Feature Request: Implement Microservice for LLM Responses

[prthm20]

Description

Implement a scalable microservice for handling responses from a Large Language Model (LLM) to support the Debate AI project. The microservice will manage interactions for the following use cases:

1. User vs. User: Facilitate AI-assisted monitoring and real-time response suggestions.
2. User vs. AI: Provide direct responses from the LLM.
3. Multiple Users: Handle multiple simultaneous debates efficiently by queuing and managing LLM requests.

Requirements

Core Functionalities

• Request Handling: Accept input prompts from the Debate AI platform and send them to the LLM.
• Response Generation: Process LLM responses and send them back to the requesting client.
• Multi-User Support: Handle concurrent user requests and ensure fair allocation of resources.
• User Context Management:
  • Maintain session states for active debates.
  • Persist context for multi-turn conversations.

Additional Features

• Rate Limiting: Implement rate limiting to prevent abuse.
• Error Handling: Manage errors from the LLM API and provide fallback responses.
• Scalability: Ensure the microservice can handle increased traffic.
• Logging and Monitoring:
  • Log all interactions for debugging and analysis.
  • Integrate monitoring tools for system health and performance.

Technical Specifications

Architecture

• Backend Framework: Python (Flask or FastAPI preferred) or Node.js (Express.js).
• LLM Integration: Integrate with OpenAI GPT, Google Gemini, or other LLMs.
• Database: Use Redis for caching session states and context.
• Queue Management: Use a message queue like RabbitMQ or Kafka for managing requests in high-load scenarios.

API Endpoints

1. POST /generate-response

   • Input: { "user_id": string, "debate_id": string, "prompt": string, "context": array }
   • Output: { "response": string, "status": string }

2. GET /health-check

   • Output: { "status": "healthy", "uptime": number, "active_sessions": number }

3. GET /logs (Admin only)

   • Output: { "logs": array }

Deployment

• Containerization: Use Docker for deployment.
• Cloud Provider: AWS/GCP/Azure for hosting.
• Scaling: Use Kubernetes for load balancing and scaling.

Acceptance Criteria

• The microservice can handle at least 500 concurrent requests.
• Responses are generated within 1-3 seconds on average.
• All API endpoints are tested for correctness and reliability.
• Logs are stored securely and can be retrieved for analysis.

Tasks

1. Set up the project structure for the microservice.
2. Integrate with the chosen LLM API.
3. Implement core API endpoints (/generate-response, /health-check, /logs).
4. Add middleware for rate limiting and error handling.
5. Configure Redis for session state management.
6. Write unit tests and integration tests.
7. Containerize the microservice using Docker.
8. Deploy to a cloud provider and set up monitoring tools.

Priority

High

[ParagGhatage]

@prthm20 ,
We will see about caching and rate limiting once core functionality is running.

[ParagGhatage]

@keshav-nischal ,
Please assign this issue to both of us.
We will submit a PR in a few days.

[Adityakk9031]

please assign me this issue we may look after caching and rate limiting

[ParagGhatage]

please assign me this issue we may look after caching and rate limiting

@Adityakk9031 ,
I am working on this issue.
I will be submitting PR in a few days.