📄 中文摘要
通过构建一个智能文档问答系统，用户可以快速获取有关金融文档、季度报告、市场分析和政策文件的信息，而无需手动查找文本。该系统利用检索增强生成（RAG）技术，使用 Spring Boot 开发。教程中将详细介绍如何处理 PDF 文档并提取其内容，利用 AI 模型将文本转换为语义嵌入，并将这些嵌入存储在 PostgreSQL 向量数据库中。最终，系统能够根据自然语言查询提供上下文准确的回答。
构建基于 RAG 的金融助手：使用 Spring AI 和 LM Studio
出处: Building a RAG Powered Financial Assistant with Spring AI and LM Studio
发布: 2026年2月17日
📄 English Summary
Building a RAG Powered Financial Assistant with Spring AI and LM Studio
An intelligent document Q&A system allows users to quickly obtain information about financial documents, quarterly reports, market analyses, and policy papers without manual text searching. This system leverages Retrieval Augmented Generation (RAG) technology and is built using Spring Boot. The tutorial details how to ingest PDF documents and extract their content, convert text into semantic embeddings using AI models, and store these embeddings in a PostgreSQL vector database. Ultimately, the system can provide contextually accurate answers to natural language queries.