AI-Powered Group Chat sample with SignalR and OpenAI

The AI-Powered Group Chat sample demonstrates how to integrate OpenAI's capabilities into a real-time group chat application by using ASP.NET Core SignalR.

• View or download the complete sample code.

Overview

Integrating AI into applications is becoming essential for developers aiming to enhance user creativity, productivity, and overall experience. AI-powered features, such as intelligent chatbots, personalized recommendations, and contextual responses, add significant value to modern apps. While many AI-powered applications, like those inspired by ChatGPT, focus on interactions between a single user and an AI assistant, there's growing interest in exploring AI's potential within team environments. Developers are now asking, "What value can AI add to a team of collaborators?"

This sample guide highlights the process of building a real-time group chat application. In this chat, a group of human collaborators can interact with an AI assistant that has access to the chat history. Any collaborator can invite the AI to assist by starting their message with @gpt. The finished app looks like this:

This sample uses OpenAI for generating intelligent, context-aware responses and SignalR for delivering the response to users in a group. You can find the complete code in this repo.

Dependencies

You can use either Azure OpenAI or OpenAI for this project. Make sure to update the endpoint and key in appsettings.json. OpenAIExtensions reads the configuration when the app starts. You need to provide configuration values for endpoint and key to authenticate and use either service.

OpenAI

To build this application, you need the following resources:

• ASP.NET Core: To create the web application and host the SignalR hub.
• SignalR: For real-time communication between clients and the server.
• OpenAI Client: To interact with OpenAI's API for generating AI responses.

Azure OpenAI

To build this application, you need the following resources:

• ASP.NET Core: To create the web application and host the SignalR hub.
• SignalR: For real-time communication between clients and the server.
• Azure OpenAI: Azure.AI.OpenAI

Implementation

This section highlights the key parts of the code that integrate SignalR with OpenAI to create an AI-enhanced group chat experience.

Data flow

The following diagram highlights the step-by-step communication and processing involved in using OpenAI services, employing an iterative approach to responses and data handling:

In the previous diagram:

• The client sends instructions to the server. The server communicates with OpenAI to process these instructions.
• OpenAI responds with partial completion data. The server forwards this data back to the client. This process repeats multiple times for an iterative exchange of data between these components.

SignalR Hub integration

The GroupChatHub class manages user connections, message broadcasting, and AI interactions.

When a user sends a message that starts with @gpt:

• The hub forwards the message to OpenAI, which generates a response.
• The AI's response streams back to the group in real time.

The following code snippet demonstrates how the CompleteChatStreamingAsync method streams responses from OpenAI incrementally:

var chatClient = _openAI.GetChatClient(_options.Model);

await foreach (var completion in 
    chatClient.CompleteChatStreamingAsync(messagesInludeHistory))
{   
    // ...
    // Buffering and sending the AI's response in chunks
    await Clients.Group(groupName).SendAsync(
        "newMessageWithId",
        "ChatGPT",
        id,
        totalCompletion.ToString());
    // ...
}

In the previous code:

• chatClient.CompleteChatStreamingAsync(messagesIncludeHistory) starts streaming AI responses.
• The totalCompletion.Append(content) line accumulates the AI's response.
• If the buffered content length exceeds 20 characters, the hub sends the buffered content to clients by using Clients.Group(groupName).SendAsync.

By using this approach, the AI's response reaches users in real time, creating a seamless and interactive chat experience.

Maintain context with history

Every request to OpenAI's Chat Completions API is stateless. OpenAI doesn't store past interactions. In a chat app, what a user or an assistant says is important for generating a response that's contextually relevant. To achieve this relevance, include chat history in every request to the Completions API.

The GroupHistoryStore class manages chat history for each group. It stores messages posted by both the users and AI assistants, ensuring that the conversation context is preserved across interactions. This context is crucial for generating coherent AI responses.

The following code demonstrates how to store messages generated by the AI assistant in memory. The UpdateGroupHistoryForAssistant method is called to add the AI assistant's message to the group history, ensuring that the conversation context is maintained:

public void UpdateGroupHistoryForAssistant(string groupName, string message)
{
    var chatMessages = _store.GetOrAdd(groupName, _ => InitiateChatMessages());
    chatMessages.Add(new AssistantChatMessage(message));
}

The _history.GetOrAddGroupHistory method is called to add the user's message to the group history, ensuring that the conversation context is maintained:

_history.GetOrAddGroupHistory(groupName, userName, message);

Stream AI responses

The CompleteChatStreamingAsync method streams responses from OpenAI incrementally, which allows the app to send partial responses to the client as they're generated.

The code uses a StringBuilder to accumulate the AI's response. It checks the length of the buffered content and sends it to the clients when it exceeds a certain threshold, for example, 20 characters. This approach ensures that users see the AI's response as it forms, mimicking a human-like typing effect.

totalCompletion.Append(content);

if (totalCompletion.Length - lastSentTokenLength > 20)
{
    await Clients.Group(groupName).SendAsync(
        "newMessageWithId",
        "ChatGPT",
        id,
        totalCompletion.ToString());

    lastSentTokenLength = totalCompletion.Length;
}

Explore further

This project opens up exciting possibilities for further enhancement:

1. Advanced AI features: Leverage other OpenAI capabilities like sentiment analysis, translation, or summarization.
2. Incorporating multiple AI agents: You can introduce multiple AI agents with distinct roles or expertise areas within the same chat. For example, one agent might focus on text generation while the other provides image or audio generation. This approach creates a richer and more dynamic user experience where different AI agents interact seamlessly with users and each other.
3. Share chat history between server instances: Implement a database layer to persist chat history across sessions, allowing conversations to resume even after a disconnect. Beyond SQL or NoSQL based solutions, you can also explore using a caching service like Redis. It can significantly improve performance by storing frequently accessed data, such as chat history or AI responses, in memory. This reduces latency and offloads database operations, leading to faster response times, particularly in high-traffic scenarios.
4. Leveraging Azure SignalR Service: Azure SignalR Service provides scalable and reliable real-time messaging for your application. By offloading the SignalR backplane to Azure, you can scale out the chat application easily to support thousands of concurrent users across multiple servers. Azure SignalR also simplifies management and provides built-in features like automatic reconnections.