rocky-lti-assist

To add a FastAPI endpoint for LTI 1.3 launches, you need to follow specific route structures, use Pydantic v2 models for data validation, and implement the necessary OAuth and LTI authentication steps within the backend.

For a Canvas LTI integration project, the best way to manage Python environments is using `uv`. This ensures consistent dependency management across the monorepo when developing FastAPI endpoints and SQLAlchemy models.

Creating Alembic migrations for SQLAlchemy models in a FastAPI backend involves defining your Pydantic v2 schemas and SQLAlchemy ORM models, then running Alembic commands to generate and apply database schema changes.

Yes, this coding assistant supports React and TypeScript frontend development. It provides context-aware guidance for the frontend components within the monorepo alongside the Python FastAPI backend and Azure infrastructure.

No, you cannot use Pydantic v1 with this FastAPI Canvas LTI 1.3 integration. The project strictly adheres to specific conventions requiring Pydantic v2 for data validation across its MCP server components and endpoints.