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+# 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**
+
+```java
+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**
+
+```java
+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：
+
+```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) {
+        //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:
+
+```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'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."
+}
+```