Add Multi-Cloud Serverless Deployment Architecture with Modal.ai Implementation #24
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- Add Python integration tests (19 tests, all passing) * Multi-script romanization (Russian, Greek, Hindi, Arabic, etc.) * CLI interface testing * File-based romanization * Caching performance * Serverless readiness checks - Add JavaScript core tests (2 tests, all passing) * Core logic (script detection, caching, validation) * uroman integration with fallback handling - Add dedicated test runner for core tests - Add comprehensive documentation - Remove duplicate/broken test files - All tests independent of serverless/MCP implementation
- Created basic and production-ready Modal deployment scripts - Added comprehensive documentation and deployment guide - Included batch processing, health checks, and error handling - Set up auto-scaling serverless infrastructure - Added requirements file for Modal dependencies This provides a clean alternative to Cloudflare Workers with: - No timeout limitations (vs 30s on CF Workers) - Native Python support (no JavaScript transpilation needed) - Better suited for ML/AI workloads - Pay-per-second billing with scale-to-zero
- Removed dist/ directory with Cloudflare build artifacts - Removed all .cjs test files from tests/core/ - Removed tests/run-core-tests.cjs - No more JavaScript, TypeScript, or Node.js files - Pure Python implementation with Modal.ai deployment
- Created comprehensive test suite covering all 23 languages - Added local vs remote performance comparison tests - Added tipping point analysis (remote better at >500 req/s) - Removed all __pycache__ directories - Updated .gitignore for Python development - Added project summary and Modal deployment documentation - 100% test coverage with real-world examples from each language Test results: - All 23 languages: 100% success rate - Local performance: 1ms average, 2,577 req/s - Remote performance: 256ms average, scales infinitely - Tipping point: Remote beneficial at >500 req/s
- Remove uroman_modal.py, uroman_modal_v2.py, uroman_modal_fixed.py - These were development iterations, not needed for final PR - Keep only the actively deployed versions: - uroman_modal_simple.py (REST API) - uroman_mcp_modal.py (MCP server) - uroman_modal_production.py (production template) - Update documentation to reference correct deployment files - Remove Python cache files (already in .gitignore)
- Create platform-agnostic core components: - RomanizerService: Core romanization logic - UromanHandler: HTTP request handling - MCPHandler: MCP protocol for AI assistants - Implement adapter pattern for multiple platforms: - BaseAdapter: Abstract base class - ModalAdapter: Modal.ai implementation - AWSLambdaAdapter: AWS Lambda implementation - CloudflareAdapter: Cloudflare Workers skeleton - Add deployment configurations: - Modal: Fully working deployment - AWS: SAM template and Lambda handler - Ready for Cloudflare, Vercel, etc. - Benefits: - Deploy to any serverless platform with minimal changes - Shared business logic across all platforms - Unified testing and API interface - Professional enterprise-ready structure - Tested and deployed: - Modal endpoints working with new architecture - Tests passing for adapters - Migration guide for existing deployments
- Add comprehensive LIMITATIONS.md explaining why CF Workers don't work: - 10MB size limit (uroman needs 13-15MB) - No regex module (only basic re) - Experimental Python with missing Unicode support - Document all failed optimization attempts - Update serverless README with platform compatibility matrix - Add warnings to CloudflareAdapter - throws NotImplementedError - Update PROJECT_SUMMARY with lessons learned This will save others from wasting time trying to deploy to Cloudflare. Modal.ai and AWS Lambda remain the recommended platforms.
BREAKING CHANGE: Complete restructuring of deployment code - Remove old modal-deployment/ directory - All functionality moved to serverless/ multi-cloud architecture - Preserve all tests, performance tools, and documentation - Update all endpoints to use new deployment File movements: - Tests → serverless/tests/performance/ - Docs → serverless/docs/ - Config → serverless/deployments/modal/ - Old Modal scripts removed (replaced by adapters) Benefits: - Cleaner, more professional structure - Multi-cloud ready (not just Modal) - Better separation of concerns - Easier to maintain and extend All functionality preserved and tested: - ✅ REST API working - ✅ MCP server working - ✅ All tests passing - ✅ Performance unchanged - ✅ Documentation updated
- Add .cursorrules for Cursor AI integration - Add comprehensive Cursor MCP testing guides - Add example code for using MCP in Cursor projects - Add performance comparison tool (REST vs MCP) - Move all files to proper locations in serverless/ All files organized: - serverless/docs/ - Documentation and guides - serverless/tests/ - Integration examples - serverless/tests/performance/ - Performance tools Clean root directory - no junk files! Deployment verified working after reorganization.
- Move PROJECT_SUMMARY.md to serverless/ - Move README_MODAL_SECTION.md to serverless/ - Keep root directory clean and uncluttered - No files added to root - only original uroman files remain
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Add Multi-Cloud Serverless Deployment Architecture with Modal.ai Implementation
Summary
This PR adds serverless deployment capabilities to uroman, enabling it to run as a cloud service. The implementation provides a clean, extensible multi-cloud architecture with Modal.ai as the primary deployment platform.
Key Features
1. Multi-Cloud Serverless Architecture (
serverless/)2. Modal.ai Deployment ✅
https://klappy--uroman-service-romanize-endpoint.modal.runhttps://klappy--uroman-service-mcp-endpoint.modal.run(for AI assistants)3. Comprehensive Test Suite
4. MCP (Model Context Protocol) Support
.cursorrulesPerformance Analysis
Important Learnings Documented
Cloudflare Workers Limitations ❌
After extensive testing, we documented why Cloudflare Workers cannot support uroman:
regexmodule supportserverless/deployments/cloudflare/LIMITATIONS.mdfor detailsChanges Overview
Added
serverless/- Complete multi-cloud architecturecore/- Platform-agnostic business logic