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A highly efficient, isomorphic, full-featured, multilingual text search engine library, providing full-text search, fuzzy matching, phonetic scoring, document indexing and more, with micro JSON state hydration/dehydration in-browser and server-side.
An AI-powered solution for efficient document querying. It uses Llama Index for vector-based indexing and OpenAI's GPT to interpret natural language queries, providing accurate search results.
Educational Document Base prototype to perform queries based on similarity and dissimilarity measures of documents to which stemming, lemmantization and latent semantic indexing was applied.
The purpose of this project is also to compare the efficiency and performance of two different methods for handling search operations: the inverted index and the term-document matrix