
Name	CohereTextEmbedder
Source	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/cohere
Most common position in a pipeline	Before an embedding Retriever in a query/RAG pipeline
Mandatory input variables	“text”: A string
Output variables	“embedding”: A list of float numbers (vectors) “meta”: A dictionary of metadata strings

Overview

CohereTextEmbedder embeds a simple string (such as a query) into a vector. For embedding lists of documents, use the use the CohereDocumentEmbedder, which enriches the document with the computed embedding, also known as vector.

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 a Secret and Secret.from_token static 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

Here is how you can use the component on its own. You’ll need to pass in your Cohere API key via Secret or set it as an environment variable called COHERE_API_KEY. The examples below assume you've set the environment variable.

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

text_to_embed = "I love pizza!"

text_embedder = CohereTextEmbedder()

print(text_embedder.run(text_to_embed))
# {'embedding': [-0.453125, 1.2236328, 2.0058594, 0.67871094...], 
#  'meta': {'api_version': {'version': '1'}, 'billed_units': {'input_tokens': 4}}}

In a pipeline

from haystack import Document
from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.embedders.cohere.text_embedder import CohereTextEmbedder
from haystack_integrations.components.embedders.cohere.document_embedder import CohereDocumentEmbedder
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever

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")]

document_embedder = CohereDocumentEmbedder()
documents_with_embeddings = document_embedder.run(documents)['documents']
document_store.write_documents(documents_with_embeddings)

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=..., content: 'My name is Wolfgang and I live in Berlin')