A community-fridge leftover-share handbook is tidied, turned into high-dimensional vectors with a pre-trained embedder, and saved for later lookup. Which sequence best summarizes that preprocess path?
Select an answer to reveal the explanation.
Short Explanation
First tidy the text, then stamp each piece with a meaning vector, then file the stamps. Clean and process, encode as embeddings, store in a vector database — that is the RAG preprocess path. Chat-first invention or dumping raw PDFs into a case table skips the store that semantic search needs.
Full Explanation
For RAG, source text is cleaned and processed, encoded as embeddings with a pre-trained embedder, and stored in a vector database for later similarity search. Embeddings here are the retrieval store mechanism, not a Chapter 1 basics lecture. Skipping cleaning or storing only relational cases leaves semantic lookup incomplete.