Follow along with a comprehensive tutorial video that demonstrates how to build an AI-powered financial advisor using Java and Retrieval Augmented Generation (RAG) with Spring AI. Master essential concepts including token management, context windows, and vector databases while constructing a practical application that analyzes financial documents. Explore the fundamentals of RAG implementation, understand its limitations and importance, and learn to work with technologies like Spring Boot 3.3.4, PG Vector, Docker, and the OpenAI API. Progress through hands-on demonstrations covering document ingestion, vector database setup, REST controller creation, and thorough testing of the RAG implementation. Gain valuable insights into best practices for API usage, cost management, and error handling, all while building a complete RAG application from scratch using Java 23. Access accompanying resources including a GitHub repository, Spring Initializer setup, and comprehensive Spring AI documentation to support the learning process.
Creating an AI-Powered Financial Advisor with Spring AI and RAG
Overview
Syllabus
Introduction to RAG
Why RAG Matters & Limitations
Understanding Tokens & Costs
What is RAG vs Not RAG
Building RAG Demo Application
Setting Up Vector Database
Creating REST Controller
Testing RAG Implementation
Conclusion & Next Steps
Taught by
Dan Vega
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