---
title: "Generating image descriptions using OpenAI | Nutrient Java SDK"
canonical_url: "https://www.nutrient.io/guides/java/extraction/describe-image-with-openai/"
md_url: "https://www.nutrient.io/guides/java/extraction/describe-image-with-openai.md"
last_updated: "2026-06-10T14:59:56.779Z"
description: "Generate accessible image descriptions using OpenAI with Nutrient Java SDK."
---

# Generating image descriptions using OpenAI

Use OpenAI-powered image description to generate alt text and visual summaries in cloud workflows.

Common use cases include:

- Accessibility pipelines for screen readers

- Content management and image cataloging

- Document workflows across regions

- Enterprise integrations with managed API infrastructure

- Fast prototyping without local model hosting

This guide uses OpenAI as the VLM provider through Nutrient Vision API.

[Download sample](https://www.nutrient.io/downloads/samples/java/describe-image-with-openai.zip)

## How Nutrient helps

Nutrient Java SDK handles provider configuration, request handling, and response parsing.

The SDK handles:

- OpenAI API authentication, endpoint configuration, and request formatting

- Image encoding and multimodal API request structures

- Model parameters such as temperature, max tokens, and provider-specific settings

- Vision service failures and API rate limits

## Complete implementation

This example generates an image description using OpenAI:

```java

package io.nutrient.Sample;

```

Import the required classes from the SDK:

```java

import io.nutrient.sdk.Document;
import io.nutrient.sdk.Vision;
import io.nutrient.sdk.enums.VlmProvider;
import io.nutrient.sdk.exceptions.NutrientException;
import io.nutrient.sdk.settings.OpenAIApiEndpointSettings;
import io.nutrient.sdk.settings.VisionSettings;

import java.io.FileWriter;
import java.io.IOException;

public class DescribeImageWithOpenai {

```

Create the main method and declare thrown exceptions:

```java

    public static void main(String[] args) throws NutrientException, IOException {

```

## Configuring the OpenAI provider

Open the image in try-with-resources and configure OpenAI as the provider.

In this sample:

- `setProvider(VlmProvider.OpenAI)` selects OpenAI.

- `setApiKey("OPENAI_API_KEY")` sets your API key.

- Input can be PNG, JPEG, GIF, BMP, or TIFF.

```java

        try (Document document = Document.open("input_photo.png")) {
            // Configure OpenAI as the VLM provider
            VisionSettings visionSettings = document.getSettings().getVisionSettings();
            visionSettings.setProvider(VlmProvider.OpenAI);

            // Set the OpenAI API key
            OpenAIApiEndpointSettings openaiSettings = document.getSettings().getOpenAIApiEndpointSettings();
            openaiSettings.setApiKey("OPENAI_API_KEY");

```

## Creating a vision instance and generating the description

Create a vision instance and call `describe()` to generate text.

In this sample:

- `Vision.set(document)` binds processing to the opened image.

- `vision.describe()` returns a description string.

- The SDK handles encoding, request construction, and response parsing.

```java

            Vision vision = Vision.set(document);
            String description = vision.describe();

```

## Saving the description

Write the description to a text file.

This sample uses try-with-resources for both document and file-writer cleanup:

```java

            try (FileWriter writer = new FileWriter("output.txt")) {
                writer.write(description);
            }
        }
    }
}

```

## Understanding the output

`describe()` returns natural language text for accessibility and content understanding.

Descriptions are typically:

- **Concise** — Focused on key subjects and details, often one to three sentences

- **Accessible** — Suitable for users who rely on screen readers

- **Accurate** — Based on visible content only

- **Contextual** — Include relevant relationships and scene context

Use this output for accessibility metadata, image search, and document workflows.

## OpenAI API settings

The OpenAI provider uses these `OpenAIApiEndpointSettings` properties:

- **ApiEndpoint** — The OpenAI API endpoint (default: `https://api.openai.com/v1`).

- **ApiKey** — Your OpenAI API key for authentication.

- **Model** — The model identifier to use.

- **Temperature** — Controls response creativity (0.0 = deterministic, 1.0 = creative).

- **MaxTokens** — Maximum tokens in the response (default: 16384).

## Error handling

The sample can throw:

- `NutrientException` for vision and API issues

- `IOException` for file I/O operations

Common failure scenarios include:

- The input image can’t be read due to path, permission, or format issues

- The OpenAI API key is missing or invalid

- The OpenAI API is unavailable

- Rate limits are exceeded

- Network requests fail before reaching the API

- Image data is too large or corrupted

- File writing fails due to path, disk, or permission issues

In production code:

- Catch `NutrientException` and `IOException`.

- Return clear error messages.

- Log failure details for debugging.

- Add retry logic for transient API failures.

## Conclusion

Use this workflow to generate image descriptions with OpenAI:

1. Open the image file using try-with-resources for automatic resource cleanup.

2. The SDK supports multiple image formats, including PNG, JPEG, GIF, BMP, and TIFF.

3. Retrieve the vision settings with `document.getSettings().getVisionSettings()` to configure the VLM provider.

4. Set the provider to OpenAI with `setProvider(VlmProvider.OpenAI)` instead of alternatives like Claude or local models.

5. Retrieve OpenAI-specific settings with `document.getSettings().getOpenAIApiEndpointSettings()` for API configuration.

6. Set the OpenAI API key with `setApiKey()` using credentials obtained from the OpenAI platform.

7. OpenAI API settings control endpoint URLs, model selection, temperature, and max tokens.

8. Create a vision instance with `Vision.set()` bound to the document with configured provider settings.

9. Generate the description with `vision.describe()` which sends the image to OpenAI’s vision endpoint and returns natural language text.

10. The SDK encodes image data, constructs multimodal API requests, and parses responses automatically.

11. Generated descriptions are concise (1–3 sentences), accessible (WCAG-compliant alt text), accurate (observable details only), and contextual.

12. Write the description to a file using try-with-resources with `FileWriter` for automatic resource cleanup.

13. Handle `NutrientException` for vision processing failures, including authentication errors, API failures, and rate limits.

14. Handle `IOException` for file operations, including read failures or write errors when saving output.

For related image workflows, refer to the [Java SDK guides](https://www.nutrient.io/guides/java.md).

Download [this ready-to-use sample package](https://www.nutrient.io/downloads/samples/java/describe-image-with-openai.zip) to explore OpenAI-based image description.
---

## Related pages

- [Applying OCR to a PDF page](/guides/java/extraction/apply-ocr-to-pdf-page.md)
- [Applying OCR to a PDF document](/guides/java/extraction/apply-ocr-to-pdf.md)
- [Generating image descriptions using local AI](/guides/java/extraction/describe-image-with-local-ai.md)
- [Generating image descriptions using Claude](/guides/java/extraction/describe-image-with-claude.md)
- [Extracting data from images using ICR](/guides/java/extraction/extract-data-from-image-icr.md)
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- [Extracting data from images using vision language models](/guides/java/extraction/extract-data-from-image-vlm.md)
- [Nutrient Java SDK extraction guides](/guides/java/extraction.md)
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- [Extracting form fields from images](/guides/java/extraction/extract-form-fields-from-image.md)
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- [Extracting text from multilingual images](/guides/java/extraction/read-text-from-image-multi-language.md)
- [Extracting text from images](/guides/java/extraction/read-text-from-image.md)
- [Extracting text from PDF documents](/guides/java/extraction/pdf-to-text.md)
- [Speeding up first ICR operation by predownloading models](/guides/java/extraction/speed-up-first-icr-by-downloading-requirements.md)