In Action Pdf [upd] — Spring Ai

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// Load a template: "Tell me a joke about topic" PromptTemplate promptTemplate = new PromptTemplate( "Tell me a funny joke about topic" ); spring ai in action pdf

@RestController public class ChatController 👇 // Load a template: "Tell me a

One of the most critical components discussed in any practical application of Spring AI is the implementation of Retrieval-Augmented Generation (RAG). While LLMs are powerful, they are often limited by their training cutoff dates and a lack of access to private enterprise data. Spring AI facilitates the RAG pattern by providing built-in support for vector databases and document ETL (Extract, Transform, Load) pipelines. Developers can ingest PDFs, text files, or database records, convert them into vector embeddings, and store them in specialized databases like Pinecone, Weaviate, or Pgvector. When a user asks a question, the system retrieves relevant data fragments to provide context to the LLM, ensuring the output is grounded in factual, up-to-date information. Developers can ingest PDFs, text files, or database