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: 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, Line Counter, Circle Visualization, OCR Model, Clip Comparison, Crop Visualization, Template Matching, SIFT Comparison, OpenAI, Stitch OCR Detections, OpenAI, Image Preprocessing, Pixel Color Count, Line Counter, 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, Distance Measurement
• 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 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>"
}