The vectors computed by this component are necessary to perform embedding retrieval on a collection of documents. At retrieval time, the vector that represents the query is compared with those of the documents to find the most similar or relevant documents.


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 to be embedded
Output variables	“documents”: A list of documents (enriched with embeddings) “meta”: A dictionary of metadata strings
API reference	Cohere
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/cohere

Overview

CohereDocumentEmbedder enriches the metadata of documents with an embedding of their content. To embed a string, you should use the CohereTextEmbedder.

The component supports the following Cohere models:
"embed-english-v3.0", "embed-english-light-v3.0", "embed-multilingual-v3.0",
"embed-multilingual-light-v3.0", "embed-english-v2.0", "embed-english-light-v2.0",
"embed-multilingual-v2.0". The default model is embed-english-v2.0. This list of all supported models can be found in Cohere’s model documentation.

To start using this integration with Haystack, install it with:

pip install cohere-haystack

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 api_key:

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

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

Embedding Metadata

Text documents often come with a set of metadata. If they are distinctive and semantically meaningful, you can embed them along with the text of the document to improve retrieval.

You can do this by using the Document Embedder:

from haystack import Document
from cohere_haystack.embedders.document_embedder import CohereDocumentEmbedder

doc = Document(content="some text", meta={"title": "relevant title", "page number": 18})

embedder = CohereDocumentEmbedder(api_key=Secret.from_token("<your-api-key>", meta_fields_to_embed=["title"])

docs_w_embeddings = embedder.run(documents=[doc])["documents"]

Usage

On its own

Remember to set COHERE_API_KEY as an environment variable first, or pass it in directly.

Here is how you can use the component on its own:

from haystack import Document
from haystack_integrations.components.embedders.cohere.document_embedder import CohereDocumentEmbedder

doc = Document(content="I love pizza!")

embedder = CohereDocumentEmbedder()

result = embedder.run([doc])
print(result['documents'][0].embedding)
# [-0.453125, 1.2236328, 2.0058594, 0.67871094...]

In a pipeline

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

from haystack_integrations.components.embedders.cohere.document_embedder import CohereDocumentEmbedder
from haystack_integrations.components.embedders.cohere.text_embedder import CohereTextEmbedder

document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")

documents = [Document(content="My name is Wolfgang and I live in Berlin"),
             Document(content="I saw a black horse running"),
             Document(content="Germany has many big cities")]

indexing_pipeline = Pipeline()
indexing_pipeline.add_component("embedder", CohereDocumentEmbedder())
indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
indexing_pipeline.connect("embedder", "writer")

indexing_pipeline.run({"embedder": {"documents": documents}})

query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", CohereTextEmbedder())
query_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

query = "Who lives in Berlin?"

result = query_pipeline.run({"text_embedder":{"text": query}})

print(result['retriever']['documents'][0])

# Document(id=..., text: 'My name is Wolfgang and I live in Berlin')