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CohereDocumentEmbedder

This component computes the embeddings of a list of Documents and stores the obtained vectors in the embedding field of each Document. It uses Cohere embedding models.

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.

NameCohereDocumentEmbedder
TypeDocument Embedder
Position in a PipelineBefore a DocumentWriter in an indexing pipeline
Inputs“documents”: List of Document objects to be embedded
Outputs“documents”: List of Document objects (enriched with embeddings)

“meta”: dictionary of metadata strings

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="YOUR COHERE 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 cohere_haystack.embedders.document_embedder import CohereDocumentEmbedder

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

embedder = CohereDocumentEmbedder("YOUR COHERE API KEY")

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 cohere_haystack.embedders.document_embedder import CohereDocumentEmbedder
from cohere_haystack.embedders.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("YOUR COHERE API KEY"))
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("YOUR COHERE API KEY"))
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')

Related Links

Check out the API reference in the GitHub repo or in our docs: