VoyageTextEmbedder

Embeds text strings, such as user queries, using Voyage AI models and returns the embedding for use in retrieval pipelines.

Key Features

• Supports Voyage AI embedding models optimized for retrieval and semantic similarity.
• input_type parameter for query-specific embedding optimization.
• Configurable output dimensions for voyage-3-large and voyage-code-3 models.
• Adds optional prefix and suffix strings to the text before embedding.
• Configurable timeout and retry settings.

Configuration

Authentication

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

1. Drag the VoyageTextEmbedder 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, and other options.

Connections

VoyageTextEmbedder accepts a text string as input and outputs a floating-point embedding vector.

Use this component in query pipelines for semantic search. Connect its embedding output to an embedding retriever like OpenSearchEmbeddingRetriever.

Usage Example

This is an example RAG pipeline with VoyageTextEmbedder for query embedding:

components:
  bm25_retriever:
    type: haystack_integrations.components.retrievers.opensearch.bm25_retriever.OpenSearchBM25Retriever
    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:
      top_k: 20
      fuzziness: 0

  query_embedder:
    type: haystack_integrations.components.embedders.voyage_embedders.voyage_text_embedder.VoyageTextEmbedder
    init_parameters:
      api_key:
        type: env_var
        env_vars:
        - VOYAGE_API_KEY
        strict: false
      model: voyage-3
      input_type: query
      truncate: true
      prefix:
      suffix:
      output_dimension:
      output_dtype: float
      timeout:
      max_retries:

  embedding_retriever:
    type: haystack_integrations.components.retrievers.opensearch.embedding_retriever.OpenSearchEmbeddingRetriever
    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:
      top_k: 20

  document_joiner:
    type: haystack.components.joiners.document_joiner.DocumentJoiner
    init_parameters:
      join_mode: concatenate

  ranker:
    type: haystack_integrations.components.rankers.voyage.ranker.VoyageRanker
    init_parameters:
      api_key:
        type: env_var
        env_vars:
        - VOYAGE_API_KEY
        strict: false
      model: rerank-2
      top_k: 8

  answer_builder:
    type: deepset_cloud_custom_nodes.augmenters.deepset_answer_builder.DeepsetAnswerBuilder
    init_parameters:
      reference_pattern: acm

  PromptBuilder:
    type: haystack.components.builders.prompt_builder.PromptBuilder
    init_parameters:
      template: "You are a helpful assistant answering the user's questions based on the provided documents.\nDo not use your own knowledge.\n\nProvided documents:\n{% for document in documents %}\nDocument [{{ loop.index }}]:\n{{ document.content }}\n{% endfor %}\n\nQuestion: {{ query }}\nAnswer:"

  generator:
    type: haystack.components.generators.chat.openai.OpenAIChatGenerator
    init_parameters:
      api_key:
        type: env_var
        env_vars:
        - OPENAI_API_KEY
        strict: false
      model: gpt-4o
      generation_kwargs:
        max_tokens: 1000
        temperature: 0.7

connections:
- sender: bm25_retriever.documents
  receiver: document_joiner.documents
- sender: query_embedder.embedding
  receiver: embedding_retriever.query_embedding
- sender: embedding_retriever.documents
  receiver: document_joiner.documents
- sender: document_joiner.documents
  receiver: ranker.documents
- sender: ranker.documents
  receiver: answer_builder.documents
- sender: ranker.documents
  receiver: PromptBuilder.documents
- sender: PromptBuilder.prompt
  receiver: generator.messages
- sender: generator.replies
  receiver: answer_builder.replies

inputs:
  query:
  - "bm25_retriever.query"
  - "query_embedder.text"
  - "ranker.query"
  - "answer_builder.query"
  - "PromptBuilder.query"
  filters:
  - "bm25_retriever.filters"
  - "embedding_retriever.filters"

outputs:
  documents: "ranker.documents"
  answers: "answer_builder.answers"

max_runs_per_component: 100

metadata: {}

Parameters

Inputs

Parameter	Type	Default	Description
text	str		The text to embed.

Outputs

Parameter	Type	Default	Description
embedding	List[float]		The embedding of the input text.
meta	Dict[str, Any]		Metadata related to 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 "query" for search queries or "document" for documents. 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 the text.
suffix	str	""	A string to add to the end of the 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".
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
text	str		The text to embed.