In classroom question-answering, vector RAG (OpenAI) excels at fact lookup, GraphRAG Global at thematic questions, and GraphRAG Local at dense altered textbooks; a simple query router combines their strengths.
Foundations of Vector Retrieval
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abstract
Vectors are universal mathematical objects that can represent text, images, speech, or a mix of these data modalities. That happens regardless of whether data is represented by hand-crafted features or learnt embeddings. Collect a large enough quantity of such vectors and the question of retrieval becomes urgently relevant: Finding vectors that are more similar to a query vector. This monograph is concerned with the question above and covers fundamental concepts along with advanced data structures and algorithms for vector retrieval. In doing so, it recaps this fascinating topic and lowers barriers of entry into this rich area of research.
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Aligning LLMs for the Classroom with Knowledge-Based Retrieval -- A Comparative RAG Study
In classroom question-answering, vector RAG (OpenAI) excels at fact lookup, GraphRAG Global at thematic questions, and GraphRAG Local at dense altered textbooks; a simple query router combines their strengths.