# 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
English | 简体中文
# 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

## Modules
