= cohere_embeddings
:type: processor
:status: experimental
:categories: ["AI"]



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component_type_dropdown::[]


Generates vector embeddings to represent input text, using the Cohere API.

Introduced in version 4.37.0.

```yml
# Config fields, showing default values
label: ""
cohere_embeddings:
  base_url: https://api.cohere.com
  api_key: "" # No default (required)
  model: embed-english-v3.0 # No default (required)
  text_mapping: "" # No default (optional)
  dimensions: search_document
```

This processor sends text strings to the Cohere API, which generates vector embeddings. By default, the processor submits the entire payload of each message as a string, unless you use the `text_mapping` configuration field to customize it.

To learn more about vector embeddings, see the https://docs.cohere.com/docs/embeddings[Cohere API documentation^].

== Examples

[tabs]
======
Store embedding vectors in Qdrant::
+
--

Compute embeddings for some generated data and store it within xrefs:component:outputs/qdrant.adoc[Qdrant]

```yamlinput:
  generate:
    interval: 1s
    mapping: |
      root = {"text": fake("paragraph")}
pipeline:
  processors:
  - cohere_embeddings:
      model: embed-english-v3
      api_key: "${COHERE_API_KEY}"
      text_mapping: "root = this.text"
output:
  qdrant:
    grpc_host: localhost:6334
    collection_name: "example_collection"
    id: "root = uuid_v4()"
    vector_mapping: "root = this"```

--
======

== Fields

=== `base_url`

The base URL to use for API requests.


*Type*: `string`

*Default*: `"https://api.cohere.com"`

=== `api_key`

The API key for the Cohere API.
[CAUTION]
====
This field contains sensitive information that usually shouldn't be added to a config directly, read our xref:configuration:secrets.adoc[secrets page for more info].
====



*Type*: `string`


=== `model`

The name of the Cohere model to use.


*Type*: `string`


```yml
# Examples

model: embed-english-v3.0

model: embed-english-light-v3.0

model: embed-multilingual-v3.0

model: embed-multilingual-light-v3.0
```

=== `text_mapping`

The text you want to generate a vector embedding for. By default, the processor submits the entire payload as a string.


*Type*: `string`


=== `dimensions`

Specifies the type of input passed to the model.


*Type*: `string`

*Default*: `"search_document"`

|===
| Option | Summary

| `classification`
| Used for embeddings passed through a text classifier.
| `clustering`
| Used for the embeddings run through a clustering algorithm.
| `search_document`
| Used for embeddings stored in a vector database for search use-cases.
| `search_query`
| Used for embeddings of search queries run against a vector DB to find relevant documents.

|===