1 Knowledge base
no document loaded yet — drop a file or load the sample.
2 Ask a question
load a document first — the embeddings need to be ready before retrieval can run.
3 Retrieval
assembled CONTEXT sent to the model (top-k joined)
4 Same model, two arms
Without RAG generic, no document
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With RAG grounded in your CV
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i How this works
embed → retrieve → augment → generate. Your document is split into chunks and
embedded with
all-MiniLM-L6-v2 (transformers.js, 384-dim, in your browser).
A question is embedded the same way; the closest chunks by cosine similarity become the CONTEXT for the answer.
The left arm gets no context and guesses; the right arm reads your actual CV. Load a document to begin.