Spring Ai In Action Pdf Github Review

The ChatModel and ChatClient interfaces are the primary entry points for interacting with LLMs. They handle the serialization of Java objects into JSON payloads required by external APIs.

For decades, Spring has been the backbone of enterprise Java. However, connecting Java applications to AI models has historically been a messy affair involving hand-rolled HTTP clients, parsing raw JSON streams, and managing API keys in an ad-hoc manner.

import org.springframework.ai.chat.model.ChatModel; import org.springframework.ai.chat.prompt.Prompt; import org.springframework.ai.chat.prompt.PromptTemplate; import org.springframework.stereotype.Service; import java.util.Map; @Service public class SupportAiService private final ChatModel chatModel; public SupportAiService(ChatModel chatModel) this.chatModel = chatModel; public String generateResponse(String customerName, String issue) String template = "You are a helpful customer service agent. Address the customer as name. Solve this issue: issue"; PromptTemplate promptTemplate = new PromptTemplate(template); Prompt prompt = promptTemplate.create(Map.of("name", customerName, "issue", issue)); return chatModel.call(prompt).getResult().getOutput().getContent(); Use code with caution. Advanced Patterns: Retrieval-Augmented Generation (RAG) spring ai in action pdf github

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package com.example.ai.service; import org.springframework.ai.chat.client.ChatClient; import org.springframework.ai.document.Document; import org.springframework.ai.reader.tika.TikaDocumentReader; import org.springframework.ai.transformer.splitter.TokenTextSplitter; import org.springframework.ai.vectorstore.VectorStore; import org.springframework.ai.vectorstore.SearchRequest; import org.springframework.core.io.Resource; import org.springframework.stereotype.Service; import java.util.List; import java.util.stream.Collectors; @Service public class KnowledgeBaseService private final VectorStore vectorStore; private final ChatClient chatClient; public KnowledgeBaseService(VectorStore vectorStore, ChatClient.Builder chatClientBuilder) this.vectorStore = vectorStore; this.chatClient = chatClientBuilder.build(); // Ingest a PDF file into the vector database public void ingestPdf(Resource pdfResource) TikaDocumentReader reader = new TikaDocumentReader(pdfResource); List rawDocuments = reader.get(); TokenTextSplitter splitter = new TokenTextSplitter(); List splitDocuments = splitter.apply(rawDocuments); this.vectorStore.accept(splitDocuments); // Query the system using RAG public String answerQuery(String userQuery) // Retrieve relevant context pieces from the database List similarDocuments = this.vectorStore.similaritySearch( SearchRequest.query(userQuery).withTopK(4) ); String context = similarDocuments.stream() .map(Document::getContent) .collect(Collectors.joining("\n")); // Ask the LLM to generate an answer based purely on the context return this.chatClient.prompt() .system(sp -> sp.text(""" You are an expert enterprise assistant. Answer the user question based only on the provided context. If you do not know the answer from the context, say 'I cannot find that in the documents.' Context: context """).param("context", context)) .user(userQuery) .call() .content(); Use code with caution. 5. Structuring a Production GitHub Repository The ChatModel and ChatClient interfaces are the primary

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This comprehensive guide explores the core concepts of Spring AI, practical implementation strategies, and how to access production-ready repositories and reference materials. What is Spring AI? However, connecting Java applications to AI models has

The repo showed how to use PGVector to give the AI "memory" of their specific financial data. 2. Spring AI in Action

He wired up a document reader that scanned the company’s internal market research.

The rest of the team was blown away, but they were also confused. "How do we maintain this?" his manager asked.

Creating AI agents that can use tools and take actions.