parent
60c38614e4
commit
115982fe1e
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package xyz.wbsite.ai;
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/**
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* 最简单的聊天示例
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*/
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public class Base_Chat_Example {
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public static void main(String[] args) {
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String chat = Helper.getChatModel().chat("你是谁");
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System.out.println(chat);
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}
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}
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package xyz.wbsite.ai;
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import cn.hutool.core.thread.ThreadUtil;
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import dev.langchain4j.memory.chat.MessageWindowChatMemory;
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import dev.langchain4j.service.AiServices;
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import dev.langchain4j.service.TokenStream;
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/**
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* 简单的流式聊天示例
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*/
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public class Easy_StreamChat_Example {
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public static void main(String[] args) {
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String question = "你是谁?";
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Assistant assistant = AiServices.builder(Assistant.class)
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.streamingChatLanguageModel(Helper.getStreamChatModel())
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.chatMemory(MessageWindowChatMemory.withMaxMessages(10))
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.build();
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assistant.chat(question)
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.onPartialResponse(System.out::print)
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.onCompleteResponse(chatResponse -> {
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System.out.println();
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System.out.println("onComplete");
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})
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.ignoreErrors()
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.start();
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ThreadUtil.sleep(10 * 1000);
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}
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/**
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* 助手
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*/
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interface Assistant {
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TokenStream chat(String userMessage);
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}
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}
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package xyz.wbsite.ai;
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import cn.hutool.core.collection.CollUtil;
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import dev.langchain4j.data.document.Document;
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import dev.langchain4j.model.openai.OpenAiChatModel;
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import dev.langchain4j.model.openai.OpenAiStreamingChatModel;
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import java.util.List;
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public class Helper {
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private static OpenAiStreamingChatModel openAiStreamingChatModel = OpenAiStreamingChatModel.builder()
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.baseUrl("http://36.138.207.178:11434/v1")
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.apiKey("1")
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.modelName("deepseek-r1:14B")
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.logRequests(true)
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.logResponses(true)
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.build();
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private static OpenAiChatModel openAiChatModel = OpenAiChatModel.builder()
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.baseUrl("http://36.138.207.178:11434/v1")
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.apiKey("1")
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.modelName("deepseek-r1:14B")
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.logRequests(true)
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.logResponses(true)
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.build();
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private static OpenAiChatModel toolChatModel = OpenAiChatModel.builder()
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.baseUrl("http://36.138.207.178:11434/v1")
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.apiKey("1")
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.modelName("qwen2.5:7b")
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.build();
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public static OpenAiStreamingChatModel getStreamChatModel() {
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return openAiStreamingChatModel;
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}
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public static OpenAiChatModel getChatModel() {
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return openAiChatModel;
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}
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public static OpenAiChatModel getToolChatModel() {
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return toolChatModel;
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}
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public static Document getDocument() {
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return Document.from("人往往在做梦的时候会打呼噜");
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}
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public static Document getDocument(String text) {
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return Document.from(text);
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}
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public static List<Document> getDocuments() {
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return CollUtil.newArrayList(
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Document.from("人往往在做梦的时候会打呼噜"),
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Document.from("小猪在睡觉的时候会扭屁股"),
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Document.from("有一只蟑螂在床底下跳舞"),
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Document.from("小狗在睡觉的时候会磨牙"),
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Document.from("我家的小鸡喜欢吃虫子")
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);
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}
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}
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package xyz.wbsite.ai;
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import dev.langchain4j.data.document.splitter.DocumentSplitters;
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import dev.langchain4j.data.segment.TextSegment;
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import dev.langchain4j.memory.chat.MessageWindowChatMemory;
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import dev.langchain4j.model.embedding.EmbeddingModel;
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import dev.langchain4j.model.embedding.onnx.bgesmallenv15q.BgeSmallEnV15QuantizedEmbeddingModel;
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import dev.langchain4j.rag.DefaultRetrievalAugmentor;
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import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
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import dev.langchain4j.rag.query.transformer.CompressingQueryTransformer;
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import dev.langchain4j.service.AiServices;
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import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
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import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
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/**
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* 对上下文对话进行查询压缩
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*/
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public class Query_Compression_Example {
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public static void main(String[] args) {
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EmbeddingModel embeddingModel = new BgeSmallEnV15QuantizedEmbeddingModel();
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InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
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EmbeddingStoreIngestor ingestor = EmbeddingStoreIngestor.builder()
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.documentSplitter(DocumentSplitters.recursive(300, 0))
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.embeddingStore(embeddingStore)
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.embeddingModel(embeddingModel)
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.build();
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ingestor.ingest(Helper.getDocument());
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EmbeddingStoreContentRetriever retriever = EmbeddingStoreContentRetriever.builder()
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.embeddingModel(embeddingModel)
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.embeddingStore(embeddingStore)
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.maxResults(2)
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.minScore(0.5)
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.build();
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CompressingQueryTransformer queryTransformer = new CompressingQueryTransformer(Helper.getChatModel());
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// // 其他实现
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// QueryTransformer queryTransformer = new QueryTransformer() {
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// @Override
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// public Collection<Query> transform(Query query) {
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// return CollUtil.newArrayList(new Query(query.text().toLowerCase()));
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// }
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// };
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DefaultRetrievalAugmentor retrievalAugmentor = DefaultRetrievalAugmentor.builder()
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.queryTransformer(queryTransformer)
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.contentRetriever(retriever)
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.build();
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Assistant assistant = AiServices.builder(Assistant.class)
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.chatLanguageModel(Helper.getChatModel())
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.retrievalAugmentor(retrievalAugmentor)
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.chatMemory(MessageWindowChatMemory.withMaxMessages(4))
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.build();
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String chat = assistant.chat("人在睡觉时会做什么了？");
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System.out.println(chat);
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String chat1 = assistant.chat("小猪在睡觉的时候会什么了？");
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System.out.println(chat1);
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}
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/**
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* 助手
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*/
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interface Assistant {
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String chat(String userMessage);
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}
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}
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package xyz.wbsite.ai;
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import dev.langchain4j.data.document.Document;
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import dev.langchain4j.data.document.DocumentParser;
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import dev.langchain4j.data.document.DocumentSplitter;
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import dev.langchain4j.data.document.loader.FileSystemDocumentLoader;
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import dev.langchain4j.data.document.parser.TextDocumentParser;
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import dev.langchain4j.data.document.splitter.DocumentSplitters;
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import dev.langchain4j.data.embedding.Embedding;
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import dev.langchain4j.data.message.ChatMessage;
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import dev.langchain4j.data.segment.TextSegment;
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import dev.langchain4j.memory.chat.MessageWindowChatMemory;
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import dev.langchain4j.model.embedding.EmbeddingModel;
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import dev.langchain4j.model.embedding.onnx.bgesmallenv15q.BgeSmallEnV15QuantizedEmbeddingModel;
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import dev.langchain4j.model.openai.OpenAiChatModel;
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import dev.langchain4j.model.openai.OpenAiStreamingChatModel;
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import dev.langchain4j.rag.content.Content;
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import dev.langchain4j.rag.content.retriever.ContentRetriever;
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import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
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import dev.langchain4j.rag.query.Query;
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import dev.langchain4j.service.AiServices;
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import dev.langchain4j.service.TokenStream;
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import dev.langchain4j.store.embedding.EmbeddingStore;
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import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
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import java.util.List;
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import static dev.langchain4j.data.document.loader.FileSystemDocumentLoader.loadDocument;
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/**
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* 主函数入口
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*/
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public class TestRagNativeChat {
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public static void main(String[] args) {
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OpenAiChatModel model = OpenAiChatModel.builder()
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.baseUrl("http://36.138.207.178:11434/v1")
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.apiKey("1")
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.modelName("deepseek-r1:14B")
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.build();
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// Now, let's load a document that we want to use for RAG.
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// We will use the terms of use from an imaginary car rental company, "Miles of Smiles".
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// For this example, we'll import only a single document, but you can load as many as you need.
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// LangChain4j offers built-in support for loading documents from various sources:
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// File System, URL, Amazon S3, Azure Blob Storage, GitHub, Tencent COS.
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// Additionally, LangChain4j supports parsing multiple document types:
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// text, pdf, doc, xls, ppt.
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// However, you can also manually import your data from other sources.
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DocumentParser documentParser = new TextDocumentParser();
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Document document = loadDocument("D:\\docs\\人才公共服务平台会议纪要20210720.txt", documentParser);
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// Now, we need to split this document into smaller segments, also known as "chunks."
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// This approach allows us to send only relevant segments to the LLM in response to a user query,
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// rather than the entire document. For instance, if a user asks about cancellation policies,
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// we will identify and send only those segments related to cancellation.
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// A good starting point is to use a recursive document splitter that initially attempts
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// to split by paragraphs. If a paragraph is too large to fit into a single segment,
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// the splitter will recursively divide it by newlines, then by sentences, and finally by words,
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// if necessary, to ensure each piece of text fits into a single segment.
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DocumentSplitter splitter = DocumentSplitters.recursive(300, 0);
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List<TextSegment> segments = splitter.split(document);
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// Now, we need to embed (also known as "vectorize") these segments.
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// Embedding is needed for performing similarity searches.
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// For this example, we'll use a local in-process embedding model, but you can choose any supported model.
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// Langchain4j currently supports more than 10 popular embedding model providers.
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EmbeddingModel embeddingModel = new BgeSmallEnV15QuantizedEmbeddingModel();
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List<Embedding> embeddings = embeddingModel.embedAll(segments).content();
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// Next, we will store these embeddings in an embedding store (also known as a "vector database").
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// This store will be used to search for relevant segments during each interaction with the LLM.
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// For simplicity, this example uses an in-memory embedding store, but you can choose from any supported store.
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// Langchain4j currently supports more than 15 popular embedding stores.
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EmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
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embeddingStore.addAll(embeddings, segments);
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// We could also use EmbeddingStoreIngestor to hide manual steps above behind a simpler API.
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// See an example of using EmbeddingStoreIngestor in _01_Advanced_RAG_with_Query_Compression_Example.
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// The content retriever is responsible for retrieving relevant content based on a user query.
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// Currently, it is capable of retrieving text segments, but future enhancements will include support for
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// additional modalities like images, audio, and more.
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ContentRetriever contentRetriever = EmbeddingStoreContentRetriever.builder()
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.embeddingStore(embeddingStore)
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.embeddingModel(embeddingModel)
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.maxResults(2) // on each interaction we will retrieve the 2 most relevant segments
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.minScore(0.1) // we want to retrieve segments at least somewhat similar to user query
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.build();
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List<Content> retrieve = contentRetriever.retrieve(new Query("参会人员有哪些人？"));
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System.out.println();
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Assistant assistant = AiServices.builder(Assistant.class)
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.chatLanguageModel(model)
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.chatMemory(MessageWindowChatMemory.withMaxMessages(10))
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.contentRetriever(contentRetriever)
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.build();
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// String chat = assistant.chat("参会人员有哪些人？");
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//System.out.println(chat);
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}
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// 创建一个助手接口
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interface Assistant {
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String chat(String userMessage);
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TokenStream chatStream(List<ChatMessage> messages);
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TokenStream chatStream(ChatMessage message);
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TokenStream chatStream(String message);
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}
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}
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@ -1,33 +0,0 @@
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package xyz.wbsite.ai;
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import dev.langchain4j.data.message.ChatMessage;
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import dev.langchain4j.data.message.UserMessage;
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import dev.langchain4j.model.chat.request.ChatRequest;
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import dev.langchain4j.model.chat.response.ChatResponse;
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import dev.langchain4j.model.openai.OpenAiChatModel;
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/**
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* 主函数入口
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*/
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public class TestSimpleChat {
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public static void main(String[] args) {
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OpenAiChatModel model = OpenAiChatModel.builder()
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.baseUrl("http://36.138.207.178:11434/v1")
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.apiKey("1")
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.modelName("deepseek-r1:14B")
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.build();
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String generate = model.chat("你好");
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System.out.println(generate);
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ChatRequest chatRequest = ChatRequest.builder()
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.messages(new ChatMessage[]{
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UserMessage.from("你是谁")
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})
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.build();
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ChatResponse chatResponse = model.chat(chatRequest);
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System.out.println(chatResponse.aiMessage().text());
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}
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}
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Loading…
Reference in new issue