The complexity and sheer volume of information encompassing documents,
papers, data, and other resources from large-scale experiments demand
significant time and effort to navigate, making the task of accessing and
utilizing these varied forms of information daunting, particularly for new
collaborators and early-career scientists. To tackle this issue, a Retrieval
Augmented Generation (RAG)--based Summarization AI for EIC (RAGS4EIC) is under
development. This AI-Agent not only condenses information but also effectively
references relevant responses, offering substantial advantages for
collaborators. Our project involves a two-step approach: first, querying a
comprehensive vector database containing all pertinent experiment information;
second, utilizing a Large Language Model (LLM) to generate concise summaries
enriched with citations based on user queries and retrieved data. We describe
the evaluation methods that use RAG assessments (RAGAs) scoring mechanisms to
assess the effectiveness of responses. Furthermore, we describe the concept of
prompt template-based instruction-tuning which provides flexibility and
accuracy in summarization. Importantly, the implementation relies on LangChain,
which serves as the foundation of our entire workflow. This integration ensures
efficiency and scalability, facilitating smooth deployment and accessibility
for various user groups within the Electron Ion Collider (EIC) community. This
innovative AI-driven framework not only simplifies the understanding of vast
datasets but also encourages collaborative participation, thereby empowering
researchers. As a demonstration, a web application has been developed to
explain each stage of the RAG Agent development in detail.

研究正在开发基于检索增强生成（RAG）的电子离子对撞机（EIC）AI 摘要代理（RAGS4EIC）。这一创新型的人工智能驱动框架不仅简化了庞大数据集的理解，还鼓励协作参与，从而增强了研究人员的能力。

面向电子离子对撞机的基于 RAG 的摘要生成代理

Towards a \textbf{RAG}-based Summarization Agent for the Electron-Ion  Collider

Breast ultrasound (BUS) is a critical diagnostic tool in the field of breast
imaging, aiding in the early detection and characterization of breast
abnormalities. Interpreting breast ultrasound images commonly involves creating
comprehensive medical reports, containing vital information to promptly assess
the patient's condition. However, the ultrasound imaging system necessitates
capturing multiple images of various parts to compile a single report,
presenting a time-consuming challenge. To address this problem, we propose the
integration of multiple image analysis tools through a LangChain using Large
Language Models (LLM), into the breast reporting process. Through a combination
of designated tools and text generation through LangChain, our method can
accurately extract relevant features from ultrasound images, interpret them in
a clinical context, and produce comprehensive and standardized reports. This
approach not only reduces the burden on radiologists and healthcare
professionals but also enhances the consistency and quality of reports. The
extensive experiments shows that each tools involved in the proposed method can
offer qualitatively and quantitatively significant results. Furthermore,
clinical evaluation on the generated reports demonstrates that the proposed
method can make report in clinically meaningful way.

通过 LangChain 和大型语言模型 (Large Language Models, LLM) 的组合，我们提出了一种整合多种图像分析工具的方法，可以准确提取超声图像的相关特征，将其在临床背景中进行解释，并生成全面且标准化的报告，从而减轻放射科医师和医疗保健专业人员的负担，提高报告的一致性和质量。大量实验证明了所提出方法中涉及的每个工具均能提供定性和定量上显著的结果。此外，对所生成报告的临床评估表明，所提出的方法可以以临床意义的方式进行报告生成。

利用 LangChain 生成乳腺超声报告

Breast Ultrasound Report Generation using LangChain

In the digital age, the dynamics of customer service are evolving, driven by
technological advancements and the integration of Large Language Models (LLMs).
This research paper introduces a groundbreaking approach to automating customer
service using LangChain, a custom LLM tailored for organizations. The paper
explores the obsolescence of traditional customer support techniques,
particularly Frequently Asked Questions (FAQs), and proposes a paradigm shift
towards responsive, context-aware, and personalized customer interactions. The
heart of this innovation lies in the fusion of open-source methodologies, web
scraping, fine-tuning, and the seamless integration of LangChain into customer
service platforms. This open-source state-of-the-art framework, presented as
"Sahaay," demonstrates the ability to scale across industries and
organizations, offering real-time support and query resolution. Key elements of
this research encompass data collection via web scraping, the role of
embeddings, the utilization of Google's Flan T5 XXL, Base and Small language
models for knowledge retrieval, and the integration of the chatbot into
customer service platforms. The results section provides insights into their
performance and use cases, here particularly within an educational institution.
This research heralds a new era in customer service, where technology is
harnessed to create efficient, personalized, and responsive interactions.
Sahaay, powered by LangChain, redefines the customer-company relationship,
elevating customer retention, value extraction, and brand image. As
organizations embrace LLMs, customer service becomes a dynamic and
customer-centric ecosystem.

这篇研究论文介绍了一种创新的方法，使用定制化的大型语言模型（LLM）LangChain 来自动化客户服务，通过融合开源方法、网页抓取、微调和将 LangChain 无缝集成到客户服务平台中，将传统客服技术（尤其是常见问题解答（FAQ））的过时性以及现实质感、上下文感知和个性化客户交互等向新的方向进行转变。