Spring AI 高级RAG功能实现：检索增强顾问（基础用法和高级用法）

src/main/java/com/huangge1199/aiagent/Service/RagService.java

@@ -20,4 +20,8 @@ public interface RagService {
     String queryTranslation(String question);
 
     String contextAwareQueries(String question);
+
+    String baseAdvisor(String question);
+
+    String advancedAdvisor(String question);
 }

src/main/java/com/huangge1199/aiagent/Service/impl/RagServiceImpl.java

@@ -6,15 +6,23 @@ import com.huangge1199.aiagent.rag.MyMultiQueryExpander;
 import jakarta.annotation.Resource;
 import org.springframework.ai.chat.client.ChatClient;
 import org.springframework.ai.chat.client.advisor.QuestionAnswerAdvisor;
+import org.springframework.ai.chat.client.advisor.RetrievalAugmentationAdvisor;
+import org.springframework.ai.chat.client.advisor.api.Advisor;
 import org.springframework.ai.chat.messages.AssistantMessage;
 import org.springframework.ai.chat.messages.UserMessage;
 import org.springframework.ai.chat.model.ChatModel;
+import org.springframework.ai.document.Document;
+import org.springframework.ai.embedding.EmbeddingModel;
 import org.springframework.ai.rag.Query;
+import org.springframework.ai.rag.generation.augmentation.ContextualQueryAugmenter;
 import org.springframework.ai.rag.preretrieval.query.transformation.CompressionQueryTransformer;
 import org.springframework.ai.rag.preretrieval.query.transformation.QueryTransformer;
 import org.springframework.ai.rag.preretrieval.query.transformation.RewriteQueryTransformer;
 import org.springframework.ai.rag.preretrieval.query.transformation.TranslationQueryTransformer;
+import org.springframework.ai.rag.retrieval.search.VectorStoreDocumentRetriever;
 import org.springframework.ai.vectorstore.VectorStore;
+import org.springframework.ai.vectorstore.SimpleVectorStore;
+import org.springframework.ai.vectorstore.filter.FilterExpressionBuilder;
 import org.springframework.stereotype.Service;
 
 import java.util.List;
@@ -37,6 +45,9 @@ public class RagServiceImpl implements RagService {
     @Resource
     private ChatModel ollamaChatModel;
 
+    @Resource
+    private EmbeddingModel ollamaEmbeddingModel;
+
     @Override
     public String localDoc(String question) {
         return chatClient.prompt().user(question).advisors(new MyLoggerAdvisor()).advisors(new QuestionAnswerAdvisor(vectorStore)).call().content();
@@ -113,4 +124,69 @@ public class RagServiceImpl implements RagService {
         return transformedQuery.text();
 
     }
+
+    @Override
+    public String baseAdvisor(String question) {
+        Advisor advisor = RetrievalAugmentationAdvisor.builder()
+                .documentRetriever(VectorStoreDocumentRetriever.builder()
+                        .vectorStore(vectorStore)
+                        .build())
+                .build();
+        return advisor(question, advisor);
+    }
+
+    @Override
+    public String advancedAdvisor(String question) {
+        Advisor advisor = RetrievalAugmentationAdvisor.builder()
+                // 配置查询增强器
+                .queryAugmenter(ContextualQueryAugmenter.builder()
+                        // 允许空上下文查询
+                        .allowEmptyContext(true)
+                        .build())
+                // 配置文档检索器
+                .documentRetriever(VectorStoreDocumentRetriever.builder()
+                        .vectorStore(vectorStore)
+                        // 相似度阈值
+                        .similarityThreshold(0.5)
+                        // 返回文档数量
+                        .topK(3)
+                        .filterExpression(new FilterExpressionBuilder()
+                                .eq("genre", "fairytale")
+                                .build())     // 文档过滤表达式
+                        .build())
+                .build();
+        return advisor(question, advisor);
+    }
+
+    /**
+     * 检索增强顾问
+     * @param question 问题
+     * @param advisor 检索增强顾问
+     * @return 查询结果
+     */
+    private String advisor(String question, Advisor advisor) {
+        // 1. 初始化向量存储
+        SimpleVectorStore vectorStore = SimpleVectorStore.builder(ollamaEmbeddingModel)
+                .build();
+
+        // 2. 添加文档到向量存储
+        List<Document> documents = List.of(
+                new Document("产品说明书:产品名称：智能机器人\n" +
+                        "产品描述：智能机器人是一个智能设备，能够自动完成各种任务。\n" +
+                        "功能：\n" +
+                        "1. 自动导航：机器人能够自动导航到指定位置。\n" +
+                        "2. 自动抓取：机器人能够自动抓取物品。\n" +
+                        "3. 自动放置：机器人能够自动放置物品。\n"));
+        vectorStore.add(documents);
+
+        // 3. 创建检索增强顾问 advisor
+
+        // 4. 在聊天客户端中使用顾问
+        return chatClient.prompt()
+                .user(question)
+                // 添加检索增强顾问
+                .advisors(advisor)
+                .call()
+                .content();
+    }
 }

src/main/java/com/huangge1199/aiagent/controller/RagController.java

@@ -61,4 +61,18 @@ public class RagController {
         String queryList = ragService.contextAwareQueries(question);
         return R.ok(queryList);
     }
+
+    @PostMapping("/baseAdvisor")
+    @Operation(summary = "检索增强顾问：基础用法")
+    public R<String> baseAdvisor(@RequestBody String question) {
+        String queryList = ragService.baseAdvisor(question);
+        return R.ok(queryList);
+    }
+
+    @PostMapping("/advancedAdvisor")
+    @Operation(summary = "检索增强顾问：高级用法")
+    public R<String> advancedAdvisor(@RequestBody String question) {
+        String queryList = ragService.advancedAdvisor(question);
+        return R.ok(queryList);
+    }
 }