feat: RAG

pom.xml

@@ -77,12 +77,25 @@
         </dependency>
 
 
-        <!--mysql驱动-->
+        <!-- PostgreSQL：聊天记忆 JDBC + pgvector 共用同一数据源 -->
         <dependency>
-            <groupId>com.mysql</groupId>
-            <artifactId>mysql-connector-j</artifactId>
+            <groupId>org.postgresql</groupId>
+            <artifactId>postgresql</artifactId>
             <scope>runtime</scope>
         </dependency>
+
+        <dependency>
+            <groupId>org.springframework.ai</groupId>
+            <artifactId>spring-ai-starter-vector-store-pgvector</artifactId>
+            <version>1.1.2</version>
+        </dependency>
+
+        <!--   向量数据库     -->
+        <dependency>
+            <groupId>org.springframework.ai</groupId>
+            <artifactId>spring-ai-advisors-vector-store</artifactId>
+            <version>1.1.2</version>
+        </dependency>
     </dependencies>
 
 </project>

src/main/java/com/baoshi/conf/AiConfig.java

@@ -1,14 +1,19 @@
 package com.baoshi.conf;
 import com.alibaba.cloud.ai.dashscope.chat.DashScopeChatModel;
+import com.alibaba.cloud.ai.dashscope.spec.DashScopeModel;
 import org.springframework.ai.chat.client.ChatClient;
 import org.springframework.ai.chat.client.advisor.PromptChatMemoryAdvisor;
 import org.springframework.ai.chat.memory.ChatMemory;
 import org.springframework.ai.chat.memory.MessageWindowChatMemory;
 import org.springframework.ai.chat.memory.repository.jdbc.JdbcChatMemoryRepository;
+import org.springframework.ai.embedding.EmbeddingModel;
 import org.springframework.ai.model.ollama.autoconfigure.OllamaChatProperties;
 import org.springframework.ai.ollama.OllamaChatModel;
+import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.beans.factory.annotation.Qualifier;
 import org.springframework.context.annotation.Bean;
 import org.springframework.context.annotation.Configuration;
+import org.springframework.context.annotation.Primary;
 
 @Configuration
 public class AiConfig {
@@ -16,7 +21,7 @@ public class AiConfig {
     @Bean
     public ChatMemory chatMemory(JdbcChatMemoryRepository chatMemoryRepository) {
         return MessageWindowChatMemory.builder()
-                .maxMessages(1)
+                .maxMessages(3)
                 .chatMemoryRepository(chatMemoryRepository)
                 .build();
     }
@@ -37,4 +42,10 @@ public class AiConfig {
                 .defaultAdvisors(PromptChatMemoryAdvisor.builder(chatMemory).build())
                 .build();
     }
+
+    @Primary
+    @Bean
+    public EmbeddingModel embeddingModel(@Autowired @Qualifier("ollamaEmbeddingModel") EmbeddingModel embeddingModel) {
+        return embeddingModel;
+    }
 }

src/main/resources/application.yml

@@ -5,6 +5,8 @@ spring:
       base-url: http://100.121.13.117:11434
       chat:
         model: qwen2.5:7b
+      embedding:
+        model: bge-m3
     dashscope:
       api-key: sk-36a57382b9ae4db696ecd07eb7150a88
       chat:
@@ -14,11 +16,18 @@ spring:
         repository:
           jdbc:
             initialize-schema: always
+    vectorstore:
+      pgvector:
+        initialize-schema: true
+        index-type: HNSW
+        distance-type: COSINE_DISTANCE
 
   datasource:
-    username: root
-    password: 123456
-    url: jdbc:mysql://localhost:3306/springai?characterEncoding=utf8&useSSL=false&serverTimezone=UTC&
-    driver-class-name: com.mysql.cj.jdbc.Driver
+    username: postgres
+    password: postgres
+    url: jdbc:postgresql://localhost:5432/springai
+    driver-class-name: org.postgresql.Driver
+
+    
 server:
   port: 8080

src/test/java/com/baoshi/JDBCMemoryTest.java

@@ -6,6 +6,7 @@ import org.springframework.ai.chat.client.ChatClient;
 import org.springframework.ai.chat.memory.ChatMemory;
 import org.springframework.ai.chat.memory.repository.jdbc.JdbcChatMemoryRepository;
 import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.beans.factory.annotation.Qualifier;
 import org.springframework.boot.test.context.SpringBootTest;
 import org.springframework.jdbc.core.JdbcTemplate;
 
@@ -23,14 +24,12 @@ class JDBCMemoryTest {
     private static final String CONVERSATION_ID = "1";
 
     @Autowired
-    private ChatClient ollama;
+    @Qualifier("ollama")
+    private ChatClient chatClient;
 
     @Autowired
     private ChatMemory chatMemory;
 
-    @Autowired
-    private JdbcChatMemoryRepository chatMemoryRepository;
-
     @Autowired
     private JdbcTemplate jdbcTemplate;
 
@@ -41,20 +40,23 @@ class JDBCMemoryTest {
 
     @Test
     void shouldPersistMessagesIntoJdbcRepositoryByChatModelConversation() {
-        ollama.prompt()
+        chatClient.prompt()
                 .advisors(advisor -> advisor.param(ChatMemory.CONVERSATION_ID, CONVERSATION_ID))
                 .user("我叫邹志文")
                 .call()
                 .content();
-
-        String secondResponse = ollama.prompt()
+        chatClient.prompt()
+                .advisors(advisor -> advisor.param(ChatMemory.CONVERSATION_ID, CONVERSATION_ID))
+                .user("她叫冬马和纱")
+                .call()
+                .content();
+        String secondResponse = chatClient.prompt()
                 .advisors(advisor -> advisor.param(ChatMemory.CONVERSATION_ID, CONVERSATION_ID))
                 .user("我叫什么")
                 .call()
                 .content();
 
-        assertThat(secondResponse).isNotBlank();
-        assertThat(chatMemoryRepository.findByConversationId(CONVERSATION_ID)).isNotEmpty();
+        System.out.println(secondResponse);
         Integer rowCount = jdbcTemplate.queryForObject(
                 "SELECT COUNT(*) FROM SPRING_AI_CHAT_MEMORY WHERE conversation_id = ?",
                 Integer.class,

src/test/java/com/baoshi/SimpleVectorStoreTest.java

@@ -0,0 +1,76 @@
+package com.baoshi;
+
+import com.alibaba.cloud.ai.dashscope.chat.DashScopeChatModel;
+import org.junit.jupiter.api.BeforeEach;
+import org.junit.jupiter.api.Test;
+import org.springframework.ai.chat.client.ChatClient;
+import org.springframework.ai.chat.client.advisor.SimpleLoggerAdvisor;
+import org.springframework.ai.chat.client.advisor.vectorstore.QuestionAnswerAdvisor;
+import org.springframework.ai.document.Document;
+import org.springframework.ai.vectorstore.SearchRequest;
+import org.springframework.ai.vectorstore.VectorStore;
+import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.beans.factory.annotation.Qualifier;
+import org.springframework.boot.test.context.SpringBootTest;
+
+import java.util.Arrays;
+
+@SpringBootTest
+public class SimpleVectorStoreTest {
+
+    @BeforeEach
+    public void init( @Autowired
+                      VectorStore vectorStore) {
+        // 1. 声明内容文档
+        Document doc = Document.builder()
+                .text("""
+                预订航班:
+                - 通过我们的网站或移动应用程序预订。
+                - 预订时需要全额付款。
+                - 确保个人信息（姓名、ID 等）的准确性，因为更正可能会产生 25 的费用。
+                """)
+                .build();
+        Document doc2 = Document.builder()
+                .text("""
+                取消预订:
+                - 最晚在航班起飞前 48 小时取消。
+                - 取消费用：经济舱 75 美元，豪华经济舱 50 美元，商务舱 25 美元。
+                - 退款将在 7 个工作日内处理。
+                """)
+                .build();
+
+
+        // 2. 将文本进行向量化，并且存入向量数据库（无需再手动向量化）
+        vectorStore.add(Arrays.asList(doc,doc2));
+    }
+ 
+
+    @Test
+    void chatRagTest(
+            @Autowired
+            VectorStore vectorStore,
+            @Autowired
+            @Qualifier("ollama")
+            ChatClient chatClient
+            ) {
+
+
+        String message="退费需要多少费用?";
+        String content = chatClient.prompt().user(message)
+                .advisors(
+                        new SimpleLoggerAdvisor(),
+                        QuestionAnswerAdvisor.builder(vectorStore)
+                                .searchRequest(
+                                        SearchRequest
+                                        .builder().query(message)
+                                                .topK(5)
+                                                .similarityThreshold(0.3)
+                                                .build())
+                                .build()
+                ).call().content();
+
+        System.out.println(content);
+
+    }
+
+}