// tag::cohere[]

[source,console]
------------------------------------------------------------
PUT _inference/text_embedding/cohere_embeddings <1>
{
    "service": "cohere",
    "service_settings": {
        "api_key": "<api_key>", <2>
        "model_id": "embed-english-v3.0", <3>
        "embedding_type": "byte"
    }
}
------------------------------------------------------------
// TEST[skip:TBD]
<1> The task type is `text_embedding` in the path.
<2> The API key of your Cohere account. You can find your API keys in your
Cohere dashboard under the
https://dashboard.cohere.com/api-keys[API keys section]. You need to provide
your API key only once. The <<get-inference-api>> does not return your API
key.
<3> The name of the embedding model to use. You can find the list of Cohere
embedding models https://docs.cohere.com/reference/embed[here].

NOTE: When using this model the recommended similarity measure to use in the
`dense_vector` field mapping is `dot_product`. In the case of Cohere models, the
embeddings are normalized to unit length in which case the `dot_product` and
the `cosine` measures are equivalent.



// end::cohere[]


// tag::openai[]

[source,console]
------------------------------------------------------------
PUT _inference/text_embedding/openai_embeddings <1>
{
    "service": "openai",
    "service_settings": {
        "api_key": "<api_key>", <2>
        "model_id": "text-embedding-ada-002" <3>
    }
}
------------------------------------------------------------
// TEST[skip:TBD]
<1> The task type is `text_embedding` in the path.
<2> The API key of your OpenAI account. You can find your OpenAI API keys in
your OpenAI account under the
https://platform.openai.com/api-keys[API keys section]. You need to provide
your API key only once. The <<get-inference-api>> does not return your API
key.
<3> The name of the embedding model to use. You can find the list of OpenAI
embedding models
https://platform.openai.com/docs/guides/embeddings/embedding-models[here].

NOTE: When using this model the recommended similarity measure to use in the
`dense_vector` field mapping is `dot_product`. In the case of OpenAI models, the
embeddings are normalized to unit length in which case the `dot_product` and
the `cosine` measures are equivalent.

// end::openai[]