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RAG Notebook

A retrieval-augmented generation prototype that answers questions grounded in personal notes, slides, and publications.

Objective
Test chunking and retrieval strategies so answers cite real source documents instead of hallucinating.
Status
Experimental
Stack
PythonVector DBLLM APIs
Notes
Benchmarks different embedding models against a fixed question set built from real course material.
Prototype. This experiment is not a production application.