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CJCrafterCJCrafter
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add examples and dsl
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‎examples/build.gradle.kts

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plugins {
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`java-library`
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kotlin("jvm") version "1.7.20-RC"
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}
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repositories {
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mavenCentral()
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}
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dependencies {
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implementation(project(":"))
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implementation("com.squareup.okhttp3:okhttp:4.9.2")
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implementation("com.fasterxml.jackson.core:jackson-core:2.15.3")
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implementation("com.fasterxml.jackson.core:jackson-databind:2.15.3")
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implementation("io.github.cdimascio:dotenv-kotlin:6.4.1")
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// https://mvnrepository.com/artifact/org.mariuszgromada.math/MathParser.org-mXparser
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// Used for tool tests
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implementation("org.mariuszgromada.math:MathParser.org-mXparser:5.2.1")
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}
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kotlin {
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jvmToolchain {
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languageVersion.set(JavaLanguageVersion.of(8))
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}
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}
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tasks.withType<org.jetbrains.kotlin.gradle.tasks.KotlinCompile>().all {
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kotlinOptions {
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jvmTarget = "1.8"
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}
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}

‎examples/src/main/java/chat/ChatCompletion.java

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package chat;
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import com.cjcrafter.openai.OpenAI;
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import com.cjcrafter.openai.chat.ChatMessage;
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import com.cjcrafter.openai.chat.ChatRequest;
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import com.cjcrafter.openai.chat.ChatResponse;
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import io.github.cdimascio.dotenv.Dotenv;
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import java.util.ArrayList;
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import java.util.List;
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import java.util.Scanner;
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/**
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* In this Java example, we will be using the Chat API to create a simple chatbot.
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*/
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public class ChatCompletion {
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public static void main(String[] args) {
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// To use dotenv, you need to add the "io.github.cdimascio:dotenv-kotlin:version"
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// dependency. Then you can add a .env file in your project directory.
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String key = Dotenv.load().get("OPENAI_TOKEN");
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OpenAI openai = OpenAI.builder()
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.apiKey(key)
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.build();
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List<ChatMessage> messages = new ArrayList<>();
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messages.add(ChatMessage.toSystemMessage("Help the user with their problem."));
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// Here you can change the model's settings, add tools, and more.
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ChatRequest request = ChatRequest.builder()
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.model("gpt-3.5-turbo")
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.messages(messages)
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.build();
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Scanner scan = new Scanner(System.in);
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while (true) {
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System.out.println("What are you having trouble with?");
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String input = scan.nextLine();
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messages.add(ChatMessage.toUserMessage(input));
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ChatResponse response = openai.createChatCompletion(request);
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System.out.println("Generating Response...");
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System.out.println(response.get(0).getMessage().getContent());
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// Make sure to add the response to the messages list!
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messages.add(response.get(0).getMessage());
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}
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}
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}

‎examples/src/main/java/chat/StreamChatCompletion.java

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package chat;
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import com.cjcrafter.openai.OpenAI;
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import com.cjcrafter.openai.chat.ChatMessage;
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import com.cjcrafter.openai.chat.ChatRequest;
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import com.cjcrafter.openai.chat.ChatResponseChunk;
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import io.github.cdimascio.dotenv.Dotenv;
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import java.util.ArrayList;
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import java.util.List;
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import java.util.Scanner;
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/**
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* In this Java example, we will be using the Chat API to create a simple chatbot.
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* Instead of waiting for the full response to generate, we will "stream" tokens
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* 1 by 1 as they are generated.
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*/
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public class StreamChatCompletion {
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public static void main(String[] args) {
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// To use dotenv, you need to add the "io.github.cdimascio:dotenv-kotlin:version"
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// dependency. Then you can add a .env file in your project directory.
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String key = Dotenv.load().get("OPENAI_TOKEN");
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OpenAI openai = OpenAI.builder()
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.apiKey(key)
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.build();
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// Notice that this is a *mutable* list. We will be adding messages later
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// so we can continue the conversation.
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List<ChatMessage> messages = new ArrayList<>();
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messages.add(ChatMessage.toSystemMessage("Help the user with their problem."));
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// Here you can change the model's settings, add tools, and more.
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ChatRequest request = ChatRequest.builder()
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.model("gpt-3.5-turbo")
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.messages(messages)
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.build();
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Scanner scan = new Scanner(System.in);
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while (true) {
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System.out.println("What are you having trouble with?");
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String input = scan.nextLine();
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messages.add(ChatMessage.toUserMessage(input));
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System.out.println("Generating Response...");
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for (ChatResponseChunk chunk : openai.streamChatCompletion(request)) {
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// This is nullable! ChatGPT will return null AT LEAST ONCE PER MESSAGE.
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String delta = chunk.get(0).getDeltaContent();
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if (delta != null)
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System.out.print(delta);
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// When the response is finished, we can add it to the messages list.
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if (chunk.get(0).isFinished())
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messages.add(chunk.get(0).getMessage());
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}
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// Print a new line to separate the messages
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System.out.println();
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}
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}
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}

‎examples/src/main/java/chat/StreamChatCompletionFunction.java

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package chat;
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import com.cjcrafter.openai.OpenAI;
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import com.cjcrafter.openai.chat.ChatMessage;
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import com.cjcrafter.openai.chat.ChatRequest;
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import com.cjcrafter.openai.chat.ChatResponseChunk;
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import com.cjcrafter.openai.chat.ChatUser;
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import com.cjcrafter.openai.chat.tool.*;
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import com.cjcrafter.openai.exception.HallucinationException;
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import com.fasterxml.jackson.databind.JsonNode;
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import io.github.cdimascio.dotenv.Dotenv;
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import org.mariuszgromada.math.mxparser.Expression;
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import org.mariuszgromada.math.mxparser.License;
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import java.util.ArrayList;
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import java.util.List;
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import java.util.Map;
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import java.util.Scanner;
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/**
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* In this Java example, we will be using the Chat API to create a simple chatbot.
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* Instead of waiting for the full response to generate, we will "stream" tokens
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* 1 by 1 as they are generated. We will also add a Math tool so that the chatbot
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* can solve math problems with a math parser.
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*/
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public class StreamChatCompletionFunction {
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public static void main(String[] args) {
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// Use mXparser
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License.iConfirmNonCommercialUse("CJCrafter");
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// To use dotenv, you need to add the "io.github.cdimascio:dotenv-kotlin:version"
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// dependency. Then you can add a .env file in your project directory.
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String key = Dotenv.load().get("OPENAI_TOKEN");
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OpenAI openai = OpenAI.builder()
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.apiKey(key)
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.build();
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// Notice that this is a *mutable* list. We will be adding messages later
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// so we can continue the conversation.
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List<ChatMessage> messages = new ArrayList<>();
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messages.add(ChatMessage.toSystemMessage("Help the user with their problem."));
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// Here you can change the model's settings, add tools, and more.
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ChatRequest request = ChatRequest.builder()
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.model("gpt-3.5-turbo")
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.messages(messages)
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.addTool(FunctionTool.builder()
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.name("solve_math_problem")
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.description("Returns the result of a math problem as a double")
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.addStringParameter("equation", "The math problem for you to solve", true)
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.build()
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)
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.build();
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Scanner scan = new Scanner(System.in);
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while (true) {
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System.out.println("What are you having trouble with?");
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String input = scan.nextLine();
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messages.add(ChatMessage.toUserMessage(input));
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System.out.println("Generating Response...");
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boolean madeToolCall;
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do {
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madeToolCall = false;
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for (ChatResponseChunk chunk : openai.streamChatCompletion(request)) {
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String delta = chunk.get(0).getDeltaContent();
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if (delta != null)
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System.out.print(delta);
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// When the response is finished, we can add it to the messages list.
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if (chunk.get(0).isFinished())
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messages.add(chunk.get(0).getMessage());
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}
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// If the API returned a tool call to us, we need to handle it.
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List<ToolCall> toolCalls = messages.get(messages.size() - 1).getToolCalls();
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if (toolCalls != null) {
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madeToolCall = true;
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for (ToolCall call : toolCalls) {
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ChatMessage response = handleToolCall(call, request.getTools());
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messages.add(response);
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}
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}
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// Loop until we get a message without tool calls
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} while (madeToolCall);
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// Print a new line to separate the messages
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System.out.println();
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}
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}
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public static ChatMessage handleToolCall(ToolCall call, List<Tool> validTools) {
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// The try-catch here is *crucial*. ChatGPT *isn't very good*
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// at tool calls (And you probably aren't very good at prompt
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// engineering yet!). OpenAI will often "Hallucinate" arguments.
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try {
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if (call.getType() != ToolType.FUNCTION)
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throw new HallucinationException("Unknown tool call type: " + call.getType());
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FunctionCall function = call.getFunction();
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Map<String, JsonNode> arguments = function.tryParseArguments(validTools); // You can pass null here for less strict parsing
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String equation = arguments.get("equation").asText();
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double result = solveEquation(equation);
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// NaN implies that the equation was invalid
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if (Double.isNaN(result))
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throw new HallucinationException("Format was invalid: " + equation);
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// Add the result to the messages list
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String json = "{\"result\": " + result + "}";
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return new ChatMessage(ChatUser.TOOL, json, null, call.getId());
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} catch (HallucinationException ex) {
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// Lets let ChatGPT know it made a mistake so it can correct itself
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String json = "{\"error\": \"" + ex.getMessage() + "\"}";
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return new ChatMessage(ChatUser.TOOL, json, null, call.getId());
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}
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}
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public static double solveEquation(String equation) {
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Expression expression = new Expression(equation);
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return expression.calculate();
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}
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}

‎examples/src/main/java/completion/Completion.java

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package completion;
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import com.cjcrafter.openai.OpenAI;
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import com.cjcrafter.openai.completions.CompletionRequest;
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import io.github.cdimascio.dotenv.Dotenv;
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/**
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* In this Java example, we will be using the Legacy Completion API to generate
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* a response.
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*/
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public class Completion {
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public static void main(String[] args) {
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// To use dotenv, you need to add the "io.github.cdimascio:dotenv-kotlin:version"
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// dependency. Then you can add a .env file in your project directory.
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String key = Dotenv.load().get("OPENAI_TOKEN");
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OpenAI openai = OpenAI.builder()
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.apiKey(key)
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.build();
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String input = "What is 9+10?";
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// We use 128 tokens, so we have minimal delay before the response (for testing).
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CompletionRequest request = CompletionRequest.builder()
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.model("davinci")
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.prompt(input)
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.maxTokens(128).build();
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System.out.println("Generating Response...");
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String text = openai.createCompletion(request).get(0).getText();
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System.out.println(text);
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}
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}

‎examples/src/main/kotlin/chat/ChatCompletion.kt

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package chat
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import com.cjcrafter.openai.chat.*
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import com.cjcrafter.openai.chat.ChatMessage.Companion.toSystemMessage
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import com.cjcrafter.openai.chat.ChatMessage.Companion.toUserMessage
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import com.cjcrafter.openai.openAI
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import io.github.cdimascio.dotenv.dotenv
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import java.util.*
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/**
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* In this Kotlin example, we will be using the Chat API to create a simple chatbot.
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*/
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fun main() {
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// To use dotenv, you need to add the "io.github.cdimascio:dotenv-kotlin:version"
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// dependency. Then you can add a .env file in your project directory.
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val key = dotenv()["OPENAI_TOKEN"]
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val openai = openAI { apiKey(key) }
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// Here you can change the model's settings, add tools, and more.
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val request = chatRequest {
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model("gpt-3.5-turbo")
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addMessage("Help the user with their problem.".toSystemMessage())
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}
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val messages: MutableList<ChatMessage> = request.messages // We'll update this list as the conversation continues
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val scan = Scanner(System.`in`)
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while (true) {
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println("What are you having trouble with?")
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val input = scan.nextLine()
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messages.add(input.toUserMessage())
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println("Generating Response...")
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val completion = openai.createChatCompletion(request)[0]
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println(completion.message.content)
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messages.add(completion.message)
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}
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}

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