https://docs.ai.gwdg.de/en/categories/arcana/index.htmlHugoenhttps://docs.ai.gwdg.de/en/user/ai-services/arcana/transparency/index.htmlMon, 01 Jan 0001 00:00:00 +0000https://docs.ai.gwdg.de/en/user/ai-services/arcana/transparency/index.htmlGoal Typical LLMs have the advantage, but also the problem, of having been trained on an incredible amount of data. This means that they know a lot, but are often unable to answer very specific questions. In these cases, LLMs are very prone to hallucination, which means that they basically make things up. One way to improve the performance of LLMs for very specific questions is to use Retrieval-Augmented-Generation (RAG). Here, users provide custom documents that contain the knowledge base they want to ask questions about later. Before an LLM responds to a user’s query, the most relevant documents previously provided by the user are retrieved and provided to the LLM as additional context.https://docs.ai.gwdg.de/en/user/ai-services/arcana/migration-guide/index.htmlMon, 01 Jan 0001 00:00:00 +0000https://docs.ai.gwdg.de/en/user/ai-services/arcana/migration-guide/index.htmlWarning This service is currently in beta phase and is updated regularly. The same applies to the documentation. With the introduction of our new RAG Manager, we have improved the UI and indexing process. Due to architectural changes, the new interface is only partially backward compatible, particularly in terms of file storage and indexing. This guide explains: What still works between the old and new interface What does not work How to migrate your existing Arcanas from the old manager to the new System Details The old version is still accessible during the transition period and will be removed within the next months. You can continue to use it herehttps://docs.ai.gwdg.de/en/user/ai-services/arcana/rag-service/index.htmlMon, 01 Jan 0001 00:00:00 +0000https://docs.ai.gwdg.de/en/user/ai-services/arcana/rag-service/index.htmlWarning This service is currently in beta phase and is updated regularly. The same applies to the documentation. RAG (Retrieval-Augmented Generation) is an advanced AI technique designed to improve the accuracy, reliability, and contextual relevance of AI-generated responses. Traditional AI models, such as large language models (LLMs), rely solely on pre-trained data to generate answers. While these models can provide insightful responses, they are limited by the information they were trained on, which may become outdated or may not cover specific topics in detail.https://docs.ai.gwdg.de/en/user/ai-services/arcana/getting-started/index.htmlMon, 01 Jan 0001 00:00:00 +0000https://docs.ai.gwdg.de/en/user/ai-services/arcana/getting-started/index.htmlWarning This service is currently in beta phase and is updated regularly. The same applies to the documentation. Table of contents: First Login Creating an Arcana Uploading Files Viewing your Access Link Updating files in an Arcana The process screenshots contain blank blocks. These are in the positions that will be replaced with the username. First Login Go to the main Arcana page and click on register. You will need to log in with your Academic Cloud account first.https://docs.ai.gwdg.de/en/user/ai-services/arcana/how-to-use/index.htmlMon, 01 Jan 0001 00:00:00 +0000https://docs.ai.gwdg.de/en/user/ai-services/arcana/how-to-use/index.htmlWarning This service is currently in beta phase and is updated regularly. This also applies to the documentation. Warning Previously, RAG/Arcanas only worked with specific models. This is no longer the case, and Arcanas now work with every model. The previous method of using Arcanas with models that have ‘RAG’ in their name (or the little book icon) will not be supported in future. Please use the GWDG Tools as described below!https://docs.ai.gwdg.de/en/procurement/index.htmlMon, 01 Jan 0001 00:00:00 +0000https://docs.ai.gwdg.de/en/procurement/index.htmlAvailable AI Products Within the Academic Cloud The Academic Cloud is a modular service portal available nationwide for universities, colleges, and research institutions. As a shared platform, it enables the provision, development, and use of digital services across institutional boundaries and supports cross-institutional collaboration.https://docs.ai.gwdg.de/en/user/ai-services/arcana/api-usage/index.htmlMon, 01 Jan 0001 00:00:00 +0000https://docs.ai.gwdg.de/en/user/ai-services/arcana/api-usage/index.htmlWarning This service is currently in beta phase and is updated regularly. The same applies to the documentation. The Arcana Manager exposes and api endpoint for you to use. With the api endpoint you can perform all the actions that the frontend Arcana Manager is capable of. To use the API, you’ll first need an API key, which you can request on our website. This key works across all our services. If you’re already using the LLMs via the api, you can use the same key. Otherwise you can request your key following this documentation.https://docs.ai.gwdg.de/en/user/ai-services/arcana/index.htmlMon, 01 Jan 0001 00:00:00 +0000https://docs.ai.gwdg.de/en/user/ai-services/arcana/index.htmlArcana is a Retrieval-Augmented Generation (RAG) service that enables you to interact with Documents - such as research papers, manuals, or study materials - using natural language. The Arcana service works together with our Chat AI Service. LLMs benefit from RAG (Retrieval-Augmented Generation) by using the relevant information provided in a Document Collection (Arcana) for generating more accurate, reliable, and contextually grounded responses. In order to use it, you need to activate your account for the Arcana page and set up an Arcana following the Getting Started Guide. Once this is done, you can share it with colleagues or the public. Details about this process can be found in the usage examples.https://docs.ai.gwdg.de/en/user/ai-services/arcana/docling-process/index.htmlMon, 01 Jan 0001 00:00:00 +0000https://docs.ai.gwdg.de/en/user/ai-services/arcana/docling-process/index.htmlWarning This service is currently in beta phase and is updated regularly. The same applies to the documentation. The RAG Service provides users with an efficient way to upload and process PDF documents using Docling. The system converts uploaded PDFs into Markdown format while also automatically annotating them. This enhanced Markdown output, referred to as Markdown Plus, includes metadata and structural annotations for improved document parsing and customization.https://docs.ai.gwdg.de/en/user/ai-services/arcana/public-arcanas/index.htmlMon, 01 Jan 0001 00:00:00 +0000https://docs.ai.gwdg.de/en/user/ai-services/arcana/public-arcanas/index.htmlWarning This service is currently in beta phase and is updated regularly. The same applies to the documentation. Here is a list of public links for Arcanas. These are sorted in categories and contain public material. Please refer to the How to use section of you need help working with these. GWDG Services Contains knowledge about the GWDG Services and Documentation. Example question: Welche Dienste könnte ich als Forscher bei der GWDG nutzen? Institute for Computer Science Contain knowledge about the computer science study track. Example question: Wie funktioniert der Studiengang angewandte Informatik? In case you find a broken link, please let us know via our support contacts.https://docs.ai.gwdg.de/en/user/ai-services/arcana/support/index.htmlMon, 01 Jan 0001 00:00:00 +0000https://docs.ai.gwdg.de/en/user/ai-services/arcana/support/index.htmlWarning This service is currently in beta phase and is updated regularly. The same applies to the documentation. If you run into problem using this service please contact our KISSKI support We also need your help providing publicly available Arcana links. If you have one that you think is relevant to a larger group of users, please also reach out to us so we can publish it.