Large Language Models (LLMs)
Agents-Flex provides an abstract implementation interface for Large Language Models (LLMs) called Llm.java
, supporting two different chat types: chat
and chatStream
.
For various vendors, Agents-Flex offers different implementation classes and communication protocols, including HTTP
, SSE
, and WebSocket
clients.
Chat
In chat conversations of AI LLMs, we need to consider several different scenarios:
- Simple chat
- Chat with Histories
- Function Calling
These capabilities are determined by prompts, so Agents-Flex provides three implementations of prompts:
- SimplePrompt: Used for simple chat
- HistoriesPrompt: Used for chat with Histories
- FunctionPrompt: Used for Function Calling
During the interaction between prompts and LLMs, messages are exchanged. Therefore, Agents-Flex also provides different message implementations:
- AiMessage: The message returned by the LLMs, which includes not only the message content but also data such as token consumption.
- FunctionMessage: A subclass of AiMessage, returned when using FunctionPrompt in the
chat
method. - HumanMessage: Represents messages input by Human during conversations.
- SystemMessage: Represents system messages, often used to inform the LLM's role, for fine-tuning prompts.
Example Code
Simple Chat
public static void main(String[] args) {
Llm llm = new OpenAiLlm.of("sk-rts5NF6n*******");
Prompt prompt = new SimplePrompt("what is your name?");
String response = llm.chat(prompt);
System.out.println(response);
}
Chat with Histories
public static void main(String[] args) {
Llm llm = new OpenAiLlm.of("sk-rts5NF6n*******");
HistoriesPrompt prompt = new HistoriesPrompt();
prompt.addMessage(new SystemMessage("You are now a database development engineer...."));
prompt.addMessage(new HumanMessage("Please provide the DDL content for...."));
String response = llm.chat(prompt);
System.out.println(response);
}
Function Calling
Utility class definition:
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) {
//Here, we should call API through third-party interfaces.
return name + "The weather is cloudy turning to overcast. ";
}
}
Create FunctionPrompt and pass it to the LLMs through the chat
method:
public static void main(String[] args) {
OpenAiLlmConfig config = new OpenAiLlmConfig();
config.setApiKey("sk-rts5NF6n*******");
OpenAiLlm llm = new OpenAiLlm(config);
FunctionPrompt prompt = new FunctionPrompt("how's the weather in New York?", WeatherUtil.class);
FunctionResultResponse response = llm.chat(prompt);
//Execute utility class method to obtain result.
Object result = response.getFunctionResult();
System.out.println(result);
//"The weather in New York is cloudy turning overcast."
}