Denser Retriever combines multiple search technologies into a single platform. It utilizes gradient boosting ( xgboost) machine learning technique to combine: * Keyword-based searches that focus on fetching precisely what the query mentions. * Vector databases that are great for finding a wide range of potentially relevant answers. * Machine Learning rerankers that fine-tune the results to ensure the most relevant answers top the list. Our experiments on MTEB datasets show that the combination of keyword search, vector search and a reranker via an xgboost model (denoted as ES+VS+RR_n) can significantly improve the vector search (VS) baseline. The initial release of Denser Retriever provides the following features. * Supporting heterogeneous retrievers such as keyword search, vector search, and ML model reranking * Leveraging xgboost ML technique to effectively combine heterogeneous retrievers * State-of-the-art accuracy on MTEB Retrieval benchmarking * Demonstrating how to use Denser retriever to power an end-to-end applications such as chatbot and semantic search

Denser Retriever

Cutting-Edge AI Retriever for RAG

Cutting-Edge AI Retriever for RAG