TTT #51: Designing Event-Driven Systems for LLMs

One significant hurdle we face when working with LLMs (Large Language Models) is the time it can take for these models to generate responses, especially when dealing with complex queries.

Today, I want to talk about the importance of designing event-driven systems to manage these tasks efficiently and how this approach is integral to the AI-powered document processing service I’m developing.

Embracing Event-Driven Architecture

In an event-driven architecture, components communicate through events, allowing for asynchronous processing. This design decouples the producers, who generate events, from the consumers, who process them. This decoupling is critical when dealing with LLMs because it allows the system to handle long-running tasks without blocking other operations.

The Role of Message Queues

Message queues are at the heart of this architecture. They act as intermediaries, storing and managing events (or messages) until they are processed by consumers. For LLMs, a message queue can hold requests for text generation, ensuring that each request is handled efficiently and without overloading the system.

Using message queues has several benefits:

• Asynchronous Processing: Requests are queued and processed asynchronously, preventing system bottlenecks.
• Scalability: The system can scale consumers independently based on the load, ensuring consistent performance.
• Reliability: Messages are not lost and can be retried if processing fails, enhancing system robustness.

Popular message queue systems include Amazon SQS, RabbitMQ, and Apache Kafka, each offering unique features to cater to different needs.

Real-Time Communication with WebSockets

WebSockets provide a full-duplex communication channel over a single TCP connection, making them perfect for real-time updates between the server and clients. This capability is particularly useful for keeping users informed about the progress of their requests.

WebSockets offer several advantages:

• Real-Time Updates: Clients receive immediate updates on the progress of their requests.
• Reduced Latency: Unlike polling, WebSockets maintain a persistent connection, reducing the overhead of repeated HTTP requests.
• Efficient Resource Usage: Only one connection is needed for continuous data exchange.

Implementing the System

Here’s how an event-driven system with message queues and WebSockets can be implemented for LLM tasks:

1. Client Request Handling:

   • The client sends a request to the server to generate text using an LLM.
   • The server acknowledges the request and places it in a message queue.

2. Task Processing:

   • A worker (or a set of workers) consumes requests from the queue and processes them.
   • Each worker is responsible for invoking the LLM to generate the desired text.

3. Progress Updates:

   • As the worker processes the request, it can generate intermediate progress updates.
   • These updates are sent to the client via WebSockets, using a service like AWS API Gateway’s Websockets API.

4. Completion Notification:

   • Once the worker completes the task, the final result is delivered back to the client via WebSockets.

A Glimpse into My AI-Powered Document Processing Service

This approach is not just theoretical. It’s a core part of how my upcoming AI-powered document processing service operates. By leveraging an event-driven architecture with message queues and WebSockets, it ensures efficient handling of document processing tasks, keeping users informed with real-time updates and delivering results reliably.

Conclusion

By designing systems that can handle the asynchronous nature of LLM tasks, we can build more responsive, scalable, and user-friendly applications. Whether you’re processing documents, generating text, or performing any other complex task, adopting an event-driven approach with message queues and WebSockets can make a significant difference.