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Prompts store all configuration for interacting with a model: system instructions, messages, model selection, and parameters. A Prompt can be reused across Playgrounds, Experiments, Deployments, and Agents.
Controls creativity and predictability. Higher values produce more diverse, surprising responses. Lower values produce more predictable output that sticks closely to learned patterns.
Max Tokens
Sets the upper limit on tokens the model can produce in a single output. Prevents excessively long responses while ensuring enough room for a complete answer.
Top K
Narrows the model’s choices to the k most likely tokens at each generation step. Helps refine output to follow particular patterns or meet specific criteria.
Top P
Nucleus sampling. Selects a pool of likely words based on a probability threshold, balancing creativity with coherence.
Frequency Penalty
Discourages the model from reusing the same words or phrases, encouraging richer and more varied language.
Presence Penalty
Discourages repeated topics or ideas rather than specific words. Higher values lead to more creative and diverse responses.
Use {{variable_name}} syntax inside a message to declare a dynamic input. All declared inputs appear in the Inputs block at the top-right of the page, where default values can be set for testing.
In a Deployment, populate variables through the inputs field when invoking.
Image generation models create images from text descriptions. Select a model with an image tag in the model picker.Selecting an Image Generation Model
Configuring Parameters for Image ModelsImage generation models have different parameters compared to chat models. Parameters vary per model and affect the generated output.
Using Image Generation in PlaygroundUse image models in the Playground like any other model. Generated images appear as regular messages and can be viewed fullscreen.
Use Cases
Creative Content: Generate artwork, illustrations, and visual content for marketing materials
Product Design: Create mockups and visual prototypes based on descriptions
Content Creation: Generate images for blogs, social media, and presentations
Concept Visualization: Turn abstract ideas into visual representations
Best Practices
Be Specific: Provide detailed descriptions for better results
Style Guidelines: Include artistic style, mood, and visual elements in your prompts
Parameter Tuning: Experiment with model-specific parameters to achieve desired output quality
Iterative Refinement: Use generated images as starting points for further refinement
Vision models analyze and interpret images. Select a model with a vision tag in the model picker.
To include an image as input in the Playground, click the image icon at the top-right of the message. Share a link or upload a file to pass to the model.
Use Cases
Document Processing: Extract text and information from scanned documents and forms
Visual Quality Control: Analyze product images for defects or compliance
Content Moderation: Automatically review images for inappropriate content
Medical Imaging: Analyze medical scans and diagnostic images (with appropriate models)
Insurance Claims: Process damage assessment photos and documentation
Best Practices
Image Quality: Ensure images are clear and well-lit for best analysis results
Specific Questions: Ask focused questions about what you want to extract or understand
Context Provision: Provide context about what the image represents for better interpretation
Multiple Angles: For complex analysis, consider providing multiple views of the same subject
Pass images to a vision-capable model via a Deployment invocation using the image_url content type with a URL or base64-encoded data URI.
curl --request POST \ --url https://api.orq.ai/v2/deployments/invoke \ --header 'accept: application/json' \ --header 'content-type: application/json' \ --data '{ "messages": [ { "role": "user", "content": "describe what you see in this image" }, { "content": [ { "type": "image_url", "image_url": { "url": "Either a URL of the image or the base64 encoded image data." } } ] } ]}'