API Reference (gRPC) for NVIDIA NeMo Retriever Embedding NIM#

This documentation contains the gRPC reference for NVIDIA NeMo Retriever Embedding NIM.

gRPC Models#

The gRPC model names differ from the NIM model IDs shown in the Support Matrix. The following table contains the mapping of the names.

Model ID	gRPC Model Name
nvidia/llama-nemotron-embed-1b-v2	nvidia_llama_nemotron_embed_1b_v2
nvidia/nv-embedqa-e5-v5	nvidia_nv_embedqa_e5_v5

Input	Shape	Data Type	Description	Required
`text`	[batch_size, 1]	BYTES	A list of UTF-8 encoded strings to embed. For details on how to encode multimodal data as string, refer to How to Specify Modality.	Yes
`modality`	[batch_size, 1]	BYTES	A list of UTF-8 modality strings for each of the text input elements. If you don’t specify modality, the modality is inferred. For supported modalities, refer to How to Specify Modality.	No

Parameter	Data Type	Description	Valid Values	Default	Required
`input_type`	String	The context of the embedding.	`"query"`, `"passage"`	`"query"`	Yes
`truncate`	String	How to handle text that exceeds the maximum token length.	`"END"`, `"START"`, `"NONE"`	`"NONE"`	Yes
`dimensions`	Integer	The desired dimensionality of the output embeddings. Must be supported by the model.	—	The model’s default dimension.	No
`embedding_type`	String	The output type of the embeddings. See How to Specify Embedding Type for how the output type is handled.	`"float", "binary", "ubinary", "int8", "uint8"`	`"float"`	No
`nvcf_asset_dir`	String	Directory path where NVCF (NVIDIA Cloud Functions) asset files are stored. See API Reference (OpenAI) for more details.	-	No

Output	Shape	Data Type	Description
`token_count`	[batch_size]	INT32	The number of tokens in each input text.
`embeddings`	[batch_size, embedding_dimension]	Configurable using the `embedding_type` request parameter. The default is FLOAT32.	The resulting embedding vectors.