From c1d53b15dc217852eb8d466ee608f4f86d11886a Mon Sep 17 00:00:00 2001 From: Joachim Date: Tue, 10 Dec 2024 09:39:45 +0100 Subject: [PATCH] chore: vault backup from Joachims-PC at 2024-12-10T09:39 --- Rentry OER Klappe/rentry.md | 5 +++++ relichat.png | Bin 0 -> 1296800 bytes relichat/Skizze.md | 24 ++++++++++++++++++++++++ 3 files changed, 29 insertions(+) create mode 100644 Rentry OER Klappe/rentry.md create mode 100644 relichat.png create mode 100644 relichat/Skizze.md diff --git a/Rentry OER Klappe/rentry.md b/Rentry OER Klappe/rentry.md new file mode 100644 index 0000000..1c09fa4 --- /dev/null +++ b/Rentry OER Klappe/rentry.md @@ -0,0 +1,5 @@ +https://rentry.co/ + +https://github.com/radude + + diff --git a/relichat.png b/relichat.png new file mode 100644 index 0000000000000000000000000000000000000000..5a631fead4bdbc354ef74530f76f256c0bb9be33 GIT binary patch literal 1296800 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Der Prozessablauf lässt sich in mehrere Kernschritte aufteilen: + +Der Prozess beginnt mit der Benutzereingabe über die Chat-Oberfläche, wo Lehrkräfte ihre Anfragen zum Religionsunterricht eingeben können. Das System analysiert zunächst die Fragestellung und den Bedarf (dargestellt durch die erste Raute "klärt Fragestellung/Bedarf"). + +Nach der Analyse generiert das System eine RAG-Anfrage (Retrieval Augmented Generation). Diese Technologie ermöglicht es, relevante Informationen aus verschiedenen Datenquellen gezielt abzurufen und zu verarbeiten. + +Die Wissensbasis des Systems ist mehrschichtig aufgebaut: + +- Eine materialbasierte Datenbank enthält strukturierte Informationen zu Unterrichtsentwürfen, Lernaktivitäten, Methoden, Medien und allgemeinen Informationen. Jeder dieser Bereiche hat spezifische Attribute - beispielsweise enthält ein Unterrichtsentwurf Angaben zu Verlaufsplan, Intention, Kompetenzen und Klassenstufe. + +Das System kann bei Bedarf auf externe Ressourcen zugreifen: + +- Über GraphQL-Schnittstellen werden Community-Ressourcen eingebunden, die Themen, Keywords und Zielgruppen-Informationen enthalten +- API-Zugriffe ermöglichen die Anbindung an OER-Portale wie Openverse, OERSI, WLO und Mundo +- Ein EDU-Feed-System ist ebenfalls angebunden + +Der Kern der Verarbeitung liegt in der KI-gestützten Analyse ("prüft die Ergebnisse, wählt relevante aus und formuliert eine Antwort"). Hier werden die unterschiedlichen Aufgaben und Fähigkeiten der KI eingesetzt, um die passendsten Ressourcen auszuwählen und eine kohärente Antwort zu formulieren. + +Besonders interessant ist die Integration von relilab Tutorials mit speziellen Kategorien für Kollaboration, Editor-Funktionen, generative KI und Bildbearbeitung. Dies deutet darauf hin, dass das System nicht nur Informationen bereitstellt, sondern auch aktiv bei der Materialerstellung und -bearbeitung unterstützt. + +Die Architektur folgt damit einem modernen Multi-Agenten-Ansatz, wie er in den Dokumenten beschrieben ist, mit spezialisierten Agenten für verschiedene Aufgabenbereiche und einem übergeordneten Koordinator-Agenten. Dies ermöglicht eine flexible und effiziente Verarbeitung der Anfragen unter Einbeziehung aller verfügbaren Ressourcen.

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