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最后，Before comparing BM25 and vector search, we need a shared knowledge base to search over. We define 12 short text chunks covering a range of topics — Python, machine learning, BM25, transformers, embeddings, RAG, databases, and more. The topics are deliberately varied: some chunks are closely related (BM25 and TF-IDF, embeddings and cosine similarity), while others are completely unrelated (PostgreSQL, Django). This variety is what makes the comparison meaningful — a retrieval method that works well should surface the relevant chunks and ignore the noise.
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