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feat(ai): 集成多种大模型并支持会话记忆功能

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- 新增 Ollama、智谱 AI 和 OpenAI 大模型接入配置- 实现基于 ChatMemory 的会话上下文管理
- 添加流式输出接口以提升响应体验
- 更新加密工具类密钥及测试数据
- 引入多个 AI 控制器用于不同模型的服务调用
- 在 pom.xml 中添加相关依赖项以支持多模型集成
This commit is contained in:
Hanserwei
2025-10-21 22:33:19 +08:00
parent ef527aab00
commit bfbfdbc90d
9 changed files with 212 additions and 3 deletions
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17
pom.xml
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@@ -35,6 +35,23 @@
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-starter-model-deepseek</artifactId>
</dependency>
<!-- Ollama -->
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-starter-model-ollama</artifactId>
</dependency>
<!-- 智谱AI -->
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-starter-model-zhipuai</artifactId>
</dependency>
<!-- OpenAI -->
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-starter-model-openai</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>

5
src/main/java/com/hanserwei/airobot/config/ChatClientConfig.java
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@@ -1,14 +1,19 @@
package com.hanserwei.airobot.config;
import com.hanserwei.airobot.advisor.MyLoggerAdvisor;
import jakarta.annotation.Resource;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.client.advisor.MessageChatMemoryAdvisor;
import org.springframework.ai.chat.client.advisor.SimpleLoggerAdvisor;
import org.springframework.ai.chat.memory.ChatMemory;
import org.springframework.ai.deepseek.DeepSeekChatModel;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class ChatClientConfig {
@Resource
private ChatMemory chatMemory;
/**
* 初始化 ChatClient 客户端

30
src/main/java/com/hanserwei/airobot/config/ChatMemoryConfig.java Normal file
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@@ -0,0 +1,30 @@
package com.hanserwei.airobot.config;
import jakarta.annotation.Resource;
import org.springframework.ai.chat.memory.ChatMemory;
import org.springframework.ai.chat.memory.ChatMemoryRepository;
import org.springframework.ai.chat.memory.MessageWindowChatMemory;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class ChatMemoryConfig {
/**
* 记忆存储
*/
@Resource
private ChatMemoryRepository chatMemoryRepository;
/**
* 初始化一个 ChatMemory 实例，并注入到 Spring 容器中
* @return ChatMemory
*/
@Bean
public ChatMemory chatMemory() {
return MessageWindowChatMemory.builder()
.maxMessages(50) // 最大消息窗口为 50，默认值为 20
.chatMemoryRepository(chatMemoryRepository) // 记忆存储
.build();
}
}

5
src/main/java/com/hanserwei/airobot/controller/ChatClientController.java
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@@ -2,6 +2,7 @@ package com.hanserwei.airobot.controller;
import jakarta.annotation.Resource;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.memory.ChatMemory;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestParam;
@@ -36,9 +37,11 @@ public class ChatClientController {
* @return 对话结果
*/
@GetMapping(value = "/generateStream", produces = "text/html;charset=utf-8")
public Flux<String> generateStream(@RequestParam(value = "message", defaultValue = "你是谁？") String message) {
public Flux<String> generateStream(@RequestParam(value = "message", defaultValue = "你是谁？") String message,
@RequestParam(value = "chatId") String chatId) {
return chatClient.prompt()
.user(message) // 提示词
.advisors(a -> a.param(ChatMemory.CONVERSATION_ID, chatId))
.stream() // 流式输出
.content();

33
src/main/java/com/hanserwei/airobot/controller/OllamaController.java Normal file
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@@ -0,0 +1,33 @@
package com.hanserwei.airobot.controller;
import jakarta.annotation.Resource;
import org.springframework.ai.chat.model.ChatResponse;
import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.ai.ollama.OllamaChatModel;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
@RestController
@RequestMapping("/v3/ai")
public class OllamaController {
@Resource
private OllamaChatModel chatModel;
/**
* 普通对话
* @param message 对话输入内容
* @return 对话结果
*/
@GetMapping("/generate")
public String generate(@RequestParam(value = "message", defaultValue = "你是谁？") String message) {
// 构建提示词，调用大模型
ChatResponse chatResponse = chatModel.call(new Prompt(message));
// 响应回答内容
return chatResponse.getResult().getOutput().getText();
}
}

53
src/main/java/com/hanserwei/airobot/controller/OpenAIController.java Normal file
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@@ -0,0 +1,53 @@
package com.hanserwei.airobot.controller;
import jakarta.annotation.Resource;
import org.springframework.ai.chat.messages.UserMessage;
import org.springframework.ai.chat.model.Generation;
import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.ai.openai.OpenAiChatModel;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
import reactor.core.publisher.Flux;
import java.util.Objects;
@RestController
@RequestMapping("/v5/ai")
public class OpenAIController {
@Resource
private OpenAiChatModel chatModel;
/**
* 普通对话
* @param message 对话输入内容
* @return 对话结果
*/
@GetMapping("/generate")
public String generate(@RequestParam(value = "message", defaultValue = "你是谁？") String message) {
// 一次性返回结果
return chatModel.call(message);
}
/**
* 流式对话
* @param message 对话输入内容
* @return 对话结果
*/
@GetMapping(value = "/generateStream", produces = "text/html;charset=utf-8")
public Flux<String> generateStream(@RequestParam(value = "message", defaultValue = "你是谁？") String message) {
// 构建提示词
Prompt prompt = new Prompt(new UserMessage(message));
// 流式输出
return chatModel.stream(prompt)
.mapNotNull(chatResponse -> {
Generation generation = chatResponse.getResult();
return generation.getOutput().getText();
});
}
}

47
src/main/java/com/hanserwei/airobot/controller/ZhiPuController.java Normal file
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@@ -0,0 +1,47 @@
package com.hanserwei.airobot.controller;
import jakarta.annotation.Resource;
import org.springframework.ai.chat.messages.UserMessage;
import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.ai.zhipuai.ZhiPuAiChatModel;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
import reactor.core.publisher.Flux;
@RestController
@RequestMapping("/v4/ai")
public class ZhiPuController {
@Resource
private ZhiPuAiChatModel chatModel;
/**
* 普通对话
* @param message 对话输入内容
* @return 对话结果
*/
@GetMapping("/generate")
public String generate(@RequestParam(value = "message", defaultValue = "你是谁？") String message) {
// 一次性返回结果
return chatModel.call(message);
}
/**
* 流式对话
* @param message 对话输入内容
* @return 对话结果
*/
@GetMapping(value = "/generateStream", produces = "text/html;charset=utf-8")
public Flux<String> generateStream(@RequestParam(value = "message", defaultValue = "你是谁？") String message) {
// 构建提示词
Prompt prompt = new Prompt(new UserMessage(message));
// 流式输出
return chatModel.stream(prompt)
.mapNotNull(chatResponse -> chatResponse.getResult().getOutput().getText());
}
}

4
src/main/java/com/hanserwei/airobot/utils/EncryptorUtil.java
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@@ -5,7 +5,7 @@ import org.jasypt.util.text.AES256TextEncryptor;
public class EncryptorUtil {
public static void main(String[] args) {
AES256TextEncryptor textEncryptor = new AES256TextEncryptor();
textEncryptor.setPassword("password");
System.out.println(textEncryptor.encrypt("sk-xxxxxxxxxxxxxx"));
textEncryptor.setPassword("hanserwei");
System.out.println(textEncryptor.encrypt("sk-QXBlsyIonybNTcG5tt5GvmMpg2WpdMLPTvU55TXrt9urWpL8"));
}
}

21
src/main/resources/application.yml
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@@ -10,6 +10,27 @@ spring:
options:
model: deepseek-chat # 使用哪个模型
temperature: 0.8 # 温度值
ollama:
base-url: http://localhost:11434 # Ollama 服务的访问地址, 11434 端口是 Ollama 默认的启动端口
chat:
options: # 模型参数
model: qwen3:14b # 指定 Ollama 使用的大模型名称，根据你实际安装的来，我运行的是 14b
temperature: 0.7 # 温度值
zhipuai:
base-url: https://open.bigmodel.cn/api/paas # 智谱 AI 的请求 URL, 可不填，默认值为 open.bigmodel.cn/api/paas
api-key: ENC(Rz1O0AygSzG3q4UrIpIPHRwFoTQXCUZkWZ54vNzl1kgdBkQECzCYa3LoOADM9NlGLlAwCKTMtkj0nd6cP98T59DohcKtzc3iYyiAoNRfH0rsiu483CpaCciyMwxCUi5O) # 填写智谱 AI 的 API Key, 该成你自己的
chat:
options: # 模型参数
model: GLM-4.6 # 模型名称，使用智谱 AI 哪个模型
temperature: 0.7 # 温度值
openai:
base-url: https://api.master-jsx.top # OpenAI 服务的访问地址，这里使用的第三方代理商：智增增
api-key: ENC(D6ETp0VBeDYXvM612dcoGkyHaGUcPuwOVuSLtL92TOCxydyMKXL7/VBndWjFkxAQP/AS7TeQeegla+Ny6TrLStwdJtd28mVhoyf2YsKuXIdRnKF/mv8/uZ0MpzMdv9YR) # 填写智增增的 API Key, 该成你自己的
chat:
options:
model: gpt-4o # 模型名称
temperature: 0.7 # 温度值
jasypt:
encryptor:
password: ${jasypt.encryptor.password}