用向量与图检索增强生成，提升银行客服与年报解读效率
CAPRAG: A Large Language Model Solution for Customer Service and Automatic Reporting using Vector and Graph Retrieval-Augmented Generation
- 融合向量与图数据库的双通道检索架构
- 支持关系型与上下文型问题的精准回答
- 适合需处理复杂金融咨询的银行智能客服系统
银行推出新功能时常令客户困惑，我们设计了一款基于大语言模型的AI客服代理，帮助客户获取银行业务信息并理解年报内容。提出混合式客户分析流水线检索增强生成（CAPRAG），有效应对关系型与上下文型查询，提升数字银行场景下的客户参与度。构建文本处理流水线，分别在向量和图数据库中存储结构化数据；利用Cypher查询图数据库，用户提问经扩展后由混合知识库生成最终查询，再交由开源LLM生成回复。该方案专为国际银行设计，助力客户在日益复杂的数字环境中更清晰、便捷地获取信息。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。