# Agents-Flex **Repository Path**: bigsnake/agents-flex ## Basic Information - **Project Name**: Agents-Flex - **Description**: Agents-Flex: 一个优雅的 LLM(大语言模型) 应用开发框架,使用 Java 开发。 - **Primary Language**: Java - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: https://github.com/agents-flex/agents-flex - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 852 - **Created**: 2024-01-24 - **Last Updated**: 2024-01-24 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

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# Agents-Flex is an elegant LLM Application Framework like LangChain with Java. --- ## Features - LLM Visit - Prompt、Prompt Template Loader - Function Calling Definer, Invoker、Running - Memory - Embedding - Vector Storage - Resource Loaders - Text Splitter - LLMs Chain - Agents Chain ## Simple Chat use OpenAi LLM: ```java public static void main(String[] args) throws InterruptedException { OpenAiConfig config = new OpenAiConfig(); config.setApiKey("sk-rts5NF6n*******"); Llm llm = new OpenAiLlm(config); String prompt = "Please write a story about a little rabbit defeating a big bad wolf"; llm.chat(prompt, (llmInstance, message) -> { System.out.println("--->" + message.getContent()); }); Thread.sleep(10000); } ``` use Qwen LLM: ```java public static void main(String[] args) throws InterruptedException { QwenLlmConfig config = new QwenLlmConfig(); config.setApiKey("sk-28a6be3236****"); config.setModel("qwen-turbo"); Llm llm = new QwenLlm(config); String prompt = "Please write a story about a little rabbit defeating a big bad wolf"; llm.chat(prompt, (llmInstance, message) -> { System.out.println("--->" + message.getContent()); }); Thread.sleep(10000); } ``` use SparkAi LLM: ```java public static void main(String[] args) throws InterruptedException { SparkLlmConfig config = new SparkLlmConfig(); config.setAppId("****"); config.setApiKey("****"); config.setApiSecret("****"); Llm llm = new SparkLlm(config); String prompt = "Please write a story about a little rabbit defeating a big bad wolf"; llm.chat(prompt, (llmInstance, message) -> { System.out.println("--->" + message.getContent()); }); Thread.sleep(10000); } ``` ## Chat With Histories ```java public static void main(String[] args) { SparkLlmConfig config = new SparkLlmConfig(); config.setAppId("****"); config.setApiKey("****"); config.setApiSecret("****"); // Create LLM Llm llm = new SparkLlm(config); // Create Histories prompt HistoriesPrompt prompt = new HistoriesPrompt(); System.out.println("ask for something..."); Scanner scanner = new Scanner(System.in); //wait for user input String userInput = scanner.nextLine(); while (userInput != null){ prompt.addMessage(new HumanMessage(userInput)); //chat with llm llm.chat(prompt, (instance, message) -> { System.out.println(">>>> " + message.getContent()); }); //wait for user input userInput = scanner.nextLine(); } } ``` ## Function Calling - step 1: define the function native ```java 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 ```java public static void main(String[] args) throws InterruptedException { OpenAiLlmConfig config = new OpenAiLlmConfig(); config.setApiKey("sk-rts5NF6n*******"); OpenAiLlm llm = new OpenAiLlm(config); Functions functions = Functions.from(WeatherUtil.class, String.class); String result = llm.call("How is the weather in Beijing today?", functions); System.out.println(result); // "Today it will be dull and overcast in Beijing"; Thread.sleep(10000); } ``` ## Communication ![](./docs/assets/images/wechat-group.jpg) ## Modules ![](./docs/assets/images/modules.jpg)