# 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:
```java
@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:
```java
@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:
```java
@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
```java
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
```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) {
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
