# Overview

The <code class="expression">space.vars.ionos\_cloud\_ai\_model\_hub</code> is designed to simplify the deployment and management of advanced machine learning models, eliminating the complexities associated with hardware and infrastructure. This inference service serves a range of powerful AI models that enable developers to implement sophisticated AI solutions without concerns about underlying hardware and operational overhead.

IONOS' AI Model Hub supports various use cases, including:

* **Text Generation**: Utilize pre-trained Large Language Models (LLMs) to generate text and answer queries using textual descriptions.
* **Image Generation**: Utilize pre-trained text-to-image models to create images based on textual descriptions.
* **Retrieval Augmented Generation (RAG)**: Enhance responses by combining Large Language Models with contextually relevant documents stored in a vector database.
* **Tool Calling**: Enable AI models to interact with external systems by invoking APIs or executing predefined functions. This allows for dynamic, task-based automation such as triggering workflows, retrieving real-time data, or integrating with business applications, all initiated through natural language prompts.

## Features

The <code class="expression">space.vars.ionos\_cloud\_ai\_model\_hub</code> Service offers a wide array of features tailored to meet the needs of modern developers:

* **Managed Hosting**: Utilize AI models without needing to maintain the underlying infrastructure.
* **Security and Compliance**: Keep your data secure and compliant with regulations, as data processing is confined within Germany. Your input data remains exclusively for your use and is excluded from training purposes.
* **Scalability**: Scale your AI deployments seamlessly to meet your needs.
* **Integration Options**: Integrate with your applications using REST APIs that are fully OpenAI-compatible, with support for popular programming languages like Python and Bash.
* **Diverse Model Offerings**: Choose from various foundation models, including Large Language Models and text-to-image models, each capable of generating innovative, and sophisticated AI outputs.
* **Retrieval Augmented Generation**: Combine vector databases and Large Language Models to generate enhanced outputs that are contextually aware, providing more accurate and helpful responses.
* **Token-based Billing**: Pay for the services based on the number of tokens used, enabling cost-efficient usage and transparency in billing.

## Concepts

Understanding the foundational concepts of the <code class="expression">space.vars.ionos\_cloud\_ai\_model\_hub</code> will help you leverage its full potential:

### Foundation Models

Foundation models are pre-trained on massive datasets to perform a wide range of language and image processing tasks. They can generate text, answer questions, and create images based on textual descriptions. With IONOS, you can access these models through APIs, simplifying the process of integrating advanced AI capabilities into your applications.

#### Key points

* Access various open-source Large Language Models for text generation and text-to-image models for image generation.
* Use models without managing underlying hardware.
* Maintain data privacy and comply with German data protection regulations.

### Retrieval augmented generation (RAG)

Retrieval Augmented Generation enhances the performance of Large Language Models by combining their inherent capabilities with contextually relevant information retrieved from document collections stored in vector databases. This approach allows the model to produce highly accurate and detailed responses tailored to specific queries.

#### Key points

* Use Large Language Models together with document collections from vector databases.
* Improve response accuracy and relevance by incorporating more context.
* Implement sophisticated AI solutions using a combination of querying and generation.

## Components

### API Endpoints

Use dedicated REST API endpoints to interact with various models and services. These endpoints are designed to facilitate the quick integration of AI capabilities into your applications. The <code class="expression">space.vars.ionos\_cloud\_ai\_model\_hub</code> provides two API options for maximum flexibility: its native <code class="expression">space.vars.ionos\_cloud\_ai\_model\_hub</code> API and an OpenAI-compatible API, enabling you to work with tools that support OpenAI endpoints.

#### OpenAI-Compatible endpoints

These endpoints mirror [<mark style="color:blue;">OpenAI’s API structure</mark>](https://platform.openai.com/docs/api-reference), allowing for seamless integration with tools and platforms already designed for OpenAI:

1. [<mark style="color:blue;">**Models**</mark>](https://platform.openai.com/docs/api-reference/models): Retrieve the list of available models and their details.
2. [<mark style="color:blue;">**Chat Completions**</mark>](https://platform.openai.com/docs/api-reference/chat): Generate conversational responses using supported Large Language Models.
3. [<mark style="color:blue;">**Image Generations**</mark>](https://platform.openai.com/docs/api-reference/images): Generate high-quality images based on text prompts.
4. [<mark style="color:blue;">**Embeddings**</mark>](https://platform.openai.com/docs/api-reference/embeddings): Generate text embeddings as numerical vectors for semantic search, text similarity, and clustering.

#### Native IONOS CLOUD AI Model Hub endpoints

These endpoints are used for managing document collections and performing semantic searches:

1. **Document Management**: Endpoints to create, modify, retrieve, delete document collections, and individual documents.
2. **Collection Query**: Semantic similarity search against your document collections (`POST /collections/{collectionId}/query`). Use together with the OpenAI-compatible Chat Completions endpoint to implement Retrieval Augmented Generation.

{% hint style="info" %}
**Recommendation:**

* **Text and image generation**: Use the **OpenAI-compatible endpoints** (`POST /v1/chat/completions` for text, `POST /v1/images/generations` for images).
* **Retrieval Augmented Generation**: Query your document collection with the native `/collections/{collectionId}/query` endpoint, then pass the retrieved context to the OpenAI-compatible `POST /v1/chat/completions` endpoint for response generation.
  {% endhint %}

### Authentication and Authorization

Security is paramount, and IONOS provides robust mechanisms to authenticate and authorize API requests. You must generate and use API tokens to access the AI services securely. For more information about generating a corresponding token, see [<mark style="color:blue;">Access Management</mark>](/cloud/ai/ai-model-hub/how-tos/access-management.md).

### Data privacy and compliance

IONOS ensures that all data processing complies with German and European data protection regulations. Your data is processed within Germany, providing an additional layer of security and compliance. For more information, see [<mark style="color:blue;">Data Handling</mark>](/cloud/ai/ai-model-hub/governance-and-compliance/data-handling.md).

### Technical support

IONOS offers expert technical support to help you troubleshoot and optimize your AI deployments. Whether you need assistance with API integration or model performance, the support and Professional Service team is available to ensure your success during German business hours.

### Backup of Collections in Vector Database

IONOS recommends implementing a backup strategy for the data saved to collections in the vector database. This ensures that your collections can be restored in case of accidental deletion or other unforeseen events.

## Next steps

* [<mark style="color:blue;">Text Generation</mark>](/cloud/ai/ai-model-hub/how-tos/text-generation.md) — Generate content using Large Language Models.
* [<mark style="color:blue;">Image Generation</mark>](/cloud/ai/ai-model-hub/how-tos/image-generation.md) — Create images from text prompts.
* [<mark style="color:blue;">Retrieval Augmented Generation</mark>](/cloud/ai/ai-model-hub/how-tos/retrieval-augmented-generation.md) — Combine your knowledge base with Large Language Models.
* [<mark style="color:blue;">Tool Calling</mark>](/cloud/ai/ai-model-hub/how-tos/tool-calling.md) — Connect models to external APIs and tools.
* [<mark style="color:blue;">Text Embeddings</mark>](/cloud/ai/ai-model-hub/how-tos/text-embeddings.md) — Generate vector embeddings for semantic search.


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.ionos.com/cloud/ai/ai-model-hub/ai-model-hub.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.