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首先,With the corpus defined, we can build the BM25 index. The process has two steps: tokenization and indexing. The tokenize function lowercases the text and splits on any non-alphanumeric character — so “TF-IDF” becomes [“tf”, “idf”] and “bag-of-words” becomes [“bag”, “of”, “words”]. This is intentionally simple: BM25 is a bag-of-words model, so there is no stemming, no stopword removal, and no linguistic preprocessing. Every word is treated as an independent token.

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其次,result = await orchestrator.execute_notebook(notebook_cells)

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第三,print(f" Example {i + 1}:")

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