Foundations of Retrieval Augmented Generation Systems with Java

Learn how to represent text effectively for Retrieval-Augmented Generation (RAG). Explore the importance of text representation, compare Bag-of-Words and embeddings, visualize embeddings with t-SNE, and assess their performance in document retrieval and semantic search.

Scale your RAG system with a vector database. Learn to preprocess documents, store embeddings in ChromaDB, retrieve chunks using filters and weights, and build prompts for multi-chunk context. Also, manage updates and handle large-scale ingestion with batch strategies.

Enhance your RAG pipeline with advanced retrieval techniques. Combine BM25 with embeddings, refine queries iteratively, build final context, and constrain LLMs to use only retrieved data. Optionally, summarize multiple chunks into a concise context to improve accuracy and responses.