// Splitting the document into manageable chunks var textSplitter = new TokenTextSplitter(); var splitDocuments = textSplitter.split(documents);
import org.springframework.ai.chat.model.ChatModel; import org.springframework.web.bind.annotation.GetMapping; import org.springframework.web.bind.annotation.RequestParam; import org.springframework.web.bind.annotation.RestController; @RestController public class AIController private final ChatModel chatModel; public AIController(ChatModel chatModel) this.chatModel = chatModel; @GetMapping("/api/generate") public String generateText(@RequestParam(value = "message") String message) return chatModel.call(message); Use code with caution. Mastering Advanced Spring AI Capabilities spring ai in action pdf github
The ChatModel interface is the central abstraction for interacting with text-based LLMs. It handles the request-response lifecycle, streaming responses, and configuration overrides. 2. Prompts and Prompt Templates // Splitting the document into manageable chunks var
+-------------------------------------------------------------+ | Spring AI | +-------------------------------------------------------------+ | | | v v v +---------------+ +---------------+ +---------------+ | Chat Models | | Vector Stores | | RAG Pipelines| | (OpenAI, | | (Pgvector, | | (Document | | Anthropic, | | Pinecone, | | Readers, | | Ollama) | | Chroma) | | Transformers) +---------------+ +---------------+ +---------------+ 1. Chat and Language Models for Spring AI 1
Import data via text, PDF, JSON, or Office documents.
for Spring AI 1.0, including RAG and Model Context Protocol (MCP) implementations. Baeldung Introduction : A popular technical
You don’t need to wait for the PDF. Let’s build a microservice that uses the core concept from Spring AI in Action : .
