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feat: add nemo retriever text embedding models to nvidia inference provider (#1218)

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# What does this PR do?

add the NeMo Retriever Embedding models from
https://docs.nvidia.com/nim/nemo-retriever/text-embedding/latest/support-matrix.html
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Matthew Farrellee 2025-02-26 23:18:34 -06:00 • committed by GitHub
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41
llama_stack/providers/remote/inference/nvidia/models.py
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@ -48,12 +48,49 @@ _MODEL_ENTRIES = [
"meta/llama-3.2-90b-vision-instruct",
CoreModelId.llama3_2_90b_vision_instruct.value,
),
# NeMo Retriever Text Embedding models -
#
# https://docs.nvidia.com/nim/nemo-retriever/text-embedding/latest/support-matrix.html
#
# +-----------------------------------+--------+-----------+-----------+------------+
# | Model ID | Max | Publisher | Embedding | Dynamic |
# | | Tokens | | Dimension | Embeddings |
# +-----------------------------------+--------+-----------+-----------+------------+
# | nvidia/llama-3.2-nv-embedqa-1b-v2 | 8192 | NVIDIA | 2048 | Yes |
# | nvidia/nv-embedqa-e5-v5 | 512 | NVIDIA | 1024 | No |
# | nvidia/nv-embedqa-mistral-7b-v2 | 512 | NVIDIA | 4096 | No |
# | snowflake/arctic-embed-l | 512 | Snowflake | 1024 | No |
# +-----------------------------------+--------+-----------+-----------+------------+
ProviderModelEntry(
provider_model_id="baai/bge-m3",
provider_model_id="nvidia/llama-3.2-nv-embedqa-1b-v2",
model_type=ModelType.embedding,
metadata={
"embedding_dimension": 2048,
"context_length": 8192,
},
),
ProviderModelEntry(
provider_model_id="nvidia/nv-embedqa-e5-v5",
model_type=ModelType.embedding,
metadata={
"embedding_dimension": 1024,
"context_length": 8192,
"context_length": 512,
},
),
ProviderModelEntry(
provider_model_id="nvidia/nv-embedqa-mistral-7b-v2",
model_type=ModelType.embedding,
metadata={
"embedding_dimension": 4096,
"context_length": 512,
},
),
ProviderModelEntry(
provider_model_id="snowflake/arctic-embed-l",
model_type=ModelType.embedding,
metadata={
"embedding_dimension": 1024,
"context_length": 512,
},
),
# TODO(mf): how do we handle Nemotron models?