OpenAIDocumentEmbedder

Compute document embeddings using OpenAI models. Use this component in indexes to embed documents before storing them in a vector database.

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

• Computes dense vector embeddings for documents using OpenAI embedding models.
• Supports all OpenAI embedding models, including the text-embedding-3 series.
• Embeds documents in configurable batch sizes for efficient processing.
• Optionally embeds metadata fields along with document content.
• Configurable error handling: raises or logs errors on failure.

Configuration

1. Drag the OpenAIDocumentEmbedder component onto the canvas from the Component Library.
2. Click on the component to open the configuration panel.
3. On the General tab:
   • Make sure Haystack Platform is connected to your OpenAI account. For help, see Use OpenAI Models.
   • Select the embedding model to use.
4. Go to the Advanced tab to configure additional settings such as timeout, max_retries, http_client_kwargs, prefix, suffix, and metadata embedding options.

Connections

OpenAIDocumentEmbedder receives documents to embed from PreProcessors like DocumentSplitter. It sends the embedded documents to DocumentWriter, which writes them into a document store.

Source Code

To check this component's source code, open openai_document_embedder.py in the Haystack repository.

Usage Examples

Basic Configuration

  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

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

Outputs

Parameter	Type	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.

Related Information

• Use OpenAI Models
• Add Integrations