RagE (RAG Engine) - A tool supporting the construction and training of components of the Retrieval-Augmented-Generation (RAG) model. It also facilitates the rapid development of Q&A systems and chatbots following the RAG model.

Performance Evaluation of Rankers and RRF Techniques for Retrieval Pipelines: Employs Diversity, Lost-in-the-Middle, and Similarity rankers to reorder documents and maximize LLM context window performance. Implements Hybrid Retrieval with Reciprocal Rank Fusion (RRF) and rigorous BEIR evaluation (NDCG, MAP, Recall, Precision).

Modular LLM ranking library for Information Retrieval and RAG. Implements state-of-the-art Pairwise, Setwise, and Listwise ranking with structured generation and specialized models (RankZephyr, RankLlama). Features efficient sorting algorithms, sliding windows, and zero-shot capabilities.

Pairwise Ranking Prompting (PRP): Zero-shot LLM reranking library implementing efficient pairwise strategies (Heapsort, Sliding Window, All-Pairs). Mitigates position bias via bidirectional comparison and ensures reliability with structured Pydantic validation. Built for Haystack pipelines.

My preferred method for sorting lists by user preference. A potentially more accurate albeit much slower method is Elo Sort.

Python scripts that grabs users ratings from the USCF website and then writes them to a google sheet

A PyTorch implementation of Neural Ranker-Reader model for Machine Reading Comprehension

A sorting method that uses the Elo Sort system, originally designed to rank chess players. Potentially more accurate than Merge Sort, though will take much longer as it needs to compare every single combination of items.