CAPRAG：一种使用向量和图形检索增强生成的大型语言模型客户服务与自动报告解决方案
CAPRAG: A Large Language Model Solution for Customer Service and Automatic Reporting using Vector and Graph Retrieval-Augmented Generation
AI总结：
针对银行客户服务，提出混合CAPRAG框架，结合向量与图检索增强生成，处理关系与上下文查询，提升数字银行中的客户参与度。
摘要：
银行业新功能和新服务的引入常常使客户感到不知所措，这为银行通过由大型语言模型（LLM）驱动的金融聊天机器人来提升用户体验创造了机会。我们启动了一个AI代理，旨在为客户提供有关银行服务的相关信息以及年度报告中的见解。我们提出了一种混合式客户分析流水线检索增强生成（CAPRAG）方法，该方法有效处理基于关系的查询和基于上下文的查询，从而在数字银行环境中提高客户参与度。为了实现这一点，我们开发了一个处理流水线来精炼文本数据，并将其应用于两个主要框架：向量RAG和图RAG。这种双重方法使我们能够用处理后的数据填充向量数据库和图数据库，以实现高效检索。Cypher查询组件用于有效查询图数据库。当用户提交查询时，首先由查询扩展模块进行扩展，然后路由以从混合知识库（KB）构建最终查询。然后将此最终查询发送到开源LLM以生成响应。总体而言，我们为国际银行设计的创新方案，在日益复杂的数字环境中为银行客户提供服务，增强了信息的清晰度和可访问性。
英文摘要：
The introduction of new features and services in the banking sector often overwhelms customers, creating an opportunity for banks to enhance user experience through financial chatbots powered by large language models (LLMs). We initiated an AI agent designed to provide customers with relevant information about banking services and insights from annual reports. We proposed a hybrid Customer Analysis Pipeline Retrieval-Augmented Generation (CAPRAG) that effectively addresses both relationship-based and contextual queries, thereby improving customer engagement in the digital banking landscape. To implement this, we developed a processing pipeline to refine text data, which we utilized in two main frameworks: Vector RAG and Graph RAG. This dual approach enables us to populate both vector and graph databases with processed data for efficient retrieval. The Cypher query component is employed to effectively query the graph database. When a user submits a query, it is first expanded by a query expansion module before being routed to construct a final query from the hybrid Knowledge Base (KB). This final query is then sent to an open-source LLM for response generation. Overall, our innovative, designed to international banks, serves bank's customers in an increasingly complex digital environment, enhancing clarity and accessibility of information.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
