AI2.0 【Mcp-client】 20260611

 

1、pom

<properties>
    <java.version>17</java.version>
    <spring-ai.version>2.0.0-M5</spring-ai.version>
</properties>
<dependencies>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-webmvc</artifactId>
    </dependency>
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-starter-model-openai</artifactId>
    </dependency>
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-starter-model-ollama</artifactId>
    </dependency>
    <!-- Redis 向量库-->
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-starter-vector-store-redis</artifactId>
    </dependency>
    <!--Redis 客户端-->
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-data-redis</artifactId>
        <exclusions>
            <exclusion>
                <groupId>io.lettuce</groupId>
                <artifactId>lettuce-core</artifactId>
            </exclusion>
        </exclusions>
    </dependency>
    <dependency>
        <groupId>redis.clients</groupId>
        <artifactId>jedis</artifactId>
    </dependency>
    <!-- Tika 通用文档读取器 -->
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-tika-document-reader</artifactId>
    </dependency>
    <!-- Markdown 文档读取器 -->
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-markdown-document-reader</artifactId>
    </dependency>
    <!-- 问答顾问器 advisor-->
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-advisors-vector-store</artifactId>
    </dependency>
    <!-- MCP 客户端：通过 SSE 连接本机 mcp-server（默认端口 18089） -->
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-starter-mcp-client</artifactId>
    </dependency>
</dependencies>

<dependencyManagement>
    <dependencies>
        <dependency>
            <groupId>org.springframework.ai</groupId>
            <artifactId>spring-ai-bom</artifactId>
            <version>${spring-ai.version}</version>
            <type>pom</type>
            <scope>import</scope>
        </dependency>
    </dependencies>
</dependencyManagement>

 

2、application.yml

2.1

${OPENAI_API_KEY}

 

 

spring:
  autoconfigure:
    exclude:  #禁用 Ollama Embedding（如果你用 OpenAI），不然会冲突
      - org.springframework.ai.model.ollama.autoconfigure.OllamaEmbeddingAutoConfiguration
  application:
    name: dashscope18088
  data:
    redis:
      url: redis://192.168.91.165:6379
      client-type: jedis
  ai:
    openai:
      api-key: ${OPENAI_API_KEY}
      base-url: https://dashscope.aliyuncs.com/compatible-mode/v1
      chat:
        options:
          model: qwen3.6-plus
      embedding:
        options:
          model: text-embedding-v4
          dimensions: 2048 # 输出向量维度2048
        #      chat:
#        model: qwen3.6-plus
    mcp:
      client:
        enabled: true
        type: SYNC
        # 与 mcp-server 一致；须先启动 mcp-server（默认 http://localhost:18089）
        sse:
          connections:
            mcp-weather-server:
              url: http://localhost:18089
    vectorstore:
      redis:
        initialize-schema: true   #是否在启动时创建/初始化向量索引等结构
        index-name: sf2026_ai_vector_index  # RediSearch 里的索引名
        prefix: sf2026   #存向量文档用的Redis键前级
    ollama:
      base-url: http://192.168.91.164:11434
      chat:
        model: qwen2.5:0.5b

#        options:
#          temperature: 0.7
#          max-tokens: 2000
server:
  port: 18088
logging:
  level:
    org.springframework.ai: debug
    org.springframework.ai.mcp: debug

3、controller

 

import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.memory.ChatMemory;
import org.springframework.ai.mcp.SyncMcpToolCallbackProvider;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RestController;

@RestController
public class MyMcpChatController {

    @Autowired
    private ChatClient chatClient;

    @Autowired
    private SyncMcpToolCallbackProvider mcpToolCallbackProvider;

//    /**
//     *
//     * @return
//     */
//    @RequestMapping("/aimcp")
//    public String aiWithMcp() {
//        String q = "查一下上海天气";
//        String result = chatClient.prompt()
//                .system("用户询问天气时，你必须调用工具查询后再用简短中文回答。")
//                .user(q)
//                .toolCallbacks(mcpToolCallbackProvider)
//                .call()
//                .content();
//        System.out.println(result);
//        return "OK";
//    }

    @RequestMapping("/aimcp")
    public String aiWithMcp() {
        String q = "查一下上海天气";
        String result = chatClient.prompt()
                .system("用户询问天气时，你必须调用工具查询后再用简短中文回答。")
                .user(q)
                .toolCallbacks(mcpToolCallbackProvider)
                .advisors(a->a.param(ChatMemory.CONVERSATION_ID,"0001"))
                .call()
                .content();
        System.out.println(result);
        return result; // 直接返回回答，而不是固定的"OK"
    }
}

 

 

4、http://localhost:18088/aimcp