semantic-kernel/python/FEATURE_PARITY.md

Feature matrix Python and C#

The Python codebase has a bulk of the Semantic Kernel architecture re-implemented, but is not yet fully complete. Major things like tests and docs are work in progress.

This is a high-level overview of where things stand towards reaching feature parity with the C# reference implementation.

	Python	Notes
./ai/embeddings	🔄	Using Numpy for embedding representation. Vector operations not yet implemented
./ai/openai	🔄	Makes use of the OpenAI Python package. AzureOpenAI* not implemented
./configuration	✅	Direct port. Check inline docs
./core_skills	🔄	TextMemorySkill implemented. Others not
./diagnostics	✅	Direct port of custom exceptions and validation helpers
./kernel_extensions	🔄	Extensions take kernel as first argument and are exposed via sk.extensions.*
./memory	🔄	Can simplify by relying on Numpy NDArray
./planning	❌	Not yet implemented
./semantic_functions/partitioning	❌	Not yet implemented

Design Choices

The overall architecture of the core kernel is consistent across Python and C#, however, the code should follow common paradigms and style of each language.

During the initial development phase, many Python best practices have been ignored in the interest of velocity and feature parity. The project is now going through a refactoring exercise to increase code quality.

To make the Kernel as lightweight as possible, the core pip package should have a minimal set of external dependencies. On the other hand, the SDK should not reinvent mature solutions already available, unless of major concerns.