VoyageDocumentEmbedder

Computes document embeddings using Voyage AI models and stores them in each document's embedding field for use in indexing pipelines.

Key Features

• Supports Voyage AI embedding models optimized for document retrieval.
• input_type parameter for document-specific embedding optimization.
• Configurable output dimensions for voyage-3-large and voyage-code-3 models.
• Processes documents in batches with an optional progress bar.
• Embeds metadata fields alongside document content.

Configuration

Authentication

To use this component, connect Haystack Platform with Voyage AI first. For detailed instructions, see Use Voyage AI Models.

1. Drag the VoyageDocumentEmbedder component onto the canvas from the Component Library.
2. Click the component to open the configuration panel.
3. On the General tab:
   1. Enter the name of the Voyage AI embedding model to use. See Voyage Embeddings documentation for available models.
4. Go to the Advanced tab to configure the API key, input_type, truncation, batch size, and metadata fields to embed.

Connections

VoyageDocumentEmbedder accepts a list of documents as input. It outputs the same documents with embeddings stored in the embedding field.

Use this component in indexing pipelines. Connect a preprocessor like DocumentSplitter to its documents input, and connect its documents output to DocumentWriter.

Usage Example

This is an example index with VoyageDocumentEmbedder for document embedding:

components:
  converter:
    type: haystack.components.converters.multi_file_converter.MultiFileConverter
    init_parameters:
      encoding: utf-8

  cleaner:
    type: haystack.components.preprocessors.document_cleaner.DocumentCleaner
    init_parameters:
      remove_empty_lines: true
      remove_extra_whitespaces: true
      remove_repeated_substrings: false
      keep_id: false

  splitter:
    type: haystack.components.preprocessors.document_splitter.DocumentSplitter
    init_parameters:
      split_by: sentence
      split_length: 5
      split_overlap: 1
      split_threshold: 0

  document_embedder:
    type: haystack_integrations.components.embedders.voyage_embedders.voyage_document_embedder.VoyageDocumentEmbedder
    init_parameters:
      api_key:
        type: env_var
        env_vars:
        - VOYAGE_API_KEY
        strict: false
      model: voyage-3
      input_type: document
      truncate: true
      prefix:
      suffix:
      output_dimension:
      output_dtype: float
      batch_size: 32
      metadata_fields_to_embed:
      embedding_separator: "\n"
      progress_bar: true
      timeout:
      max_retries:

  writer:
    type: haystack.components.writers.document_writer.DocumentWriter
    init_parameters:
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          hosts:
          index: 'default'
          max_chunk_bytes: 104857600
          embedding_dim: 1024
          return_embedding: false
          method:
          mappings:
          settings:
          create_index: true
          http_auth:
          use_ssl:
          verify_certs:
          timeout:
      policy: OVERWRITE

connections:
- sender: converter.documents
  receiver: cleaner.documents
- sender: cleaner.documents
  receiver: splitter.documents
- sender: splitter.documents
  receiver: document_embedder.documents
- sender: document_embedder.documents
  receiver: writer.documents

max_runs_per_component: 100

metadata: {}

Parameters

Inputs

Parameter	Type	Default	Description
documents	List[Document]		A list of documents to embed.

Outputs

Parameter	Type	Default	Description
documents	List[Document]		Documents with embeddings stored in the embedding field.
meta	Dict[str, Any]		Metadata about the embedding operation.

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
api_key	Secret	Secret.from_env_var('VOYAGE_API_KEY')	The Voyage AI API key. It can be explicitly provided or automatically read from the environment variable VOYAGE_API_KEY.
model	str	voyage-3	The name of the Voyage model to use. See the Voyage Embeddings documentation for available models.
input_type	Optional[str]	None	Type of the input text. Set to "document" for indexing documents or "query" for search queries. When set, prepends an appropriate prompt to the text.
truncate	bool	True	Whether to truncate the input text to fit within the context length. If False, an error is raised when the text exceeds the context length.
prefix	str	""	A string to add to the beginning of each text.
suffix	str	""	A string to add to the end of each text.
output_dimension	Optional[int]	None	The dimension of the output embedding. Only supported by voyage-3-large and voyage-code-3 models.
output_dtype	str	float	The data type for the embeddings. Options: "float", "int8", "uint8", "binary", "ubinary".
batch_size	int	32	Number of documents to encode at once.
metadata_fields_to_embed	Optional[List[str]]	None	List of metadata fields to embed along with the document content.
embedding_separator	str	"\n"	Separator used to concatenate metadata fields to the document content.
progress_bar	bool	True	Whether to show a progress bar during processing.
timeout	Optional[int]	None	Timeout for Voyage AI client calls. If not set, it is inferred from the VOYAGE_TIMEOUT environment variable or set to 30.
max_retries	Optional[int]	None	Maximum retries if Voyage AI returns an internal error. If not set, it is inferred from the VOYAGE_MAX_RETRIES environment variable or set to five.

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]		A list of documents to embed.