package com.ard.agent.service;
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import jakarta.annotation.Resource;
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import lombok.extern.slf4j.Slf4j;
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import org.springframework.ai.chat.client.ChatClient;
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import org.springframework.ai.chat.prompt.Prompt;
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import org.springframework.ai.chat.prompt.PromptTemplate;
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import org.springframework.ai.document.Document;
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import org.springframework.ai.vectorstore.SearchRequest;
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import org.springframework.ai.vectorstore.VectorStore;
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import org.springframework.beans.factory.annotation.Qualifier;
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import org.springframework.stereotype.Service;
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import reactor.core.publisher.Flux;
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import java.util.List;
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import java.util.stream.Collectors;
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@Slf4j
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@Service
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public class RagChatService {
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@Resource
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private ChatClient chatClient;
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@Resource
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private VectorStore vectorStore;
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/**
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* RAG问答流式方法（推荐使用）
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*/
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public Flux<String> chatStream(String question) {
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log.info("收到RAG流式请求，question={}", question);
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// 1. 向量检索：召回Top3相关文档（同步执行）
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List<org.springframework.ai.document.Document> similarDocs = vectorStore.similaritySearch(
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SearchRequest.builder()
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.query(question)
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.topK(3)
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.build()
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);
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// 2. 拼接上下文
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String context = similarDocs.stream()
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.map(doc -> "【参考内容】：" + doc.getText())
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.collect(Collectors.joining("\n\n"));
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log.info("检索到{}条相关文档，开始流式生成回答", similarDocs.size());
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// 3. 构建Prompt模板
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String promptText = """
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你是专业知识库助手，基于以下参考内容回答用户问题，禁止编造，答案必须准确：
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{context}
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用户问题：{question}
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""";
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PromptTemplate promptTemplate = new PromptTemplate(promptText);
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promptTemplate.add("context", context);
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promptTemplate.add("question", question);
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Prompt prompt = promptTemplate.create();
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// 4. 流式调用大模型
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return chatClient.prompt(prompt)
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.stream()
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.content()
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.doOnNext(chunk -> log.debug("输出数据块: {}", chunk))
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.doOnComplete(() -> log.info("流式输出完成"))
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.doOnError(error -> log.error("流式输出出错: {}", error.getMessage()));
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}
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}
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