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

NameTypeDescription
documentsList of Document objectsThe documents to embed.

Outputs

NameTypeDescription
documentsList of Document objectsDocuments with their embeddings added to the metadata.
metaDictionaryMetadata regarding the 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.

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Embedding Models in Query Pipelines and Indexes

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

This means the embedders for your indexes and 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

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: {}

Init Parameters

Parameter

Type

Possible values

Description

model

DeepsetNVIDIAEmbeddingModels

Default: 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.
Required.

prefix

String

Default: ""

A string to add at the beginning of each document text, useful for instructions required by some embedding models.
Required

suffix

String

Default: ""

A string to add at the end of each document text.
Required

batch_size

Integer

Default: 32

The number of documents to embed at once.
Required

meta_fields_to_embed

List of strings

Default: None

A list of metadata fields to embed along with the document text.
Required.

embedding_separator

String

Default: "\n"

The separator used to concatenate the metadata fields to the document text.
Required.

truncate

EmbeddingTruncateMode

START, END, NONE
Default: None

Specifies how to truncate inputs longer than the maximum token length. Possible options are: START, END, NONE.
If set to START, the input is truncated from the start.
If set to END, the input is truncated from the end.
If set to NONE, returns an error if the input is too long.
Required.

normalize_embeddings

Boolean

True
False
Default: False

Whether to normalize the embeddings by dividing the embedding by its L2 norm.
Required.

timeout

Float

Default: None

Timeout for request calls in seconds.
Required.

backend_kwargs

Dictionary

Default: None

Keyword arguments to further customize the model behavior.
Required.

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

Description

documents

List of Document objects

The documents to embed.
Required.