OpenAITextEmbedder

Embed strings, such as user queries, using OpenAI models. Use this component in query pipelines when you want to perform semantic search.

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

• Embeds text strings (queries) using OpenAI embedding models.
• Supports all OpenAI embedding models, including the text-embedding-3 series.
• Outputs the embedding for use with embedding-based retrievers.
• Supports configurable prefix and suffix for text preprocessing.

Configuration

1. Drag the OpenAITextEmbedder 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, and suffix.

Connections

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

Source Code

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

Usage Examples

Basic Configuration

  OpenAITextEmbedder:
    type: haystack.components.embedders.openai_text_embedder.OpenAITextEmbedder
    init_parameters:
      api_key:
        type: env_var
        env_vars:
        - OPENAI_API_KEY
        strict: false
      model: text-embedding-ada-002
      prefix: ''
      suffix: ''

This is a query pipeline that uses OpenAITextEmbedder to embed a query and retrieve documents:

components:
  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:
          - ${OPENSEARCH_HOST}
          index: ''
          max_chunk_bytes: 104857600
          embedding_dim: 768
          return_embedding: false
          method:
          mappings:
          settings:
          create_index: true
          http_auth:
          - ${OPENSEARCH_USER}
          - ${OPENSEARCH_PASSWORD}
          use_ssl: true
          verify_certs: false
          timeout:
      top_k: 20

  OpenAITextEmbedder:
    type: haystack.components.embedders.openai_text_embedder.OpenAITextEmbedder
    init_parameters:
      api_key:
        type: env_var
        env_vars:
        - OPENAI_API_KEY
        strict: false
      model: text-embedding-ada-002
      dimensions:
      api_base_url:
      organization:
      prefix: ''
      suffix: ''
      timeout:
      max_retries:
      http_client_kwargs:

connections:
- sender: OpenAITextEmbedder.embedding
  receiver: embedding_retriever.query_embedding

inputs:
  query:
  - OpenAITextEmbedder.text
  filters:
  - embedding_retriever.filters

outputs:
  documents: embedding_retriever.documents

max_runs_per_component: 100

metadata: {}

Parameters

Inputs

Parameter	Type	Description
text	str	Text to embed.

Outputs

Parameter	Type	Description
embedding	List[float]	The embedding of the input text.
meta	Dict[str, Any]	Information about the usage of the model.

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 default base URL for all HTTP requests.
organization	Optional[str]	None	Your organization ID. See OpenAI's production best practices for more information.
prefix	str		A string to add at the beginning of each text to embed.
suffix	str		A string to add at the end of each text to embed.
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 set to 5.
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.

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		Text to embed.

Related Information

• Use OpenAI Models
• Add Integrations