
Most common position in a pipeline	Before a `DocumentWriter` in an indexing pipeline
Mandatory init variables	"api_key": The Cohere API key. Can be set with `COHERE_API_KEY` or `CO_API_KEY` env var.
Mandatory run variables	"documents": A list of documents, with a meta field containing an image file path
Output variables	"documents": A list of documents (enriched with embeddings)
API reference	Cohere
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/cohere

Overview

CohereDocumentImageEmbedder expects a list of documents containing an image or a PDF file path in a meta field. The meta field can be specified with the file_path_meta_field init parameter of this component.

The embedder efficiently loads the images, computes the embeddings using a Cohere model, and stores each of them in the embedding field of the document.

CohereDocumentImageEmbedder is commonly used in indexing pipelines. At retrieval time, you need to use the same model with a CohereTextEmbedder to embed the query, before using an Embedding Retriever.

This component is compatible with Cohere Embed models v3 and later. For a complete list of supported models, see the Cohere documentation.

Installation

To start using this integration with Haystack, install the package with:

pip install cohere-haystack

Authentication

The component uses a COHERE_API_KEY or CO_API_KEY environment variable by default. Otherwise, you can pass an API key at initialization with a Secret and Secret.from_token method:

embedder = CohereTextEmbedder(api_key=Secret.from_token("<your-api-key>"))

To get a Cohere API key, head over to https://cohere.com/.

Usage

On its own

Remember to set COHERE_API_KEY as an environment variable first.

from haystack import Document
from haystack_integrations.components.embedders.cohere import CohereDocumentImageEmbedder

embedder = CohereDocumentImageEmbedder(model="embed-v4.0")
embedder.warm_up()

documents = [
    Document(content="A photo of a cat", meta={"file_path": "cat.jpg"}),
    Document(content="A photo of a dog", meta={"file_path": "dog.jpg"}),
]

result = embedder.run(documents=documents)
documents_with_embeddings = result["documents"]
print(documents_with_embeddings)

# [Document(id=...,
#           content='A photo of a cat',
#           meta={'file_path': 'cat.jpg',
#                 'embedding_source': {'type': 'image', 'file_path_meta_field': 'file_path'}},
#           embedding=vector of size 1536),
#  ...]

In a pipeline

In this example, we can see an indexing pipeline with three components:

ImageFileToDocument converter that creates empty documents with a reference to an image in the meta.file_path field;
CohereDocumentImageEmbedder that loads the images, computes embeddings and store them in documents;
DocumentWriter that writes the documents in the InMemoryDocumentStore.

There is also a multimodal retrieval pipeline, composed of a CohereTextEmbedder (using the same model as before) and an InMemoryEmbeddingRetriever.

from haystack import Pipeline
from haystack.components.converters.image import ImageFileToDocument
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
from haystack.components.writers import DocumentWriter
from haystack.document_stores.in_memory import InMemoryDocumentStore

from haystack_integrations.components.embedders.cohere import CohereDocumentImageEmbedder, CohereTextEmbedder

document_store = InMemoryDocumentStore()

# Indexing pipeline
indexing_pipeline = Pipeline()
indexing_pipeline.add_component("image_converter", ImageFileToDocument())
indexing_pipeline.add_component(
    "embedder",
    CohereDocumentImageEmbedder(model="embed-v4.0")
)
indexing_pipeline.add_component(
    "writer", DocumentWriter(document_store=document_store)
)
indexing_pipeline.connect("image_converter", "embedder")
indexing_pipeline.connect("embedder", "writer")

indexing_pipeline.run(data={"image_converter": {"sources": ["dog.jpg", "hyena.jpeg"]}})

# Multimodal retrieval pipeline
retrieval_pipeline = Pipeline()
retrieval_pipeline.add_component(
    "embedder",
    CohereTextEmbedder(model="embed-v4.0")
)
retrieval_pipeline.add_component(
    "retriever",
    InMemoryEmbeddingRetriever(document_store=document_store, top_k=2)
)
retrieval_pipeline.connect("embedder.embedding", "retriever.query_embedding")

result = retrieval_pipeline.run(data={"text": "man's best friend"})
print(result)

# {
#     'retriever': {
#         'documents': [
#             Document(
#                 id=0c96...,
#                 meta={
#                     'file_path': 'dog.jpg',
#                     'embedding_source': {
#                         'type': 'image',
#                         'file_path_meta_field': 'file_path'
#                     }
#                 },
#                 score=0.288
#             ),
#             Document(
#                 id=5e76...,
#                 meta={
#                     'file_path': 'hyena.jpeg',
#                     'embedding_source': {
#                         'type': 'image',
#                         'file_path_meta_field': 'file_path'
#                     }
#                 },
#                 score=0.248
#             )
#         ]
#     }
# }

Additional References

📓 Tutorial: Creating Vision+Text RAG Pipelines