2025-07-13-Sun-T-AI-LangChain4j

1. 认识AI

1.1 神经元

介绍

黑马LangChain4j入门到实战项目： 项目地址

软件架构

前端：

• 静态页面

后端框架：

• Springboot

• Langchain4j + Ollama

持久化：

• 本地文件存储对话记录 （resources/memory)
• 用户预约信息存储在内存（map结构）
• RAG向量存储在内存

1. 快速入门：Ollama本地部署大模型

• 安装ollama

下载地址：Ollama

• 运行阿里千问小小模型

ollama run qwen3:0.6b

• POST请求ollama本地服务

参考Thinking · Ollama Blog

//http://localhost:11434/api/chat
{"model":"qwen3:0.6b","messages":[{"role":"system","content": "你是一个ai助手"},{"role": "user","content": "叫什么名字？什么是PEFT？"},{"role":"assistant","content" : "我是小小傻瓜蛋"}],"think":true,"stream":false
}

2. 整合spring-boot

2.1 引入ollama依赖

<dependency><groupId>dev.langchain4j</groupId><artifactId>langchain4j-ollama</artifactId><version>1.0.1-beta6</version>
</dependency>

2.2 配置模型

//Properties
package com.learning.langchain4j.config;import lombok.Data;
import org.springframework.boot.context.properties.ConfigurationProperties;@ConfigurationProperties(prefix = "ollama")
@Data
public class OllamaConfigProperties {private String modelName;private String baseUrl; // 本地 ollama 基础 URL
}

//Ollama 配置
package com.learning.langchain4j.config;import dev.langchain4j.model.ollama.OllamaChatModel;
import org.springframework.boot.context.properties.ConfigurationProperties;
import org.springframework.boot.context.properties.EnableConfigurationProperties;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;import java.time.Duration;@Configuration
@EnableConfigurationProperties(OllamaConfigProperties.class)
public class OllamaConfig {@Beanpublic OllamaChatModel ollamaChatModel(OllamaConfigProperties ollamaConfigProperties){OllamaChatModel model = OllamaChatModel.builder().modelName(ollamaConfigProperties.getModelName()).baseUrl(ollamaConfigProperties.getBaseUrl()).timeout(Duration.ofSeconds(30)) // 超时时间.logRequests(true).logResponses(true).maxRetries(3) // 超时最大重试次数.build();return model;}}

# yaml
server:port: 8099
spring:application:name: ai-consultant
ollama:base-url:  http://localhost:11434model-name: qwen3:0.6b

2.3 编写controller

package com.learning.langchain4j;import dev.langchain4j.model.ollama.OllamaChatModel;
import lombok.extern.slf4j.Slf4j;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RestController;@RestController
@Slf4j
public class ChatController {@Autowiredprivate OllamaChatModel ollamaChatModel;@RequestMapping("/chat")public String chat(String message) {log.info("用户: {}", message);return ollamaChatModel.generate(message);}
}

2.4 测试

http://localhost:8099/chat?message="你好"

3. 功能会话 AIServices

3. 1 引入依赖

<dependency><groupId>dev.langchain4j</groupId><artifactId>langchain4j-spring-boot-starter</artifactId><version>1.0.1-beta6</version>
</dependency>

3.2 Service 和Controller

// AiService
@AiService(chatModel = "ollamaChatModel",wiringMode = AiServiceWiringMode.EXPLICIT
)
public interface ConsultantService {// 用于聊天的方法，message为用户输入内容String chat(String message);
}// Controller
@Autowired
private ConsultantService consultantService;@RequestMapping("/streamChat")
public String streamChat(String message) {log.info("用户: {}", message);return consultantService.chat(message);
}

4. 流式调用

4.1 依赖

    <dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-webflux</artifactId></dependency><dependency><groupId>dev.langchain4j</groupId><artifactId>langchain4j-reactor</artifactId><version>1.0.1-beta6</version></dependency>

4.2 Config, Service, Controller

• config

@Bean
public OllamaStreamingChatModel ollamaStreamingChatModel(OllamaConfigProperties ollamaConfigProperties){return OllamaStreamingChatModel.builder().modelName(ollamaConfigProperties.getModelName()).baseUrl(ollamaConfigProperties.getBaseUrl()).timeout(Duration.ofSeconds(ollamaConfigProperties.getTimeOut())) // 超时时间.logRequests(ollamaConfigProperties.getLogRequests()).logResponses(ollamaConfigProperties.getLogResponses()).build();
}

• service

@AiService(chatModel = "ollamaChatModel",wiringMode = AiServiceWiringMode.EXPLICIT,streamingChatModel = "ollamaStreamingChatModel"
)
public interface ConsultantService {// 用于聊天的方法，message为用户输入内容Flux<String> chat(String message);
}

• controller

    @Autowiredprivate ConsultantService consultantService;@RequestMapping(value = "/streamChat", produces = "text/html;charset=utf-8") // produces指定响应类型，解决响应乱码问题public Flux<String> streamChat(String message) {log.info("用户: {}", message);return consultantService.chat(message);}

5 消息会话

5.1 message注解

@AiService(chatModel = "ollamaChatModel",wiringMode = AiServiceWiringMode.EXPLICIT,streamingChatModel = "ollamaStreamingChatModel"
)
public interface ConsultantService {// 用于聊天的方法，message为用户输入内容@SystemMessage(fromResource = "system.txt")Flux<String> chat(String message);
}

5.2 会话记忆

• config

    @Beanpublic ChatMemory chatMemory(OllamaConfigProperties ollamaConfigProperties){return MessageWindowChatMemory.builder().maxMessages(ollamaConfigProperties.getMaxMessages()).build();}

• service

@AiService(chatModel = "ollamaChatModel",wiringMode = AiServiceWiringMode.EXPLICIT,streamingChatModel = "ollamaStreamingChatModel",chatMemory = "chatMemory"
)
public interface ConsultantService {// 用于聊天的方法，message为用户输入内容@SystemMessage(fromResource = "system.txt")Flux<String> chat(String message);
}

5.3 会话记忆隔离

• config

    @Beanpublic ChatMemoryProvider chatMemoryProvider(OllamaConfigProperties ollamaConfigProperties){return new ChatMemoryProvider(){@Overridepublic ChatMemory get(Object o) {return MessageWindowChatMemory.builder().id(o).maxMessages(ollamaConfigProperties.getMaxMessages()).build();}};}

• service

@AiService(chatModel = "ollamaChatModel",wiringMode = AiServiceWiringMode.EXPLICIT,streamingChatModel = "ollamaStreamingChatModel",chatMemoryProvider = "chatMemoryProvider"
)
public interface ConsultantService {// 用于聊天的方法，message为用户输入内容@SystemMessage(fromResource = "system.txt")Flux<String> chat(@MemoryId String memoryId, @UserMessage String message);
}

• controller

    @RequestMapping(value = "/streamChat", produces = "text/html;charset=utf-8")public Flux<String> streamChat(String memoryId,String message) {log.info("用户: {}", message);return consultantService.chat(memoryId,message);}

5.4 数据持久化- 本地文件

• repository

package com.learning.langchain4j.repository;import dev.langchain4j.data.message.ChatMessage;
import dev.langchain4j.data.message.ChatMessageDeserializer;
import dev.langchain4j.data.message.ChatMessageSerializer;
import dev.langchain4j.store.memory.chat.ChatMemoryStore;
import org.springframework.stereotype.Repository;import java.io.*;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.HashMap;
import java.util.List;
import java.util.Map;@Repository
public class MyChatMemoryStore implements ChatMemoryStore {// 存储路径：优先使用resource目录下的memory文件private static final Path STORAGE_PATH = Paths.get("src/main/resources/memory");private final Map<Object, String> memoryMap = new HashMap<>();public MyChatMemoryStore() {loadFromFile(); // 初始化时加载持久化数据}// 从文件加载会话记忆private synchronized void loadFromFile() {try (ObjectInputStream ois = new ObjectInputStream(new FileInputStream(STORAGE_PATH.toFile()))) {Map<Object, String> loadedMap = (Map<Object, String>) ois.readObject();memoryMap.putAll(loadedMap);} catch (FileNotFoundException e) {initStorageFile(); // 首次使用时创建文件} catch (IOException | ClassNotFoundException e) {throw new RuntimeException("Failed to load chat memory", e);}}// 初始化存储文件private void initStorageFile() {try {File file = STORAGE_PATH.toFile();if (!file.getParentFile().exists()) {file.getParentFile().mkdirs();}file.createNewFile();saveToFile(); // 创建空文件} catch (IOException e) {throw new RuntimeException("Failed to initialize storage file", e);}}// 保存会话记忆到文件private synchronized void saveToFile() {try (ObjectOutputStream oos = new ObjectOutputStream(new FileOutputStream(STORAGE_PATH.toFile()))) {oos.writeObject(memoryMap);} catch (IOException e) {throw new RuntimeException("Failed to save chat memory", e);}}@Overridepublic List<ChatMessage> getMessages(Object memoryId) {String s = memoryMap.get(memoryId);List<ChatMessage> list = ChatMessageDeserializer.messagesFromJson(s);return list;}@Overridepublic void updateMessages(Object memoryId, List<ChatMessage> messages) {String s = ChatMessageSerializer.messagesToJson(messages);memoryMap.put(memoryId, s);saveToFile(); // 更新后立即持久化}@Overridepublic void deleteMessages(Object memoryId) {memoryMap.remove(memoryId);saveToFile(); // 删除后立即持久化}
}

• config

@Bean
public ChatMemoryProvider chatMemoryProvider(OllamaConfigProperties ollamaConfigProperties,ChatMemoryStore myChatMemoryStore){return new ChatMemoryProvider(){@Overridepublic ChatMemory get(Object o) {return MessageWindowChatMemory.builder().id(o).maxMessages(ollamaConfigProperties.getMaxMessages()).chatMemoryStore(myChatMemoryStore).build();}};
}

6. RAG知识库

6.1 原理

1. 知识文本通过向量模型转为向量
2. 将向量和文本一一存储到向量数据库
3. 用户输入时，先计算用户提问的文本对应的向量，然后再到向量数据库中去匹配，计算用户输入向量与数据库中向量余弦相似度
4. 设定余弦相似度阈值，例如0.5，那么余弦相似度大于0.5的数据就会被提取出来，和用户提问数据一并交给大模型处理

6.2 快速入门

• 引入依赖

    <!--RAG数据库相关依赖--><dependency><groupId>dev.langchain4j</groupId><artifactId>langchain4j-easy-rag</artifactId><version>1.0.1-beta6</version></dependency>

• config

    /*** 加载文件进内存，当作RAG* @return*/@Beanpublic EmbeddingStore embeddingStore(){  // 出现重名需要重命名// 1.加载文件进内存，构建向量数据库操作对象List<Document> contents = ClassPathDocumentLoader.loadDocuments("content");InMemoryEmbeddingStore store = new InMemoryEmbeddingStore();  // 使用内存当作向量数据库// 文本切割，向量化EmbeddingStoreIngestor ingestor = EmbeddingStoreIngestor.builder().embeddingStore(store).build();IngestionResult ingest = ingestor.ingest(contents);return store;}//构建向量数据库检索对象@Beanpublic ContentRetriever contentRetriever(EmbeddingStore embeddingStore){return EmbeddingStoreContentRetriever.builder().embeddingStore(embeddingStore).minScore(0.5).maxResults(3).build();}

• service

@AiService(chatModel = "ollamaChatModel",wiringMode = AiServiceWiringMode.EXPLICIT,streamingChatModel = "ollamaStreamingChatModel",chatMemoryProvider = "chatMemoryProvider",contentRetriever = "contentRetriever" 
)
public interface ConsultantService {// 用于聊天的方法，message为用户输入内容@SystemMessage(fromResource = "system.txt")Flux<String> chat(@MemoryId String memoryId, @UserMessage String message);
}

6.3 核心API

1 内容加载

• FileSystemDocumentLoader
• ClassPathDocumentLoader
• UrlDocumentLoader
• ...

2 文本分割器

• DocumentByParagraphSplitter 按照段落
• DocumentByRegexSpliter 按照正则表达式分割
• DocumentSplitters.recursive(...)(默认) 递归分割器，优先段落，再按照行，句子和词

3 配置自己想要的向量模型

具体操作：略，本项目采用内存向量数据库，理解即可

4 部署向量数据库

具体操作：略，本项目采用内存向量数据库，理解即可

常用向量数据库：

• Milvus
• Chroma
• Pinecone
• RedisSearch（Redis）
• pgvector（PostgreSQL)

使用向量数据库流程

1. 安装好向量数据库
2. 引入maven依赖
3. 配置向量数据库信息
4. 注入向量数据库对象并引用

7. Tools

7.1 开发预约服务，可以读写mysql中预约表中的信息

具体操作：本项目采用Map作为数据库，理解即可

数据库准备：

1. 准备数据库环境
2. 引入依赖
3. 配置连接信息
4. 准备实体类
5. 开发mapper
6. 开发service
7. 完成测试

7.2 原理

7.3 实现

1. 准备工具方法
2. 配置工具方法

• tool配置

@Component
public class ReservationTool {@Autowiredprivate ReservationService reservationService;// 1. 工具：添加预约信息@Tool("预约志愿填报服务")public void addReservation(@P("考生姓名") String name,@P("考生性别") String gender,@P("考生电话") String phone,@P("会话时间") LocalDateTime communicationTime,@P("考生所在省份") String province,@P("考生预估分数") Integer estimateScore) {Reservation build = Reservation.builder().id(null).name(name).gender(gender).phone(phone).communicationTime(communicationTime).province(province).estimateScore(estimateScore).build();reservationService.insert(build);}// 2. 工具： 查询预约信息@Tool("根据用户手机号码查询预约单")public Reservation findReservation(@P("考生电话") String phone){return reservationService.findByPhone(phone);}
}

• service 使用

@AiService(chatModel = "ollamaChatModel",wiringMode = AiServiceWiringMode.EXPLICIT,streamingChatModel = "ollamaStreamingChatModel",chatMemoryProvider = "chatMemoryProvider",contentRetriever = "contentRetriever",tools = {"reservationTool"}
)
public interface ConsultantService {// 用于聊天的方法，message为用户输入内容@SystemMessage(fromResource = "system.txt")Flux<String> chat(@MemoryId String memoryId, @UserMessage String message);
}

qwen3:0.6b似乎不能完成工具的调用