diff --git a/src/main/java/com/huangge1199/aiagent/Service/RagService.java b/src/main/java/com/huangge1199/aiagent/Service/RagService.java
index 054fb58..8d7884b 100644
--- a/src/main/java/com/huangge1199/aiagent/Service/RagService.java
+++ b/src/main/java/com/huangge1199/aiagent/Service/RagService.java
@@ -24,4 +24,6 @@ public interface RagService {
     String baseAdvisor(String question);
 
     String advancedAdvisor(String question);
+
+    String documentSelection();
 }
diff --git a/src/main/java/com/huangge1199/aiagent/Service/impl/RagServiceImpl.java b/src/main/java/com/huangge1199/aiagent/Service/impl/RagServiceImpl.java
index 531d18e..94b18da 100644
--- a/src/main/java/com/huangge1199/aiagent/Service/impl/RagServiceImpl.java
+++ b/src/main/java/com/huangge1199/aiagent/Service/impl/RagServiceImpl.java
@@ -19,13 +19,16 @@ import org.springframework.ai.rag.preretrieval.query.transformation.CompressionQ
 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.DocumentRetriever;
 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.ArrayList;
 import java.util.List;
+import java.util.Map;
 
 /**
  * RagServiceImpl
@@ -160,8 +163,9 @@ public class RagServiceImpl implements RagService {
 
     /**
      * 检索增强顾问
+     *
      * @param question 问题
-     * @param advisor 检索增强顾问
+     * @param advisor  检索增强顾问
      * @return 查询结果
      */
     private String advisor(String question, Advisor advisor) {
@@ -189,4 +193,100 @@ public class RagServiceImpl implements RagService {
                 .call()
                 .content();
     }
+
+    @Override
+    public String documentSelection() {
+        ChatClient.Builder builder = ChatClient.builder(ollamaChatModel);
+        // 生成室内设计案例文档
+        List<Document> documents = new ArrayList<>();
+
+        // 现代简约风格客厅案例
+        documents.add(new Document(
+                "案例编号：LR-2023-001\n" +
+                        "项目概述：180平米大平层现代简约风格客厅改造\n" +
+                        "设计要点：\n" +
+                        "1. 采用5.2米挑高的落地窗，最大化自然采光\n" +
+                        "2. 主色调：云雾白(哑光，NCS S0500-N)配合莫兰迪灰\n" +
+                        "3. 家具选择：意大利B&B品牌真皮沙发，北欧白橡木茶几\n" +
+                        "4. 照明设计：嵌入式筒灯搭配意大利Flos吊灯\n" +
+                        "5. 软装配饰：进口黑胡桃木电视墙，几何图案地毯\n" +
+                        "空间效果：通透大气，适合商务接待和家庭日常起居",
+                Map.of(
+                        // 文档类型
+                        "type", "interior",
+                        // 年份
+                        "year", "2023",
+                        // 月份
+                        "month", "06",
+                        // 位置类型
+                        "location", "indoor",
+                        // 装修风格
+                        "style", "modern",
+                        // 房间类型
+                        "room", "living_room"
+                )
+        ));
+        // 1. 初始化向量存储
+        SimpleVectorStore vectorStore = SimpleVectorStore.builder(ollamaEmbeddingModel)
+                .build();
+
+        // 2. 配置AI助手角色
+        ChatClient chatClient = builder
+                .defaultSystem("你是一位专业的室内设计顾问，精通各种装修风格、材料选择和空间布局。请基于提供的参考资料，为用户提供专业、详细且实用的建议。在回答时，请注意：\n" +
+                        "1. 准确理解用户的具体需求\n" +
+                        "2. 结合参考资料中的实际案例\n" +
+                        "3. 提供专业的设计理念和原理解释\n" +
+                        "4. 考虑实用性、美观性和成本效益\n" +
+                        "5. 如有需要，可以提供替代方案")
+                .build();
+
+        // 3. 构建复杂的文档过滤条件
+        var filterExpression = getOp();
+
+        // 4. 配置文档检索器
+        DocumentRetriever retriever = VectorStoreDocumentRetriever.builder()
+                .vectorStore(vectorStore)
+                // 设置相似度阈值
+                .similarityThreshold(0.5)
+                // 返回前3个最相关的文档
+                .topK(3)
+                .filterExpression(filterExpression.build())
+                .build();
+
+        // 5. 创建上下文感知的查询增强器
+        Advisor advisor = RetrievalAugmentationAdvisor.builder()
+                .queryAugmenter(ContextualQueryAugmenter.builder()
+                        .allowEmptyContext(true)
+                        .build())
+                .documentRetriever(retriever)
+                .build();
+
+        // 6. 执行查询并获取响应
+        String userQuestion = "根据已经提供的资料，请描述所有相关的场景风格，输出案例编号，尽可能详细地描述其内容。";
+        return chatClient.prompt()
+                .user(userQuestion)
+                .advisors(advisor)
+                .call()
+                .content();
+    }
+
+    private static FilterExpressionBuilder.Op getOp() {
+        var b = new FilterExpressionBuilder();
+        // 筛选2023年的案例
+        // 仅选择室内案例
+        // 类型为室内设计
+        // 指定房间类型
+        return b.and(
+                b.and(
+                        // 筛选2023年的案例
+                        b.eq("year", "2023"),
+                        // 仅选择室内案例
+                        b.eq("location", "indoor")),
+                b.and(
+                        // 类型为室内设计
+                        b.eq("type", "interior"),
+                        // 指定房间类型
+                        b.in("room", "living_room", "study", "kitchen")
+                ));
+    }
 }
diff --git a/src/main/java/com/huangge1199/aiagent/controller/RagController.java b/src/main/java/com/huangge1199/aiagent/controller/RagController.java
index e26e329..ba523cd 100644
--- a/src/main/java/com/huangge1199/aiagent/controller/RagController.java
+++ b/src/main/java/com/huangge1199/aiagent/controller/RagController.java
@@ -75,4 +75,11 @@ public class RagController {
         String queryList = ragService.advancedAdvisor(question);
         return R.ok(queryList);
     }
+
+    @PostMapping("/documentSelection")
+    @Operation(summary = "文档选择")
+    public R<String> documentSelection() {
+        String queryList = ragService.documentSelection();
+        return R.ok(queryList);
+    }
 }