Retrieval-Augmented Generation Pipelines

Retrieval-Augmented Generation (RAG) grounds an LLM's answers in your own documents instead of relying solely on what the model memorized during training.

• Fresh & private data — answer questions about internal docs the model never saw.
• Less hallucination — the model cites retrieved context rather than inventing facts.
• Cheaper than fine-tuning — you update a vector store, not model weights.

A Spring AI RAG pipeline has two phases: an ingestion phase (read → split → embed → store) and a query phase (embed question → retrieve → augment prompt → generate).

Spring AI gives you composable building blocks for both phases. The core types you will assemble are:

• DocumentReader — loads raw sources (PDF, Markdown, JSON, web pages).
• DocumentTransformer — splits documents into chunks (e.g. TokenTextSplitter).
• EmbeddingModel — turns text into vectors.
• VectorStore — stores and similarity-searches those vectors.
• ChatClient with a RAG advisor — wires retrieval into the prompt automatically.

The first three feed ingestion; the last two power querying.