Google Gemini

Class: GoogleGeminiBlockV1

Source: inference.core.workflows.core_steps.models.foundation.google_gemini.v1.GoogleGeminiBlockV1

Ask a question to Google's Gemini 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 Google AI API key to use the Gemini model.

WARNING!

This block makes use of /v1beta API of Google Gemini model - the implementation may change in the future, without guarantee of backward compatibility.

Type identifier

Use the following identifier in step "type" field: roboflow_core/google_gemini@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 Gemini 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 Google AI 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_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 Google Gemini 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 Google Gemini in version v1.

• inputs: Circle Visualization, Polygon Zone Visualization, Image Slicer, Reference Path Visualization, Image Contours, Dynamic Crop, Size Measurement, Blur Visualization, Gaze Detection, Stability AI Inpainting, SIFT Comparison, OpenAI, LMM For Classification, Instance Segmentation Model, Absolute Static Crop, Trace Visualization, Classification Label Visualization, Crop Visualization, Camera Calibration, Stitch Images, Line Counter Visualization, Email Notification, Corner Visualization, VLM as Classifier, Google Vision OCR, Dimension Collapse, Multi-Label Classification Model, Perspective Correction, Object Detection Model, Triangle Visualization, Image Blur, Clip Comparison, Webhook Sink, Twilio SMS Notification, VLM as Detector, Image Preprocessing, CSV Formatter, Pixelate Visualization, Anthropic Claude, Buffer, Background Color Visualization, Roboflow Custom Metadata, Polygon Visualization, Stability AI Outpainting, Grid Visualization, CogVLM, Identify Changes, Color Visualization, Google Gemini, Model Monitoring Inference Aggregator, Relative Static Crop, Bounding Box Visualization, Llama 3.2 Vision, Halo Visualization, Florence-2 Model, Image Convert Grayscale, Camera Focus, Clip Comparison, OpenAI, Ellipse Visualization, Single-Label Classification Model, OpenAI, Mask Visualization, Roboflow Dataset Upload, Dynamic Zone, Model Comparison Visualization, Dot Visualization, Stability AI Image Generation, Depth Estimation, Keypoint Visualization, LMM, Keypoint Detection Model, OCR Model, Stitch OCR Detections, Image Threshold, Roboflow Dataset Upload, Florence-2 Model, SIFT, Local File Sink, Label Visualization, Image Slicer, Cosine Similarity, Slack Notification
• outputs: Detections Consensus, Circle Visualization, Polygon Zone Visualization, Reference Path Visualization, Dynamic Crop, Size Measurement, Distance Measurement, VLM as Detector, Perception Encoder Embedding Model, Cache Set, Stability AI Inpainting, SIFT Comparison, OpenAI, LMM For Classification, Instance Segmentation Model, Trace Visualization, Classification Label Visualization, Crop Visualization, Segment Anything 2 Model, Line Counter Visualization, Email Notification, Corner Visualization, Time in Zone, CLIP Embedding Model, VLM as Classifier, Google Vision OCR, Path Deviation, Line Counter, Moondream2, Perspective Correction, Object Detection Model, Triangle Visualization, Path Deviation, Image Blur, Pixel Color Count, Cache Get, Clip Comparison, Detections Classes Replacement, Webhook Sink, Twilio SMS Notification, VLM as Detector, Image Preprocessing, Anthropic Claude, Keypoint Detection Model, Buffer, JSON Parser, Background Color Visualization, Roboflow Custom Metadata, Polygon Visualization, Stability AI Outpainting, Grid Visualization, CogVLM, Color Visualization, Google Gemini, Model Monitoring Inference Aggregator, Bounding Box Visualization, Llama 3.2 Vision, YOLO-World Model, Halo Visualization, Florence-2 Model, Clip Comparison, OpenAI, Ellipse Visualization, OpenAI, Mask Visualization, Roboflow Dataset Upload, Object Detection Model, Model Comparison Visualization, Line Counter, Dot Visualization, Stability AI Image Generation, Instance Segmentation Model, Keypoint Visualization, LMM, VLM as Classifier, Keypoint Detection Model, Time in Zone, Roboflow Dataset Upload, Florence-2 Model, Local File Sink, Image Threshold, PTZ Tracking (ONVIF).md), Label Visualization, Detections Stitch, Slack Notification

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Google Gemini in version v1 has.

Bindings

• input

  • images (image): The image to infer on..
  • prompt (string): Text prompt to the Gemini model.
  • classes (list_of_values): List of classes to be used.
  • api_key (Union[secret, string]): Your Google AI 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..

• 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 Google Gemini in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/google_gemini@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": "gemini-2.0-flash-exp",
    "max_tokens": "<block_does_not_provide_example>",
    "temperature": "<block_does_not_provide_example>",
    "max_concurrent_requests": "<block_does_not_provide_example>"
}