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Agents-Flex is a LLM Application Framework like LangChain base on Java.

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Features

• LLM Visit
• Prompt、Prompt Template
• Function Calling Definer, Invoker、Running
• Memory
• Embedding
• Vector Store
• Resource Loaders
• Document
  • Splitter
  • Loader
  • Parser
    • PoiParser
    • PdfBoxParser
• Agent
  • LLM Agent
• Chain
  • SequentialChain
  • ParallelChain
  • LoopChain
  • ChainNode
    • AgentNode
    • EndNode
    • RouterNode
      • GroovyRouterNode
      • QLExpressRouterNode
      • LLMRouterNode

Simple Chat

use OpenAi LLM:

 @Test
public void testChat() {
    OpenAiLlmConfig config = new OpenAiLlmConfig();
    config.setApiKey("sk-rts5NF6n*******");

    Llm llm = new OpenAiLlm(config);
    String response = llm.chat("what is your name?");

    System.out.println(response);
}

use Qwen LLM:

 @Test
public void testChat() {
    QwenLlmConfig config = new QwenLlmConfig();
    config.setApiKey("sk-28a6be3236****");
    config.setModel("qwen-turbo");

    Llm llm = new QwenLlm(config);
    String response = llm.chat("what is your name?");

    System.out.println(response);
}

use SparkAi LLM:

 @Test
public void testChat() {
    SparkLlmConfig config = new SparkLlmConfig();
    config.setAppId("****");
    config.setApiKey("****");
    config.setApiSecret("****");

    Llm llm = new SparkLlm(config);
    String response = llm.chat("what is your name?");

    System.out.println(response);
}

Chat With Histories

public static void main(String[] args) {
    SparkLlmConfig config = new SparkLlmConfig();
    config.setAppId("****");
    config.setApiKey("****");
    config.setApiSecret("****");

    Llm llm = new SparkLlm(config);

    HistoriesPrompt prompt = new HistoriesPrompt();

    System.out.println("ask for something...");
    Scanner scanner = new Scanner(System.in);
    String userInput = scanner.nextLine();

    while (userInput != null) {

        prompt.addMessage(new HumanMessage(userInput));

        llm.chatStream(prompt, (context, response) -> {
            System.out.println(">>>> " + response.getMessage().getContent());
        });

        userInput = scanner.nextLine();
    }
}

Function Calling

• step 1: define the function native

public class WeatherUtil {

    @FunctionDef(name = "get_the_weather_info", description = "get the weather info")
    public static String getWeatherInfo(
        @FunctionParam(name = "city", description = "the city name") String name
    ) {
        //we should invoke the third part api for weather info here
        return "Today it will be dull and overcast in " + name;
    }
}

• step 2: invoke the function from LLM

 public static void main(String[] args) {
    OpenAiLlmConfig config = new OpenAiLlmConfig();
    config.setApiKey("sk-rts5NF6n*******");

    OpenAiLlm llm = new OpenAiLlm(config);

    FunctionPrompt prompt = new FunctionPrompt("How is the weather in Beijing today?", WeatherUtil.class);
    FunctionResultResponse response = llm.chat(prompt);

    Object result = response.invoke();

    System.out.println(result);
    //Today it will be dull and overcast in Beijing
}

Communication

• Twitter: https://twitter.com/yangfuhai

Modules