deepset-ai--haystack/docs-website/versioned_docs/version-2.21/pipeline-components/embedders/coheretextembedder.mdx

---
title: "CohereTextEmbedder"
id: coheretextembedder
slug: "/coheretextembedder"
description: "This component transforms a string into a vector that captures its semantics using a Cohere embedding model.  When you perform embedding retrieval, you use this component to transform your query into a vector. Then, the embedding Retriever looks for similar or relevant documents."
---

# CohereTextEmbedder

This component transforms a string into a vector that captures its semantics using a Cohere embedding model.  When you perform embedding retrieval, you use this component to transform your query into a vector. Then, the embedding Retriever looks for similar or relevant documents.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Before an embedding [Retriever](../retrievers.mdx)  in a query/RAG 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** | `text`: A string |
| **Output variables** | `embedding`: A list of float numbers (vectors)  <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 |

</div>

## Overview

`CohereTextEmbedder` embeds a simple string (such as a query) into a vector. For embedding lists of documents, use the use the [`CohereDocumentEmbedder`](coheredocumentembedder.mdx), 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](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 a [Secret](../../concepts/secret-management.mdx) and `Secret.from_token` static method:

```python
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.

```python
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

```python
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')
```