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