Hybrid Retrieval System combining keyword matching (BM25) with semantic similarity (Vectorstore) for improved retrieval.
-
Updated
Jul 21, 2024 - Jupyter Notebook
Hybrid Retrieval System combining keyword matching (BM25) with semantic similarity (Vectorstore) for improved retrieval.
A distributed search engine with a C++ Crawler/Indexer, Python Ranker (BM25), and Ruby on Rails Interface. Uses RocksDB & Redis
B-SERS: Books Search Engine and Recommendation System. This system combines the functionalities of a search engine and a recommendation system to effectively address the challenges of finding and suggesting books
BM25 (Okapi BM25) implementation in TypeScript with field boosting and parallel processing support.
RAG Application with Contextual Retrieval and Lexical Retrieval.
Implementation of BM25/Okapi and BIM models for probabilistic document ranking in Information Retrieval.
Repository for the CENG543 Course Information Retrieval Term Project
An Information Retrieval system that processes and ranks news articles. It parses XML files, applies stop-word removal and stemming, and uses TF-IDF and BM25 algorithms to score documents against user queries, sorting them by relevance.
Add a description, image, and links to the bm25-okapi topic page so that developers can more easily learn about it.
To associate your repository with the bm25-okapi topic, visit your repo's landing page and select "manage topics."