Tutorials

The IONOS AI Model Hub offers powerful AI capabilities to meet various needs. Here are three pivotal use cases you can implement with this service:

Use Case 1: Foundation Models

Foundation models are pre-trained on extensive datasets, allowing you to leverage state-of-the-art AI for text and image generation. These models can streamline tasks such as content generation, summarization, and question-answering.

Key Features:

• Access various open-source Large Language Models (LLMs) and text-to-image models without managing the hardware.

• Ensure data privacy with processing confined within Germany.

For a step-by-step guide on using Foundation Models, see Foundation Models tutorial.

Use Case 2: 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 3: 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 RAG, see the Retrieval Augmented Generation 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.