unknown

A comprehensive guide to building a Retrieval Augmented Generation (RAG) system using Spring AI, PostgreSQL with pgvector, and LM Studio. The tutorial covers document ingestion from PDFs, text chunking, vector embedding generation, semantic search, and query processing. It includes practical implementation details like conditional ingestion to avoid reprocessing, Docker setup for PostgreSQL, LM Studio configuration, and production considerations such as chunk size optimization, hybrid search strategies, and scaling approaches. The complete source code demonstrates a three-component architecture: IngestionService for document processing, ChatController for REST endpoints, and pgvector for efficient similarity searches.

Building a RAG Powered Financial Assistant with Spring AI and LM Studio

Squad for devs building apps with GenAI and LLMs! Connect, share, exchange ideas, and get feedback. 