OpenAI

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-4 with Vision model.

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

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

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
}