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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
- 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);
Prompt prompt = new SimplePrompt("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);
Prompt prompt = new SimplePrompt("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);
Prompt prompt = new SimplePrompt("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(new SimplePrompt("How is the weather like today?"), functions);
System.out.println(result);
// "Today it will be dull and overcast in Beijing";
Thread.sleep(10000);
}
```