Anthropic Claude

Class: AnthropicClaudeBlockV1

Source: inference.core.workflows.core_steps.models.foundation.anthropic_claude.v1.AnthropicClaudeBlockV1

Ask a question to Anthropic Claude model with vision capabilities.

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

You need to provide your Anthropic API key to use the Claude model.

Type identifier

Use the following identifier in step "type" field: roboflow_core/anthropic_claude@v1to 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 Claude 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 Anthropic API key.	✅
model_version	str	Model to be used.	✅
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_image_size	int	Maximum size of the image - if input has larger side, it will be downscaled, keeping aspect ratio.	✅
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 Anthropic API 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 Anthropic Claude in version v1.

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

Input and Output Bindings

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

Bindings

• input

  • images (image): The image to infer on..
  • prompt (string): Text prompt to the Claude model.
  • classes (list_of_values): List of classes to be used.
  • api_key (Union[string, secret]): Your Anthropic API key.
  • model_version (string): Model to be used.
  • 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_image_size (integer): Maximum size of the image - if input has larger side, it will be downscaled, keeping aspect ratio.

• 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 Anthropic Claude in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/anthropic_claude@v1",
    "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": "claude-3-5-sonnet",
    "max_tokens": "<block_does_not_provide_example>",
    "temperature": "<block_does_not_provide_example>",
    "max_image_size": "<block_does_not_provide_example>",
    "max_concurrent_requests": "<block_does_not_provide_example>"
}