max_chunk_size: (Optional, integer) Specifies the maximum size of a chunk in words. Defaults to 250. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).
overlap: (Optional, integer) Only for word chunking strategy. Specifies the number of overlapping words for chunks. Defaults to 100. This value cannot be higher than the half of max_chunk_size.
sentence_overlap: (Optional, integer) Only for sentence chunking strategy. Specifies the numnber of overlapping sentences for chunks. It can be either 1 or 0. Defaults to 1.
strategy: (Optional, string) Specifies the chunking strategy. It could be either sentence or word.

service

(Required, string) The type of service supported for the specified task type. In this case, cohere.

service_settings

(Required, object) Settings used to install the inference model.

These settings are specific to the cohere service.

api_key

(Required, string) A valid API key of your Cohere account. You can find your Cohere API keys or you can create a new one on the API keys settings page.

You need to provide the API key only once, during the inference model creation. The Get inference API does not retrieve your API key. After creating the inference model, you cannot change the associated API key. If you want to use a different API key, delete the inference model and recreate it with the same name and the updated API key.

rate_limit

(Optional, object) By default, the cohere service sets the number of requests allowed per minute to 10000. This value is the same for all task types. This helps to minimize the number of rate limit errors returned from Cohere. To modify this, set the requests_per_minute setting of this object in your service settings:

"rate_limit": {
    "requests_per_minute": <<number_of_requests>>
}

More information about Cohere’s rate limits can be found in Cohere’s production key docs.

service_settings for the completion task type

model_id: (Optional, string) The name of the model to use for the inference task. To review the available completion models, refer to the Cohere docs.

service_settings for the rerank task type

model_id: (Optional, string) The name of the model to use for the inference task. To review the available rerank models, refer to the Cohere docs.

service_settings for the text_embedding task type

embedding_type

(Optional, string) Specifies the types of embeddings you want to get back. Defaults to float. Valid values are:

byte: use it for signed int8 embeddings (this is a synonym of int8).
float: use it for the default float embeddings.
int8: use it for signed int8 embeddings.
binary: use it for binary embeddings, which are encoded as bytes with signed int8 precision.
bit: use it for binary embeddings, which are encoded as bytes with signed int8 precision (this is a synonym of binary).

model_id

(Optional, string) The name of the model to use for the inference task. To review the available text_embedding models, refer to the Cohere docs. The default value for text_embedding is embed-english-v2.0.

similarity

(Optional, string) Similarity measure. One of cosine, dot_product, l2_norm. Defaults based on the embedding_type (float → dot_product, int8/byte → cosine).

task_settings

(Optional, object) Settings to configure the inference task. These settings are specific to the <task_type> you specified.

task_settings for the rerank task type

return_documents: (Optional, boolean) Specify whether to return doc text within the results.
top_n: (Optional, integer) The number of most relevant documents to return, defaults to the number of the documents. If this inference endpoint is used in a text_similarity_reranker retriever query and top_n is set, it must be greater than or equal to rank_window_size in the query.

task_settings for the text_embedding task type

input_type

(Optional, string) Specifies the type of input passed to the model. Valid values are:

classification: use it for embeddings passed through a text classifier.
clusterning: use it for the embeddings run through a clustering algorithm.
ingest: use it for storing document embeddings in a vector database.
search: use it for storing embeddings of search queries run against a vector database to find relevant documents.

The input_type field is required when using embedding models v3 and higher.

truncate

(Optional, string) Specifies how the API handles inputs longer than the maximum token length. Defaults to END. Valid values are:

NONE: when the input exceeds the maximum input token length an error is returned.
START: when the input exceeds the maximum input token length the start of the input is discarded.
END: when the input exceeds the maximum input token length the end of the input is discarded.

Cohere service examples

edit

The following example shows how to create an inference endpoint called cohere-embeddings to perform a text_embedding task type.

resp = client.inference.put(
    task_type="text_embedding",
    inference_id="cohere-embeddings",
    inference_config={
        "service": "cohere",
        "service_settings": {
            "api_key": "<api_key>",
            "model_id": "embed-english-light-v3.0",
            "embedding_type": "byte"
        }
    },
)
print(resp)

const response = await client.inference.put({
  task_type: "text_embedding",
  inference_id: "cohere-embeddings",
  inference_config: {
    service: "cohere",
    service_settings: {
      api_key: "<api_key>",
      model_id: "embed-english-light-v3.0",
      embedding_type: "byte",
    },
  },
});
console.log(response);

PUT _inference/text_embedding/cohere-embeddings
{
    "service": "cohere",
    "service_settings": {
        "api_key": "<api_key>",
        "model_id": "embed-english-light-v3.0",
        "embedding_type": "byte"
    }
}

The following example shows how to create an inference endpoint called cohere-rerank to perform a rerank task type.

resp = client.inference.put(
    task_type="rerank",
    inference_id="cohere-rerank",
    inference_config={
        "service": "cohere",
        "service_settings": {
            "api_key": "<API-KEY>",
            "model_id": "rerank-english-v3.0"
        },
        "task_settings": {
            "top_n": 10,
            "return_documents": True
        }
    },
)
print(resp)

const response = await client.inference.put({
  task_type: "rerank",
  inference_id: "cohere-rerank",
  inference_config: {
    service: "cohere",
    service_settings: {
      api_key: "<API-KEY>",
      model_id: "rerank-english-v3.0",
    },
    task_settings: {
      top_n: 10,
      return_documents: true,
    },
  },
});
console.log(response);

PUT _inference/rerank/cohere-rerank
{
    "service": "cohere",
    "service_settings": {
        "api_key": "<API-KEY>",
        "model_id": "rerank-english-v3.0"
    },
    "task_settings": {
        "top_n": 10,
        "return_documents": true
    }
}

For more examples, also review the Cohere documentation.

« Azure OpenAI inference integration Elasticsearch inference integration »