OSCAR: Smarter RAG Compression
Enhancing LLM efficiency without sacrificing performance
OSCAR introduces a query-dependent soft compression technique that optimizes Retrieval-Augmented Generation (RAG) pipelines by reducing computational overhead while maintaining accuracy.
- Addresses scaling challenges as retrieval sizes grow in RAG systems
- Implements online soft compression that adapts to each specific query
- Combines compression with reranking for optimal context selection
- Achieves computational efficiency without performance degradation
This innovation matters for engineering teams building LLM applications, offering a practical solution to balance computational costs with model performance in production RAG systems.