Streaming input and output using WebSockets

TL;DR#

• Learn how to build an agent chat application using WebSockets and IOStream
• Explore a hands-on example of connecting a web application to a responsive chat with agents over WebSockets.
• Streamlined Real-Time Interactions: WebSockets offer a low-latency, persistent connection for sending and receiving data in real time.

Real-Time Applications: Why WebSockets?#

WebSockets provide a powerful framework for real-time communication between a client and server. Unlike traditional HTTP requests, which require polling for updates, WebSockets establish a persistent, full-duplex connection that allows for continuous data exchange.

This capability is critical for applications that use AG2, where seamless interaction is essential.

Key Benefits of WebSockets#

1. Low Latency: WebSockets reduce latency by maintaining a direct, open connection, avoiding the overhead of repeated HTTP handshakes.
2. Efficient Data Streaming: Continuous, two-way data streams enable smooth user experiences in real-time applications.
3. Event-Driven Communication: With WebSocket protocols, the server can "push" updates to the client as events occur.
4. Simplified Architecture: WebSockets eliminate the need for separate polling mechanisms, reducing server load and complexity.

Building a chat System#

This example demonstrates how to create a WebSocket-based chat system that streams real-time input and output from AG2 Agents.

How It Works#

1. WebSocket Connection: The client establishes a persistent WebSocket connection to the server.
2. Real-Time Data Flow: Events in the conversation are streamed over WebSockets to the browser where they can be displayed.

Example: Creating a Weather chat app#

Let’s walk through an example that integrates WebSockets with a weather-focused chat.

1. Clone the Repository#

git clone https://github.com/ag2ai/agentchat-over-websockets.git
cd agentchat-over-websockets

2. Set Up Environment Variables#

Create a OAI_CONFIG_LIST file based on the provided OAI_CONFIG_LIST_sample:

cp OAI_CONFIG_LIST_sample OAI_CONFIG_LIST

(Optional) Create and use a virtual environment#

To reduce cluttering your global Python environment on your machine, you can create a virtual environment. On your command line, enter:

python3 -m venv env
source env/bin/activate

3. Install Dependencies#

Install the required Python packages using pip:

pip install -r requirements.txt

4. Start the Server#

Run the main.py file:

python agentchat-over-websockets/main.py

Test the App#

With the server running, open the client application in your browser by navigating to http://localhost:8001/. And send a message to the chat and watch the conversation between agents roll out in your browser.

Code review#

Backend Code: main.py#

The backend is responsible for serving the frontend, managing WebSocket connections, and hosting the AI-powered conversational agent. Below is a step-by-step breakdown.

Setting Up the WebSocket Server#

The IOWebsockets.run_server_in_thread utility is used to run a WebSocket server. The on_connect function handles new client connections and initializes the chatbot.

from autogen.io.websockets import IOWebsockets
from datetime import datetime

def on_connect(iostream: IOWebsockets) -> None:
    print(f"Connected to client: {iostream}")
    initial_msg = iostream.input()  # Receive the first message from the client.
    print(f"Initial message: {initial_msg}")

    # Define the agent
    agent = autogen.ConversableAgent(
        name="chatbot",
        system_message="Complete tasks and reply TERMINATE when done. Use the 'weather_forecast' tool for weather-related queries.",
        llm_config={"stream": False},
    )

    # Define the user proxy
    user_proxy = autogen.UserProxyAgent(
        name="user_proxy",
        system_message="A proxy for the user.",
        is_termination_msg=lambda msg: msg.get("content", "").endswith("TERMINATE"),
        human_input_mode="NEVER",
    )

    # Register tool functions
    def weather_forecast(city: str) -> str:
        return f"The weather forecast for {city} is sunny as of {datetime.now()}."

    autogen.register_function(
        weather_forecast,
        caller=agent,
        executor=user_proxy,
        description="Provides a mock weather forecast.",
    )

    # Initiate conversation
    user_proxy.initiate_chat(agent, message=initial_msg)

Explanation: 1. on_connect: Handles client connections and manages the interaction between the ConversableAgent and the client. 2. Tool Registration: The weather_forecast function provides a mock weather report and is linked to the agent for handling weather-related queries.

Serving the Frontend#

The SimpleHTTPRequestHandler is used to serve HTML files. A custom handler class overrides the behavior for the root path to serve chat.html.

class MyRequestHandler(SimpleHTTPRequestHandler):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, directory=Path(__file__).parent / "website_files" / "templates", **kwargs)

    def do_GET(self):
        if self.path == "/":
            self.path = "/chat.html"
        return super().do_GET()

Explanation: - The MyRequestHandler class ensures that the default page served is chat.html. - Files are served from the website_files/templates directory.

Running the Servers#

Finally, both the WebSocket and HTTP servers are started.

from http.server import HTTPServer

PORT = 8001

handler = MyRequestHandler

# Start WebSocket server
with IOWebsockets.run_server_in_thread(on_connect=on_connect, port=8080) as uri:
    print(f"WebSocket server started at {uri}")

    # Start HTTP server
    with HTTPServer(("", PORT), handler) as httpd:
        print(f"HTTP server started at http://localhost:{PORT}")
        try:
            httpd.serve_forever()
        except KeyboardInterrupt:
            print("HTTP server stopped.")

Explanation: - The WebSocket server listens on port 8080, while the HTTP server listens on port 8001. - The WebSocket server handles real-time communication, while the HTTP server serves static files.

Frontend Code: chat.html#

The frontend provides a simple interface for users to interact with the chatbot.

HTML Structure#

The HTML structure defines an input form for sending messages and a list for displaying them.

<!DOCTYPE html>
<html>
<head>
    <title>Chat Interface</title>
    <style>
        body { font-family: monospace; max-width: 800px; margin: 20px auto; }
        #messages { list-style: none; padding: 0; }
        #messages li { background: #f1f3f4; padding: 8px; border-radius: 4px; margin: 4px 0; }
    </style>
</head>
<body>
    <h1>AI Chat Interface</h1>
    <form onsubmit="sendMessage(event)">
        <input type="text" id="messageText" autocomplete="off" />
        <button>Send</button>
    </form>
    <ul id="messages"></ul>
</body>
</html>

JavaScript Logic#

The JavaScript code establishes a WebSocket connection, handles incoming messages, and sends user input to the backend.

var ws = new WebSocket("ws://localhost:8080");

ws.onmessage = function(event) {
    var messages = document.getElementById('messages');
    var message = document.createElement('li');
    message.textContent = event.data;  // Display the message content.
    messages.appendChild(message);
};

function sendMessage(event) {
    var input = document.getElementById("messageText");
    ws.send(input.value);  // Send the input value to the backend.
    input.value = '';  // Clear the input field.
    event.preventDefault();  // Prevent form submission.
}

Explanation: 1. WebSocket Initialization: Connects to the WebSocket server at ws://localhost:8080. 2. Message Display: Appends incoming messages to the #messages list. 3. Sending Messages: Captures user input, sends it to the server, and clears the input field.

Conclusion#

Building an AgentChat system with WebSockets unlocks the potential for real-time, interactive applications. By maintaining a persistent connection, WebSockets enable seamless communication, enhancing user experience with minimal latency.

The example of a weather chatbot demonstrates the ease of integrating AG2 with WebSockets to create dynamic conversational agents. Whether for customer support, virtual assistants, or personalized services, this architecture provides a robust foundation for building next-generation applications.

Ready to start building? Explore the full example code here.