BM25: lexical search without embeddings
A post published on February 9, 2026 explains how BM25 scores documents using term rarity, diminishing returns for repetitions, and document-length normalization.
Source: Como o BM25 classifica documentos sem embeddings (x.com). Text prepared with AI from this source.
What happened and what to do
The post, published on February 9, 2026, explains that BM25 ranks documents by considering the rarity of query terms, reducing the weight of repetitions, and normalizing for document length. According to the post, the method requires no training, embeddings, or fine-tuning; it is a traditional lexical approach to information retrieval.
A company can implement BM25 to search internal documents and compare its results with vector search on real queries. An indexing pipeline, relevance metrics, and an evaluation dashboard can help determine when to use each method or combine them, without assuming that every search needs an AI solution.
How the consultancy can help
Wendelmaques can diagnose the search use case and document sources, define a BM25 implementation and evaluation criteria, and deliver a scoped solution, including indexing, monitoring, and operation as agreed.
Next step
Send a short description of your search use case to receive a scoped proposal.
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