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