FEATURE_MATRIX.md

Kernel Feature Matrix by Language

Legend

✅ - Feature implemented
🔄 - Feature partially implemented (see associated Note column)
❌ - Feature not implemented

AI Services

	C#	Python	Java	Notes
TextGeneration	✅	✅	✅	Example: Text-Davinci-003
TextEmbeddings	✅	✅	✅	Example: Text-Embeddings-Ada-002
ChatCompletion	✅	✅	❌	Example: GPT4, Chat-GPT
Image Generation	✅	❌	❌	Example: Dall-E

AI Service Endpoints

	C#	Python	Java	Notes
OpenAI	✅	✅	✅
AzureOpenAI	✅	✅	✅
Hugging Face Inference API	🔄	❌	❌	Coming soon to Python, not all scenarios are covered for .NET
Hugging Face Local	❌	✅	❌
Custom	✅	🔄	❌	Requires the user to define the service schema in their application

Tokenizers

	C#	Python	Java	Notes
GPT2	✅	✅	✅
GPT3	✅	❌	❌
tiktoken	🔄	❌	❌	Coming soon to Python and C#. Can be manually added to Python via pip install tiktoken

Core Skills

	C#	Python	Java	Notes
TextMemorySkill	✅	✅	🔄
ConversationSummarySkill	✅	✅	❌
FileIOSkill	✅	✅	❌
HttpSkill	✅	✅	❌
MathSkill	✅	✅	❌
TextSkill	✅	✅	🔄
TimeSkill	✅	✅	🔄
WaitSkill	✅	❌	❌

Planning

	C#	Python	Java	Notes
Plan	✅	✅	❌	Need to port the Plan object model
BasicPlanner	❌	✅	❌
ActionPlanner	✅	❌	❌
SequentialPlanner	✅	❌	❌

Connectors and Skill Libraries

	C#	Python	Java	Notes
Qdrant (Memory)	✅	❌	❌
ChromaDb (Memory)	❌	🔄	❌
Milvus (Memory)	❌	❌	❌
Pinecone (Memory)	✅	❌	❌
Weaviate (Memory)	❌	❌	❌
CosmosDB (Memory)	✅	❌	❌	CosmosDB is not optimized for vector storage
Sqlite (Memory)	✅	❌	❌	Sqlite is not optimized for vector storage
Postgres (Memory)	✅	❌	❌	Vector optimized (required the pgvector extension)
Azure Cognitive Search	✅	🔄	❌
MsGraph	✅	❌	❌	Contains connectors for OneDrive, Outlook, ToDos, and Organization Hierarchies
Document and Data Loading Skills (i.e. pdf, csv, docx, pptx)	✅	❌	❌	Currently only supports Word documents
OpenAPI	✅	❌	❌
Web Search Skills (i.e. Bing, Google)	✅	❌	❌
Text Chunkers	🔄	🔄	❌

Design Choices

The overall architecture of the core kernel is consistent across all languages, 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.