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Integration Service for HR Platform Using Event-Driven Microkernel Architecture

Date: 2024-09-24

Status

Proposed

Context

We are developing an AI service to connect with various external LLM cloud providers, focusing on workflow requirements and high evolvability. This service should leverage an event-driven architecture within a microkernel style, allowing for easy integration with various LLM model provider systems without compromising on performance. This approach will facilitate scalability, adaptability, and responsiveness to changing business needs.

Alternatives

  1. Event-driven-microkernel: Microkernel for external LLM cloud providers and event driven for workflow service
  2. Point-to-Point Integration: Directly connect each application to the LLM provider.
  3. Monolithic Middleware: Use a monolithic middleware service to handle all LLM providers.

PrOACT

  • Problem: How can we design a configurable/evolvable AI service that integrates easily with various LLM providers and supports workflow requirements without compromising on performance?

  • Objectives:

    • Enable seamless integration with multiple LLM providers.
    • Support high configurability/performance for different model workflows.
    • Ensure minimal disruption when migrating to new LLM providers.

  • Alternatives: As described above.

  • Consequences:

    • Event driven microkernel requires additional maintainance
    • Point-to-Point Integration leads to scalability issues and high maintenance overhead.
    • Monolithic Middleware lacks flexibility and complicates future integrations.

  • Trade-offs:

    • An event-driven microkernel architecture allows for extensibility but may require additional infrastructure for event management.
    • Monolithic solutions are simpler initially but limit scalability and adaptability over time.

Decision

Adopt an event-driven microkernel architecture for the AI service. This design will use a core microkernel that handles common AI functionalities, while modular plugins will manage specific integrations with various LLM providers. Events will drive the communication between services, and workflow enabling real-time processing and easy scalability.

Tradeoffs - Mitigations

  • Complexity: The event-driven microkernel can introduce complexity in managing events and plugins.

    • Mitigation: Implement robust documentation and training programs, alongside a user-friendly interface for managing integrations and events.

  • Performance: Potential latency due to the overhead of event processing and additional modules.

    • Mitigation: Optimize event handling and employ asynchronous processing to minimize latency. Utilize event streaming platforms for high throughput.

  • Plugin Management: Risk of plugin conflicts and difficulties in managing multiple integrations.

    • Mitigation: Establish clear versioning and compatibility guidelines for plugins, along with automated testing for new integrations.

  • Monitoring and Debugging: Event-driven architectures can complicate monitoring and debugging efforts.

    • Mitigation: Integrate comprehensive logging and monitoring solutions to track events and identify issues quickly.

  • Learning Curve: Teams may need time to adapt to the new architecture style.

    • Mitigation: Provide ongoing training and support to help teams become proficient in managing the event-driven microkernel architecture.

This decision aims to enhance our ability to integrate with various LLM providers and workflow effectively while ensuring adaptability, scalability, and minimal disruption in future integrations.