---
title: "CohereDocumentEmbedder"
id: coheredocumentembedder
slug: "/coheredocumentembedder"
description: "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."
---

# 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.

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Before a [`DocumentWriter`](../writers/documentwriter.mdx)   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)  <br /> <br />“meta”: A dictionary of metadata strings |
| **API reference** | [Cohere](/reference/integrations-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`](coheretextembedder.mdx).

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](https://docs.cohere.com/docs/models#representation).

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

```shell
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`:

```python
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:

```python
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:

```python
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

```python
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')
```
