为大模型检索增强生成提供标准化接口库，让跨平台开发更顺畅。
A Library of LLM Intrinsics for Retrieval-Augmented Generation
- 提出大模型原语概念，通过标准API调用核心能力。
- 在vLLM上实现可复用的LoRA适配器，支持多厂商协同开发。
- 提供完整文档与代码，适合希望统一RAG开发流程的团队。
在大语言模型开发者社区中，尚缺乏类似软件库的清晰协作模式，以支持大规模协同开发。即使在常见的检索增强生成（RAG）场景下，也难以基于不同大模型提供商共同认可的标准化API构建应用。受编译器内建函数（intrinsic）理念启发，本文提出一种面向RAG的大型语言模型原语（LLM Intrinsics）库。一个LLM原语被定义为可通过明确、稳定且与实现无关的API调用的能力。本库中的原语以LoRA适配器形式发布于HuggingFace，同时在vLLM推理平台上提供具有清晰输入输出结构的软件接口，并配备完整的文档与代码。本文详述了各原语的使用方式、训练细节及评估结果，以及多个原语的组合使用方法。
原文摘要 · Abstract (English)
In the developer community for large language models (LLMs), there is not yet a clean pattern analogous to a software library, to support very large scale collaboration. Even for the commonplace use case of Retrieval-Augmented Generation (RAG), it is not currently possible to write a RAG application against a well-defined set of APIs that are agreed upon by different LLM providers. Inspired by the idea of compiler intrinsics, we propose some elements of such a concept through introducing a library of LLM Intrinsics for RAG. An LLM intrinsic is defined as a capability that can be invoked through a well-defined API that is reasonably stable and independent of how the LLM intrinsic itself is implemented. The intrinsics in our library are released as LoRA adapters on HuggingFace, and through a software interface with clear structured input/output characteristics on top of vLLM as an inference platform, accompanied in both places with documentation and code. This article describes the intended usage, training details, and evaluations for each intrinsic, as well as compositions of multiple intrinsics.
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