This connector enables integration with NVIDIA NIM API for text embeddings and chat completion. It allows you to use NVIDIA's models within the Semantic Kernel framework.

Quick start

Initialize the kernel

import semantic_kernel as sk
kernel = sk.Kernel()

Add NVIDIA text embedding service

You can provide your API key directly or through environment variables

from semantic_kernel.connectors.ai.nvidia import NvidiaTextEmbedding

embedding_service = NvidiaTextEmbedding(
ai_model_id="nvidia/nv-embedqa-e5-v5", # Default model if not specified
api_key="your-nvidia-api-key", # Can also use NVIDIA_API_KEY env variable
service_id="nvidia-embeddings" # Optional service identifier
)

Add the embedding service to the kernel

kernel.add_service(embedding_service)

Generate embeddings for text

texts = ["Hello, world!", "Semantic Kernel is awesome"]
embeddings = await kernel.get_service("nvidia-embeddings").generate_embeddings(texts)

Add NVIDIA chat completion service

from semantic_kernel.connectors.ai.nvidia import NvidiaChatCompletion

chat_service = NvidiaChatCompletion(
    ai_model_id="meta/llama-3.1-8b-instruct", # Default model if not specified
    api_key="your-nvidia-api-key", # Can also use NVIDIA_API_KEY env variable
    service_id="nvidia-chat" # Optional service identifier
)
kernel.add_service(chat_service)

Basic chat completion

response = await kernel.invoke_prompt("Hello, how are you?")

Using with Chat Completion Agent

from semantic_kernel.agents import ChatCompletionAgent
from semantic_kernel.connectors.ai.nvidia import NvidiaChatCompletion

agent = ChatCompletionAgent(
    service=NvidiaChatCompletion(),
    name="SK-Assistant",
    instructions="You are a helpful assistant.",
)
response = await agent.get_response(messages="Write a haiku about Semantic Kernel.")
print(response.content)