Implementing RAG with Spring AI and Pinecone: A Practical Guide

Retrieval-Augmented Generation (RAG) combines information retrieval with generative language models to build AI applications. This guide demonstrates how to implement a RAG system using Spring AI and Pinecone as the vector database for creating a documentation chatbot. The system architecture consists of a documentation website, scraper, chunking, and Pinecone vector database. The prerequisites include a Pinecone account, Spring Boot application, and basic understanding of vector databases. The implementation steps include setting up Pinecone integration, document processing pipeline, knowledge base initialization, and implementing RAG in chat completions. The document processing pipeline involves web scraping, document chunking, and knowledge base initialization. Best practices for optimal chunking, enhanced metadata, hybrid search, and prompt engineering are also discussed. Performance optimization techniques such as caching, async processing, and batch processing are recommended. Evaluation metrics for retrieval precision, response latency, and user satisfaction are provided. The implementation demonstrates how to build a production-ready RAG system with accurate context-aware responses, scalable vector search capabilities, and easy integration with existing Spring applications.