OpenAIDocumentEmbedder

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

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.

Key Features

• Supports all OpenAI embedding models, including text-embedding-3-small and text-embedding-ada-002.
• Configurable embedding dimensions for text-embedding-3 and later models.
• Processes documents in batches with an optional progress bar.
• Embeds metadata fields alongside document content.
• Adds optional prefix and suffix strings to each document before embedding.
• Configurable timeout, retries, and custom HTTP client settings.

Configuration

Authentication

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

1. Drag the OpenAIDocumentEmbedder 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 OpenAI embedding model to use.
4. Go to the Advanced tab to configure the API key, API base URL, organization, timeout, maximum retries, HTTP client settings, and other options.

Connections

OpenAIDocumentEmbedder 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 index that uses OpenAIDocumentEmbedder to embed documents before storing them:

components:
  document_embedder:
    type: haystack.components.embedders.openai_document_embedder.OpenAIDocumentEmbedder
    init_parameters:
      api_key:
        type: env_var
        env_vars:
          - OPENAI_API_KEY
        strict: false
      model: text-embedding-ada-002

  document_writer:
    type: haystack.components.writers.document_writer.DocumentWriter
    init_parameters:
      policy: NONE
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          hosts:
            - ${OPENSEARCH_HOST}
          http_auth:
            - ${OPENSEARCH_USER}
            - ${OPENSEARCH_PASSWORD}
          use_ssl: true
          verify_certs: false
          index: my_index
          embedding_dim: 1536

connections:
  - sender: document_embedder.documents
    receiver: document_writer.documents

inputs:
  documents:
    - document_embedder.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]		A list of documents with embeddings.
meta	Dict[str, Any]		Information about the usage of the model, including model name and token usage.

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
api_key	Secret	Secret.from_env_var('OPENAI_API_KEY')	The OpenAI API key. You can set it with an environment variable OPENAI_API_KEY, or pass with this parameter during initialization.
model	str	text-embedding-ada-002	The name of the model to use for calculating embeddings. The default model is text-embedding-ada-002.
dimensions	Optional[int]	None	The number of dimensions of the resulting embeddings. Only text-embedding-3 and later models support this parameter.
api_base_url	Optional[str]	None	Overrides the default base URL for all HTTP requests.
organization	Optional[str]	None	Your OpenAI organization ID. See OpenAI's Setting Up Your Organization for more information.
prefix	str	""	A string to add at the beginning of each text.
suffix	str	""	A string to add at the end of each text.
batch_size	int	32	Number of documents to embed at once.
progress_bar	bool	True	If True, shows a progress bar when running.
meta_fields_to_embed	Optional[List[str]]	None	List of metadata fields to embed along with the document text.
embedding_separator	str	\n	Separator used to concatenate the metadata fields to the document text.
timeout	Optional[float]	None	Timeout for OpenAI client calls. If not set, it defaults to either the OPENAI_TIMEOUT environment variable, or 30 seconds.
max_retries	Optional[int]	None	Maximum number of retries to contact OpenAI after an internal error. If not set, it defaults to either the OPENAI_MAX_RETRIES environment variable, or 5 retries.
http_client_kwargs	Optional[Dict[str, Any]]	None	A dictionary of keyword arguments to configure a custom httpx.Clientor httpx.AsyncClient. For more information, see the HTTPX documentation.
raise_on_failure	bool	False	Whether to raise an exception if the embedding request fails. If False, the component will log the error and continue processing the remaining documents. If True, it will raise an exception on failure.

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.