As artificial intelligence continues to evolve, it is paramount to ensure that user data is handled responsibly and transparently. This commitment protects user privacy and builds trust in the technology we provide. At IONOS, we strive to uphold the highest standards in data handling while delivering innovative AI solutions through our AI Model Hub.

Data Collection, Usage, and Storage

• Stateless Service: AI Model Hub operates as a stateless service, meaning it does not retain user prompts or outputs. Each session is independent, ensuring that your interactions are not logged or recorded for future reference or used to train or improve models.

• Data Collections: Customers can upload their documents to a Vector database. Each collection is dedicated to a customer by design and is securely stored in a database in our Berlin data center, ensuring that user data is subject to strict geographic regulations and protections.

• Service Logs: We collect standard service logs like any other application. These logs are essential for operational integrity and performance tracking. They are used to:

  • Monitor the overall performance and health of the AI Model Hub service.

  • Identify and troubleshoot any potential issues that may arise.

  • Provide support services effectively when users encounter difficulties.

Data Security

Our infrastructure is hosted in our Berlin data center, designed to meet stringent security standards.

We employ multiple security measures, including:

• Encryption protocols to protect data both at rest and in transit.

• The use of API key tokens protects API endpoints.

• Regular security audits and compliance checks to identify and mitigate potential vulnerabilities.

Privacy Guidelines

GDPR compliance: AI Model Hub is designed to comply with the General Data Protection Regulation (GDPR), ensuring that we respect and uphold user rights regarding personal data. For more information, please check our Data Processing Agreement.

The IONOS AI Model Hub 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 facilitate developers' implementation of sophisticated AI solutions without worrying about underlying hardware and operational overheads.

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.

• Document Embeddings: Store and query extensive document collections based on semantic similarity.

• Retrieval Augmented Generation (RAG): Enhance responses by combining Large Language Models with contextually relevant documents stored in a vector database.

Features

The IONOS AI Model Hub Service offers a wide array of features tailored to meet the needs of modern developers:

1. Managed Hosting: Utilize AI models without needing to maintain the underlying infrastructure.

2. Security and Compliance: Keep your data secure and compliant with regulations, as data processing is confined within Germany. Your input data is not used for training purposes in any way.

3. Scalability: Scale your AI deployments seamlessly based on your needs.

4. Integration Options: Easily integrate with your applications using REST APIs that are fully OpenAI-compatible, with support for popular programming languages like Python and Bash.

5. 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.

6. Document Embeddings: Store and manage document collections and perform semantic similarity searches to extract contextually relevant information.

7. Retrieval Augmented Generation: Combine vector databases and Large Language Models to generate enhanced outputs that are contextually aware, providing more accurate and helpful responses.

8. 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 IONOS AI Model Hub 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 via 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.

Document Embeddings

Vector databases provide a way to store and manage document collections, enabling semantic similarity searches. Documents are converted to embeddings (vector representations), allowing the discovery of related content through similarity searches.

Key Points:

• Persist documents and search for semantically similar content.

• Use API endpoints to manage document collections and perform searches.

• Ensure document storage and processing stays within Germany.

Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation combines the capabilities of Large Language Models and vector databases to improve the quality of responses. By supplementing the inherent knowledge of Large Language Models with specific, contextually relevant information from document collections, Retrieval Augmented Generation provides more accurate and detailed answers.

Key Points:

• Use Large Language Models together with document collections from vector databases.

• Improve response accuracy and relevance by incorporating additional 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 and easy integration of AI capabilities into your applications. The IONOS AI Model Hub provides two API options for maximum flexibility: its native IONOS AI Model Hub API and an OpenAI-compatible API, making it easy to work with tools that support OpenAI endpoints.

OpenAI-Compatible Endpoints:

These endpoints mirror OpenAI’s API structure, allowing for seamless integration with tools and platforms already designed for OpenAI:

1. Models: Retrieve the list of available models and their details.

2. Chat Completions: Generate conversational responses using supported Large Language Models.

3. Image Generations: Create high-quality images based on text prompts.

Native IONOS AI Model Hub Endpoints:

1. Model Management: Endpoints for retrieving model lists, querying models, and managing predictions.

2. Document Management: Endpoints for creating, modifying, retrieving, and deleting document collections and individual documents.

3. Querying and Generating: Endpoints for combining semantic searches with Large Language Models to implement Retrieval Augmented Generation scenarios.

Authentication and Authorization

Security is paramount, and IONOS provides robust mechanisms to authenticate and authorize API requests. Users must generate and use API tokens to access the AI services securely.

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.

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 does not backup the data saved to collections in the vector database. Please ensure that you can restore the content of your collections in case of deletion.

The IONOS AI Model Hub API allows you to access vector databases to persist your document collections and find semantically similar documents.

The vector database is used to persist documents in document collections. Each document is any form of pure text. In the document collection not only the input text is persisted, but also a transformation of the input text into an embedding. Each embedding is a vector of numbers. Input texts which are semantically similar have similar embeddings. A similarity search on a document collection finds the most similar embeddings for a given input text. These embeddings and the corresponding input text are returned to the user.

Overview

This tutorial is intended for developers. It assumes you have basic knowledge of:

• REST APIs and how to call them

• A programming language to handle REST API endpoints (for illustration purposes, the tutorials uses Python and Bash scripting)

By the end of this tutorial, you'll be able to:

• Create, delete and query a document collection in the IONOS vector database

• Save, delete and modify documents in the document collection and

• Answer customer queries using the document collection.

Background

• The IONOS AI Model Hub API offers a vector database that you can use to persist text in document collections without having to manage corresponding hardware yourself.

• Our AI Model Hub API provides all required functionality without your data being transfered out of Germany.

Before you begin

Manage document collections

In this section you learn how to create a document collection. We will use this document collection to fill it with the data from your knowledge base in the next step.

To track, if something went wrong this section also shows how to:

• List existing document collections

• Remove document collections

• Get meta data of a document collection

Create document collections

To create a document collection, you have to specify the name of the collection and a description and invoke the endpoint to generate document collections:

If the creation of the document collection was successful, the status code of the request is 201 and it returns a JSON document with all relevant information concerning the document collection.

To modify the document collection you need its identifier. You can extract it from the returned JSON document in the variable id.

List existing document collections

To ensure that the previous step went as expected, you can list the existing document collections.

To retrieve a list of all document collections saved by you:

This query returns a JSON document consisting of your document collections and corresponding meta information

The result consists of 8 attributes per collection of which 3 are relevant for you:

• id: The identifier of the document collection

• properties.description: The textual description of the document collection

• properties.documentsCount: The number of documents persisted in the document collection

If you have not created a collection yet, the field items is an empty list.

Remove a document collection

If the list of document collections consists of document collections you do not need anymore, you can remove a document collection by invoking:

This query returns a status code which indicates whether the deletion was successful:

• 204: Status code for successfull deletion

• 404: Status code given the collection did not exist

Get meta data for a document collection

If you are interested in the meta data of a collection, you can extract it by invoking:

This query returns a status code which indicates whether the collection exists:

• 200: Status code if the collection exists

• 404: Status code given the collection does not exist

The body of the request consists of all meta data of the document collection.

Manage documents in document collection

In this section, you learn how to add documents to the newly created document collection. To validate your insertion, this section also shows how to

• List the documents in the document collection,

• Get meta data for a document,

• Update an existing document and

• Prune a document collection.

Add documents to document collection

To add an entry to the document collection, you need to at least specify the content, the name of the content and the contentType:

This request returns a status code 200 if adding the document to the document collection was successful.

List existing documents in document collection

To ensure that the previous step went as expected, you can list the existing documents of your document collection.

To retrieve a list of all documents in the document collection saved by you:

This query returns a JSON document consisting of your documents in the document collection and corresponding meta information

The result has a field items with all documents in the collection. This field consists of 10 attributes per entry of which 5 are relevant for you:

• id: The identifier of the document

• properties.content: The base64 encoded content of the document

• properties.name: The name of the document

• properties.description: The description of the document

• properties.labels.number_of_tokens: The number of tokens in the document

If you have not created the collection yet, the request will return a status code 404. It will return a JSON document with the field items set to an empty list if no documents were added yet.

Get meta data for a document

If you are interested in the metadata of a document, you can extract it by invoking:

This query returns a status code which indicates whether the document exists:

• 200: Status code if the document exists

• 404: Status code given the document does not exist

The body of the request consists of all meta data of the document.

Update a document

If you want to update a document, invoke:

This will replace the existing entry in the document collection with the given id by the payload of this request.

Prune a document collection

If you want to remove all documents from a document collection invoke:

This query returns the status code 204 if pruning the document collection was successful.

Query documents in the document collection

Finally, this section shows how to use the document collection and the contained documents to answer a user query.

To retrieve the documents relevant for answering the user query, invoke the query endpoint as follows:

This will return a list of the NUM_OF_DOCUMENTS most relevant documents in your document collection for answering the user query.

Summary

In this tutorial you learned how to use the IONOS AI Model Hub API to conduct semantic similarity searches using our vector database.

Namely, you learned how to:

• Create a necessary document collection in the vector database and modify it

• Insert your documents into the document collection and modify the documents

• Conduct semantic similarity searches using your document collection.

The IONOS AI Model Hub offers an OpenAI-compatible API that enables powerful text generation capabilities through foundation models. These Large Language Models (LLMs) can perform a wide variety of tasks, such as generating conversational responses, summaries, and contextual answers, without requiring you to manage hardware or extensive infrastructure.

Supported Text Generation Models

The following models are currently available for text generation, each suited to different applications:

Overview

In this tutorial, you will learn how to generate text using foundation models via the IONOS API. This tutorial is intended for developers with basic knowledge of:

• REST APIs

• A programming language for handling REST API endpoints (Python and Bash examples are provided)

By the end, you will be able to:

1. Retrieve a list of text generation models available in the IONOS AI Model Hub.

2. Apply prompts to these models to generate text responses, supporting applications like virtual assistants and content creation.

Getting Started with Text Generation

To use text generation models, first set up your environment and authenticate using the OpenAI-compatible API endpoints.

Step 1: Retrieve Available Models

Fetch a list of models to see which are available for your use case:

# Python example to retrieve available models
import requests

IONOS_API_TOKEN = "[YOUR API TOKEN HERE]"

endpoint = "https://openai.inference.de-txl.ionos.com/v1/models"

header = {
    "Authorization": f"Bearer {IONOS_API_TOKEN}", 
    "Content-Type": "application/json"
}
requests.get(endpoint, headers=header).json()

#!/bin/bash

IONOS_API_TOKEN=[YOUR API TOKEN HERE]

curl -H "Authorization: Bearer ${IONOS_API_TOKEN}" \
     --get https://openai.inference.de-txl.ionos.com/v1/models

This query returns a JSON document listing each models name, which you’ll use to specify a model for text generation in later steps.

Step 2: Generate Text with Your Prompt

To generate text, send a prompt to the chat/completions endpoint.

# Python example for text generation
import requests

IONOS_API_TOKEN = "[YOUR API TOKEN HERE]"
MODEL_NAME = "[MODEL NAME HERE]"
PROMPT = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Hello!"}
  ]

endpoint = "https://openai.inference.de-txl.ionos.com/v1/chat/completions"

header = {
    "Authorization": f"Bearer {IONOS_API_TOKEN}", 
    "Content-Type": "application/json"
}
body = {
    "model": MODEL_NAME,
    "messages": PROMPT,
}
requests.post(endpoint, json=body, headers=header).json()

#!/bin/bash

IONOS_API_TOKEN=[YOUR API TOKEN HERE]
MODEL_NAME=meta-llama/Meta-Llama-3.1-8B-Instruct 
PROMPT='[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Hello!"}
]'

BODY="{ 
    \"model\": \"$MODEL_NAME\",
    \"messages\": $PROMPT
}"
echo $BODY

curl -H "Authorization: Bearer ${IONOS_API_TOKEN}" \
     -H "Content-Type: application/json" \
     -d "$BODY" \
     https://openai.inference.de-txl.ionos.com/v1/chat/completions

Step 3: Extract and Interpret the Result

The returned JSON includes several key fields, most importantly:

• choices.[].message.content: The generated text based on your prompt.

• usage.prompt_tokens: Token count for the input prompt.

• usage.completion_tokens: Token count for the generated output.

Summary

In this tutorial, you learned how to:

1. Access available text generation models.

2. Use prompts to generate text responses, ideal for applications such as conversational agents, content creation, and more.

For information on image generation, refer to our dedicated tutorial on text-to-image models.

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 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 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.

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.

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.

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.

The IONOS AI Model Hub provides an OpenAI-compatible API that enables high-quality image generation using state-of-the-art foundation models. By inputting descriptive prompts, users can create detailed images directly through the API, without the need for managing underlying hardware or infrastructure.

Supported Image Generation Models

The following models are currently available for image generation, each suited to different types of visual outputs:

Overview

In this tutorial, you will learn how to generate images using foundation models via the IONOS API. This tutorial is intended for developers with basic knowledge of:

• REST APIs

• A programming language for handling REST API endpoints (Python and Bash examples are provided)

By the end, you will be able to:

1. Retrieve a list of available image generation models in the IONOS AI Model Hub.

2. Use prompts to generate images with these models.

Getting Started with Image Generation

To use image generation models, first set up your environment and authenticate using the OpenAI-compatible API endpoints.

Step 1: Retrieve Available Models

Fetch a list of models to see which are available for your use case:

This query returns a JSON document listing each model's name, which you’ll use to specify a model for image generation in later steps.

Step 2: Generate an Image with Your Prompt

To generate an image, send a prompt to the /images/generations endpoint. Customize parameters like size for the resolution of the output image.

Step 3: Extract and Interpret the Result

The returned JSON includes several key fields, most importantly:

• data.[].b64_json: The generated image in base64 format.

• usage.prompt_tokens: Token count for the input prompt.

• usage.total_tokens: Token count for the entire process (usually zero for image generation, as billing is per image).

Summary

In this tutorial, you learned how to:

1. Access available image generation models.

2. Use descriptive prompts to generate high-quality images, ideal for applications in design, creative work, and more.

The IONOS AI Model Hub provides an OpenAI-compatible API, allowing seamless integration with various frontend tools that use Large Language Models (LLMs). This guide walks you through the setup process, using as an example tool.

By the end of this tutorial, you will be able to configure AnythingLLM to use the IONOS AI Model Hub as its backend for AI-powered responses.

Step 1: Get an Authentication Token

You will need an authentication token to access the IONOS AI Model Hub. For more information about how to generate your token in the IONOS DCD, see .

Save this token in a secure place, as you’ll need to enter it into AnythingLLM during setup.

Step 2: Select a Language Model

The IONOS AI Model Hub offers a variety of Large Language Models to suit different needs. Choose the model that best fits your use case from the table below:

During setup, you’ll enter the model’s "Model Name" value into AnythingLLM’s configuration.

Step 3: Obtain the Base URL

For connecting to the IONOS AI Model Hub, use the following Base URL for the OpenAI-compatible API:

You will enter this URL in the configuration settings of AnythingLLM.

Step 4: Configure AnythingLLM

With your authentication token, selected model name, and base URL in hand, you’re ready to set up AnythingLLM:

1. Open AnythingLLM and go to the configuration page for the Large Language Model (LLM) settings.

   • In AnythingLLM, this can be accessed by clicking the wrench icon in the lower left corner, then navigating to AI Providers -> LLM.

2. Choose Generic OpenAI as the provider.

3. Enter the following information in the respective fields:

   • API Key: Your IONOS authentication token.

   • Model Name: The name of the model you selected from the table (e.g., meta-llama/Meta-Llama-3.1-8B-Instruct).

   • Base URL: https://openai.inference.de-txl.ionos.com/v1

Your screen should look similar to the image below:

1. Click Save Changes to apply the settings.

From now on, AnythingLLM will use the IONOS AI Model Hub as its backend, enabling AI-powered functionality based on your chosen Large Language Model.

Summary

This guide provides a straightforward path for integrating the IONOS AI Model Hub into third-party frontend tools using the OpenAI-compatible API. For other tools and more advanced configurations, the steps will be similar: generate an API key, select a model, and configure the tool’s API settings.

The IONOS AI Model Hub allows you to combine Large Language Models and a vector database to implement Retrieval Augmented Generation use cases.

Retrieval Augmented Generation is an approach that allows you to teach an existing Large Language Model, such as LLama or Mistral, to answer not only based on the knowledge the model learned during training, but also based on the knowledge you specified yourself.

Retrieval Augmented Generation uses two components:

• a Large Language Model (we offer corresponding models for ) and

If one of your users queries your Retrieval Augmented Generation system, you first get the most similar documents from the corresponding document collection. Second, you ask the Large Language Model to answer the query by using both the knowledge it was trained on and the most similar documents from your document collection.

Overview

This tutorial is intended for developers. It assumes you have basic knowledge of:

• REST APIs and how to call them

• A programming language to handle REST API endpoints (for illustration purposes, the tutorials use Python and Bash scripting)

You should also be familiar with the IONOS:

By the end of this tutorial, you'll be able to: Answer customer queries using a Large Language Model which adds data from your document collections to the answers.

Background

• The IONOS AI Model Hub API offers both document embeddings and Large Language Models that you can use to implement retrieval augmented generation without having to manage corresponding hardware yourself.

• Our AI Model Hub API provides all required functionality without your data being transferred out of Germany.

Before you begin

You will need this identifier in the subsequent steps.

Access list of available Large Language Models

To retrieve a list of Large Language Models supported by the IONOS AI Model Hub API enter:

This query returns a JSON document consisting of all foundation models and corresponding meta information.

The JSON document consists an entry items*. This is a list of all available foundation models. Of the 7 attributes per foundation model 3 are relevant for you:

• id: The identifier of the foundation model

• properties.description: The textual description of the model

• properties.name: The name of the model

From the list you generated in the previous step, choose the model you want to use and the id. You will use this id in the next step to use the foundation model.

Manual retrieval augmented generation

This section shows how to use the document collection and the contained documents to answer a user query.

Step 1: Retrieve relevant documents

To retrieve the documents relevant to answering the user query, invoke the query endpoint as follows:

This will return a list of the NUM_OF_DOCUMENTS most relevant documents in your document collection for answering the user query.

Step 2: Generate final answer

Now, combine the user query and the result from the document collection in one prompt:

The result will be a JSON-Document consisting of the answer to the customer and some meta information. You can access it in the field at properties.output

Automated Retrieval Augmented Generation

The IONOS AI Model Hub allows for automating the process described above. Namely, by specifying the collection ID and the collection query directly to our foundation model endpoint, it first queries the document collection and returns it in a variable which you can then directly use in your prompt. This section describes how to do this.

Apply combined retrieval augmented generation prompt to foundation model

To implement a Retrieval Augmented Generation use case with only one prompt, you have to invoke the /predictions endpoint of the Large Language Model you want to use and send the prompt as part of the body of this query:

This query conducts all steps necessary to answer a user query using Retrieval Augmented Generation:

• The user query (saved at collectionQuery) is sent to the collection (specified at collectionId).

• The results of this query are saved in a variable .context, while the user query is saved in a variable .collection_query. You can use both variables in your prompt.

• The example prompt uses the variables .context and .collection_query to answer the customer query.

Summary

In this tutorial, you learned how to use the IONOS AI Model Hub API to implement Retrieval Augmented Generation use cases.

Namely, you learned how to: Derive answers to user queries using the content of your document collection and one of the IONOS foundation models.

The IONOS AI Model Hub is a comprehensive platform that empowers developers to easily implement advanced AI functionalities. You can enhance your applications' capabilities by leveraging managed foundation models, vector databases, and advanced retrieval augmented generation techniques while ensuring security and compliance. Explore the potential of IONOS AI Model Hub Service to transform your AI projects today.

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