EdenAITextEmbedder

Embed a query string using Eden AI's multi-provider embedding API. Use this component in query pipelines to transform user queries into vectors for embedding-based retrieval.

Key Features

• Routes embedding requests through Eden AI to multiple providers—including OpenAI, Mistral, Cohere, and Google—using a single API key.
• Uses Eden AI's provider/model naming convention, for example openai/text-embedding-3-small or mistral/mistral-embed.
• Outputs a float vector embedding suitable for use with embedding retrievers.
• Supports EU data residency through Eden AI's infrastructure.
• The model must match the one used in the corresponding EdenAIDocumentEmbedder in the indexing pipeline.

Configuration

1. Drag the EdenAITextEmbedder component onto the canvas from the Component Library.
2. Click on the component to open the configuration panel.
3. On the General tab:
   1. Create a secret with your Eden AI API key. Use EDENAI_API_KEY as the secret key. For instructions, see Create Secrets. Get your API key from Eden AI.
   2. Set the model field to the same embedding model you used for indexing, in provider/model format, for example openai/text-embedding-3-small.
4. Go to the Advanced tab to configure timeout and max_retries.

Connections

EdenAITextEmbedder receives the user query as a text string through its text input, typically from the Input component. It outputs a float vector through its embedding output, which you connect to an embedding retriever such as OpenSearchEmbeddingRetriever.

Source Code

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

Usage Examples

Basic Configuration

  EdenAITextEmbedder:
    type: haystack_integrations.components.embedders.edenai.text_embedder.EdenAITextEmbedder
    init_parameters:
      api_key:
        type: env_var
        env_vars:
        - EDENAI_API_KEY
        strict: false
      model: openai/text-embedding-3-small
      api_base_url: https://api.edenai.run/v3

Using the Component in a Pipeline

This example shows a query pipeline with EdenAITextEmbedder that embeds the user query and sends it to OpenSearchEmbeddingRetriever to find matching documents.

# haystack-pipeline
components:
  EdenAITextEmbedder:
    type: haystack_integrations.components.embedders.edenai.text_embedder.EdenAITextEmbedder
    init_parameters:
      api_key:
        type: env_var
        env_vars:
        - EDENAI_API_KEY
        strict: false
      model: openai/text-embedding-3-small
      api_base_url: https://api.edenai.run/v3
      prefix: ""
      suffix: ""
  OpenSearchEmbeddingRetriever:
    type: haystack_integrations.components.retrievers.opensearch.embedding_retriever.OpenSearchEmbeddingRetriever
    init_parameters:
      filters:
      top_k: 10
      filter_policy: replace
      custom_query:
      raise_on_failure: true
      efficient_filtering: true
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          hosts:
          index: Standard-Index-English
          max_chunk_bytes: 104857600
          embedding_dim: 1536
          return_embedding: false
          method:
          mappings:
          settings:
          create_index: true
          http_auth:
          use_ssl:
          verify_certs:
          timeout:

connections:
- sender: EdenAITextEmbedder.embedding
  receiver: OpenSearchEmbeddingRetriever.query_embedding

max_runs_per_component: 100

metadata: {}

inputs:
  query:
  - EdenAITextEmbedder.text

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 about the request, including model name and usage statistics.

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
api_key	Secret	Secret.from_env_var('EDENAI_API_KEY')	The Eden AI API key.
model	str	openai/text-embedding-3-small	The Eden AI embedding model in provider/model format. For a full list, see the Eden AI models catalog.
api_base_url	Optional[str]	https://api.edenai.run/v3	The Eden AI API base URL.
prefix	str	""	A string to add to the beginning of the text.
suffix	str	""	A string to add to the end of the text.
timeout	Optional[float]	None	Timeout for API calls in seconds. Defaults to the OPENAI_TIMEOUT environment variable or 30 seconds.
max_retries	Optional[int]	None	Maximum number of retries after an internal error. Defaults to the OPENAI_MAX_RETRIES environment variable or 5.
http_client_kwargs	Optional[Dict[str, Any]]	None	Keyword arguments for a custom httpx.Client or httpx.AsyncClient.

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	Description
text	str	The text to embed.

Related Information

• EdenAIDocumentEmbedder
• Pipeline Components