diff --git a/pom.xml b/pom.xml index ae35c2b..6f87006 100644 --- a/pom.xml +++ b/pom.xml @@ -10,8 +10,10 @@ 1.8 - - 1.0.0-beta1 + + 0.34.0 + + @@ -38,8 +40,8 @@ org.apache.maven.plugins maven-compiler-plugin - 8 - 8 + 9 + 9 diff --git a/src/main/java/xyz/wbsite/ai/Main.java b/src/main/java/xyz/wbsite/ai/Main.java deleted file mode 100644 index 5d98fbf..0000000 --- a/src/main/java/xyz/wbsite/ai/Main.java +++ /dev/null @@ -1,178 +0,0 @@ -package xyz.wbsite.ai; - -import cn.hutool.core.thread.ThreadUtil; -import cn.hutool.json.JSONUtil; -import dev.langchain4j.agent.tool.*; -import dev.langchain4j.data.document.Document; -import dev.langchain4j.data.document.loader.FileSystemDocumentLoader; -import dev.langchain4j.data.document.parser.TextDocumentParser; -import dev.langchain4j.data.message.*; -import dev.langchain4j.memory.ChatMemory; -import dev.langchain4j.model.chat.ChatLanguageModel; -import dev.langchain4j.model.chat.StreamingChatLanguageModel; -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.chat.response.StreamingChatResponseHandler; -import dev.langchain4j.model.ollama.OllamaChatModel; -import dev.langchain4j.model.ollama.OllamaStreamingChatModel; -import dev.langchain4j.service.AiServices; -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 Main { - - public static void main(String[] args) { -// testSimpleChat(args); -// testStreamChat(args); -// testTool(args); -// testRagChat(args); - } - - public static void testSimpleChat(String[] args) { - ChatLanguageModel model = OllamaChatModel.builder() - .baseUrl("http://36.138.207.178:11434") - .modelName("deepseek-r1:32B") - .build(); - - String generate = model.chat("请问，你是谁？"); - System.out.println(generate); - - ChatResponse chat = model.chat(new UserMessage("请问，你是谁？")); - String text = chat.aiMessage().text(); - System.out.println(text); - - ChatRequest build = ChatRequest.builder() - .messages(new ChatMessage[]{UserMessage.from("我想知道1+1等于几")}) - .build(); - ChatResponse chatResponse = model.chat(build); - System.out.println(chatResponse.aiMessage().text()); - } - - public static void testRagChat(String[] args) { - ChatLanguageModel model = OllamaChatModel.builder() - .baseUrl("http://36.138.207.178:11434") - .modelName("deepseek-r1:32B") - .build(); - - ChatRequest build = ChatRequest.builder() - .messages(new ChatMessage[]{ - SystemMessage.from("你需要使用文档内容对用户提出的问题进行回复，这点非常重要。\n" + - "当用户提出的问题无法根据文档内容进行回复时，回复不知道即可。请明确说明，你的答案是否是从文档中获取的。\n" + - "文档内容如下:小王和小吴是情侣关系，他们两个年龄一样都是20岁，小王有一辆大众牌小轿车，小吴有一辆雅迪电动车。"), - UserMessage.from("小王的车是什么牌子") - }) - .build(); - ChatResponse chatResponse = model.chat(build); - System.out.println(chatResponse.aiMessage().text()); - } - - public static void testStreamChat(String[] args) { - final boolean[] end = {false}; - - StreamingChatLanguageModel model = OllamaStreamingChatModel.builder() - .baseUrl("http://36.138.207.178:11434") - .modelName("deepseek-r1:32B") - .build(); - - String question = "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?"; - - System.out.println("开始提问"); - - ChatRequest chatRequest = ChatRequest.builder().messages(new ChatMessage[]{UserMessage.from(question)}).build(); - - final StringBuilder sb = new StringBuilder(); - - model.chat(chatRequest, new StreamingChatResponseHandler() { - @Override - public void onPartialResponse(String s) { - System.out.print(s); - sb.append(s); - } - - @Override - public void onCompleteResponse(ChatResponse chatResponse) { - System.out.println("-----回答结束-----"); - System.out.println(JSONUtil.toJsonStr(chatResponse)); - end[0] = true; - } - - @Override - public void onError(Throwable throwable) { - System.err.println("-----回答出错-----"); - end[0] = true; - } - }); - - while (!end[0]) { - ThreadUtil.safeSleep(1000); - } - } - - public static void testTool(String[] args) { - ChatLanguageModel model = OllamaChatModel.builder() - .baseUrl("http://36.138.207.178:11434") - .modelName("qwen2.5:7b") - .logRequests(true) - .logResponses(true) - .build(); - - List 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 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 toolExecutionRequests = chatResponse.aiMessage().toolExecutionRequests(); - toolExecutionRequests.forEach(new Consumer() { - @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()); - } -} \ No newline at end of file diff --git a/src/main/java/xyz/wbsite/ai/RagTest.java b/src/main/java/xyz/wbsite/ai/RagTest.java deleted file mode 100644 index 34b7b87..0000000 --- a/src/main/java/xyz/wbsite/ai/RagTest.java +++ /dev/null @@ -1,58 +0,0 @@ -package xyz.wbsite.ai; - -import dev.langchain4j.data.document.*; -import dev.langchain4j.data.document.loader.FileSystemDocumentLoader; -import dev.langchain4j.data.document.parser.TextDocumentParser; -import dev.langchain4j.data.segment.TextSegment; -import dev.langchain4j.memory.chat.MessageWindowChatMemory; -import dev.langchain4j.model.chat.ChatLanguageModel; -import dev.langchain4j.model.ollama.OllamaChatModel; -import dev.langchain4j.model.openai.OpenAiChatModel; -import dev.langchain4j.rag.content.Content; -import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever; -import dev.langchain4j.rag.query.Query; -import dev.langchain4j.service.AiServices; -import dev.langchain4j.service.SystemMessage; -import dev.langchain4j.store.embedding.EmbeddingStoreIngestor; -import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore; - -import java.io.IOException; -import java.io.InputStream; -import java.util.List; - -public class RagTest { - - public static void main(String[] args) { - - List documents = FileSystemDocumentLoader.loadDocuments("D:\\wbSource\\starter-ai\\src\\main\\resources"); - InMemoryEmbeddingStore embeddingStore = new InMemoryEmbeddingStore<>(); - EmbeddingStoreIngestor.ingest(documents, embeddingStore); - - EmbeddingStoreContentRetriever embeddingStoreContentRetriever = EmbeddingStoreContentRetriever.from(embeddingStore); - - List retrieve = embeddingStoreContentRetriever.retrieve(new Query("java中会存在内存泄漏吗，请简单描述")); - - System.out.println(); - -// ChatLanguageModel model = OllamaChatModel.builder() -// .baseUrl("http://36.138.207.178:11434") -// .modelName("deepseek-r1:32B") -// .build(); -// -// Assistant assistant = AiServices.create(Assistant.class,model); -// .chatMemory(MessageWindowChatMemory.withMaxMessages(10)) -// .contentRetriever(EmbeddingStoreContentRetriever.from(embeddingStore)) -// .build(); -// -// String chat = assistant.chat("java中会存在内存泄漏吗，请简单描述。"); -// System.out.println(chat); - } - - - interface Assistant { - -// @SystemMessage("请扮演一名小学生,根据输入的文章题目写一篇100字以内的作文") - String chat(String userMessage); - } - -} diff --git a/src/main/java/xyz/wbsite/ai/Test.java b/src/main/java/xyz/wbsite/ai/Test.java new file mode 100644 index 0000000..f15718e --- /dev/null +++ b/src/main/java/xyz/wbsite/ai/Test.java @@ -0,0 +1,210 @@ +package xyz.wbsite.ai; + +import cn.hutool.core.collection.CollUtil; +import dev.langchain4j.data.document.Document; +import dev.langchain4j.data.message.AiMessage; +import dev.langchain4j.data.message.ChatMessage; +import dev.langchain4j.data.message.SystemMessage; +import dev.langchain4j.data.message.UserMessage; +import dev.langchain4j.data.segment.TextSegment; +import dev.langchain4j.memory.chat.MessageWindowChatMemory; +import dev.langchain4j.model.StreamingResponseHandler; +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.model.output.Response; +import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever; +import dev.langchain4j.service.AiServices; +import dev.langchain4j.service.TokenStream; +import dev.langchain4j.store.embedding.EmbeddingStoreIngestor; +import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore; + +import java.util.List; + +/** + * 主函数入口 + */ +public class Test { + + public static void main(String[] args) { +// testSimpleChat(args); +// testStreamChat(args); + testRagChat(args); +// testTool(args); + } + + public static void testSimpleChat(String[] args) { + OpenAiChatModel model = OpenAiChatModel.builder() + .baseUrl("http://36.138.207.178:11434/v1") + .apiKey("1") + .modelName("deepseek-r1:14B") + .build(); + + String generate = model.generate("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()); + + } + + public static void testStreamChat(String[] args) { + OpenAiStreamingChatModel model = OpenAiStreamingChatModel.builder() + .baseUrl("http://36.138.207.178:11434/v1") + .apiKey("1") + .modelName("deepseek-r1:14B") + .build(); + + List 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() { + @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 response) { + System.out.println("onComplete"); + } + }); + } + + public static void testRagChat(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 documents = FileSystemDocumentLoader.loadDocuments("path"); + List documents = List.of( + 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 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 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) + .onNext(System.out::print) + .onError(throwable -> System.err.println("Error: " + throwable.getMessage())) + .onComplete(chatResponse -> System.out.println("Complete Response: ")) + .start(); + } + + public static void testTool(String[] args) { +// ChatLanguageModel model = OllamaChatModel.builder() +// .baseUrl("http://36.138.207.178:11434") +// .modelName("qwen2.5:7b") +// .logRequests(true) +// .logResponses(true) +// .build(); +// +// List 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 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 toolExecutionRequests = chatResponse.aiMessage().toolExecutionRequests(); +// toolExecutionRequests.forEach(new Consumer() { +// @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()); + } + + // 创建一个助手接口 + interface Assistant { + + String chat(String userMessage); + + TokenStream chatStream(List messages); + + TokenStream chatStream(ChatMessage message); + + TokenStream chatStream(String message); + } +} \ No newline at end of file diff --git a/src/main/java/xyz/wbsite/ai/Test.java35^ b/src/main/java/xyz/wbsite/ai/Test.java35^ new file mode 100644 index 0000000..f62cfa0 --- /dev/null +++ b/src/main/java/xyz/wbsite/ai/Test.java35^ @@ -0,0 +1,173 @@ +//package xyz.wbsite.ai; +// +//import cn.hutool.core.thread.ThreadUtil; +//import cn.hutool.json.JSONUtil; +//import dev.langchain4j.agent.tool.*; +//import dev.langchain4j.data.message.*; +//import dev.langchain4j.model.chat.ChatLanguageModel; +//import dev.langchain4j.model.chat.StreamingChatLanguageModel; +//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.chat.response.StreamingChatResponseHandler; +//import dev.langchain4j.model.ollama.OllamaChatModel; +//import dev.langchain4j.model.ollama.OllamaStreamingChatModel; +//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 Test { +// +// public static void main(String[] args) { +//// testSimpleChat(args); +//// testStreamChat(args); +//// testTool(args); +//// testRagChat(args); +// } +// +// public static void testSimpleChat(String[] args) { +// ChatLanguageModel model = OllamaChatModel.builder() +// .baseUrl("http://36.138.207.178:11434") +// .modelName("deepseek-r1:32B") +// .build(); +// +// String generate = model.chat("请问，你是谁？"); +// System.out.println(generate); +// +// ChatResponse chat = model.chat(new UserMessage("请问，你是谁？")); +// String text = chat.aiMessage().text(); +// System.out.println(text); +// +// ChatRequest build = ChatRequest.builder() +// .messages(new ChatMessage[]{UserMessage.from("我想知道1+1等于几")}) +// .build(); +// ChatResponse chatResponse = model.chat(build); +// System.out.println(chatResponse.aiMessage().text()); +// } +// +// public static void testRagChat(String[] args) { +// ChatLanguageModel model = OllamaChatModel.builder() +// .baseUrl("http://36.138.207.178:11434") +// .modelName("deepseek-r1:32B") +// .build(); +// +// ChatRequest build = ChatRequest.builder() +// .messages(new ChatMessage[]{ +// SystemMessage.from("你需要使用文档内容对用户提出的问题进行回复，这点非常重要。\n" + +// "当用户提出的问题无法根据文档内容进行回复时，回复不知道即可。请明确说明，你的答案是否是从文档中获取的。\n" + +// "文档内容如下:小王和小吴是情侣关系，他们两个年龄一样都是20岁，小王有一辆大众牌小轿车，小吴有一辆雅迪电动车。"), +// UserMessage.from("小王的车是什么牌子") +// }) +// .build(); +// ChatResponse chatResponse = model.chat(build); +// System.out.println(chatResponse.aiMessage().text()); +// } +// +// public static void testStreamChat(String[] args) { +// final boolean[] end = {false}; +// +// StreamingChatLanguageModel model = OllamaStreamingChatModel.builder() +// .baseUrl("http://36.138.207.178:11434") +// .modelName("deepseek-r1:32B") +// .build(); +// +// String question = "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?"; +// +// System.out.println("开始提问"); +// +// ChatRequest chatRequest = ChatRequest.builder().messages(new ChatMessage[]{UserMessage.from(question)}).build(); +// +// final StringBuilder sb = new StringBuilder(); +// +// model.chat(chatRequest, new StreamingChatResponseHandler() { +// @Override +// public void onPartialResponse(String s) { +// System.out.print(s); +// sb.append(s); +// } +// +// @Override +// public void onCompleteResponse(ChatResponse chatResponse) { +// System.out.println("-----回答结束-----"); +// System.out.println(JSONUtil.toJsonStr(chatResponse)); +// end[0] = true; +// } +// +// @Override +// public void onError(Throwable throwable) { +// System.err.println("-----回答出错-----"); +// end[0] = true; +// } +// }); +// +// while (!end[0]) { +// ThreadUtil.safeSleep(1000); +// } +// } +// +// public static void testTool(String[] args) { +// ChatLanguageModel model = OllamaChatModel.builder() +// .baseUrl("http://36.138.207.178:11434") +// .modelName("qwen2.5:7b") +// .logRequests(true) +// .logResponses(true) +// .build(); +// +// List 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 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 toolExecutionRequests = chatResponse.aiMessage().toolExecutionRequests(); +// toolExecutionRequests.forEach(new Consumer() { +// @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()); +// } +//} \ No newline at end of file diff --git a/src/main/java/xyz/wbsite/ai/TestRag.java35^ b/src/main/java/xyz/wbsite/ai/TestRag.java35^ new file mode 100644 index 0000000..b3e871e --- /dev/null +++ b/src/main/java/xyz/wbsite/ai/TestRag.java35^ @@ -0,0 +1,88 @@ +//package xyz.wbsite.ai; +// +//import dev.langchain4j.data.document.DefaultDocument; +//import dev.langchain4j.data.document.Document; +//import dev.langchain4j.data.segment.TextSegment; +//import dev.langchain4j.memory.chat.MessageWindowChatMemory; +//import dev.langchain4j.model.chat.ChatLanguageModel; +//import dev.langchain4j.model.chat.StreamingChatLanguageModel; +//import dev.langchain4j.model.ollama.OllamaChatModel; +//import dev.langchain4j.model.ollama.OllamaStreamingChatModel; +//import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever; +//import dev.langchain4j.service.AiServices; +//import dev.langchain4j.service.TokenStream; +//import dev.langchain4j.store.embedding.EmbeddingStoreIngestor; +//import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore; +// +//import java.util.List; +// +//public class TestRag { +// +// public static void main(String[] args) { +// // 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 documents = FileSystemDocumentLoader.loadDocuments("path"); +// List documents = List.of( +// new DefaultDocument("人往往在做梦的时候会打呼噜"), +// new DefaultDocument("小猪在睡觉的时候会扭屁股"), +// new DefaultDocument("有一只蟑螂在床底下跳舞"), +// new DefaultDocument("小狗在睡觉的时候会磨牙"), +// new DefaultDocument("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 embeddingStore = new InMemoryEmbeddingStore<>(); +// +// // 将文档和嵌入存储器进行关联 +// EmbeddingStoreIngestor.ingest(documents, embeddingStore); +// +// // 创建一个助手接口 +// interface Assistant { +// +// String chat(String userMessage); +// +// TokenStream chatStream(String userMessage); +// } +// +// ChatLanguageModel chatModel = OllamaChatModel.builder() +// .baseUrl("http://36.138.207.178:11434") +// .modelName("qwen2.5:7b") +// .logRequests(true) +// .logResponses(true) +// .build(); +// +// StreamingChatLanguageModel streamingChatModel = OllamaStreamingChatModel.builder() +// .baseUrl("http://36.138.207.178:11434") +// .modelName("deepseek-r1:32B") +// .logRequests(true) +// .logResponses(true) +// .build(); +// +// Assistant assistant = AiServices.builder(Assistant.class) +// .chatLanguageModel(chatModel) +// .streamingChatLanguageModel(streamingChatModel) +// .chatMemory(MessageWindowChatMemory.withMaxMessages(10)) +// .contentRetriever(EmbeddingStoreContentRetriever.from(embeddingStore)) +// .build(); +// +//// // 问题 +//// String[] questions = new String[]{ +//// "人在做梦的时候会干什么", +//// "小猪在睡觉的时候会干什么", +//// "小狗在睡觉的时候会干什么" +//// }; +//// +//// // 回答 +//// for (String question : questions) { +//// System.out.println("问题：" + question); +//// String chat = assistant.chat(question); +//// System.out.println("回答：" + chat); +//// } +// +// assistant.chatStream("小猪在睡觉的时候会干什么") +// .onPartialResponse(s -> System.out.println("Partial Response: " + s)) +// .onError(throwable -> System.err.println("Error: " + throwable.getMessage())) +// .onCompleteResponse(chatResponse -> System.out.println("Complete Response: " + chatResponse.aiMessage().text())) +// .start(); +// +// } +//}