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Integration with NVIDIA NIM for embedding generation. - NVIDIA NIM exposes an OpenAI-compatible endpoint, so this package builds on RawOpenAIEmbeddingClient from agent-framework-openai rather than reimplementing the wire format. This mirrors agent-framework-foundry-local, which builds on the OpenAI Chat Completions client for the same reason. - The default endpoint is https://integrate.api.nvidia.com/v1. baseurl overrides it, which is the hook for a self-hosted NIM container. - Model availability is per-account. An unentitled model returns 404 ... Not found for account, and a retired model…

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AGENTS.md

NVIDIA Package (agent-framework-nvidia)

Integration with NVIDIA NIM for embedding generation.

Implementation Notes

  • NVIDIA NIM exposes an OpenAI-compatible endpoint, so this package builds on RawOpenAIEmbeddingClient from agent-framework-openai rather than reimplementing the wire format. This mirrors agent-framework-foundry-local, which builds on the OpenAI Chat Completions client for the same reason.
  • The default endpoint is https://integrate.api.nvidia.com/v1. base_url overrides it, which is the hook for a self-hosted NIM container.
  • Model availability is per-account. An unentitled model returns 404 ... Not found for account, and a retired model returns 410 Gone, so neither is a request-shape error.
  • input_type and truncate are outside the OpenAI embedding schema. The client sends them as extra_body by overriding _prepare_extra_request_options, and rejects invalid values before the request. Callers set input_type per call through vector-store embeddings_options, the same route the Gemini client uses for task_type. An agent-framework-openai without that hook would drop both fields silently, so importing the package raises ImportError instead.
  • The hosted endpoint rejects requests with more than 256 inputs (MAX_BATCH_SIZE) or a body over 2 MiB (413, budgeted by MAX_BATCH_BYTES), and a vector-store upsert embeds every record in one call, so get_embeddings splits larger inputs into consecutive requests, as the Gemini client does for Enterprise.

Main Classes

  • NvidiaEmbeddingClient - Embedding client for NVIDIA NIM models, with telemetry
  • RawNvidiaEmbeddingClient - The same client without the telemetry layer
  • NvidiaEmbeddingOptions - Options TypedDict for NVIDIA-specific embedding parameters
  • NvidiaEmbeddingSettings - TypedDict settings for NVIDIA configuration

Usage

from agent_framework_nvidia import NvidiaEmbeddingClient

# Requires NVIDIA_API_KEY environment variable (or pass api_key= directly)
client = NvidiaEmbeddingClient(model="nvidia/nemotron-3-embed-1b")
result = await client.get_embeddings(["Hello, world!"])
print(result[0].vector)

Import Path

The package is alpha, so there is no agent_framework.nvidia namespace in core yet.

from agent_framework_nvidia import NvidiaEmbeddingClient

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