId: "test-image",
imageUrl:
"https://samples.clarifai.com/celebrity.jpeg",
});
const model = new Model({
authConfig: {
pat: process.env.CLARIFAI_PAT!,
userId: process.env.CLARIFAI_USER_ID!,
appId: process.env.CLARIFAI_APP_ID!
},
modelId: "celebrity-face-recognition",
});
model
.predict({
inputs: [input],
})
.then((response) => {
const result = response?.[0].data?.conceptsList[0].name ?? "unrecognized";
console.log(result);
})
.catch(console.error);
Using a custom workflow built on clarifai.com to analyze sentiment of a given image. For list of all available workflows visit clarifai workflows page
import { Input, Workflow } from "clarifai-nodejs";
const input = Input.getInputFromUrl({
inputId: "test-image",
imageUrl:
"https://samples.clarifai.com/celebrity.jpeg",
});
const workflow = new Workflow({
authConfig: {
pat: process.env.CLARIFAI_PAT!,
userId: process.env.CLARIFAI_USER_ID!,
appId: process.env.CLARIFAI_APP_ID!
},
workflowId: "workflow-238a93",
});
workflow
.predict({
inputs: [input],
})
.then((response) => {
const result =
response.resultsList[0].outputsList[0].data?.regionsList[0].data
?.conceptsList[0].name ?? "unrecognized";
console.log(result);
})
.catch(console.error);
On Clarifai, apps act as a central repository for models, datasets, inputs and other resources and information. Checkout how to create apps on clarifai portal.
import { User } from "clarifai-nodejs";
export const user = new User({
pat: process.env.CLARIFAI_PAT!,
userId: process.env.CLARIFAI_USER_ID!,
appId: process.env.CLARIFAI_APP_ID!,
});
const list = await user
.listApps({
pageNo: 1,
perPage: 20,
params: {
sortAscending: true,
},
})
.next();
const apps = list.value;
console.log(apps);
For full usage details, checkout our API Reference docs