20250330 用ollama llama3.2模型实现function call

pom.xml

@@ -46,6 +46,11 @@
             <artifactId>langchain4j-community-zhipu-ai</artifactId>
             <version>1.0.0-beta1</version>
         </dependency>
+        <dependency>
+            <groupId>dev.langchain4j</groupId>
+            <artifactId>langchain4j-ollama</artifactId>
+            <version>1.0.0-beta1</version>
+        </dependency>
 
         <dependency>
             <groupId>dev.langchain4j</groupId>
@@ -53,6 +58,30 @@
             <version>1.0.0-beta1</version>
         </dependency>
 
+
+        <!-- Unirest依赖 -->
+        <dependency>
+            <groupId>com.mashape.unirest</groupId>
+            <artifactId>unirest-java</artifactId>
+            <version>1.4.9</version>
+        </dependency>
+
+        <dependency>
+            <groupId>com.fasterxml.jackson.core</groupId>
+            <artifactId>jackson-core</artifactId>
+            <version>2.15.2</version>
+        </dependency>
+        <dependency>
+            <groupId>com.fasterxml.jackson.core</groupId>
+            <artifactId>jackson-databind</artifactId>
+            <version>2.15.2</version>
+        </dependency>
+        <dependency>
+            <groupId>com.fasterxml.jackson.core</groupId>
+            <artifactId>jackson-annotations</artifactId>
+            <version>2.15.2</version>
+        </dependency>
+
     </dependencies>
 
     <dependencyManagement>

src/main/java/com/ai/config/OllamaConfig.java

@@ -0,0 +1,28 @@
+package com.ai.config;
+
+import com.ai.function.MyCalculator;
+import com.ai.service.OllamaAssist;
+import dev.langchain4j.memory.chat.MessageWindowChatMemory;
+import dev.langchain4j.model.chat.ChatLanguageModel;
+import dev.langchain4j.model.ollama.OllamaChatModel;
+import dev.langchain4j.service.AiServices;
+import org.springframework.context.annotation.Bean;
+import org.springframework.context.annotation.Configuration;
+
+@Configuration
+public class OllamaConfig {
+
+    @Bean
+    public OllamaAssist ollamaAssist() {
+        ChatLanguageModel ollamaModel = OllamaChatModel.builder()
+                .baseUrl("http://localhost:11434")
+                .modelName("llama3.2:3b")
+                .build();
+        return AiServices.builder(OllamaAssist.class)
+                .chatMemoryProvider(memoryId -> MessageWindowChatMemory.withMaxMessages(10))
+                .chatLanguageModel(ollamaModel)
+                .tools(new MyCalculator())
+                .build();
+    }
+
+}

src/main/java/com/ai/controller/LangChainController.java

@@ -2,6 +2,8 @@ package com.ai.controller;
 
 import com.ai.service.Assist;
 import com.ai.service.LangChainService;
+import com.ai.service.NormalRequestService;
+import com.ai.service.OllamaAssist;
 import dev.langchain4j.community.model.dashscope.QwenChatModel;
 import dev.langchain4j.community.model.zhipu.ZhipuAiImageModel;
 import dev.langchain4j.data.image.Image;
@@ -31,6 +33,18 @@ public class LangChainController {
     @Autowired
     private ZhipuAiImageModel zhipuAiImageModel;
 
+    @Autowired
+    private OllamaAssist ollamaAssist;
+
+    @Autowired
+    private NormalRequestService normalRequestService;
+
+    @GetMapping("/normal/chat")
+    public String normalChat(@RequestParam("input") String input) {
+        System.out.println("start normal chat...");
+        return normalRequestService.chat(input);
+    }
+
     /**
      * 处理用户输入并调用 LangChainService 获取响应
      * @param input 用户输入的内容
@@ -65,6 +79,6 @@ public class LangChainController {
     @GetMapping("/high/call")
     public String functionCall(@RequestParam("input") String input) {
         System.out.println("start highlevel memory chat...");
-        return assist.chat(input);
+        return ollamaAssist.chat(input);
     }
 }

src/main/java/com/ai/function/MyCalculator.java

@@ -2,10 +2,31 @@ package com.ai.function;
 
 import dev.langchain4j.agent.tool.Tool;
 
+import java.util.HashMap;
+import java.util.Map;
+import java.util.Random;
+
 public class MyCalculator {
 
-    @Tool("路飞算法")
+    private int films = 10;
-    public int luffy(int a, int b){
+
-        return (a + b) / (a - b);
+    private static Map<Integer, String> agentMap = new HashMap<>();
+
+    static{
+        agentMap.put(1, "艾莲乔");
+        agentMap.put(2, "星见雅");
+        agentMap.put(3, "妮可");
+        agentMap.put(4, "安比");
+        agentMap.put(5, "耀佳音");
+    }
+
+    @Tool("用若干菲林抽若干代理人")
+    public void raffle(int raffleNum){
+        Random random = new Random();
+        for(int i = 0; i < raffleNum; i++){
+            int randomNumber = random.nextInt(5) + 1;
+            String agent = agentMap.get(randomNumber);
+            System.out.println("获得代理人:" + agent);
+        }
     }
 }

src/main/java/com/ai/http/chat.http

@@ -15,4 +15,4 @@ GET http://localhost:8080/langchain/high/memory-chat?memoryId=1&input=详细介
 GET http://localhost:8080/langchain/zhipu/img?input=请画一张魔兽世界里的兽人高举锤子的图
 
 ### 测试 LangChainController 的 highlevel chat 接口
-GET http://localhost:8080/langchain/high/call?input=我的两个数分别是9和3,请用路飞算法计算结果
+GET http://localhost:8080/langchain/high/call?input=用5个菲林抽取5个代理人

src/main/java/com/ai/service/NormalRequestService.java

@@ -0,0 +1,53 @@
+package com.ai.service;
+
+import com.fasterxml.jackson.databind.ObjectMapper;
+import com.mashape.unirest.http.HttpResponse;
+import com.mashape.unirest.http.Unirest;
+import org.springframework.stereotype.Service;
+
+import java.util.Map;
+
+@Service
+public class NormalRequestService {
+
+   public String chat(String userMessage)  {
+       try {
+           HttpResponse<String> response = Unirest.post("https://api.deepseek.com/chat/completions")
+                   .header("Authorization", "Bearer sk-3043bb4777404970a22c7544dd30aaa2")
+                   .header("Content-Type", "application/json")
+                   .body("{\n" +
+                           "        \"model\": \"deepseek-chat\",\n" +
+                           "        \"messages\": [\n" +
+                           "          {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n" +
+                           "          {\"role\": \"user\", \"content\": \"" + userMessage + "\"}\n" +
+                           "        ],\n" +
+                           "        \"stream\": false\n" +
+                           "      }")
+                   .asString();
+
+           // 检查响应状态码
+           if (response.getStatus() == 200) {
+               try {
+                   // 使用 Jackson 解析 JSON 响应
+                   ObjectMapper mapper = new ObjectMapper();
+                   Map<String, Object> jsonResponse = mapper.readValue(response.getBody(), Map.class);
+
+                   // 假设响应中包含一个名为 "content" 的字段
+                   String content = (String) jsonResponse.get("content");
+                   if (content != null) {
+                       System.out.println("响应内容: " + content);
+                   } else {
+                       System.out.println("响应中未找到 'content' 字段。");
+                   }
+               } catch (Exception e) {
+                   System.err.println("解析响应时出错: " + e.getMessage());
+               }
+           } else {
+               System.err.println("请求失败，状态码: " + response.getStatus());
+           }
+       } catch (Exception e) {
+           e.printStackTrace();
+       }
+      return "你好，" + userMessage;
+   }
+}

src/main/java/com/ai/service/OllamaAssist.java

@@ -0,0 +1,8 @@
+package com.ai.service;
+
+import dev.langchain4j.service.SystemMessage;
+
+public interface OllamaAssist {
+    @SystemMessage("你是一个助手，可以使用工具来回答问题")
+    String chat(String userMessage);
+}