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ragsystems

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The course provides a comprehensive guide to optimizing retrieval systems in large-scale RAG applications. It covers tokenization, vector quantization, and search optimization techniques to enhance search quality, reduce memory usage, and balance performance in vector search systems.

  • Updated Dec 28, 2024
  • Jupyter Notebook

Official implementation of "Anti-Rumor Context Integration: A Novel RAG System for Automated Factual Correction". Features multi-tier retrieval (Internal KB → Google Fact Check API → Live Web Search), hybrid semantic-keyword scoring, constrained T5 counter-narrative generation, and an XAI explainability module.

  • Updated Apr 5, 2026
  • Python

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