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A standard Retrieval-Augmented Generation (RAG) pipeline that converts documents into embeddings, stores them in a vector database, and generates context-aware responses using an LLM.
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The project is a brand-new repository with zero stars or forks, likely representing a personal experiment or a student project (as suggested by the username). It implements a standard RAG architecture which is now a commodity feature provided natively by frontier labs (e.g., OpenAI Assistants API) and mature open-source frameworks.
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