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Mistral inference service

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Mistral inference service

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Creates an inference endpoint to perform an inference task with the mistral service.

Request

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PUT /_inference/<task_type>/<inference_id>

Path parameters

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<inference_id>

(Required, string) The unique identifier of the inference endpoint.

<task_type>

(Required, string) The type of the inference task that the model will perform.

Available task types:

text_embedding.

Request body

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service

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

service_settings

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

These settings are specific to the mistral service.

api_key

(Required, string) A valid API key for your Mistral account. You can find your Mistral API keys or you can create a new one on the API Keys 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.

model

(Required, string) The name of the model to use for the inference task. Refer to the Mistral models documentation for the list of available text embedding models.

max_input_tokens

(Optional, integer) Allows you to specify the maximum number of tokens per input before chunking occurs.

rate_limit

(Optional, object) By default, the mistral service sets the number of requests allowed per minute to 240. This helps to minimize the number of rate limit errors returned from the Mistral API. To modify this, set the requests_per_minute setting of this object in your service settings:

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

Mistral service example

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

resp = client.inference.put(
    task_type="text_embedding",
    inference_id="mistral-embeddings-test",
    inference_config={
        "service": "mistral",
        "service_settings": {
            "api_key": "<api_key>",
            "model": "mistral-embed"
        }
    },
)
print(resp)

const response = await client.inference.put({
  task_type: "text_embedding",
  inference_id: "mistral-embeddings-test",
  inference_config: {
    service: "mistral",
    service_settings: {
      api_key: "<api_key>",
      model: "mistral-embed",
    },
  },
});
console.log(response);

PUT _inference/text_embedding/mistral-embeddings-test
{
  "service": "mistral",
  "service_settings": {
    "api_key": "<api_key>",
    "model": "mistral-embed" 
  }
}

The model must be the ID of a text embedding model which can be found in the Mistral models documentation.

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