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fix: fix nvidia provider #3716

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Merged
merged 2 commits into from
Oct 8, 2025
Merged

fix: fix nvidia provider #3716

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e2e8e7f
fix nvidia provider
wukaixingxp Oct 7, 2025
4f43f23
style: apply pre-commit fixes
github-actions[bot] Oct 7, 2025
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28 changes: 21 additions & 7 deletions llama_stack/providers/remote/inference/nvidia/nvidia.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,7 @@
OpenAIEmbeddingUsage,
)
from llama_stack.log import get_logger
from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin

from . import NVIDIAConfig
Expand All @@ -21,9 +22,7 @@
logger = get_logger(name=__name__, category="inference::nvidia")


class NVIDIAInferenceAdapter(OpenAIMixin):
config: NVIDIAConfig

class NVIDIAInferenceAdapter(OpenAIMixin, ModelRegistryHelper):
"""
NVIDIA Inference Adapter for Llama Stack.

Expand All @@ -37,12 +36,27 @@ class NVIDIAInferenceAdapter(OpenAIMixin):
- ModelRegistryHelper.check_model_availability() just returns False and shows a warning
"""

def __init__(self, config: NVIDIAConfig) -> None:
"""Initialize the NVIDIA inference adapter with configuration."""
# Initialize ModelRegistryHelper with empty model entries since NVIDIA uses dynamic model discovery
ModelRegistryHelper.__init__(self, model_entries=[], allowed_models=config.allowed_models)
self.config = config

# source: https://docs.nvidia.com/nim/nemo-retriever/text-embedding/latest/support-matrix.html
embedding_model_metadata: dict[str, dict[str, int]] = {
"nvidia/llama-3.2-nv-embedqa-1b-v2": {"embedding_dimension": 2048, "context_length": 8192},
"nvidia/llama-3.2-nv-embedqa-1b-v2": {
"embedding_dimension": 2048,
"context_length": 8192,
},
"nvidia/nv-embedqa-e5-v5": {"embedding_dimension": 512, "context_length": 1024},
"nvidia/nv-embedqa-mistral-7b-v2": {"embedding_dimension": 512, "context_length": 4096},
"snowflake/arctic-embed-l": {"embedding_dimension": 512, "context_length": 1024},
"nvidia/nv-embedqa-mistral-7b-v2": {
"embedding_dimension": 512,
"context_length": 4096,
},
"snowflake/arctic-embed-l": {
"embedding_dimension": 512,
"context_length": 1024,
},
}

async def initialize(self) -> None:
Expand Down Expand Up @@ -95,7 +109,7 @@ async def openai_embeddings(
response = await self.client.embeddings.create(
model=await self._get_provider_model_id(model),
input=input,
encoding_format=encoding_format if encoding_format is not None else NOT_GIVEN,
encoding_format=(encoding_format if encoding_format is not None else NOT_GIVEN),
dimensions=dimensions if dimensions is not None else NOT_GIVEN,
user=user if user is not None else NOT_GIVEN,
extra_body=extra_body,
Expand Down