Tutorials
The IONOS AI Model Hub offers powerful AI capabilities to meet various needs. Here are five pivotal use cases you can implement with this service:
Use Case 1: 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, allowing for high-quality text generation with minimal setup.
Key Features:
Access open-source Large Language Models (LLMs) via an OpenAI-compatible API.
Ensure data privacy with processing confined within Germany.
For step-by-step instructions on text generation, see the Text Generation tutorial.
Use Case 2: 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 realism or creative, artistic output.
To learn how to implement image generation, see the Image Generation tutorial.
Use Case 3: Document Embeddings
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 Embeddings tutorial.
Use Case 4: 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:
Use foundation models with additional context from document collections.
Enhance response accuracy and relevance for user queries.
To learn how to implement Retrieval Augmented Generation, see the Retrieval Augmented Generation tutorial.
Use Case 5: Tool Integration
The IONOS AI Model Hub can be seamlessly integrated into various frontend tools that use Large Language Models or text-to-image models through its OpenAI-compatible API. This integration allows you to leverage foundation models in applications without complex setups. For example, using the tool AnythingLLM, you can configure and connect to the IONOS AI Model Hub to serve as the backend for Large Language Model functionalities.
Key Features:
Easily connect to third-party tools with the OpenAI-compatible API.
Enable custom applications with IONOS-hosted foundation models.
For detailed guidance on integrating with tools, see the Tool Integration tutorial.
These tutorials will guide you through each use case, providing clear and actionable steps to integrate advanced AI capabilities into your applications using the IONOS AI Model Hub.
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