Getting Started

Installation

You can install the VLM Run Node.js SDK using npm, yarn, or pnpm:

# Using npm
npm install vlmrun

# Using yarn
yarn add vlmrun

# Using pnpm
pnpm add vlmrun

Authentication

To use the VLM Run API, you’ll need an API key. You can obtain one by:

Create an account at VLM Run
Navigate to dashboard Settings -> API Keys

Then use it to initialize the client:

import { VlmRun } from "vlmrun";

const client = new VlmRun({
  apiKey: "your-api-key",
});

Basic Usage

Image Predictions

Here’s a simple example of using the SDK to process an image:

import { VlmRun } from "vlmrun";

// Initialize the client
const client = new VlmRun({
  apiKey: "your-api-key",
});

// Process an image using URL
const imageUrl =
  "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/document.invoice/invoice_1.jpg";
const response = await client.image.generate({
  images: [imageUrl],
  domain: "document.invoice",
  config: {
    jsonSchema: {
      type: "object",
      properties: {
        invoice_number: { type: "string" },
        total_amount: { type: "number" },
      },
    },
  },
});

Process an image passing zod schema

import { z } from "zod";

const imageUrl =
  "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/document.invoice/invoice_1.jpg";

const schema = z.object({
  invoice_number: z.string(),
  total_amount: z.number(),
});

const apiResponse = await client.image.generate({
  images: [imageUrl],
  domain: "document.invoice",
  config: {
    responseModel: schema,
  },
});
const response = apiResponse.response as z.infer<typeof schema>;
console.log(response);

Process an image using local file

// Process an image using local file
const localResponse = await client.image.generate({
  images: ["path/to/local/image.jpg"],
  model: "vlm-1",
  domain: "document.invoice",
});

Document Predictions

Here’s how to process documents:

// Upload a document
const file = await client.files.upload({
  filePath: "path/to/invoice.pdf",
});

// Process a document using file ID
const response = await client.document.generate({
  fileId: file.id,
  model: "vlm-1",
  domain: "document.invoice",
});
console.log(response);

// Process a document using URL
const documentUrl =
  "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/document.invoice/google_invoice.pdf";
const urlResponse = await client.document.generate({
  url: documentUrl,
  model: "vlm-1",
  domain: "document.invoice",
});
console.log(urlResponse);

Process a document passing zod schema

import { z } from "zod";

const schema = z.object({
  invoice_id: z.string(),
  total: z.number(),
  sub_total: z.number(),
  tax: z.number(),
  items: z.array(
    z.object({
      name: z.string(),
      quantity: z.number(),
      price: z.number(),
      total: z.number(),
    })
  ),
});

const apiResponse = await client.document.generate({
  url: documentUrl,
  domain: "document.invoice",
  config: { responseModel: schema },
});

const response = apiResponse.response as z.infer<typeof schema>;
console.log(response);

TypeScript Support

The VLM Run Node.js SDK is written in TypeScript and provides full type definitions out of the box. When using TypeScript, you’ll get:

Full IntelliSense support
Type checking for all API calls
Type definitions for request and response objects
Autocomplete for configuration options

To get the best development experience, make sure your tsconfig.json includes:

{
  "compilerOptions": {
    "esModuleInterop": true,
    "strict": true
  }
}

​Installation

​Authentication

​Basic Usage

​Image Predictions

​Process an image passing zod schema

​Process an image using local file

​Document Predictions

​Process a document passing zod schema

​TypeScript Support