🗒 Feature request: “Conversation-as-a-Service” FastAPI demo

[Flemington8]

What problem does this solve?
In scenarios where LLM training occurs on a dedicated machine, and AIOpsLab operates on a separate server, there’s a need for a standardized interface to manage interactions between these components. Directly coupling the training loop with specific inference engines like vLLM can lead to rigid architectures that are difficult to maintain and scale. By introducing a RESTful API layer, you decouple the training process from the inference engine, promoting modularity and flexibility.

Proposed change

• Add services/server.py (≈150 LOC) - FastAPI endpoint /simulate
• Docs: docs/conversation_service.md with curl + python client snippet
• Update README quick-start: uvicorn services.service:app --reload

Why FastAPI? FastAPI is fast (<2 ms overhead), self-documenting, and already widely used in
cloud/k8s. It introduces no heavy infra dependencies (only fastapi, pydantic, uvicorn).

Alternatives

• gRPC (heavier; harder to demo)
• websocket stream (nice-to-have later)

[Flemington8]

Refer to this PR #80