SpringBoot3.0自定stater模块的操作流程(chatGPT提供的49种场景)
作者:miukoo
导读
导读 | 12月总体来说互联网的技术圈是非常热闹的,chatGPT爆火,SpringBoot3.0发布等重磅陆消息续进入大家的视线,而本文作者将以技术整合的角度,带大家把最火的两个技术整合在一起。读完本文,你将熟悉SpringBoot3.0自定stater模块的操作流程,并熟悉OpenAi为chatGPT提供的49种场景。
项目项目我已经提交GITEE:https://gitee.com/miukoo/openai-spring 欢迎Star
新建父项目
我们这个项目分为starter和test两个模块,因此需要一个父项目来包裹。
1、快速新建父项目
2、在pom.xml中引入SpringBoot3.0
- 项目的父工程设置成SpringBoot3.0
- 在项目中定义openai的版本并导入(com.theokanning.openai-gpt3-java)
<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"> <modules> <module>openai-spring-boot-starter</module> <module>openai-starter-test</module> </modules> <packaging>pom</packaging> <modelVersion>4.0.0</modelVersion> <groupId>cn.gjsm</groupId> <artifactId>openai-spring</artifactId> <version>1.0-SNAPSHOT</version> <properties> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> <project.reporting.sourceEncoding>UTF-8</project.reporting.sourceEncoding> <openai-version>0.8.1</openai-version> </properties> <parent> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-parent</artifactId> <version>3.0.0</version> </parent> <dependencyManagement> <dependencies> <dependency> <groupId>com.theokanning.openai-gpt3-java</groupId> <artifactId>client</artifactId> <version>${openai-version}</version> </dependency> </dependencies> </dependencyManagement> <dependencies> <dependency> <groupId>org.projectlombok</groupId> <artifactId>lombok</artifactId> </dependency> </dependencies> </project>
3、删除父项目的src文件夹
新建openai-spring-boot-starter模块
openai-spring-boot-starter 模块主要用来封装openai的核心api,该模块就是springboot自定starter的标准5步:
- 新建模块
- 在模块中引入相关依赖
- 定义模块外部属性有那些
- 实现核心业务逻辑
- 配置自动装配
1、新增模块
注意模块名称的规范:非官方starter命名规则为 模块名称+'-spring-boot-starter’结尾
2、在模块中引入相关依赖
<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"> <packaging>pom</packaging> <parent> <artifactId>openai-spring</artifactId> <groupId>cn.gjsm</groupId> <version>1.0-SNAPSHOT</version> </parent> <modelVersion>4.0.0</modelVersion> <groupId>cn.gjsm</groupId> <artifactId>openai-spring-boot-starter</artifactId> <version>1.0-SNAPSHOT</version> <dependencies> <!-- 自定义starter必须导入的依赖 --> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter</artifactId> </dependency> <!-- 这个包可以用来支持自定义属性的输入提示 --> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-configuration-processor</artifactId> <optional>true</optional> </dependency> <!-- 导入openai依赖,版本在父项目中已经约束 --> <dependency> <groupId>com.theokanning.openai-gpt3-java</groupId> <artifactId>client</artifactId> </dependency> </dependencies> </project>
3、定义模块外部属性有那些
通过@ConfigurationProperties配置一个类,这个类中的属性将从外部的application.yml中读取。在这里OpenAi需要两个属性需要配置,一是token秘钥,一是timeout超时时间。关于timeout可以配置时间长一点,因为OpenAi在国外有些慢。
package cn.gjsm.miukoo.properties; import cn.gjsm.miukoo.utils.OpenAiUtils; import lombok.Data; import org.springframework.beans.factory.InitializingBean; import org.springframework.boot.context.properties.ConfigurationProperties; @Data @ConfigurationProperties(prefix = "openai") public class OpenAiProperties implements InitializingBean { // 秘钥 String token; // 超时时间 Integer timeout; // 设置属性时同时设置给OpenAiUtils @Override public void afterPropertiesSet() throws Exception { OpenAiUtils.OPENAPI_TOKEN = token; OpenAiUtils.TIMEOUT = timeout; } }
4、实现核心业务逻辑
核心业务逻辑指的就是你自定义这个starter可以提供给其它模块那些api使用;在这里我们直接通过一个静态类工具OpenAiUtils,这样在引入该模块后,其它模块直接可调用该静态工具类,使用便捷一些。
同时在这个类中提供openai官方49种场景想对应的方法。
package cn.gjsm.miukoo.utils; import cn.gjsm.miukoo.pojos.OpenAi; import com.theokanning.openai.OpenAiService; import com.theokanning.openai.completion.CompletionChoice; import com.theokanning.openai.completion.CompletionRequest; import org.springframework.util.StringUtils; import java.util.*; /** * 调用OpenAi的49中方法 */ public class OpenAiUtils { public static final Map<String, OpenAi> PARMS = new HashMap<>(); static { PARMS.put("OpenAi01", new OpenAi("OpenAi01", "问&答", "依据现有知识库问&答", "text-davinci-003", "Q: %s\nA:", 0.0, 1.0, 1.0, 0.0, 0.0, "\n")); PARMS.put("OpenAi02", new OpenAi("OpenAi02", "语法纠正", "将句子转换成标准的英语,输出结果始终是英文", "text-davinci-003", "%s", 0.0, 1.0, 1.0, 0.0, 0.0, "")); PARMS.put("OpenAi03", new OpenAi("OpenAi03", "内容概况", "将一段话,概况中心", "text-davinci-003", "Summarize this for a second-grade student:\n%s", 0.7, 1.0, 1.0, 0.0, 0.0, "")); PARMS.put("OpenAi04", new OpenAi("OpenAi04", "生成OpenAi的代码", "一句话生成OpenAi的代码", "code-davinci-002", "\"\"\"\nUtil exposes the following:\nutil.openai() -> authenticates & returns the openai module, which has the following functions:\nopenai.Completion.create(\n prompt=\"<my prompt>\", # The prompt to start completing from\n max_tokens=123, # The max number of tokens to generate\n temperature=1.0 # A measure of randomness\n echo=True, # Whether to return the prompt in addition to the generated completion\n)\n\"\"\"\nimport util\n\"\"\"\n%s\n\"\"\"\n\n", 0.0, 1.0, 1.0, 0.0, 0.0, "\"\"\"")); PARMS.put("OpenAi05", new OpenAi("OpenAi05", "程序命令生成", "一句话生成程序的命令,目前支持操作系统指令比较多", "text-davinci-003", "Convert this text to a programmatic command:\n\nExample: Ask Constance if we need some bread\nOutput: send-msg `find constance` Do we need some bread?\n\n%s", 0.0, 1.0, 1.0, 0.2, 0.0, "")); PARMS.put("OpenAi06", new OpenAi("OpenAi06", "语言翻译", "把一种语法翻译成其它几种语言", "text-davinci-003", "Translate this into %s:\n%s", 0.3, 1.0, 1.0, 0.0, 0.0, "")); PARMS.put("OpenAi07", new OpenAi("OpenAi07", "Stripe国际API生成", "一句话生成Stripe国际支付API", "code-davinci-002", "\"\"\"\nUtil exposes the following:\n\nutil.stripe() -> authenticates & returns the stripe module; usable as stripe.Charge.create etc\n\"\"\"\nimport util\n\"\"\"\n%s\n\"\"\"", 0.0, 1.0, 1.0, 0.0, 0.0, "\"\"\"")); PARMS.put("OpenAi08", new OpenAi("OpenAi08", "SQL语句生成", "依据上下文中的表信息,生成SQL语句", "code-davinci-002", "### %s SQL tables, 表字段信息如下:\n%s\n#\n### %s\n %s", 0.0, 1.0, 1.0, 0.0, 0.0, "# ;")); PARMS.put("OpenAi09", new OpenAi("OpenAi09", "结构化生成", "对于非结构化的数据抽取其中的特征生成结构化的表格", "text-davinci-003", "A table summarizing, use Chinese:\n%s\n", 0.0, 1.0, 1.0, 0.0, 0.0, "")); PARMS.put("OpenAi10", new OpenAi("OpenAi10", "信息分类", "把一段信息继续分类", "text-davinci-003", "%s\n分类:", 0.0, 1.0, 1.0, 0.0, 0.0, "")); PARMS.put("OpenAi11", new OpenAi("OpenAi11", "Python代码解释", "把代码翻译成文字,用来解释程序的作用", "code-davinci-002", "# %s \n %s \n\n# 解释代码作用\n\n#", 0.0, 1.0, 1.0, 0.0, 0.0, "")); PARMS.put("OpenAi12", new OpenAi("OpenAi12", "文字转表情符号", "将文本编码成表情服务", "text-davinci-003", "转换文字为表情。\n%s:", 0.8, 1.0, 1.0, 0.0, 0.0, "\n")); PARMS.put("OpenAi13", new OpenAi("OpenAi13", "时间复杂度计算", "求一段代码的时间复杂度", "text-davinci-003", "%s\n\"\"\"\n函数的时间复杂度是", 0.0, 1.0, 1.0, 0.0, 0.0, "\n")); PARMS.put("OpenAi14", new OpenAi("OpenAi14", "程序代码翻译", "把一种语言的代码翻译成另外一种语言的代码", "code-davinci-002", "##### 把这段代码从%s翻译成%s\n### %s\n \n %s\n \n### %s", 0.0, 1.0, 1.0, 0.0, 0.0, "###")); PARMS.put("OpenAi15", new OpenAi("OpenAi15", "高级情绪评分", "支持批量列表的方式检查情绪", "text-davinci-003", "对下面内容进行情感分类:\n%s\"\n情绪评级:", 0.0, 1.0, 1.0, 0.0, 0.0, "")); PARMS.put("OpenAi16", new OpenAi("OpenAi16", "代码解释", "对一段代码进行解释", "code-davinci-002", "代码:\n%s\n\"\"\"\n上面的代码在做什么:\n1. ", 0.0, 1.0, 1.0, 0.0, 0.0, "\"\"\"")); PARMS.put("OpenAi17", new OpenAi("OpenAi17", "关键字提取", "提取一段文本中的关键字", "text-davinci-003", "抽取下面内容的关键字:\n%s", 0.5, 1.0, 1.0, 0.8, 0.0, "")); PARMS.put("OpenAi18", new OpenAi("OpenAi18", "问题解答", "类似解答题", "text-davinci-003", "Q: %s\nA: ?", 0.0, 1.0, 1.0, 0.0, 0.0, "")); PARMS.put("OpenAi19", new OpenAi("OpenAi19", "广告设计", "给一个产品设计一个广告", "text-davinci-003", "为下面的产品创作一个创业广告,用于投放到抖音上:\n产品:%s.", 0.5, 1.0, 1.0, 0.0, 0.0, "")); PARMS.put("OpenAi20", new OpenAi("OpenAi20", "产品取名", "依据产品描述和种子词语,给一个产品取一个好听的名字", "text-davinci-003", "产品描述: %s.\n种子词: %s.\n产品名称: ", 0.8, 1.0, 1.0, 0.0, 0.0, "")); PARMS.put("OpenAi21", new OpenAi("OpenAi21", "句子简化", "把一个长句子简化成一个短句子", "text-davinci-003", "%s\nTl;dr: ", 0.7, 1.0, 1.0, 0.0, 1.0, "")); PARMS.put("OpenAi22", new OpenAi("OpenAi22", "修复代码Bug", "自动修改代码中的bug", "code-davinci-002", "##### 修复下面代码的bug\n### %s\n %s\n### %s\n", 0.0, 1.0, 1.0, 0.0, 0.0, "###")); PARMS.put("OpenAi23", new OpenAi("OpenAi23", "表格填充数据", "自动为一个表格生成数据", "text-davinci-003", "spreadsheet ,%s rows:\n%s\n", 0.5, 1.0, 1.0, 0.0, 0.0, "")); PARMS.put("OpenAi24", new OpenAi("OpenAi24", "语言聊天机器人", "各种开发语言的两天机器人", "code-davinci-002", "You: %s\n%s机器人:", 0.0, 1.0, 1.0, 0.5, 0.0, "You: ")); PARMS.put("OpenAi25", new OpenAi("OpenAi25", "机器学习机器人", "机器学习模型方面的机器人", "text-davinci-003", "You: %s\nML机器人:", 0.3, 1.0, 1.0, 0.5, 0.0, "You: ")); PARMS.put("OpenAi26", new OpenAi("OpenAi26", "清单制作", "可以列出各方面的分类列表,比如歌单", "text-davinci-003", "列出10%s:", 0.5, 1.0, 1.0, 0.52, 0.5, "11.0")); PARMS.put("OpenAi27", new OpenAi("OpenAi27", "文本情绪分析", "对一段文字进行情绪分析", "text-davinci-003", "推断下面文本的情绪是积极的, 中立的, 还是消极的.\n文本: \"%s\"\n观点:", 0.0, 1.0, 1.0, 0.5, 0.0, "")); PARMS.put("OpenAi28", new OpenAi("OpenAi28", "航空代码抽取", "抽取文本中的航空diam信息", "text-davinci-003", "抽取下面文本中的航空代码:\n文本:\"%s\"\n航空代码:", 0.0, 1.0, 1.0, 0.0, 0.0, "\n")); PARMS.put("OpenAi29", new OpenAi("OpenAi29", "生成SQL语句", "无上下文,语句描述生成SQL", "text-davinci-003", "%s", 0.3, 1.0, 1.0, 0.0, 0.0, "")); PARMS.put("OpenAi30", new OpenAi("OpenAi30", "抽取联系信息", "从文本中抽取联系方式", "text-davinci-003", "从下面文本中抽取%s:\n%s", 0.0, 1.0, 1.0, 0.0, 0.0, "")); PARMS.put("OpenAi31", new OpenAi("OpenAi31", "程序语言转换", "把一种语言转成另外一种语言", "code-davinci-002", "#%s to %s:\n%s:%s\n\n%s:", 0.0, 1.0, 1.0, 0.0, 0.0, "")); PARMS.put("OpenAi32", new OpenAi("OpenAi32", "好友聊天", "模仿好友聊天", "text-davinci-003", "You: %s\n好友:", 0.5, 1.0, 1.0, 0.5, 0.0, "You:")); PARMS.put("OpenAi33", new OpenAi("OpenAi33", "颜色生成", "依据描述生成对应颜色", "text-davinci-003", "%s:\nbackground-color: ", 0.0, 1.0, 1.0, 0.0, 0.0, ";")); PARMS.put("OpenAi34", new OpenAi("OpenAi34", "程序文档生成", "自动为程序生成文档", "code-davinci-002", "# %s\n \n%s\n# 上述代码的详细、高质量文档字符串:\n\"\"\"", 0.0, 1.0, 1.0, 0.0, 0.0, "#\"\"\"")); PARMS.put("OpenAi35", new OpenAi("OpenAi35", "段落创作", "依据短语生成相关文短", "text-davinci-003", "为下面短语创建一个中文段:\n%s:\n", 0.5, 1.0, 1.0, 0.0, 0.0, "")); PARMS.put("OpenAi36", new OpenAi("OpenAi36", "代码压缩", "把多行代码简单的压缩成一行", "code-davinci-002", "将下面%s代码转成一行:\n%s\n%s一行版本:", 0.0, 1.0, 1.0, 0.0, 0.0, ";")); PARMS.put("OpenAi37", new OpenAi("OpenAi37", "故事创作", "依据一个主题创建一个故事", "text-davinci-003", "主题: %s\n故事创作:", 0.8, 1.0, 1.0, 0.5, 0.0, "")); PARMS.put("OpenAi38", new OpenAi("OpenAi38", "人称转换", "第一人称转第3人称", "text-davinci-003", "把下面内容从第一人称转为第三人称 (性别女):\n%s\n", 0.0, 1.0, 1.0, 0.0, 0.0, "")); PARMS.put("OpenAi39", new OpenAi("OpenAi39", "摘要说明", "依据笔记生成摘要说明", "text-davinci-003", "将下面内容转换成将下%s摘要:\n%s", 0.0, 1.0, 1.0, 0.0, 0.0, "")); PARMS.put("OpenAi40", new OpenAi("OpenAi40", "头脑风暴", "给定一个主题,让其生成一些主题相关的想法", "text-davinci-003", "头脑风暴一些关于%s的想法:", 0.6, 1.0, 1.0, 1.0, 1.0, "")); PARMS.put("OpenAi41", new OpenAi("OpenAi41", "ESRB文本分类", "按照ESRB进行文本分类", "text-davinci-003", "Provide an ESRB rating for the following text:\\n\\n\\\"%s\"\\n\\nESRB rating:", 0.3, 1.0, 1.0, 0.0, 0.0, "\n")); PARMS.put("OpenAi42", new OpenAi("OpenAi42", "提纲生成", "按照提示为相关内容生成提纲", "text-davinci-003", "为%s提纲:", 0.3, 1.0, 1.0, 0.0, 0.0, "")); PARMS.put("OpenAi43", new OpenAi("OpenAi43", "美食制作(后果自负)", "依据美食名称和材料生成美食的制作步骤", "text-davinci-003", "依据下面成分和美食,生成制作方法:\n%s\n成分:\n%s\n制作方法:", 0.3, 1.0, 1.0, 0.0, 0.0, "")); PARMS.put("OpenAi44", new OpenAi("OpenAi44", "AI聊天", "与AI机器进行聊天", "text-davinci-003", "Human: %s", 0.9, 1.0, 1.0, 0.0, 0.6, "Human:AI:")); PARMS.put("OpenAi45", new OpenAi("OpenAi45", "摆烂聊天", "与讽刺机器进行聊天", "text-davinci-003", "Marv不情愿的回答问题.\nYou:%s\nMarv:", 0.5, 0.3, 1.0, 0.5, 0.0, "")); PARMS.put("OpenAi46", new OpenAi("OpenAi46", "分解步骤", "把一段文本分解成几步来完成", "text-davinci-003", "为下面文本生成次序列表,并增加列表数子: \n%s\n", 0.3, 1.0, 1.0, 0.0, 0.0, "")); PARMS.put("OpenAi47", new OpenAi("OpenAi47", "点评生成", "依据文本内容自动生成点评", "text-davinci-003", "依据下面内容,进行点评:\n%s\n点评:", 0.5, 1.0, 1.0, 0.0, 0.0, "")); PARMS.put("OpenAi48", new OpenAi("OpenAi48", "知识学习", "可以为学习知识自动解答", "text-davinci-003", "%s", 0.3, 1.0, 1.0, 0.0, 0.0, "")); PARMS.put("OpenAi49", new OpenAi("OpenAi49", "面试", "生成面试题", "text-davinci-003", "创建10道%s相关的面试题(中文):\n", 0.5, 1.0, 10.0, 0.0, 0.0, "")); } public static String OPENAPI_TOKEN = ""; public static Integer TIMEOUT = null; /** * 获取ai * * @param openAi * @param prompt * @return */ public static List<CompletionChoice> getAiResult(OpenAi openAi, String prompt) { if (TIMEOUT == null || TIMEOUT < 1000) { TIMEOUT = 3000; } OpenAiService service = new OpenAiService(OPENAPI_TOKEN, TIMEOUT); CompletionRequest.CompletionRequestBuilder builder = CompletionRequest.builder() .model(openAi.getModel()) .prompt(prompt) .temperature(openAi.getTemperature()) .maxTokens(1000) .topP(openAi.getTopP()) .frequencyPenalty(openAi.getFrequencyPenalty()) .presencePenalty(openAi.getPresencePenalty()); if (!StringUtils.isEmpty(openAi.getStop())) { builder.stop(Arrays.asList(openAi.getStop().split(","))); } CompletionRequest completionRequest = builder.build(); return service.createCompletion(completionRequest).getChoices(); } /** * 问答 * * @param question * @return */ public static List<CompletionChoice> getQuestionAnswer(String question) { OpenAi openAi = PARMS.get("OpenAi01"); return getAiResult(openAi, String.format(openAi.getPrompt(), question)); } /** * 语法纠错 * * @param text * @return */ public static List<CompletionChoice> getGrammarCorrection(String text) { OpenAi openAi = PARMS.get("OpenAi02"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 将一段话,概况中心 * * @param text * @return */ public static List<CompletionChoice> getSummarize(String text) { OpenAi openAi = PARMS.get("OpenAi03"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 一句话生成OpenAi的代码 * * @param text * @return */ public static List<CompletionChoice> getOpenAiApi(String text) { OpenAi openAi = PARMS.get("OpenAi04"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 一句话生成程序的命令,目前支持操作系统指令比较多 * * @param text * @return */ public static List<CompletionChoice> getTextToCommand(String text) { OpenAi openAi = PARMS.get("OpenAi05"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 把一种语法翻译成其它几种语言 * * @param text * @return */ public static List<CompletionChoice> getTranslatesLanguages(String text, String translatesLanguages) { if (StringUtils.isEmpty(translatesLanguages)) { translatesLanguages = " 1. French, 2. Spanish and 3. English"; } OpenAi openAi = PARMS.get("OpenAi06"); return getAiResult(openAi, String.format(openAi.getPrompt(), translatesLanguages, text)); } /** * 一句话生成Stripe国际支付API * * @param text * @return */ public static List<CompletionChoice> getStripeApi(String text) { OpenAi openAi = PARMS.get("OpenAi07"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 依据上下文中的表信息,生成SQL语句 * * @param databaseType 数据库类型 * @param tables 上午依赖的表和字段 Employee(id, name, department_id) * @param text SQL描述 * @param sqlType sql类型,比如SELECT * @return */ public static List<CompletionChoice> getStripeApi(String databaseType, List<String> tables, String text, String sqlType) { OpenAi openAi = PARMS.get("OpenAi08"); StringJoiner joiner = new StringJoiner("\n"); for (int i = 0; i < tables.size(); i++) { joiner.add("# " + tables); } return getAiResult(openAi, String.format(openAi.getPrompt(), databaseType, joiner.toString(), text, sqlType)); } /** * 对于非结构化的数据抽取其中的特征生成结构化的表格 * * @param text 非结构化的数据 * @return */ public static List<CompletionChoice> getUnstructuredData(String text) { OpenAi openAi = PARMS.get("OpenAi09"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 把一段信息继续分类 * * @param text 要分类的文本 * @return */ public static List<CompletionChoice> getTextCategory(String text) { OpenAi openAi = PARMS.get("OpenAi10"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 把一段信息继续分类 * * @param codeType 代码类型,比如Python * @param code 要解释的代码 * @return */ public static List<CompletionChoice> getCodeExplain(String codeType, String code) { OpenAi openAi = PARMS.get("OpenAi11"); return getAiResult(openAi, String.format(openAi.getPrompt(), codeType, code)); } /** * 将文本编码成表情服务 * * @param text 文本 * @return */ public static List<CompletionChoice> getTextEmoji(String text) { OpenAi openAi = PARMS.get("OpenAi12"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 求一段代码的时间复杂度 * * @param code 代码 * @return */ public static List<CompletionChoice> getTimeComplexity(String code) { OpenAi openAi = PARMS.get("OpenAi13"); return getAiResult(openAi, String.format(openAi.getPrompt(), code)); } /** * 把一种语言的代码翻译成另外一种语言的代码 * * @param fromLanguage 要翻译的代码语言 * @param toLanguage 要翻译成的代码语言 * @param code 代码 * @return */ public static List<CompletionChoice> getTranslateProgramming(String fromLanguage, String toLanguage, String code) { OpenAi openAi = PARMS.get("OpenAi14"); return getAiResult(openAi, String.format(openAi.getPrompt(), fromLanguage, toLanguage, fromLanguage, code, toLanguage)); } /** * 支持批量列表的方式检查情绪 * * @param texts 文本 * @return */ public static List<CompletionChoice> getBatchTweetClassifier(List<String> texts) { OpenAi openAi = PARMS.get("OpenAi15"); StringJoiner stringJoiner = new StringJoiner("\n"); for (int i = 0; i < texts.size(); i++) { stringJoiner.add((i + 1) + ". " + texts.get(i)); } return getAiResult(openAi, String.format(openAi.getPrompt(), stringJoiner.toString())); } /** * 对一段代码进行解释 * * @param code 文本 * @return */ public static List<CompletionChoice> getExplainCOde(String code) { OpenAi openAi = PARMS.get("OpenAi16"); return getAiResult(openAi, String.format(openAi.getPrompt(), code)); } /** * 提取一段文本中的关键字 * * @param text 文本 * @return */ public static List<CompletionChoice> getTextKeywords(String text) { OpenAi openAi = PARMS.get("OpenAi17"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 事实回答答题 * * @param text 文本 * @return */ public static List<CompletionChoice> getFactualAnswering(String text) { OpenAi openAi = PARMS.get("OpenAi18"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 给一个产品设计一个广告 * * @param text 文本 * @return */ public static List<CompletionChoice> getAd(String text) { OpenAi openAi = PARMS.get("OpenAi19"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 依据产品描述和种子词语,给一个产品取一个好听的名字 * * @param productDescription 产品描述 * @param seedWords 种子词语 * @return */ public static List<CompletionChoice> getProductName(String productDescription, String seedWords) { OpenAi openAi = PARMS.get("OpenAi20"); return getAiResult(openAi, String.format(openAi.getPrompt(), productDescription, seedWords)); } /** * 把一个长句子简化成一个短句子 * * @param text 长句子 * @return */ public static List<CompletionChoice> getProductName(String text) { OpenAi openAi = PARMS.get("OpenAi21"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 自动修改代码中的bug * * @param codeType 语言类型 * @param code 代码 * @return */ public static List<CompletionChoice> getBugFixer(String codeType, String code) { OpenAi openAi = PARMS.get("OpenAi22"); return getAiResult(openAi, String.format(openAi.getPrompt(), codeType, code, codeType)); } /** * 自动为一个表格生成数据 * * @param rows 生成的行数 * @param headers 数据表头,格式如:姓名| 年龄|性别|生日 * @return */ public static List<CompletionChoice> getFillData(int rows, String headers) { OpenAi openAi = PARMS.get("OpenAi23"); return getAiResult(openAi, String.format(openAi.getPrompt(), rows, headers)); } /** * 各种开发语言的两天机器人 * * @param question 你的问题 * @param programmingLanguages 语言 比如Java JavaScript * @return */ public static List<CompletionChoice> getProgrammingLanguageChatbot(String question, String programmingLanguages) { OpenAi openAi = PARMS.get("OpenAi24"); return getAiResult(openAi, String.format(openAi.getPrompt(), question, programmingLanguages)); } /** * 机器学习模型方面的机器人 * * @param question 你的问题 * @return */ public static List<CompletionChoice> getMLChatbot(String question) { OpenAi openAi = PARMS.get("OpenAi25"); return getAiResult(openAi, String.format(openAi.getPrompt(), question)); } /** * 可以列出各方面的分类列表,比如歌单 * * @param text 清单描述 * @return */ public static List<CompletionChoice> getListMaker(String text) { OpenAi openAi = PARMS.get("OpenAi26"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 对一段文字进行情绪分析 * * @param text * @return */ public static List<CompletionChoice> getTweetClassifier(String text) { OpenAi openAi = PARMS.get("OpenAi27"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 抽取文本中的航空代码信息 * * @param text * @return */ public static List<CompletionChoice> getAirportCodeExtractor(String text) { OpenAi openAi = PARMS.get("OpenAi28"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 无上下文,语句描述生成SQL * * @param text * @return */ public static List<CompletionChoice> getSQL(String text) { OpenAi openAi = PARMS.get("OpenAi29"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 从文本中抽取联系方式 * * @param extractContent 抽取内容描述 * @param text * @return 从下面文本中抽取邮箱和电话:\n教育行业A股IPO第一股(股票代码 003032)\n全国咨询/投诉热线:400-618-4000 举报邮箱:mc@itcast.cn */ public static List<CompletionChoice> getExtractContactInformation(String extractContent, String text) { OpenAi openAi = PARMS.get("OpenAi30"); return getAiResult(openAi, String.format(openAi.getPrompt(), extractContent, text)); } /** * 把一种语言转成另外一种语言代码 * * @param fromCodeType 当前代码类型 * @param toCodeType 转换的代码类型 * @param code * @return */ public static List<CompletionChoice> getTransformationCode(String fromCodeType, String toCodeType, String code) { OpenAi openAi = PARMS.get("OpenAi31"); return getAiResult(openAi, String.format(openAi.getPrompt(), fromCodeType, toCodeType, fromCodeType, code, toCodeType)); } /** * 模仿好友聊天 * * @param question * @return */ public static List<CompletionChoice> getFriendChat(String question) { OpenAi openAi = PARMS.get("OpenAi32"); return getAiResult(openAi, String.format(openAi.getPrompt(), question)); } /** * 依据描述生成对应颜色 * * @param text * @return */ public static List<CompletionChoice> getMoodToColor(String text) { OpenAi openAi = PARMS.get("OpenAi33"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 自动为程序生成文档 * * @param codeType 语言 * @param code * @return */ public static List<CompletionChoice> getCodeDocument(String codeType, String code) { OpenAi openAi = PARMS.get("OpenAi34"); return getAiResult(openAi, String.format(openAi.getPrompt(), codeType, code)); } /** * 依据短语生成相关文短 * * @param text 短语 * @return */ public static List<CompletionChoice> getCreateAnalogies(String text) { OpenAi openAi = PARMS.get("OpenAi35"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 把多行代码简单的压缩成一行 * * @param codeType 语言 * @param code * @return */ public static List<CompletionChoice> getCodeLine(String codeType, String code) { OpenAi openAi = PARMS.get("OpenAi36"); return getAiResult(openAi, String.format(openAi.getPrompt(), codeType, code, codeType)); } /** * 依据一个主题创建一个故事 * * @param topic 创作主题 * @return */ public static List<CompletionChoice> getStory(String topic) { OpenAi openAi = PARMS.get("OpenAi37"); return getAiResult(openAi, String.format(openAi.getPrompt(), topic)); } /** * 第一人称转第3人称 * * @param text * @return */ public static List<CompletionChoice> getStoryCreator(String text) { OpenAi openAi = PARMS.get("OpenAi38"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 依据笔记生成摘要说明 * * @param scene 生成的摘要场景 * @param note 记录的笔记 * @return */ public static List<CompletionChoice> getNotesToSummary(String scene, String note) { OpenAi openAi = PARMS.get("OpenAi39"); return getAiResult(openAi, String.format(openAi.getPrompt(), note)); } /** * 给定一个主题,让其生成一些主题相关的想法 * * @param topic 头脑风暴关键词 * @return */ public static List<CompletionChoice> getIdeaGenerator(String topic) { OpenAi openAi = PARMS.get("OpenAi40"); return getAiResult(openAi, String.format(openAi.getPrompt(), topic)); } /** * 按照ESRB进行文本分类 * * @param text 文本 * @return */ public static List<CompletionChoice> getESRBRating(String text) { OpenAi openAi = PARMS.get("OpenAi41"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 按照提示为相关内容生成提纲 * * @param text 场景,比如 数据库软件生成大学毕业论文 * @return */ public static List<CompletionChoice> getEssayOutline(String text) { OpenAi openAi = PARMS.get("OpenAi42"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 依据美食名称和材料生成美食的制作步骤 * * @param name 美食名称 * @param ingredients 美食食材 * @return */ public static List<CompletionChoice> getRecipeCreator(String name, List<String> ingredients) { OpenAi openAi = PARMS.get("OpenAi43"); StringJoiner joiner = new StringJoiner("\n"); for (String ingredient : ingredients) { joiner.add(ingredient); } return getAiResult(openAi, String.format(openAi.getPrompt(), name, joiner.toString())); } /** * 与AI机器进行聊天 * * @param question * @return */ public static List<CompletionChoice> getAiChatbot(String question) { OpenAi openAi = PARMS.get("OpenAi44"); return getAiResult(openAi, String.format(openAi.getPrompt(), question)); } /** * 与讽刺机器进行聊天,聊天的机器人是一种消极情绪 * * @param question * @return */ public static List<CompletionChoice> getMarvChatbot(String question) { OpenAi openAi = PARMS.get("OpenAi45"); return getAiResult(openAi, String.format(openAi.getPrompt(), question)); } /** * 把一段文本分解成几步来完成 * * @param text * @return */ public static List<CompletionChoice> getTurnDirection(String text) { OpenAi openAi = PARMS.get("OpenAi46"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 依据文本内容自动生成点评 * * @param text * @return */ public static List<CompletionChoice> getReviewCreator(String text) { OpenAi openAi = PARMS.get("OpenAi47"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 可以为学习知识自动解答 * * @param text * @return */ public static List<CompletionChoice> getStudyNote(String text) { OpenAi openAi = PARMS.get("OpenAi48"); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } /** * 生成面试题 * * @param text * @return */ public static List<CompletionChoice> getInterviewQuestion(String text) { OpenAi openAi = PARMS.get("OpenAi49"); System.out.println(String.format(openAi.getPrompt(), text)); return getAiResult(openAi, String.format(openAi.getPrompt(), text)); } }
5、配置自动装配
这一步是非常关键的,你的项目能在其他模块启动的时候就能够用,就必须配置这一步,而这一步有两小步:
- 编写自动装配类
- 配置自动装配类
编写自动装配类,参考代码:
package cn.gjsm.miukoo.config; import cn.gjsm.miukoo.properties.OpenAiProperties; import org.springframework.boot.context.properties.EnableConfigurationProperties; import org.springframework.context.annotation.Configuration; /** * 自动配置类 */ @Configuration @EnableConfigurationProperties(OpenAiProperties.class) public class OpenAiAutoConfiguration { }
配置自动装配类:
在resources文件夹下的META-INF/spring.factories文件中配置:
org.springframework.boot.autoconfigure.EnableAutoConfiguration=cn.gjsm.miukoo.config.OpenAiAutoConfiguration
新建openai-starter-test模块
经过上述五部我们就完成了chatGPT的stater的封装,接下来我们创建一个模块来测试。
新增模块
测试模块的名称最好是以test结尾
导入依赖
在测试模块中直接可以导入我们封装好的openai-spring-boot-starter,当然还有测试spring-boot-starter-test依赖。
<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"> <parent> <artifactId>openai-spring-boot-starter</artifactId> <groupId>cn.gjsm</groupId> <version>1.0-SNAPSHOT</version> <relativePath>../openai-spring-boot-starter/pom.xml</relativePath> </parent> <modelVersion>4.0.0</modelVersion> <groupId>cn.gjsm</groupId> <artifactId>openai-starter-test</artifactId> <version>1.0-SNAPSHOT</version> <dependencies> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-test</artifactId> </dependency> <dependency> <groupId>cn.gjsm</groupId> <artifactId>openai-spring-boot-starter</artifactId> <version>1.0-SNAPSHOT</version> </dependency> </dependencies> </project>
创建启动类
我们计划使用SpringBoot去测试,因此需要创建一个启动类
package cn.gjsm.miukoo; import org.springframework.boot.SpringApplication; import org.springframework.boot.autoconfigure.SpringBootApplication; @SpringBootApplication public class OpenAiApplication { public static void main(String[] args) { SpringApplication.run(OpenAiApplication.class, args); } }
配置属性
在测试模块的application.yml中,我们需要配置,我们在openai-spring-boot-starter中定义的两个属性
server: port: 8080 openai: token: 你的token timeout: 5000
编写测试类
我们在测试包下,新建一个测试类,即可直接调用我们在stater中封装的OpenAiUtils工具类,通过其来完成chatGPT功能调用。
package cn.gjsm.miukoo; import cn.gjsm.miukoo.utils.OpenAiUtils; import com.theokanning.openai.completion.CompletionChoice; import org.junit.jupiter.api.Test; import org.springframework.boot.test.context.SpringBootTest; import java.util.List; @SpringBootTest public class OpenAiTest { /** * 测试问答 */ @Test public void testQA(){ List<CompletionChoice> questionAnswer = OpenAiUtils.getQuestionAnswer("重庆今天的天气怎么样?"); for (CompletionChoice completionChoice : questionAnswer) { System.out.println(completionChoice.getText()); } } /** * 测试面试题生成 */ @Test public void testInterview(){ List<CompletionChoice> results = OpenAiUtils.getInterviewQuestion("redis"); for (CompletionChoice completionChoice : results) { System.out.println(completionChoice.getText()); } } }
tater中封装的OpenAiUtils工具类,通过其来完成chatGPT功能调用。
package cn.gjsm.miukoo; import cn.gjsm.miukoo.utils.OpenAiUtils; import com.theokanning.openai.completion.CompletionChoice; import org.junit.jupiter.api.Test; import org.springframework.boot.test.context.SpringBootTest; import java.util.List; @SpringBootTest public class OpenAiTest { /** * 测试问答 */ @Test public void testQA(){ List<CompletionChoice> questionAnswer = OpenAiUtils.getQuestionAnswer("重庆今天的天气怎么样?"); for (CompletionChoice completionChoice : questionAnswer) { System.out.println(completionChoice.getText()); } } /** * 测试面试题生成 */ @Test public void testInterview(){ List<CompletionChoice> results = OpenAiUtils.getInterviewQuestion("redis"); for (CompletionChoice completionChoice : results) { System.out.println(completionChoice.getText()); } } }
运行报错
如果你运行代码,出现下面错误,不应紧张,那是英文springboot3.0需要jdk17的版本
选中父项目右键打开项目配置创建,修改JDK为17版本即可,重新运行即可正常。
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