Java + RAG: Create an AI-Powered Financial Advisor using Spring AI 💡

🔥 Learn how to implement Retrieval Augmented Generation (RAG) in your Java applications using Spring AI! In this easy to follow tutorial, we'll build a real-world application that analyzes financial documents using AI, while learning essential concepts about tokens, context windows, and vector databases.

⭐ What You'll Learn:
• Understanding RAG and why it matters for Java applications
• Working with Spring AI and vector databases
• Implementing document ingestion with PG Vector
• Creating an intelligent financial document analyzer
• Best practices for token management and context windows
• Building a complete RAG application from scratch

🔍 Topics Covered:
00:00:00 Introduction to RAG
00:00:24 Why RAG Matters & Limitations
00:01:58 Understanding Tokens & Costs
00:05:13 What is RAG vs Not RAG
00:15:12 Building RAG Demo Application
00:17:13 Setting Up Vector Database
00:27:34 Creating REST Controller
00:31:53 Testing RAG Implementation
00:33:51 Conclusion & Next Steps

💻 Code & Resources:
• GitHub Repository: https://github.com/danvega/java-rag
• Spring Initializer: https://start.spring.io/
• Spring AI Documentation: https://docs.spring.io/spring-ai/reference/

🛠️ Technologies Used:
• Java 23
• Spring Boot 3.3.4
• Spring AI
• PG Vector
• Docker
• OpenAI API

📌 Key Concepts:
• RAG (Retrieval Augmented Generation)
• Vector Databases
• Document Processing
• Embeddings
• Token Management
• Context Windows

💡 Pro Tips:
• Use environment variables for API keys
• Consider token limits when processing documents
• Implement proper error handling
• Monitor your API usage and costs
• Use Docker Compose for local development

Questions about RAG, Spring AI, or Java development? Drop them in the comments below! 👇


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