How-Tos

The IONOS AI Model Hub offers powerful AI capabilities to meet various needs. Here are 10 pivotal guides on how you can implement the features of this service:

Prerequisite: Authentication Tokens

Before using the IONOS AI Model Hub, you must authenticate your requests using an authentication token. These tokens ensure secure access and help manage user permissions, ensuring that only authorized users can interact with the service.

Key Features

• Tokens are associated with specific IONOS Public Cloud users and grant access to the AI Model Hub.

• Usage and billing are tied to the IONOS Public Cloud contract owner, who is responsible for the associated users.

For detailed instructions on how to generate an IONOS Public Cloud contract, create users, and generate authentication tokens, see Access Management guide.

1: Rate Limits

To ensure fair usage, maintain system stability, and prevent abuse, the IONOS AI Model Hub enforces rate limits on all API requests. This guide introduces the rationale behind these limits, how they are applied, and how you can effectively monitor and manage your requests.

Key Features

• Understand the purpose of rate limits and how they help ensure fair, secure access to the AI Model Hub.

• Learn how rate limits are applied across different endpoints and request types.

• Get an overview of the current rate limit values and how to work with them effectively.

For detailed overview of rate limiting, see the Rate Limits guide.

2: Text Generation

Text generation models enable advanced language processing tasks, such as content creation, summarization, conversational responses, and question-answering. These models are pre-trained on extensive datasets, enabling high-quality text generation with minimal setup.

Key features

• Access open-source Large Language Models (LLMs) through an OpenAI-compatible API.

• Ensure data privacy with processing confined within Germany.

For step-by-step instructions on text generation, see the Text Generation guide.

3: Enrich Text with AI-Generated Images

Adding relevant visuals to text—whether on a website, social media, or marketing materials—boosts engagement and impact. With state-of-the-art Text-to-Image models, you can generate high-quality images from descriptive text prompts.

Key Learnings

• Understand the benefits of enriching text with AI-generated images.

• Learn how to craft effective prompts for Text-to-Image models.

• Generate photorealistic or stylized images tailored to your needs.

For a step-by-step guide, see Enrich Texts with AI-Generated Images.

4: Tool Calling

Tool calling enables AI models to interact with external services and APIs by recognizing when more data or actions are required. This feature allows models to fetch external information or trigger actions through structured requests, helping to extend the model’s capabilities beyond its initial training data.

Key Features

• Enable AI models to make calls to external tools and services in response to user queries.

• Use JSON schemas to define and structure the tool requests, ensuring precise parameters.

• Seamlessly integrate external APIs for enhanced functionality, such as retrieving real-time data or performing system actions.

For a detailed walkthrough, including an example with a weather application, see the Tool Calling guide.

5: Image Generation

Image generation models allow you to create high-quality, detailed images from descriptive text prompts. These models can be used for applications in creative design, marketing visuals, and more.

Key features

• Generate photorealistic or stylized images based on specific prompts.

• Choose from models optimized for authenticity or creative and artistic outputs.

To learn how to implement image generation, see the Image Generation guide.

6: Text Embeddings

Embedding models allow you to create numerical representations of texts, which are similar if the texts are semantically similar. These models are ideal for applications like text retrieval, comparison, ranking, and so on.

Key features

• Identify texts that answer a query based on semantic similarity between the query and the potential answer.

• Compare texts to determine their semantic closeness or difference.

To learn how to derive embeddings and calculate the similarity of texts, see the Text Embeddings guide.

7: Document Collections

Vector databases enable you to store and query large collections of documents based on semantic similarity. Converting documents into embeddings allows you to perform effective similarity searches, making it ideal for applications like document retrieval and recommendation systems.

Key features

• Persist documents and search for semantically similar content.

• Manage document collections through simple API endpoints.

For detailed instructions, see Document Collections guide.

8: Intelligent Document Search with AI

Finding the right document in file storage can be frustrating when you remember the topic but not the exact location. With Retrieval Augmented Generation, AI can help you search smarter by retrieving documents based on their meaning, not just keywords.

Key Learnings

• Understand how document collections are used to store documents based on semantic similarity.

• Upload documents to a searchable collection for easy retrieval.

• Improve search accuracy and relevance for user queries.

To optimize your file searches, check out Intelligent Document Search with AI.

By leveraging these AI-powered solutions, you can streamline workflows, improve efficiency, and unlock new possibilities for your business. Explore more advanced AI features today!

9: Retrieval Augmented Generation (RAG)

RAG combines the strengths of foundation models and vector databases. It retrieves the most relevant documents from the database and uses them to augment the output of a foundation model. This approach enriches the responses, making them more accurate and context-aware.

Key features

• Combine foundation models with more context retrieved from document collections.

• Enhance response accuracy and relevance for user queries.

To learn how to implement Retrieval Augmented Generation, see the Retrieval Augmented Generation guide.

10: Tool Integration

The IONOS AI Model Hub can be integrated into third-party tools that support OpenAI-compatible APIs. This enables you to use hosted foundation models for both language and image tasks within existing platforms — without managing infrastructure or model hosting.

Key features

• Connect to frontend and development tools using the OpenAI-compatible API.

• Leverage hosted Large Language and text-to-image models in external applications.

• Integrate seamlessly with tools like AnythingLLM to power chat interfaces, document Q&A, and more.

For detailed guidance on integrating with tools, see the Tool Integration guide.

Explore further

These guides will guide you through the basic functionality of the AI Model Hub, providing clear and actionable steps to integrate advanced AI capabilities into your applications.