[DevOps]: Introduce isolated `backend-ai` service in monorepo with independent build & CI pipeline

[tomvothecoder]

Task Details

Create a new top-level backend-ai/ service within the monorepo to isolate AI inference logic from the existing backend/ API service. The new service must have its own dependency graph, Dockerfile, and CI pipeline. It must not share internal modules with backend/ and must communicate strictly via HTTP using a defined API contract. The goal is to reduce backend image size, isolate ML dependencies, and prepare for independent scaling and future GPU deployment (if possible).

Steps to Complete

1. Create backend-ai/ directory at repository root.
2. Initialize separate pyproject.toml for AI service.
3. Implement minimal FastAPI application with health endpoint.
4. Define initial inference endpoints (e.g., /summarize, /compare, /embed).
5. Add independent Dockerfile for backend-ai.
6. Update docker-compose.yml to include backend-ai service.
7. Configure internal networking between backend and backend-ai.
8. Add environment variables for service URL configuration in backend.
9. Implement async HTTP client in backend for AI service calls.
10. Add CI workflow for backend-ai (lint, test, build).
11. Ensure no cross-imports between backend and backend-ai.
12. Validate container builds independently.
13. Update documentation to describe service boundaries and deployment model.

Additional Notes

The AI service must remain stateless and should not directly access the primary database. All schema sharing should occur through explicit request/response models rather than shared internal modules. The architecture must allow future extraction into a separate repository with minimal refactoring.