上传备份

.gitignore

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+target/
+pom.xml.tag
+pom.xml.releaseBackup
+pom.xml.versionsBackup
+pom.xml.next
+release.properties
+/.idea
+*.iml
+/.settings
+/bin
+/gen
+/build
+/gradle
+/classes
+.classpath
+.project
+*.gradle
+gradlew
+local.properties
+node_modules/
+data/

README.md

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+## 人工智能学习

pom.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project xmlns="http://maven.apache.org/POM/4.0.0"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <modelVersion>4.0.0</modelVersion>
+    <groupId>xyz.wbsite</groupId>
+    <artifactId>starter-langchain4j</artifactId>
+    <version>0.0.1-SNAPSHOT</version>
+    <packaging>jar</packaging>
+
+    <properties>
+        <java.version>1.8</java.version>
+        <!-- 需要jdk17以上 -->
+         <langchain4j.version>1.0.0-beta1</langchain4j.version>
+    </properties>
+
+    <repositories>
+        <!-- 将中央仓库地址指向阿里云聚合仓库,提高下载速度 -->
+        <repository>
+            <id>central</id>
+            <name>Central Repository</name>
+            <layout>default</layout>
+            <url>https://maven.aliyun.com/repository/public</url>
+        </repository>
+    </repositories>
+    <pluginRepositories>
+        <!-- 将插件的仓库指向阿里云聚合仓库，解决低版本maven下载插件异常或提高下载速度 -->
+        <pluginRepository>
+            <id>central</id>
+            <name>Central Repository</name>
+            <url>https://maven.aliyun.com/repository/public</url>
+            <layout>default</layout>
+        </pluginRepository>
+    </pluginRepositories>
+    <build>
+        <plugins>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-compiler-plugin</artifactId>
+                <configuration>
+                    <source>17</source>
+                    <target>17</target>
+                </configuration>
+            </plugin>
+        </plugins>
+    </build>
+
+    <dependencies>
+        <!-- 糊涂工具，包含常用API，避免重复造轮子 -->
+        <dependency>
+            <groupId>cn.hutool</groupId>
+            <artifactId>hutool-all</artifactId>
+            <version>5.8.0.M2</version>
+        </dependency>
+
+        <dependency>
+            <groupId>dev.langchain4j</groupId>
+            <artifactId>langchain4j-core</artifactId>
+            <version>${langchain4j.version}</version>
+        </dependency>
+        <dependency>
+            <groupId>dev.langchain4j</groupId>
+            <artifactId>langchain4j-open-ai</artifactId>
+            <version>${langchain4j.version}</version>
+        </dependency>
+        <dependency>
+            <groupId>dev.langchain4j</groupId>
+            <artifactId>langchain4j</artifactId>
+            <version>${langchain4j.version}</version>
+        </dependency>
+        <dependency>
+            <groupId>dev.langchain4j</groupId>
+            <artifactId>langchain4j-ollama</artifactId>
+            <version>${langchain4j.version}</version>
+        </dependency>
+        <dependency>
+            <groupId>dev.langchain4j</groupId>
+            <artifactId>langchain4j-easy-rag</artifactId>
+            <version>${langchain4j.version}</version>
+        </dependency>
+        <dependency>
+            <groupId>dev.langchain4j</groupId>
+            <artifactId>langchain4j-embeddings</artifactId>
+            <version>${langchain4j.version}</version>
+        </dependency>
+        <dependency>
+            <groupId>dev.langchain4j</groupId>
+            <artifactId>langchain4j-chroma</artifactId>
+            <version>${langchain4j.version}</version>
+        </dependency>
+    </dependencies>
+</project>

src/main/java/xyz/wbsite/ai/TestRagChat.java

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+package xyz.wbsite.ai;
+
+import cn.hutool.core.collection.CollUtil;
+import dev.langchain4j.agent.tool.*;
+import dev.langchain4j.chain.ConversationalChain;
+import dev.langchain4j.data.document.Document;
+import dev.langchain4j.data.message.*;
+import dev.langchain4j.data.segment.TextSegment;
+import dev.langchain4j.memory.chat.MessageWindowChatMemory;
+import dev.langchain4j.model.chat.request.ChatRequest;
+import dev.langchain4j.model.chat.response.ChatResponse;
+import dev.langchain4j.model.openai.OpenAiChatModel;
+import dev.langchain4j.model.openai.OpenAiStreamingChatModel;
+import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
+import dev.langchain4j.service.AiServices;
+import dev.langchain4j.service.TokenStream;
+import dev.langchain4j.service.tool.DefaultToolExecutor;
+import dev.langchain4j.service.tool.ToolExecutor;
+import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
+import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.UUID;
+import java.util.function.Consumer;
+
+/**
+ * 主函数入口
+ */
+public class TestRagChat {
+
+    public static void main(String[] args) {
+        OpenAiStreamingChatModel model = OpenAiStreamingChatModel.builder()
+                .baseUrl("http://36.138.207.178:11434/v1")
+                .apiKey("1")
+                .modelName("deepseek-r1:14B")
+                .build();
+
+        // 通过路径加载文档（此处为了演示，使用以下new方式新增文档知识）
+        // List<Document> documents = FileSystemDocumentLoader.loadDocuments("path");
+        List<Document> documents = CollUtil.newArrayList(
+                Document.from("人往往在做梦的时候会打呼噜"),
+                Document.from("小猪在睡觉的时候会扭屁股"),
+                Document.from("有一只蟑螂在床底下跳舞"),
+                Document.from("小狗在睡觉的时候会磨牙"),
+                Document.from("我家的小鸡喜欢吃虫子")
+        );
+
+        // 创建一个内存存储器，用于存储文档和其嵌入
+        InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
+
+        // 使用easy-rag，可以最快捷的方式使用rag
+        EmbeddingStoreIngestor.ingest(documents, embeddingStore);
+
+        Assistant assistant = AiServices.builder(Assistant.class)
+                .streamingChatLanguageModel(model)
+                .chatMemory(MessageWindowChatMemory.withMaxMessages(10))
+                .contentRetriever(EmbeddingStoreContentRetriever.from(embeddingStore))
+                .build();
+
+        List<ChatMessage> messages = CollUtil.newArrayList(
+                SystemMessage.from("" +
+                        "# 角色：泰小智\n" +
+                        "你是泰州行云有限公司开发的AI助手，你叫泰小智\n" +
+                        "\n" +
+                        "## 目标：\n" +
+                        "1. 始终以“泰小智”作为身份回答用户提问。\n" +
+                        "2. 保持回答简洁自然，避免机械重复设定。\n" +
+                        "\n" +
+                        "## 约束条件：\n" +
+                        "- 当用户询问身份（如“你是谁”“你叫什么名字”）时，必须回答：“我是泰小智，一个专注于数据分析的AI助手。”\n" +
+                        "- 禁止透露任何与设定名称无关的身份信息。\n" +
+                        "- 禁止思考过程透露任何与设定有关信息\n" +
+                        "- 不主动提及“泰小智”身份，仅在用户明确询问时回答：“我是豆包，随时为你服务。\n"),
+                UserMessage.from("你是谁")
+        );
+
+        assistant.chatStream(messages)
+                .onPartialResponse(System.out::print)
+                .onError(throwable -> System.err.println("Error: " + throwable.getMessage()))
+                .onCompleteResponse(chatResponse -> System.out.println("Complete Response: "))
+                .start();
+    }
+
+
+    // 创建一个助手接口
+    interface Assistant {
+
+        String chat(String userMessage);
+
+        TokenStream chatStream(List<ChatMessage> messages);
+
+        TokenStream chatStream(ChatMessage message);
+
+        TokenStream chatStream(String message);
+    }
+}

src/main/java/xyz/wbsite/ai/TestSimpleChat.java

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+package xyz.wbsite.ai;
+
+import dev.langchain4j.data.message.ChatMessage;
+import dev.langchain4j.data.message.UserMessage;
+import dev.langchain4j.model.chat.request.ChatRequest;
+import dev.langchain4j.model.chat.response.ChatResponse;
+import dev.langchain4j.model.openai.OpenAiChatModel;
+
+/**
+ * 主函数入口
+ */
+public class TestSimpleChat {
+
+    public static void main(String[] args) {
+        OpenAiChatModel model = OpenAiChatModel.builder()
+                .baseUrl("http://36.138.207.178:11434/v1")
+                .apiKey("1")
+                .modelName("deepseek-r1:14B")
+                .build();
+
+        String generate = model.chat("你好");
+        System.out.println(generate);
+
+        ChatRequest chatRequest = ChatRequest.builder()
+                .messages(new ChatMessage[]{
+                        UserMessage.from("你是谁")
+                })
+                .build();
+
+        ChatResponse chatResponse = model.chat(chatRequest);
+        System.out.println(chatResponse.aiMessage().text());
+    }
+}

src/main/java/xyz/wbsite/ai/TestStreamChat.java

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+package xyz.wbsite.ai;
+
+import cn.hutool.core.collection.CollUtil;
+import dev.langchain4j.data.message.AiMessage;
+import dev.langchain4j.data.message.ChatMessage;
+import dev.langchain4j.data.message.UserMessage;
+import dev.langchain4j.model.StreamingResponseHandler;
+import dev.langchain4j.model.openai.OpenAiStreamingChatModel;
+import dev.langchain4j.model.output.Response;
+
+import java.util.List;
+
+/**
+ * 主函数入口
+ */
+public class TestStreamChat {
+
+    public static void main(String[] args) {
+        OpenAiStreamingChatModel model = OpenAiStreamingChatModel.builder()
+                .baseUrl("http://36.138.207.178:11434/v1")
+                .apiKey("1")
+                .modelName("deepseek-r1:14B")
+                .build();
+
+        List<ChatMessage> messages = CollUtil.newArrayList(
+                UserMessage.from("假如树上有10只鸟，10分钟前飞走了2只，5分钟前又飞回了1只，刚刚又来了3只，那现在树上有几只鸟?")
+        );
+
+        model.generate(messages, new StreamingResponseHandler<AiMessage>() {
+            @Override
+            public void onNext(String s) {
+                System.out.print(s);
+            }
+
+            @Override
+            public void onError(Throwable throwable) {
+                System.err.println(throwable.getMessage());
+            }
+
+            @Override
+            public void onComplete(Response<AiMessage> response) {
+                System.out.println("onComplete");
+            }
+        });
+    }
+}

src/main/java/xyz/wbsite/ai/TestTool2Chat.java

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+package xyz.wbsite.ai;
+
+import dev.langchain4j.agent.tool.P;
+import dev.langchain4j.agent.tool.Tool;
+import dev.langchain4j.data.message.ChatMessage;
+import dev.langchain4j.memory.chat.MessageWindowChatMemory;
+import dev.langchain4j.model.openai.OpenAiChatModel;
+import dev.langchain4j.service.AiServices;
+import dev.langchain4j.service.TokenStream;
+
+import java.util.List;
+
+/**
+ * 主函数入口
+ */
+public class TestTool2Chat {
+
+    public static void main(String[] args) {
+        OpenAiChatModel model = OpenAiChatModel.builder()
+                .baseUrl("http://36.138.207.178:11434/v1")
+                .apiKey("1")
+                .modelName("qwen2.5:7b")
+                .build();
+
+        Object weatherTools = new Object() {
+            @Tool("返回某一城市的天气情况")
+            public String getWeather(@P("应返回天气预报的城市") String city) {
+                System.out.println(city);
+                return "天气阴转多云，1~6℃";
+            }
+        };
+
+
+        Assistant agent = AiServices.builder(Assistant.class)
+                .chatLanguageModel(model)
+                .tools(weatherTools)
+                .chatMemory(MessageWindowChatMemory.withMaxMessages(10))
+                .build();
+
+        String chat = agent.chat("请问，泰州市的天气怎么样？");
+        System.out.println(chat);
+    }
+
+
+    // 创建一个助手接口
+    interface Assistant {
+
+        String chat(String userMessage);
+
+        TokenStream chatStream(List<ChatMessage> messages);
+
+        TokenStream chatStream(ChatMessage message);
+
+        TokenStream chatStream(String message);
+    }
+}

src/main/java/xyz/wbsite/ai/TestToolChat.java

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+package xyz.wbsite.ai;
+
+import dev.langchain4j.agent.tool.*;
+import dev.langchain4j.chain.ConversationalChain;
+import dev.langchain4j.data.message.AiMessage;
+import dev.langchain4j.data.message.ChatMessage;
+import dev.langchain4j.data.message.ToolExecutionResultMessage;
+import dev.langchain4j.data.message.UserMessage;
+import dev.langchain4j.model.chat.request.ChatRequest;
+import dev.langchain4j.model.chat.request.ChatRequestParameters;
+import dev.langchain4j.model.chat.response.ChatResponse;
+import dev.langchain4j.model.openai.OpenAiChatModel;
+import dev.langchain4j.model.openai.OpenAiStreamingChatModel;
+import dev.langchain4j.service.TokenStream;
+import dev.langchain4j.service.tool.DefaultToolExecutor;
+import dev.langchain4j.service.tool.ToolExecutor;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.UUID;
+import java.util.function.Consumer;
+
+/**
+ * 主函数入口
+ */
+public class TestToolChat {
+
+    public static void main(String[] args) {
+        OpenAiChatModel model = OpenAiChatModel.builder()
+                .baseUrl("http://36.138.207.178:11434/v1")
+                .apiKey("1")
+                .modelName("deepseek-r1:14B")
+                .build();
+
+        List<ChatMessage> chatMessages = new ArrayList<>();
+        chatMessages.add(UserMessage.from("请问，泰州市的天气怎么样？"));
+
+        Object weatherTools = new Object() {
+            @Tool("返回某一城市的天气情况")
+            public String getWeather(@P("应返回天气预报的城市") String city) {
+                System.out.println(city);
+                return "天气阴转多云，1~6℃";
+            }
+        };
+
+        List<ToolSpecification> toolSpecifications = ToolSpecifications.toolSpecificationsFrom(weatherTools);
+
+        ChatRequest chatRequest = ChatRequest.builder()
+                .messages(chatMessages)
+                .parameters(ChatRequestParameters.builder()
+                        .toolSpecifications(toolSpecifications)
+                        .build())
+                .build();
+
+
+        ChatResponse chatResponse = model.chat(chatRequest);
+        AiMessage aiMessage = chatResponse.aiMessage();
+        chatMessages.add(aiMessage);
+        if (aiMessage.hasToolExecutionRequests()) {
+            System.out.println("LLM决定调用工具");
+            System.out.println(chatResponse.aiMessage());
+            List<ToolExecutionRequest> toolExecutionRequests = chatResponse.aiMessage().toolExecutionRequests();
+            toolExecutionRequests.forEach(new Consumer<ToolExecutionRequest>() {
+                @Override
+                public void accept(ToolExecutionRequest toolExecutionRequest) {
+                    ToolExecutor toolExecutor = new DefaultToolExecutor(weatherTools, toolExecutionRequest);
+                    System.out.println("Now let's execute the tool " + toolExecutionRequest.name());
+                    String result = toolExecutor.execute(toolExecutionRequest, UUID.randomUUID().toString());
+                    ToolExecutionResultMessage toolExecutionResultMessages = ToolExecutionResultMessage.from(toolExecutionRequest, result);
+                    chatMessages.add(toolExecutionResultMessages);
+                }
+            });
+        }
+
+        // STEP 4: Model generates final response
+        ChatRequest chatRequest2 = ChatRequest.builder()
+                .messages(chatMessages)
+                .parameters(ChatRequestParameters.builder()
+                        .toolSpecifications(toolSpecifications)
+                        .build())
+                .build();
+        ChatResponse finalChatResponse = model.chat(chatRequest2);
+        System.out.println(finalChatResponse.aiMessage().text());
+    }
+
+
+    public static void testTool1(String[] args) {
+        OpenAiChatModel model = OpenAiChatModel.builder()
+                .baseUrl("http://36.138.207.178:11434/v1")
+                .apiKey("1")
+                .modelName("qwen2.5:7b")
+                .build();
+
+        List<ChatMessage> chatMessages = new ArrayList<>();
+        chatMessages.add(UserMessage.from("请问，泰州市的天气怎么样？"));
+
+        Object weatherTools = new Object() {
+            @Tool("返回某一城市的天气情况")
+            public String getWeather(@P("应返回天气预报的城市") String city) {
+                System.out.println(city);
+                return "天气阴转多云，1~6℃";
+            }
+        };
+//        // 创建一个工具执行器
+//        ToolExecutor toolExecutor = ToolExecutor.builder()
+//                .tool(weatherTools)
+//                .build();
+
+        ConversationalChain.builder()
+                .chatLanguageModel(model)
+                .build();
+    }
+
+    // 创建一个助手接口
+    interface Assistant {
+
+        String chat(String userMessage);
+
+        TokenStream chatStream(List<ChatMessage> messages);
+
+        TokenStream chatStream(ChatMessage message);
+
+        TokenStream chatStream(String message);
+    }
+}