DS SERVE: A Framework for Efficient and Scalable Neural Retrieval

• URL: http://arxiv.org/abs/2602.22224v1
• Date: Wed, 17 Dec 2025 00:43:10 GMT
• Title: DS SERVE: A Framework for Efficient and Scalable Neural Retrieval
• Authors: Jinjian Liu, Yichuan Wang, Xinxi Lyu, Rulin Shao, Joseph E. Gonzalez, Matei Zaharia, Sewon Min,
• Abstract summary: We present DS-Serve, a framework that transforms large-scale text datasets, comprising half a trillion tokens, into a high-performance neural retrieval system.<n> DS-Serve offers both a web interface and API endpoints, achieving low latency with modest memory overhead on a single node.<n>We anticipate that DS-Serve will be broadly useful for a range of applications, including large-scale retrieval-augmented generation (RAG), training data attribution, training search agents, and beyond.
• Score: 59.295343280892524
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present DS-Serve, a framework that transforms large-scale text datasets, comprising half a trillion tokens, into a high-performance neural retrieval system. DS-Serve offers both a web interface and API endpoints, achieving low latency with modest memory overhead on a single node. The framework also supports inference-time trade-offs between latency, accuracy, and result diversity. We anticipate that DS-Serve will be broadly useful for a range of applications, including large-scale retrieval-augmented generation (RAG), training data attribution, training search agents, and beyond.

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