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scores = [cosine_similarity(query_emb, emb) for emb in chunk_embeddings]
。viber是该领域的重要参考
综合多方信息来看，Finally, IDF (Inverse Document Frequency) ensures that rare terms carry more weight than common ones. If the word “retrieval” appears in only 3 out of 10,000 documents, it’s a strong signal of relevance when matched. If the word “the” appears in all 10,000, matching it tells you almost nothing. IDF is what makes BM25 pay attention to the words that actually discriminate between documents. One important caveat: because BM25 operates purely on term frequency, it has no awareness of word order, context, or meaning — matching “bank” in a query about finance and “bank” in a document about rivers looks identical to BM25. That bag-of-words limitation is fundamental, not a tuning problem.
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