DeepsetNvidiaDocumentEmbedder

Embed documents using embedding models by NVIDIA Triton.

Basic Information

• Type: deepset_cloud_custom_nodes.embedders.nvidia.document_embedder.DeepsetNvidiaDocumentEmbedder
• Components it most often connects with:
  • PreProcessors: DeepsetNvidiaDocumentEmbedder can receive documents to embed from a PreProcessor, like DocumentSplitter.
  • DocumentWriter: DeepsetNvidiaDocumentEmbedder can send embedded documents to DocumentWriter that writes them into the document store.

Inputs

Parameter	Type	Default	Description
documents	List[Document]		The documents to embed.

Outputs

Parameter	Type	Default	Description
documents	List[Document]		Documents with their embeddings added to the metadata.
meta	Dict[str, Any]		Metadata on usage statistics.

Overview

NvidiaDocumentEmbedder uses NVIDTIA Triton models to embed a list of documents. It then adds the computed embeddings to the document's embedding metadata field.

This component runs on optimized hardware in deepset AI Platform, which means it doesn't work if you export it to a local Python file. If you're planning to export, use SentenceTransformersDocumentEmbedder instead.

Embedding Models in Query Pipelines and Indexes

The embedding model you use to embed documents in your indexing pipeline must be the same as the embedding model you use to embed the query in your query pipeline.

This means the embedders for your indexing and query pipelines must match. For example, if you use CohereDocumentEmbedder to embed your documents, you should use CohereTextEmbedder with the same model to embed your queries.

Usage Example

Initializing the Component

components:
  DeepsetNvidiaDocumentEmbedder:
    type: embedders.nvidia.document_embedder.DeepsetNvidiaDocumentEmbedder
    init_parameters:

Using the Component in an Index

This is an example of a DeepsetNvidiaDocumentEmbedder used in an index. It receives a list of documents from DocumentSplitter and then sends the embedded documents to DocumentWriter:

Here's the YAML configuration:

components:
  DocumentSplitter:
    type: haystack.components.preprocessors.document_splitter.DocumentSplitter
    init_parameters:
      split_by: word
      split_length: 200
      split_overlap: 0
      split_threshold: 0
      splitting_function: null
  DeepsetNvidiaDocumentEmbedder:
    type: deepset_cloud_custom_nodes.embedders.nvidia.document_embedder.DeepsetNvidiaDocumentEmbedder
    init_parameters:
      model: intfloat/multilingual-e5-base
      prefix: ''
      suffix: ''
      batch_size: 32
      meta_fields_to_embed: null
      embedding_separator: \n
      truncate: null
      normalize_embeddings: true
      timeout: null
      backend_kwargs: null
  DocumentWriter:
    type: haystack.components.writers.document_writer.DocumentWriter
    init_parameters:
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          embedding_dim: 1024
          similarity: cosine
      policy: NONE
connections:
  - sender: DocumentSplitter.documents
    receiver: DeepsetNvidiaDocumentEmbedder.documents
  - sender: DeepsetNvidiaDocumentEmbedder.documents
    receiver: DocumentWriter.documents
max_runs_per_component: 100
metadata: {}

Parameters

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
model	DeepsetNVIDIAEmbeddingModels	DeepsetNVIDIAEmbeddingModels.INTFLOAT_MULTILINGUAL_E5_BASE	The model to use for calculating embeddings. Can be a specific model path like intfloat/multilingual-e5-base.
Choose the model from the list.
prefix	str		A string to add at the beginning of each document text. Can be used to prepend the text with an instruction, as required by some embedding models, such as E5 and bge.
suffix	str		A string to add at the end of each document text.
batch_size	int	32	The number of documents to embed at once.
meta_fields_to_embed	List[str]	None	None
embedding_separator	str	\n	Separator used to concatenate the meta fields to the document text.
truncate	EmbeddingTruncateMode	None	None
normalize_embeddings	bool	True	Whether to normalize the embeddings. Normalization is done by dividing the embedding by its L2 norm.
timeout	float	None	None
backend_kwargs	Dict[str, Any]	None	None

Run Method Parameters

These are the parameters you can configure for the component's run() method. This means you can pass these parameters at query time through the API, in Playground, or when running a job. For details, see Modify Pipeline Parameters at Query Time.

Parameter	Type	Default	Description
documents	List[Document]		Documents to embed.