VoyageTextEmbedder

Embed text, such as user queries, using Voyage AI embedding models. Use this component in query pipelines when you want to perform semantic search.

Key Features

• Embeds text strings (queries) using Voyage AI models optimized for retrieval.
• Supports configurable input types (query or document) for optimized embeddings.
• Supports configurable output dimension and data type.
• Configurable prefix and suffix for text preprocessing.

Configuration

1. Drag the VoyageTextEmbedder component onto the canvas from the Component Library.
2. Click on the component to open the configuration panel.
3. On the General tab:
   • Connect Haystack Platform to your Voyage AI account on the Integrations page. For detailed instructions, see Use Voyage AI Models.
   • Select the embedding model to use.
   • Set input_type to "query" for search queries.
4. Go to the Advanced tab to configure timeout, max_retries, output_dimension, output_dtype, prefix, and suffix.

Connections

VoyageTextEmbedder receives text to embed from the Input component. It sends the embedding to embedding-based retrievers like OpenSearchEmbeddingRetriever.

Usage Examples

Basic Configuration

  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
      output_dtype: float

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	Description
text	str	The text to embed.

Outputs

Parameter	Type	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.

Related Information

• Use Voyage AI Models
• Add Integrations