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智谱AI-SDK入门实践之Java(保姆级教程)

影子

2024-02-03 20:18

阅读3762

影子

2024-02-03 20:18

阅读 3762

大家好，我是影子，一名以Java学习为主，其余时间探索AI+编程的程序猿

今天跟大家分享一下智谱AI-SDK的入门用法，话不多说，我们直接开始

一、什么是智谱AI

智谱AI（Zhipu AI）是一家致力于人工智能技术研发和应用的公司。该公司由清华大学背景的团队创立，专注于大

模型技术的研究与推广。智谱AI在人工智能领域取得了显著成就，其发布的自研大模型GLM-4等产品。

二、SDK玩法

(一) 注册账号

进入官网（https://maas.aminer.cn/），注册账号实名后，将会赠送有效期一个月的体验包。

(二) 查看自己的API Key

注意：我们常见的API_KEY和API_SECRET，这里采用了统一为API key，使用 .这个符号进行划分。

举个栗子：yingzix688.编程导航真不错。 (本人微信号:yingzix688,欢迎大家交流探讨)

那么，API_KEY：yingzix688

API_SECRET:编程导航真不错

大家只需要看自己的API key进行分割出来即可。

(三) 查阅官方github

官方github地址：https://github.com/zhipuai/zhipuai-sdk-java-v4

引入依赖

<groupId>cn.bigmodel.openapi</groupId>

<version>release-V4-2.0.0</version>

</dependency>

官方示例代码

package com.zhipu.oapi.demo;

import com.alibaba.fastjson.JSON;

import com.fasterxml.jackson.annotation.JsonInclude;

import com.fasterxml.jackson.core.JsonProcessingException;

import com.fasterxml.jackson.core.type.TypeReference;

import com.fasterxml.jackson.databind.DeserializationFeature;

import com.fasterxml.jackson.databind.ObjectMapper;

import com.fasterxml.jackson.databind.PropertyNamingStrategy;

import com.zhipu.oapi.ClientV4;

import com.zhipu.oapi.Constants;

import com.zhipu.oapi.service.v4.embedding.EmbeddingApiResponse;

import com.zhipu.oapi.service.v4.embedding.EmbeddingRequest;

import com.zhipu.oapi.service.v4.file.FileApiResponse;

import com.zhipu.oapi.service.v4.file.QueryFileApiResponse;

import com.zhipu.oapi.service.v4.file.QueryFilesRequest;

import com.zhipu.oapi.service.v4.fine_turning.*;

import com.zhipu.oapi.service.v4.image.CreateImageRequest;

import com.zhipu.oapi.service.v4.image.ImageApiResponse;

import com.zhipu.oapi.service.v4.model.*;

import io.reactivex.Flowable;

import java.util.ArrayList;

import java.util.HashMap;

import java.util.List;

import java.util.Map;

import java.util.concurrent.atomic.AtomicBoolean;

public class V4OkHttpClientTest {

private static final String API_KEY = "";

private static final String API_SECRET = "";

private static final ClientV4 client = new ClientV4.Builder(API_KEY,API_SECRET).build();

private static final ObjectMapper mapper = defaultObjectMapper();

public static ObjectMapper defaultObjectMapper() {

ObjectMapper mapper = new ObjectMapper();

mapper.configure(DeserializationFeature.FAIL_ON_UNKNOWN_PROPERTIES, false);

mapper.setSerializationInclusion(JsonInclude.Include.NON_NULL);

mapper.setPropertyNamingStrategy(PropertyNamingStrategy.SNAKE_CASE);

mapper.addMixIn(ChatFunction.class, ChatFunctionMixIn.class);

mapper.addMixIn(ChatCompletionRequest.class, ChatCompletionRequestMixIn.class);

mapper.addMixIn(ChatFunctionCall.class, ChatFunctionCallMixIn.class);

return mapper;

}

// 请自定义自己的业务id

private static final String requestIdTemplate = "mycompany-%d";

public static void main(String[] args) throws Exception {

System.setProperty("org.slf4j.simpleLogger.logFile", "System.out");

// 1. sse-invoke调用模型，使用标准Listener，直接返回结果

testSseInvoke();

// 2. invoke调用模型,直接返回结果

// testInvoke();

// 3. 异步调用

// String taskId = testAsyncInvoke();

// 4.异步查询

// testQueryResult(taskId);

// 5.文生图

// testCreateImage();

// 6. 图生文

// testImageToWord();

// 7. 向量模型

// testEmbeddings();

// 8.微调-上传微调数据集

// testUploadFile();

// 9.微调-查询上传文件列表

// testQueryUploadFileList();

// 10.微调-创建微调任务

// testCreateFineTuningJob();

// 11.微调-查询微调任务事件

// testQueryFineTuningJobsEvents();

// 12.微调-查询微调任务

// testRetrieveFineTuningJobs();

// 13.微调-查询个人微调任务

// testQueryPersonalFineTuningJobs();

// 14.微调-调用微调模型（参考模型调用接口，并替换成要调用模型的编码model）

}

private static void testQueryPersonalFineTuningJobs() {

QueryPersonalFineTuningJobRequest queryPersonalFineTuningJobRequest = new QueryPersonalFineTuningJobRequest();

queryPersonalFineTuningJobRequest.setLimit(1);

QueryPersonalFineTuningJobApiResponse queryPersonalFineTuningJobApiResponse = client.queryPersonalFineTuningJobs(queryPersonalFineTuningJobRequest);

System.out.println("model output:" + JSON.toJSONString(queryPersonalFineTuningJobApiResponse));

}

private static void testQueryFineTuningJobsEvents() {

QueryFineTuningJobRequest queryFineTuningJobRequest = new QueryFineTuningJobRequest();

queryFineTuningJobRequest.setJobId("ftjob-20240119114544390-zkgjb");

// queryFineTuningJobRequest.setLimit(1);

// queryFineTuningJobRequest.setAfter("1");

QueryFineTuningEventApiResponse queryFineTuningEventApiResponse = client.queryFineTuningJobsEvents(queryFineTuningJobRequest);

System.out.println("model output:" + JSON.toJSONString(queryFineTuningEventApiResponse));

}

/**

* 查询微调任务

private static void testRetrieveFineTuningJobs() {

QueryFineTuningJobRequest queryFineTuningJobRequest = new QueryFineTuningJobRequest();

queryFineTuningJobRequest.setJobId("ftjob-20240119114544390-zkgjb");

// queryFineTuningJobRequest.setLimit(1);

// queryFineTuningJobRequest.setAfter("1");

QueryFineTuningJobApiResponse queryFineTuningJobApiResponse = client.retrieveFineTuningJobs(queryFineTuningJobRequest);

System.out.println("model output:" + JSON.toJSONString(queryFineTuningJobApiResponse));

}

/**

* 创建微调任务

private static void testCreateFineTuningJob() {

FineTuningJobRequest request = new FineTuningJobRequest();

String requestId = String.format(requestIdTemplate, System.currentTimeMillis());

request.setRequestId(requestId);

request.setModel("chatglm3-6b");

request.setTraining_file("file-20240118082608327-kp8qr");

CreateFineTuningJobApiResponse createFineTuningJobApiResponse = client.createFineTuningJob(request);

System.out.println("model output:" + JSON.toJSONString(createFineTuningJobApiResponse));

}

/**

* 微调文件上传列表查询

private static void testQueryUploadFileList() {

QueryFilesRequest queryFilesRequest = new QueryFilesRequest();

QueryFileApiResponse queryFileApiResponse = client.queryFilesApi(queryFilesRequest);

System.out.println("model output:" + JSON.toJSONString(queryFileApiResponse));

}

/**

* 微调上传数据集

private static void testUploadFile() {

String filePath = "/Users/wujianguo/Downloads/transaction-data.jsonl";

String purpose = "fine-tune";

FileApiResponse fileApiResponse = client.invokeUploadFileApi(purpose, filePath);

System.out.println("model output:" + JSON.toJSONString(fileApiResponse));

}

private static void testEmbeddings() {

EmbeddingRequest embeddingRequest = new EmbeddingRequest();

embeddingRequest.setInput("hello world");

embeddingRequest.setModel(Constants.ModelEmbedding2);

EmbeddingApiResponse apiResponse = client.invokeEmbeddingsApi(embeddingRequest);

System.out.println("model output:" + JSON.toJSONString(apiResponse));

}

/**

* 图生文

private static void testImageToWord() {

List<ChatMessage> messages = new ArrayList<>();

List<Map<String, Object>> contentList = new ArrayList<>();

Map<String, Object> textMap = new HashMap<>();

textMap.put("type", "text");

textMap.put("text", "图里有什么");

Map<String, Object> typeMap = new HashMap<>();

typeMap.put("type", "image_url");

Map<String, Object> urlMap = new HashMap<>();

urlMap.put("url", "https://cdn.bigmodel.cn/enterpriseAc/3f328152-e15c-420c-803d-6684a9f551df.jpeg?attname=24.jpeg");

typeMap.put("image_url", urlMap);

contentList.add(textMap);

contentList.add(typeMap);

ChatMessage chatMessage = new ChatMessage(ChatMessageRole.USER.value(), contentList);

messages.add(chatMessage);

String requestId = String.format(requestIdTemplate, System.currentTimeMillis());

ChatCompletionRequest chatCompletionRequest = ChatCompletionRequest.builder()

.model(Constants.ModelChatGLM4V)

.stream(Boolean.FALSE)

.invokeMethod(Constants.invokeMethod)

.messages(messages)

.requestId(requestId)

.build();

ModelApiResponse modelApiResponse = client.invokeModelApi(chatCompletionRequest);

System.out.println("model output:" + JSON.toJSONString(modelApiResponse));

}

private static void testCreateImage() {

CreateImageRequest createImageRequest = new CreateImageRequest();

createImageRequest.setModel(Constants.ModelCogView);

// createImageRequest.setPrompt("画一个温顺可爱的小狗");

ImageApiResponse imageApiResponse = client.createImage(createImageRequest);

System.out.println("imageApiResponse:" + JSON.toJSONString(imageApiResponse));

}

/**

* sse调用

private static void testSseInvoke() {

List<ChatMessage> messages = new ArrayList<>();

ChatMessage chatMessage = new ChatMessage(ChatMessageRole.USER.value(), "ChatGLM和你哪个更强大");

// ChatMessage chatMessage = new ChatMessage(ChatMessageRole.USER.value(), "你能帮我查询2024年1月1日从北京南站到上海的火车票吗？");

messages.add(chatMessage);

String requestId = String.format(requestIdTemplate, System.currentTimeMillis());

// 函数调用参数构建部分

List<ChatTool> chatToolList = new ArrayList<>();

ChatTool chatTool = new ChatTool();

chatTool.setType(ChatToolType.FUNCTION.value());

ChatFunctionParameters chatFunctionParameters = new ChatFunctionParameters();

chatFunctionParameters.setType("object");

Map<String, Object> properties = new HashMap<>();

properties.put("departure", new HashMap<String, Object>() {{

put("type", "string");

put("description", "出发城市或车站");

}});

properties.put("destination", new HashMap<String, Object>() {{

put("type", "string");

put("description", "目的地城市或车站");

}});

properties.put("date", new HashMap<String, Object>() {{

put("type", "string");

put("description", "要查询的车次日期");

}});

List<String> required = new ArrayList<>();

required.add("departure");

required.add("destination");

required.add("date");

chatFunctionParameters.setProperties(properties);

ChatFunction chatFunction = ChatFunction.builder()

.name("query_train_info")

.description("根据用户提供的信息，查询对应的车次")

.parameters(chatFunctionParameters)

.required(required)

.build();

chatTool.setFunction(chatFunction);

chatToolList.add(chatTool);

ChatCompletionRequest chatCompletionRequest = ChatCompletionRequest.builder()

.model(Constants.ModelChatGLM4)

.stream(Boolean.TRUE)

.messages(messages)

.requestId(requestId)

.tools(chatToolList)

.toolChoice("auto")

.build();

ModelApiResponse sseModelApiResp = client.invokeModelApi(chatCompletionRequest);

if (sseModelApiResp.isSuccess()) {

AtomicBoolean isFirst = new AtomicBoolean(true);

ChatMessageAccumulator chatMessageAccumulator = mapStreamToAccumulator(sseModelApiResp.getFlowable())

.doOnNext(accumulator -> {

{

if (isFirst.getAndSet(false)) {

System.out.print("Response: ");

}

if (accumulator.getDelta() != null && accumulator.getDelta().getTool_calls() != null) {

String jsonString = mapper.writeValueAsString(accumulator.getDelta().getTool_calls());

System.out.println("tool_calls: " + jsonString);

}

if (accumulator.getDelta() != null && accumulator.getDelta().getContent() != null) {

System.out.print(accumulator.getDelta().getContent());

}

})

.doOnComplete(System.out::println)

.lastElement()

.blockingGet();

Choice choice = new Choice(chatMessageAccumulator.getChoice().getFinishReason(), 0L, chatMessageAccumulator.getDelta());

List<Choice> choices = new ArrayList<>();

choices.add(choice);

ModelData data = new ModelData();

data.setChoices(choices);

data.setUsage(chatMessageAccumulator.getUsage());

data.setId(chatMessageAccumulator.getId());

data.setCreated(chatMessageAccumulator.getCreated());

data.setRequestId(chatCompletionRequest.getRequestId());

sseModelApiResp.setFlowable(null);

sseModelApiResp.setData(data);

}

System.out.println("model output:" + JSON.toJSONString(sseModelApiResp));

}

public static Flowable<ChatMessageAccumulator> mapStreamToAccumulator(Flowable<ModelData> flowable) {

return flowable.map(chunk -> {

return new ChatMessageAccumulator(chunk.getChoices().get(0).getDelta(), null, chunk.getChoices().get(0), chunk.getUsage(), chunk.getCreated(), chunk.getId());

});

}

/**

* 同步调用

private static void testInvoke() {

List<ChatMessage> messages = new ArrayList<>();

ChatMessage chatMessage = new ChatMessage(ChatMessageRole.USER.value(), "ChatGLM和你哪个更强大");

messages.add(chatMessage);

String requestId = String.format(requestIdTemplate, System.currentTimeMillis());

// 函数调用参数构建部分

List<ChatTool> chatToolList = new ArrayList<>();

ChatTool chatTool = new ChatTool();

chatTool.setType(ChatToolType.FUNCTION.value());

ChatFunctionParameters chatFunctionParameters = new ChatFunctionParameters();

chatFunctionParameters.setType("object");

Map<String, Object> properties = new HashMap<>();

properties.put("location", new HashMap<String, Object>() {{

put("type", "string");

put("description", "城市，如：北京");

}});

properties.put("unit", new HashMap<String, Object>() {{

put("type", "string");

put("enum", new ArrayList<String>() {{

add("celsius");

add("fahrenheit");

}});

chatFunctionParameters.setProperties(properties);

ChatFunction chatFunction = ChatFunction.builder()

.name("get_weather")

.description("Get the current weather of a location")

.parameters(chatFunctionParameters)

.build();

chatTool.setFunction(chatFunction);

chatToolList.add(chatTool);

ChatCompletionRequest chatCompletionRequest = ChatCompletionRequest.builder()

.model(Constants.ModelChatGLM4)

.stream(Boolean.FALSE)

.invokeMethod(Constants.invokeMethod)

.messages(messages)

.requestId(requestId)

.tools(chatToolList)

.toolChoice("auto")

.build();

ModelApiResponse invokeModelApiResp = client.invokeModelApi(chatCompletionRequest);

try {

System.out.println("model output:" + mapper.writeValueAsString(invokeModelApiResp));

} catch (JsonProcessingException e) {

e.printStackTrace();

}

/**

* 异步调用

private static String testAsyncInvoke() {

List<ChatMessage> messages = new ArrayList<>();

ChatMessage chatMessage = new ChatMessage(ChatMessageRole.USER.value(), "ChatLM和你哪个更强大");

messages.add(chatMessage);

String requestId = String.format(requestIdTemplate, System.currentTimeMillis());

// 函数调用参数构建部分

List<ChatTool> chatToolList = new ArrayList<>();

ChatTool chatTool = new ChatTool();

chatTool.setType(ChatToolType.FUNCTION.value());

ChatFunctionParameters chatFunctionParameters = new ChatFunctionParameters();

chatFunctionParameters.setType("object");

Map<String, Object> properties = new HashMap<>();

properties.put("location", new HashMap<String, Object>() {{

put("type", "string");

put("description", "城市，如：北京");

}});

properties.put("unit", new HashMap<String, Object>() {{

put("type", "string");

put("enum", new ArrayList<String>() {{

add("celsius");

add("fahrenheit");

}});

chatFunctionParameters.setProperties(properties);

ChatFunction chatFunction = ChatFunction.builder()

.name("get_weather")

.description("Get the current weather of a location")

.parameters(chatFunctionParameters)

.build();

chatTool.setFunction(chatFunction);

chatToolList.add(chatTool);

ChatCompletionRequest chatCompletionRequest = ChatCompletionRequest.builder()

.model(Constants.ModelChatGLM4)

.stream(Boolean.FALSE)

.invokeMethod(Constants.invokeMethodAsync)

.messages(messages)

.requestId(requestId)

.tools(chatToolList)

.toolChoice("auto")

.build();

ModelApiResponse invokeModelApiResp = client.invokeModelApi(chatCompletionRequest);

System.out.println("model output:" + JSON.toJSONString(invokeModelApiResp));

return invokeModelApiResp.getData().getTaskId();

}

/**

* 查询异步结果

* @param taskId

private static void testQueryResult(String taskId) {

QueryModelResultRequest request = new QueryModelResultRequest();

request.setTaskId(taskId);

QueryModelResultResponse queryResultResp = client.queryModelResult(request);

try {

System.out.println("model output:" + mapper.writeValueAsString(queryResultResp));

} catch (JsonProcessingException e) {

e.printStackTrace();

}

(1) 填充自己的信息

(2) 启动示例

这里解释一下四个的区别

testSseInvoke 使用的是逐渐输出，AI回答的结果是一段一段的展示。
testInvoke 使用的是同步执行，当AI全部的回答都输出后才会展示出来。
testAsyncInvoke 与testQueryResult 搭配使用，先通过testAsyncInvoke 让AI去执行，直接返回一个成功或者失败，之后通过获得的taskId，再用testQueryResult去查询获得结果即可。这个过程实践过Bi项目的小伙伴应该深有体会。