Semantic Kernel — Microsoft AI SDK Guide
Sanjeev SharmaAdvertisement
Introduction
Semantic Kernel is Microsoft's SDK for building AI orchestration layers. This guide covers setup and practical usage for production applications.
Installation
pip install semantic-kernel
Basic Setup
import semantic_kernel as sk
kernel = sk.Kernel()
# Add OpenAI service
kernel.add_text_completion_service(
"openai",
sk.OpenAI.TextCompletion(
model_id="text-davinci-003",
api_key="your-key"
)
)
Skills and Functions
skill = kernel.import_semantic_skill_from_directory("skills")
# Execute skill
result = await kernel.run_async(
skill["summarize"],
input=large_text
)
Plugin Architecture
class MyPlugin:
@sk.kernel_function
def analyze_sentiment(self, text: str) -> str:
"""Analyze text sentiment."""
pass
kernel.import_plugin(MyPlugin(), "my_plugin")
Memory
from semantic_kernel.memory import SemanticTextMemory
memory = SemanticTextMemory(
storage=VolatileMemoryStore(),
embeddings_generator=embedding_service
)
await memory.save_information_async(
collection="documents",
id="doc1",
text="Important information"
)
Conclusion
Semantic Kernel provides enterprise-grade orchestration for AI applications. Ideal for complex workflows requiring state management and plugin extensibility.
FAQ
Q: How does Semantic Kernel compare to LangChain? A: Semantic Kernel is more structured; LangChain is more flexible.
Q: Is Semantic Kernel production-ready? A: Yes, backed by Microsoft's engineering.
Q: Can I use Semantic Kernel with local models? A: Yes, with appropriate adapters.
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