tfidf-search

TF-IDF vectorization converts text into numerical representations that capture word importance. This Skill builds a searchable index from CSV data and ranks documents by cosine similarity to your query, returning the most relevant results efficiently.

Yes. This Skill loads CSV files with a text column, applies TF-IDF vectorization with scikit-learn, and scores documents using cosine similarity. It retrieves and ranks matching documents by relevance to your search query.

TF-IDF with cosine similarity provides lightweight document ranking without complex infrastructure. This Skill handles vectorization, CSV loading with validation, and top-k result retrieval through a simple command-line interface.

Yes. This Skill includes built-in missing-value handling for CSV data, so you can process datasets with incomplete text entries without preprocessing separately.

TF-IDF balances efficiency and relevance by weighting term frequency against document prevalence. It works well for document ranking at moderate scale and requires minimal setup compared to machine learning-based retrieval methods.

Load your CSV, specify the text column, run the vectorization script with a query, and retrieve top results ranked by relevance. This Skill handles indexing and ranking in one end-to-end command.