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@v1
to 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:
Identify Changes
,Camera Focus
,Line Counter
,Polygon Zone Visualization
,Slack Notification
,Local File Sink
,Grid Visualization
,Image Convert Grayscale
,Trace Visualization
,Instance Segmentation Model
,Absolute Static Crop
,Roboflow Custom Metadata
,Perspective Correction
,OpenAI
,Distance Measurement
,Circle Visualization
,Dimension Collapse
,Clip Comparison
,Image Slicer
,OpenAI
,Cosine Similarity
,Triangle Visualization
,Halo Visualization
,Gaze Detection
,Line Counter
,Size Measurement
,Corner Visualization
,Email Notification
,Object Detection Model
,LMM
,Template Matching
,Roboflow Dataset Upload
,Dynamic Crop
,Depth Estimation
,Dynamic Zone
,Model Monitoring Inference Aggregator
,Buffer
,Stitch Images
,Llama 3.2 Vision
,Anthropic Claude
,Model Comparison Visualization
,Crop Visualization
,Blur Visualization
,SIFT Comparison
,CogVLM
,Image Threshold
,Stability AI Inpainting
,VLM as Detector
,Relative Static Crop
,Image Preprocessing
,Keypoint Visualization
,Background Color Visualization
,Pixel Color Count
,Clip Comparison
,Color Visualization
,Twilio SMS Notification
,OCR Model
,Multi-Label Classification Model
,Classification Label Visualization
,Google Vision OCR
,Camera Calibration
,Pixelate Visualization
,Stitch OCR Detections
,Label Visualization
,Image Slicer
,Reference Path Visualization
,Single-Label Classification Model
,Webhook Sink
,Google Gemini
,Roboflow Dataset Upload
,Line Counter Visualization
,Image Blur
,Florence-2 Model
,SIFT Comparison
,LMM For Classification
,Image Contours
,Polygon Visualization
,CSV Formatter
,SIFT
,Florence-2 Model
,Ellipse Visualization
,Mask Visualization
,Keypoint Detection Model
,Bounding Box Visualization
,Dot Visualization
,Stability AI Image Generation
,VLM as Classifier
- outputs:
Line Counter
,Polygon Zone Visualization
,Slack Notification
,Time in Zone
,Local File Sink
,Grid Visualization
,YOLO-World Model
,Instance Segmentation Model
,Trace Visualization
,Roboflow Custom Metadata
,Perspective Correction
,OpenAI
,Distance Measurement
,Circle Visualization
,Clip Comparison
,OpenAI
,Triangle Visualization
,Halo Visualization
,Line Counter
,Size Measurement
,Corner Visualization
,Email Notification
,Object Detection Model
,LMM
,Detections Consensus
,Roboflow Dataset Upload
,Dynamic Crop
,Cache Set
,VLM as Classifier
,Model Monitoring Inference Aggregator
,Cache Get
,Buffer
,Segment Anything 2 Model
,Object Detection Model
,Llama 3.2 Vision
,Anthropic Claude
,Model Comparison Visualization
,Keypoint Detection Model
,Crop Visualization
,CLIP Embedding Model
,CogVLM
,Image Threshold
,Stability AI Inpainting
,VLM as Detector
,Image Preprocessing
,Keypoint Visualization
,Background Color Visualization
,Path Deviation
,Pixel Color Count
,Clip Comparison
,Color Visualization
,Twilio SMS Notification
,Classification Label Visualization
,Google Vision OCR
,Label Visualization
,Time in Zone
,Reference Path Visualization
,Webhook Sink
,JSON Parser
,Google Gemini
,Roboflow Dataset Upload
,Line Counter Visualization
,Image Blur
,Florence-2 Model
,SIFT Comparison
,LMM For Classification
,Instance Segmentation Model
,Polygon Visualization
,Florence-2 Model
,Mask Visualization
,Ellipse Visualization
,Keypoint Detection Model
,Detections Stitch
,Bounding Box Visualization
,Dot Visualization
,Stability AI Image Generation
,VLM as Detector
,Path Deviation
,VLM as Classifier
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 ifstring
or LLM / VLM output iflanguage_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>"
}