10 citations · 12 across the 7 of their papers we have counts for
4 papers · 1 filter
RAG-Stack: Co-Optimizing RAG Serving Performance and Quality
Haiqiang Zhang, Yuanqing Lei, Wanting Li +2
Retrieval-augmented generation (RAG), which augments large language model (LLM) generation with information retrieved from databases, has become a widely used approach for knowledg…
RAG-Stack: Co-Optimizing RAG Quality and Performance From the Vector Database Perspective
Wenqi Jiang
Retrieval-augmented generation (RAG) has emerged as one of the most prominent applications of vector databases. By integrating documents retrieved from a database into the prompt o…
Vector-Centric Machine Learning Systems: A Cross-Stack Approach
Wenqi Jiang
Today, two major trends are shaping the evolution of ML systems. First, modern AI systems are becoming increasingly complex, often integrating components beyond the model itself. A…
HEXGEN-FLOW: Optimizing LLM Inference Request Scheduling for Agentic Text-to-SQL
You Peng, Youhe Jiang, Wenqi Jiang +2
Recent advances in agentic large language models (LLMs) have substantially improved Text-to-SQL, enabling users without database expertise to query databases intuitively. However,…