This project explores the enhancement of Retrieval-Augmented Generation (RAG) systems through query expansion and the integration of advanced reranking models such as Cross Encoders, ColBERT v2, and FlashRank. Our focus is on improving the precision and recall of document retrieval processes, thereby refining the performance of RAG models in handling information retrieval tasks.
- Query Expansion: Utilizes Large Language Models (LLMs) to generate meaningful expansions of search queries, addressing issues like query ambiguity and improving document matching.
- Reranking Methods: Implements advanced reranking models including Cross-Encoder reranking, ColBERT v2, and FlashRank to refine search results and prioritize relevant documents.
For a detailed exploration of the concepts and methodologies discussed in this project, visit our blog