agents-flex

Agents-Flex is an elegant LLM Application Framework like LangChain with Java.

Stars: 170

Visit

Agents-Flex is a LLM Application Framework like LangChain base on Java. It provides a set of tools and components for building LLM applications, including LLM Visit, Prompt and Prompt Template Loader, Function Calling Definer, Invoker and Running, Memory, Embedding, Vector Storage, Resource Loaders, Document, Splitter, Loader, Parser, LLMs Chain, and Agents Chain.

README:

English | 简体中文 | 日本語

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

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.getFunctionResult();

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

Communication

Twitter: https://twitter.com/yangfuhai

Modules

For Tasks:

Click tags to check more tools for each tasks

chat with llm call function from llm build llm application train llm model deploy llm model

For Jobs:

chatbot developer llm engineer ai researcher data scientist machine learning engineer

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