⚡️A Blazing-Fast Python Library for Ranking Evaluation, Comparison, and Fusion 🐍

Repository with basic information about Rank Aggregation Problem.

Learning Latent Semantic Representations of Paintings for Personalized Recommendation

Question-Answering (QA) system powered by Retrieval-Augmented Generation (RAG). The system leverages advanced methods such as Rank Fusion and Cascading Retrieval for optimized document retrieval and contextual QA generation.

Easy to use rank aggregation software for recommendation systems

RAG pipeline for medical question-answering. Fuses lexical and dense retrieval (MedCPT, Contriever, Specter + FAISS) with OpenAI, Gemini, and HuggingFace LLMs. Supports iterative multi-round reasoning, strict typing, structured observability, and a clean layered architecture

Training-free, CPU-only lexical–dense fusion for conversational-memory retrieval — 0.752 vs 0.640 Hit@1 over BM25 on LoCoMo, fully reproducible. Paper + reproduction code.

Rank fusion and reranking

Keyless, uv-native web search + read for AI agents: ddgs multi-engine search with de-correlated rank fusion, Trafilatura extraction to paginated Markdown, plus keyless arxiv and github search. One fenced agent face (web_search/web_fetch/web_open) and a FastMCP server. Installs via npx skills add, a Claude plugin, or uvx.

Weighted, adaptive, calibration-free Reciprocal Rank Fusion (RRF) of heterogeneous retrieval channels

Implementation of various vector rank fusion algorithms

EdgeProc: open-source local-first substrate for on-device AI search & ranking — signed, content-addressed index bundles (fail-closed Ed25519), a FAISS vector runtime, and a deterministic task router. The reusable substrate; edge-reco is the reference product built on it.

Keyword, vector & hybrid search with MongoDB — interactive demo comparing $text, $vectorSearch and $rankFusion (MongoDB 8.3), with a minimal RAG pipeline using OpenAI or Ollama.