This article details the construction of a Retrieval-Augmented Generation (RAG) pipeline using Java, addressing common enterprise challenges with large language models. It outlines a six-stage process: document ingestion, chunking, embedding, vector storage, retrieval evaluation, and re-ranking. The approach emphasizes modularity with testable interfaces for each stage, allowing for easier implementation of alternative components like pgvector or domain-specific embedding models. The RAG architecture is presented as having two main pipelines—indexing and query—that share a central vector index, with quality largely determined by the pre-model stages. AI
IMPACT Provides a practical engineering blueprint for building RAG systems, crucial for enterprise AI adoption.
RANK_REASON Article provides a technical guide on implementing a specific AI technique (RAG) using a programming language and associated tools.
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