OpenAI

v4

Class: OpenAIBlockV4 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.models.foundation.openai.v4.OpenAIBlockV4

Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning

Ask a question to OpenAI's GPT models with vision capabilities (including GPT-5 and GPT-4o).

You can specify arbitrary text prompts or predefined ones, the block supports the following types of prompt:

• Open Prompt (unconstrained) - Use any prompt to generate a raw response

• Text Recognition (OCR) (ocr) - Model recognizes text in the image

• Visual Question Answering (visual-question-answering) - Model answers the question you submit in the prompt

• Captioning (short) (caption) - Model provides a short description of the image

• Captioning (detailed-caption) - Model provides a long description of the image

• Single-Label Classification (classification) - Model classifies the image content as one of the provided classes

• Multi-Label Classification (multi-label-classification) - Model classifies the image content as one or more of the provided classes

• Unprompted Object Detection (object-detection) - Model detects and returns the bounding boxes for prominent objects in the image

• Structured Output Generation (structured-answering) - Model returns a JSON response with the specified fields

Provide your OpenAI API key or set the value to rf_key:account (or rf_key:user:<id>) to proxy requests through Roboflow's API.

Type identifier

Use the following identifier in step "type" field: roboflow_core/open_ai@v4to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
task_type	str	Task type to be performed by model. Value determines required parameters and output response..	❌
prompt	str	Text prompt to the OpenAI model.	✅
output_structure	Dict[str, str]	Dictionary with structure of expected JSON response.	❌
classes	List[str]	List of classes to be used.	✅
api_key	str	Your OpenAI API key.	✅
model_version	str	Model to be used.	✅
reasoning_effort	str	Control the effort on reasoning. Reducing reasoning effort can result in faster responses and fewer tokens used. GPT-5.1 defaults to 'none' (no reasoning) and supports 'none', 'low', 'medium', 'high'. GPT-5 models default to 'medium' and support 'minimal', 'low', 'medium', 'high'..	✅
image_detail	str	Indicates the image's quality, with 'high' suggesting it is of high resolution and should be processed or displayed with high fidelity..	✅
max_tokens	int	Maximum number of tokens the model can generate in its response. If not specified, the model will use its default limit. Minimum value is 16..	❌
temperature	float	Temperature to sample from the model - value in range 0.0-2.0, the higher - the more random / "creative" the generations are..	✅
max_concurrent_requests	int	Number of concurrent requests that can be executed by block when batch of input images provided. If not given - block defaults to value configured globally in Workflows Execution Engine. Please restrict if you hit OpenAI limits..	❌

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections

Compatible Blocks

Check what blocks you can connect to OpenAI in version v4.

• inputs: Google Vision OCR, Label Visualization, LMM For Classification, Blur Visualization, Background Color Visualization, Contrast Equalization, Bounding Box Visualization, Keypoint Visualization, Stability AI Outpainting, Reference Path Visualization, Image Slicer, Pixelate Visualization, Single-Label Classification Model, Clip Comparison, CSV Formatter, Image Preprocessing, Color Visualization, SIFT Comparison, Object Detection Model, Email Notification, Anthropic Claude, Circle Visualization, Image Contours, Polygon Zone Visualization, Ellipse Visualization, Clip Comparison, Email Notification, VLM as Classifier, Model Monitoring Inference Aggregator, OCR Model, Absolute Static Crop, Depth Estimation, LMM, Morphological Transformation, Roboflow Dataset Upload, Gaze Detection, Crop Visualization, OpenAI, Image Convert Grayscale, Florence-2 Model, CogVLM, Roboflow Custom Metadata, VLM as Detector, Classification Label Visualization, Buffer, Stitch OCR Detections, Keypoint Detection Model, Cosine Similarity, Camera Calibration, Polygon Visualization, Icon Visualization, Identify Changes, Triangle Visualization, Roboflow Dataset Upload, Anthropic Claude, Model Comparison Visualization, Corner Visualization, Florence-2 Model, Google Gemini, Google Gemini, EasyOCR, Line Counter Visualization, Grid Visualization, Halo Visualization, Size Measurement, Stability AI Image Generation, QR Code Generator, Dynamic Zone, Twilio SMS Notification, Relative Static Crop, Dot Visualization, Llama 3.2 Vision, Image Blur, Slack Notification, Dimension Collapse, OpenAI, Local File Sink, Multi-Label Classification Model, Image Slicer, OpenAI, Stability AI Inpainting, Dynamic Crop, Camera Focus, Webhook Sink, Image Threshold, Instance Segmentation Model, Perspective Correction, Mask Visualization, Trace Visualization, OpenAI, Stitch Images, SIFT
• outputs: Label Visualization, Background Color Visualization, Contrast Equalization, Reference Path Visualization, Stability AI Outpainting, Clip Comparison, Perception Encoder Embedding Model, Seg Preview, Image Preprocessing, Color Visualization, SIFT Comparison, Email Notification, Cache Set, Circle Visualization, Object Detection Model, Moondream2, VLM as Classifier, Model Monitoring Inference Aggregator, Path Deviation, LMM, Time in Zone, Morphological Transformation, Detections Consensus, Crop Visualization, OpenAI, Florence-2 Model, Classification Label Visualization, Segment Anything 2 Model, Time in Zone, YOLO-World Model, PTZ Tracking (ONVIF).md), Icon Visualization, Distance Measurement, VLM as Detector, Line Counter Visualization, Grid Visualization, Halo Visualization, Size Measurement, Twilio SMS Notification, Time in Zone, Detections Stitch, Llama 3.2 Vision, Image Blur, Slack Notification, OpenAI, OpenAI, Dynamic Crop, Pixel Color Count, Mask Visualization, Google Vision OCR, LMM For Classification, Keypoint Visualization, Bounding Box Visualization, SAM 3, SAM 3, Object Detection Model, Path Deviation, Anthropic Claude, Polygon Zone Visualization, Ellipse Visualization, Line Counter, Email Notification, Clip Comparison, Roboflow Dataset Upload, SAM 3, CogVLM, Roboflow Custom Metadata, VLM as Detector, Buffer, Stitch OCR Detections, Keypoint Detection Model, Keypoint Detection Model, Line Counter, JSON Parser, Polygon Visualization, CLIP Embedding Model, Detections Classes Replacement, Cache Get, Triangle Visualization, Roboflow Dataset Upload, Anthropic Claude, Model Comparison Visualization, Florence-2 Model, Corner Visualization, Google Gemini, Google Gemini, Stability AI Image Generation, QR Code Generator, Dot Visualization, Local File Sink, Instance Segmentation Model, Stability AI Inpainting, Webhook Sink, Instance Segmentation Model, Image Threshold, OpenAI, Perspective Correction, VLM as Classifier, Trace Visualization

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds OpenAI in version v4 has.

Bindings

• input

  • images (image): The image to infer on..
  • prompt (string): Text prompt to the OpenAI model.
  • classes (list_of_values): List of classes to be used.
  • api_key (Union[string, ROBOFLOW_MANAGED_KEY, secret]): Your OpenAI API key.
  • model_version (string): Model to be used.
  • reasoning_effort (string): Control the effort on reasoning. Reducing reasoning effort can result in faster responses and fewer tokens used. GPT-5.1 defaults to 'none' (no reasoning) and supports 'none', 'low', 'medium', 'high'. GPT-5 models default to 'medium' and support 'minimal', 'low', 'medium', 'high'..
  • image_detail (string): Indicates the image's quality, with 'high' suggesting it is of high resolution and should be processed or displayed with high fidelity..
  • temperature (float): Temperature to sample from the model - value in range 0.0-2.0, the higher - the more random / "creative" the generations are..

• output

  • output (Union[string, language_model_output]): String value if string or LLM / VLM output if language_model_output.
  • classes (list_of_values): List of values of any type.

Example JSON definition of step OpenAI in version v4

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/open_ai@v4",
    "images": "$inputs.image",
    "task_type": "<block_does_not_provide_example>",
    "prompt": "my prompt",
    "output_structure": {
        "my_key": "description"
    },
    "classes": [
        "class-a",
        "class-b"
    ],
    "api_key": "xxx-xxx",
    "model_version": "gpt-5.1",
    "reasoning_effort": "<block_does_not_provide_example>",
    "image_detail": "auto",
    "max_tokens": "<block_does_not_provide_example>",
    "temperature": "<block_does_not_provide_example>",
    "max_concurrent_requests": "<block_does_not_provide_example>"
}

v3

Class: OpenAIBlockV3 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.models.foundation.openai.v3.OpenAIBlockV3

Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning

Ask a question to OpenAI's GPT models with vision capabilities (including GPT-5 and GPT-4o).

You can specify arbitrary text prompts or predefined ones, the block supports the following types of prompt:

• Open Prompt (unconstrained) - Use any prompt to generate a raw response

• Text Recognition (OCR) (ocr) - Model recognizes text in the image

• Visual Question Answering (visual-question-answering) - Model answers the question you submit in the prompt

• Captioning (short) (caption) - Model provides a short description of the image

• Captioning (detailed-caption) - Model provides a long description of the image

• Single-Label Classification (classification) - Model classifies the image content as one of the provided classes

• Multi-Label Classification (multi-label-classification) - Model classifies the image content as one or more of the provided classes

• Structured Output Generation (structured-answering) - Model returns a JSON response with the specified fields

Provide your OpenAI API key or set the value to rf_key:account (or rf_key:user:<id>) to proxy requests through Roboflow's API.

Type identifier

Use the following identifier in step "type" field: roboflow_core/open_ai@v3to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
task_type	str	Task type to be performed by model. Value determines required parameters and output response..	❌
prompt	str	Text prompt to the OpenAI model.	✅
output_structure	Dict[str, str]	Dictionary with structure of expected JSON response.	❌
classes	List[str]	List of classes to be used.	✅
api_key	str	Your OpenAI API key.	✅
model_version	str	Model to be used.	✅
image_detail	str	Indicates the image's quality, with 'high' suggesting it is of high resolution and should be processed or displayed with high fidelity..	✅
max_tokens	int	Maximum number of tokens the model can generate in it's response..	❌
temperature	float	Temperature to sample from the model - value in range 0.0-2.0, the higher - the more random / "creative" the generations are..	✅
max_concurrent_requests	int	Number of concurrent requests that can be executed by block when batch of input images provided. If not given - block defaults to value configured globally in Workflows Execution Engine. Please restrict if you hit OpenAI limits..	❌

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections

Compatible Blocks

Check what blocks you can connect to OpenAI in version v3.

• inputs: Google Vision OCR, Label Visualization, LMM For Classification, Blur Visualization, Background Color Visualization, Contrast Equalization, Bounding Box Visualization, Keypoint Visualization, Stability AI Outpainting, Reference Path Visualization, Image Slicer, Pixelate Visualization, Single-Label Classification Model, Clip Comparison, CSV Formatter, Image Preprocessing, Color Visualization, SIFT Comparison, Object Detection Model, Email Notification, Anthropic Claude, Circle Visualization, Image Contours, Polygon Zone Visualization, Ellipse Visualization, Clip Comparison, Email Notification, VLM as Classifier, Model Monitoring Inference Aggregator, OCR Model, Absolute Static Crop, Depth Estimation, LMM, Morphological Transformation, Roboflow Dataset Upload, Gaze Detection, Crop Visualization, OpenAI, Image Convert Grayscale, Florence-2 Model, CogVLM, Roboflow Custom Metadata, VLM as Detector, Classification Label Visualization, Buffer, Stitch OCR Detections, Keypoint Detection Model, Cosine Similarity, Camera Calibration, Polygon Visualization, Icon Visualization, Identify Changes, Triangle Visualization, Roboflow Dataset Upload, Anthropic Claude, Model Comparison Visualization, Corner Visualization, Florence-2 Model, Google Gemini, Google Gemini, EasyOCR, Line Counter Visualization, Grid Visualization, Halo Visualization, Size Measurement, Stability AI Image Generation, QR Code Generator, Dynamic Zone, Twilio SMS Notification, Relative Static Crop, Dot Visualization, Llama 3.2 Vision, Image Blur, Slack Notification, Dimension Collapse, OpenAI, Local File Sink, Multi-Label Classification Model, Image Slicer, OpenAI, Stability AI Inpainting, Dynamic Crop, Camera Focus, Webhook Sink, Image Threshold, Instance Segmentation Model, Perspective Correction, Mask Visualization, Trace Visualization, OpenAI, Stitch Images, SIFT
• outputs: Label Visualization, Background Color Visualization, Contrast Equalization, Reference Path Visualization, Stability AI Outpainting, Clip Comparison, Perception Encoder Embedding Model, Seg Preview, Image Preprocessing, Color Visualization, SIFT Comparison, Email Notification, Cache Set, Circle Visualization, Object Detection Model, Moondream2, VLM as Classifier, Model Monitoring Inference Aggregator, Path Deviation, LMM, Time in Zone, Morphological Transformation, Detections Consensus, Crop Visualization, OpenAI, Florence-2 Model, Classification Label Visualization, Segment Anything 2 Model, Time in Zone, YOLO-World Model, PTZ Tracking (ONVIF).md), Icon Visualization, Distance Measurement, VLM as Detector, Line Counter Visualization, Grid Visualization, Halo Visualization, Size Measurement, Twilio SMS Notification, Time in Zone, Detections Stitch, Llama 3.2 Vision, Image Blur, Slack Notification, OpenAI, OpenAI, Dynamic Crop, Pixel Color Count, Mask Visualization, Google Vision OCR, LMM For Classification, Keypoint Visualization, Bounding Box Visualization, SAM 3, SAM 3, Object Detection Model, Path Deviation, Anthropic Claude, Polygon Zone Visualization, Ellipse Visualization, Line Counter, Email Notification, Clip Comparison, Roboflow Dataset Upload, SAM 3, CogVLM, Roboflow Custom Metadata, VLM as Detector, Buffer, Stitch OCR Detections, Keypoint Detection Model, Keypoint Detection Model, Line Counter, JSON Parser, Polygon Visualization, CLIP Embedding Model, Detections Classes Replacement, Cache Get, Triangle Visualization, Roboflow Dataset Upload, Anthropic Claude, Model Comparison Visualization, Florence-2 Model, Corner Visualization, Google Gemini, Google Gemini, Stability AI Image Generation, QR Code Generator, Dot Visualization, Local File Sink, Instance Segmentation Model, Stability AI Inpainting, Webhook Sink, Instance Segmentation Model, Image Threshold, OpenAI, Perspective Correction, VLM as Classifier, Trace Visualization

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds OpenAI in version v3 has.

Bindings

• input

  • images (image): The image to infer on..
  • prompt (string): Text prompt to the OpenAI model.
  • classes (list_of_values): List of classes to be used.
  • api_key (Union[string, ROBOFLOW_MANAGED_KEY, secret]): Your OpenAI API key.
  • model_version (string): Model to be used.
  • image_detail (string): Indicates the image's quality, with 'high' suggesting it is of high resolution and should be processed or displayed with high fidelity..
  • temperature (float): Temperature to sample from the model - value in range 0.0-2.0, the higher - the more random / "creative" the generations are..

• output

  • output (Union[string, language_model_output]): String value if string or LLM / VLM output if language_model_output.
  • classes (list_of_values): List of values of any type.

Example JSON definition of step OpenAI in version v3

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/open_ai@v3",
    "images": "$inputs.image",
    "task_type": "<block_does_not_provide_example>",
    "prompt": "my prompt",
    "output_structure": {
        "my_key": "description"
    },
    "classes": [
        "class-a",
        "class-b"
    ],
    "api_key": "xxx-xxx",
    "model_version": "gpt-5",
    "image_detail": "auto",
    "max_tokens": "<block_does_not_provide_example>",
    "temperature": "<block_does_not_provide_example>",
    "max_concurrent_requests": "<block_does_not_provide_example>"
}

v2

Class: OpenAIBlockV2 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.models.foundation.openai.v2.OpenAIBlockV2

Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning

Ask a question to OpenAI's GPT models with vision capabilities (including GPT-4o and GPT-5).

You can specify arbitrary text prompts or predefined ones, the block supports the following types of prompt:

• Open Prompt (unconstrained) - Use any prompt to generate a raw response

• Text Recognition (OCR) (ocr) - Model recognizes text in the image

• Visual Question Answering (visual-question-answering) - Model answers the question you submit in the prompt

• Captioning (short) (caption) - Model provides a short description of the image

• Captioning (detailed-caption) - Model provides a long description of the image

• Single-Label Classification (classification) - Model classifies the image content as one of the provided classes

• Multi-Label Classification (multi-label-classification) - Model classifies the image content as one or more of the provided classes

• Structured Output Generation (structured-answering) - Model returns a JSON response with the specified fields

You need to provide your OpenAI API key to use the GPT-4 with Vision model.

Type identifier

Use the following identifier in step "type" field: roboflow_core/open_ai@v2to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
task_type	str	Task type to be performed by model. Value determines required parameters and output response..	❌
prompt	str	Text prompt to the OpenAI model.	✅
output_structure	Dict[str, str]	Dictionary with structure of expected JSON response.	❌
classes	List[str]	List of classes to be used.	✅
api_key	str	Your OpenAI API key.	✅
model_version	str	Model to be used.	✅
image_detail	str	Indicates the image's quality, with 'high' suggesting it is of high resolution and should be processed or displayed with high fidelity..	✅
max_tokens	int	Maximum number of tokens the model can generate in it's response..	❌
temperature	float	Temperature to sample from the model - value in range 0.0-2.0, the higher - the more random / "creative" the generations are..	✅
max_concurrent_requests	int	Number of concurrent requests that can be executed by block when batch of input images provided. If not given - block defaults to value configured globally in Workflows Execution Engine. Please restrict if you hit OpenAI limits..	❌

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections

Compatible Blocks

Check what blocks you can connect to OpenAI in version v2.

• inputs: Google Vision OCR, Label Visualization, LMM For Classification, Blur Visualization, Background Color Visualization, Contrast Equalization, Bounding Box Visualization, Keypoint Visualization, Stability AI Outpainting, Reference Path Visualization, Image Slicer, Pixelate Visualization, Single-Label Classification Model, Clip Comparison, CSV Formatter, Image Preprocessing, Color Visualization, SIFT Comparison, Object Detection Model, Email Notification, Anthropic Claude, Circle Visualization, Image Contours, Polygon Zone Visualization, Ellipse Visualization, Clip Comparison, Email Notification, VLM as Classifier, Model Monitoring Inference Aggregator, OCR Model, Absolute Static Crop, Depth Estimation, LMM, Morphological Transformation, Roboflow Dataset Upload, Gaze Detection, Crop Visualization, OpenAI, Image Convert Grayscale, Florence-2 Model, CogVLM, Roboflow Custom Metadata, VLM as Detector, Classification Label Visualization, Buffer, Stitch OCR Detections, Keypoint Detection Model, Cosine Similarity, Camera Calibration, Polygon Visualization, Icon Visualization, Identify Changes, Triangle Visualization, Roboflow Dataset Upload, Anthropic Claude, Model Comparison Visualization, Corner Visualization, Florence-2 Model, Google Gemini, Google Gemini, EasyOCR, Line Counter Visualization, Grid Visualization, Halo Visualization, Size Measurement, Stability AI Image Generation, QR Code Generator, Dynamic Zone, Twilio SMS Notification, Relative Static Crop, Dot Visualization, Llama 3.2 Vision, Image Blur, Slack Notification, Dimension Collapse, OpenAI, Local File Sink, Multi-Label Classification Model, Image Slicer, OpenAI, Stability AI Inpainting, Dynamic Crop, Camera Focus, Webhook Sink, Image Threshold, Instance Segmentation Model, Perspective Correction, Mask Visualization, Trace Visualization, OpenAI, Stitch Images, SIFT
• outputs: Label Visualization, Background Color Visualization, Contrast Equalization, Reference Path Visualization, Stability AI Outpainting, Clip Comparison, Perception Encoder Embedding Model, Seg Preview, Image Preprocessing, Color Visualization, SIFT Comparison, Email Notification, Cache Set, Circle Visualization, Object Detection Model, Moondream2, VLM as Classifier, Model Monitoring Inference Aggregator, Path Deviation, LMM, Time in Zone, Morphological Transformation, Detections Consensus, Crop Visualization, OpenAI, Florence-2 Model, Classification Label Visualization, Segment Anything 2 Model, Time in Zone, YOLO-World Model, PTZ Tracking (ONVIF).md), Icon Visualization, Distance Measurement, VLM as Detector, Line Counter Visualization, Grid Visualization, Halo Visualization, Size Measurement, Twilio SMS Notification, Time in Zone, Detections Stitch, Llama 3.2 Vision, Image Blur, Slack Notification, OpenAI, OpenAI, Dynamic Crop, Pixel Color Count, Mask Visualization, Google Vision OCR, LMM For Classification, Keypoint Visualization, Bounding Box Visualization, SAM 3, SAM 3, Object Detection Model, Path Deviation, Anthropic Claude, Polygon Zone Visualization, Ellipse Visualization, Line Counter, Email Notification, Clip Comparison, Roboflow Dataset Upload, SAM 3, CogVLM, Roboflow Custom Metadata, VLM as Detector, Buffer, Stitch OCR Detections, Keypoint Detection Model, Keypoint Detection Model, Line Counter, JSON Parser, Polygon Visualization, CLIP Embedding Model, Detections Classes Replacement, Cache Get, Triangle Visualization, Roboflow Dataset Upload, Anthropic Claude, Model Comparison Visualization, Florence-2 Model, Corner Visualization, Google Gemini, Google Gemini, Stability AI Image Generation, QR Code Generator, Dot Visualization, Local File Sink, Instance Segmentation Model, Stability AI Inpainting, Webhook Sink, Instance Segmentation Model, Image Threshold, OpenAI, Perspective Correction, VLM as Classifier, Trace Visualization

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds OpenAI in version v2 has.

Bindings

• input

  • images (image): The image to infer on..
  • prompt (string): Text prompt to the OpenAI model.
  • classes (list_of_values): List of classes to be used.
  • api_key (Union[string, secret]): Your OpenAI API key.
  • model_version (string): Model to be used.
  • image_detail (string): Indicates the image's quality, with 'high' suggesting it is of high resolution and should be processed or displayed with high fidelity..
  • temperature (float): Temperature to sample from the model - value in range 0.0-2.0, the higher - the more random / "creative" the generations are..

• output

  • output (Union[string, language_model_output]): String value if string or LLM / VLM output if language_model_output.
  • classes (list_of_values): List of values of any type.

Example JSON definition of step OpenAI in version v2

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/open_ai@v2",
    "images": "$inputs.image",
    "task_type": "<block_does_not_provide_example>",
    "prompt": "my prompt",
    "output_structure": {
        "my_key": "description"
    },
    "classes": [
        "class-a",
        "class-b"
    ],
    "api_key": "xxx-xxx",
    "model_version": "gpt-4o",
    "image_detail": "auto",
    "max_tokens": "<block_does_not_provide_example>",
    "temperature": "<block_does_not_provide_example>",
    "max_concurrent_requests": "<block_does_not_provide_example>"
}

v1

Class: OpenAIBlockV1 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.models.foundation.openai.v1.OpenAIBlockV1

Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning

Ask a question to OpenAI's GPT-4 with Vision model.

You can specify arbitrary text prompts to the OpenAIBlock.

You need to provide your OpenAI API key to use the GPT-4 with Vision model.

This model was previously part of the LMM block.

Type identifier

Use the following identifier in step "type" field: roboflow_core/open_ai@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
prompt	str	Text prompt to the OpenAI model.	✅
openai_api_key	str	Your OpenAI API key.	✅
openai_model	str	Model to be used.	✅
json_output_format	Dict[str, str]	Holds dictionary that maps name of requested output field into its description.	❌
image_detail	str	Indicates the image's quality, with 'high' suggesting it is of high resolution and should be processed or displayed with high fidelity..	✅
max_tokens	int	Maximum number of tokens the model can generate in it's response..	❌

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections

Compatible Blocks

Check what blocks you can connect to OpenAI in version v1.

• inputs: Google Vision OCR, Label Visualization, LMM For Classification, Blur Visualization, Background Color Visualization, Contrast Equalization, Bounding Box Visualization, Keypoint Visualization, Stability AI Outpainting, Reference Path Visualization, Image Slicer, Pixelate Visualization, Single-Label Classification Model, CSV Formatter, Image Preprocessing, Color Visualization, SIFT Comparison, Object Detection Model, Email Notification, Anthropic Claude, Circle Visualization, Image Contours, Polygon Zone Visualization, Ellipse Visualization, Clip Comparison, Email Notification, VLM as Classifier, Model Monitoring Inference Aggregator, OCR Model, Absolute Static Crop, Depth Estimation, LMM, Morphological Transformation, Roboflow Dataset Upload, Crop Visualization, OpenAI, Image Convert Grayscale, Florence-2 Model, CogVLM, Roboflow Custom Metadata, VLM as Detector, Classification Label Visualization, Stitch OCR Detections, Keypoint Detection Model, Camera Calibration, Polygon Visualization, Icon Visualization, Triangle Visualization, Roboflow Dataset Upload, Anthropic Claude, Model Comparison Visualization, Corner Visualization, Florence-2 Model, Google Gemini, Google Gemini, EasyOCR, Line Counter Visualization, Grid Visualization, Halo Visualization, Stability AI Image Generation, QR Code Generator, Twilio SMS Notification, Relative Static Crop, Dot Visualization, Llama 3.2 Vision, Image Blur, Slack Notification, OpenAI, Local File Sink, Multi-Label Classification Model, Image Slicer, OpenAI, Stability AI Inpainting, Dynamic Crop, Camera Focus, Webhook Sink, Image Threshold, Instance Segmentation Model, Perspective Correction, Mask Visualization, Trace Visualization, OpenAI, Stitch Images, SIFT
• outputs: Label Visualization, Blur Visualization, Background Color Visualization, Contrast Equalization, Reference Path Visualization, Detections Filter, Stability AI Outpainting, Image Slicer, Pixelate Visualization, Single-Label Classification Model, Clip Comparison, CSV Formatter, Perception Encoder Embedding Model, Seg Preview, Overlap Filter, Image Preprocessing, Rate Limiter, Color Visualization, SIFT Comparison, Email Notification, Cache Set, Dominant Color, Property Definition, Circle Visualization, Object Detection Model, QR Code Detection, Moondream2, VLM as Classifier, Model Monitoring Inference Aggregator, OCR Model, Absolute Static Crop, Path Deviation, LMM, Time in Zone, Morphological Transformation, Gaze Detection, Detections Consensus, Crop Visualization, OpenAI, Florence-2 Model, Barcode Detection, Classification Label Visualization, Byte Tracker, Segment Anything 2 Model, Cosine Similarity, SIFT Comparison, Time in Zone, YOLO-World Model, PTZ Tracking (ONVIF).md), Detection Offset, Icon Visualization, Detections Transformation, Distance Measurement, Data Aggregator, VLM as Detector, Line Counter Visualization, Grid Visualization, Halo Visualization, Size Measurement, Dynamic Zone, Twilio SMS Notification, Time in Zone, Detections Stitch, Llama 3.2 Vision, Image Blur, Slack Notification, Velocity, OpenAI, Byte Tracker, First Non Empty Or Default, Multi-Label Classification Model, Image Slicer, OpenAI, Dynamic Crop, Pixel Color Count, Mask Visualization, Detections Merge, Stitch Images, SIFT, Google Vision OCR, LMM For Classification, Keypoint Visualization, Bounding Box Visualization, SAM 3, Byte Tracker, SAM 3, Qwen2.5-VL, Object Detection Model, Path Deviation, Detections Combine, Anthropic Claude, Image Contours, Polygon Zone Visualization, Ellipse Visualization, Line Counter, Email Notification, Clip Comparison, Expression, Depth Estimation, Roboflow Dataset Upload, SAM 3, CogVLM, Roboflow Custom Metadata, VLM as Detector, Multi-Label Classification Model, Image Convert Grayscale, Buffer, Stitch OCR Detections, Keypoint Detection Model, Bounding Rectangle, Keypoint Detection Model, Line Counter, JSON Parser, Polygon Visualization, CLIP Embedding Model, Camera Calibration, Detections Classes Replacement, Cache Get, Identify Changes, Triangle Visualization, Template Matching, Roboflow Dataset Upload, Anthropic Claude, Model Comparison Visualization, Florence-2 Model, Corner Visualization, Google Gemini, Google Gemini, EasyOCR, Delta Filter, SmolVLM2, Stability AI Image Generation, Identify Outliers, QR Code Generator, Dot Visualization, Relative Static Crop, Dimension Collapse, Continue If, Local File Sink, Instance Segmentation Model, Stability AI Inpainting, Single-Label Classification Model, Camera Focus, Detections Stabilizer, Webhook Sink, Instance Segmentation Model, Image Threshold, OpenAI, Perspective Correction, VLM as Classifier, Trace Visualization

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds OpenAI in version v1 has.

Bindings

• input

  • images (image): The image to infer on..
  • prompt (string): Text prompt to the OpenAI model.
  • openai_api_key (Union[string, secret]): Your OpenAI API key.
  • openai_model (string): Model to be used.
  • image_detail (string): Indicates the image's quality, with 'high' suggesting it is of high resolution and should be processed or displayed with high fidelity..

• output

  • parent_id (parent_id): Identifier of parent for step output.
  • root_parent_id (parent_id): Identifier of parent for step output.
  • image (image_metadata): Dictionary with image metadata required by supervision.
  • structured_output (dictionary): Dictionary.
  • raw_output (string): String value.
  • * (*): Equivalent of any element.

Example JSON definition of step OpenAI in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/open_ai@v1",
    "images": "$inputs.image",
    "prompt": "my prompt",
    "openai_api_key": "xxx-xxx",
    "openai_model": "gpt-4o",
    "json_output_format": {
        "count": "number of cats in the picture"
    },
    "image_detail": "auto",
    "max_tokens": 450
}