EduPLEx_API
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Recommendation, reporting & analytics
Recommendation, reporting & analytics
  • Experiments report
    • Key concepts
    • Data sources
    • First demonstrator: ESCO ontologies and semantic matching
    • Software design
      • Endpoints Sbert_eduplex
      • Setup Sbert_eduplex
    • AI Applications
    • Conclusions
    • Recommendation
    • Bibliography
  • Recommendation Engine
  • Reporting and predictive analytics
  • LRS User Journey Visualizer
  • AI Tutor - RAG system
    • LLM-augmented Retrieval and Ranking for Course Recommendations
    • Retrieval of course candidates when searching via title.
    • Answer Generation Evaluation
    • Chunk Size and Retrieval Evaluation
    • Chunking Techniques – Splitters
    • Golden Case CLAPNQ
    • Comparative Retrieval Performance: Modules vs Golden Case
    • LLM-based Evaluator for Context Relevance
    • Retrieval Performance Indexing pdf vs xapi, and Keywords vs Questions
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AI Tutor - RAG system

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Last updated 3 months ago

RAG System Overview

Our Retrieval-Augmented Generation (RAG) system is a robust framework designed to enhance the accessibility and usability of learning content. The system operates through three interconnected processes.

First, during the ingestion phase, the learning content is processed into structured text chunks, which are then stored in an OpenSearch index to enable efficient retrieval.

Next, in the retrieval phase, the system leverages the capabilities of OpenSearch to identify and retrieve the most relevant text chunks based on user queries, ensuring accuracy and relevance.

Finally, the answer generation phase utilizes a Large Language Model (LLM) to synthesize coherent and contextually appropriate responses, drawing from the retrieved content. This integrated approach ensures that users receive precise and informative answers tailored to their needs, making the system a powerful tool for knowledge exploration and learning.

RAG Overview created by EduPLEx