CohereDocumentImageEmbedder

Compute document embeddings based on images using Cohere's multimodal embedding API. Use this component in indexing pipelines to embed image documents before writing them to a document store.

Key Features

• Uses Cohere multimodal models (such as embed-v4.0) to embed documents containing images or PDFs.
• Stores the computed embedding in each document's embedding field, enabling image-based semantic search and retrieval.
• Reads image file paths from a configurable document metadata field (default: file_path).
• Supports JPEG and PNG images, as well as PDF pages converted to JPEG automatically.
• Configurable embedding type (float, int8, uint8, binary, ubinary) and optional embedding dimension for v4 and newer models.
• Supports both synchronous and asynchronous operation via run() and run_async().

Configuration

1. Drag the CohereDocumentImageEmbedder component onto the canvas from the Component Library.
2. Click on the component to open the configuration panel.
3. On the General tab:
   1. Select the embedding model. Make sure Haystack Platform is connected to your Cohere account. For details, see Use Cohere Models.
   2. Set the file_path_meta_field to the document metadata key that holds the image file path (default: file_path).
4. Go to the Advanced tab to configure embedding_type, embedding_dimension, image_size, timeout, and progress_bar.

Connections

CohereDocumentImageEmbedder receives a list of documents through its documents input. Each document must include an image file path in its metadata under the field specified by file_path_meta_field. It outputs the same documents with their embeddings added through its documents output.

Connect it after a converter that creates documents with image paths in their metadata. Connect its documents output to DocumentWriter to store embedded documents in a document store.

Source Code

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

Usage Examples

Basic Configuration

  CohereDocumentImageEmbedder:
    type: haystack_integrations.components.embedders.cohere.document_image_embedder.CohereDocumentImageEmbedder
    init_parameters:
      api_key:
        type: env_var
        env_vars:
        - COHERE_API_KEY
        - CO_API_KEY
        strict: false
      model: embed-v4.0
      file_path_meta_field: file_path
      api_base_url: https://api.cohere.com
      timeout: 120.0
      progress_bar: true

Using the Component in a Pipeline

This example shows an indexing pipeline that takes documents with image file paths in their metadata, embeds the images using Cohere's multimodal API, and writes them to a document store.

# haystack-pipeline
components:
  CohereDocumentImageEmbedder:
    type: haystack_integrations.components.embedders.cohere.document_image_embedder.CohereDocumentImageEmbedder
    init_parameters:
      api_key:
        type: env_var
        env_vars:
        - COHERE_API_KEY
        - CO_API_KEY
        strict: false
      model: embed-v4.0
      file_path_meta_field: file_path
      root_path:
      image_size:
      api_base_url: https://api.cohere.com
      timeout: 120.0
      embedding_dimension:
      embedding_type: float
      progress_bar: true
  DocumentWriter:
    type: haystack.components.writers.document_writer.DocumentWriter
    init_parameters:
      policy: OVERWRITE
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          hosts:
          index: Image-Index
          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: CohereDocumentImageEmbedder.documents
  receiver: DocumentWriter.documents

max_runs_per_component: 100

metadata: {}

inputs:
  documents:
  - CohereDocumentImageEmbedder.documents

Parameters

Inputs

Parameter	Type	Description
documents	List[Document]	A list of Documents to embed. Each document must have an image file path in its metadata under the field specified by file_path_meta_field.

Outputs

Parameter	Type	Description
documents	List[Document]	Documents with their embedding field populated and an embedding_source entry added to their metadata.

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
api_key	Secret	Secret.from_env_var(['COHERE_API_KEY', 'CO_API_KEY'])	The Cohere API key.
model	str	embed-v4.0	The Cohere multimodal embedding model to use. Supported models: embed-v4.0, embed-english-v3.0, embed-english-light-v3.0, embed-multilingual-v3.0, embed-multilingual-light-v3.0. See Cohere documentation for the full list.
api_base_url	str	https://api.cohere.com	The Cohere API base URL.
file_path_meta_field	str	file_path	The document metadata field that contains the file path to the image or PDF.
root_path	Optional[str]	None	The root directory for resolving relative file paths. When None, file paths are treated as absolute.
image_size	Optional[tuple[int, int]]	None	Target dimensions (width, height) to resize images while maintaining aspect ratio. Reduces file size, memory usage, and processing time.
timeout	float	120.0	Request timeout in seconds.
embedding_dimension	Optional[int]	None	The dimension of the embeddings to return. Only valid for v4 and newer models.
embedding_type	EmbeddingTypes	float	The type of embeddings to return. Specifying a type other than float is only supported for Embed v3.0 and newer models.
progress_bar	bool	True	Whether to show a progress bar. Disable in production to keep logs clean.

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

Related Information

• Use Cohere Models
• CohereDocumentEmbedder
• Pipeline Components