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-nodejsTo 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 = nextConfigClarifai 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
