Create an Azure AI studio inference endpoint
Generally available; Added in 8.14.0
Path parameters
-
The type of the inference task that the model will perform.
Values are
completion
ortext_embedding
. -
The unique identifier of the inference endpoint.
PUT
/_inference/{task_type}/{azureaistudio_inference_id}
Console
PUT _inference/text_embedding/azure_ai_studio_embeddings
{
"service": "azureaistudio",
"service_settings": {
"api_key": "Azure-AI-Studio-API-key",
"target": "Target-Uri",
"provider": "openai",
"endpoint_type": "token"
}
}
resp = client.inference.put(
task_type="text_embedding",
inference_id="azure_ai_studio_embeddings",
inference_config={
"service": "azureaistudio",
"service_settings": {
"api_key": "Azure-AI-Studio-API-key",
"target": "Target-Uri",
"provider": "openai",
"endpoint_type": "token"
}
},
)
const response = await client.inference.put({
task_type: "text_embedding",
inference_id: "azure_ai_studio_embeddings",
inference_config: {
service: "azureaistudio",
service_settings: {
api_key: "Azure-AI-Studio-API-key",
target: "Target-Uri",
provider: "openai",
endpoint_type: "token",
},
},
});
response = client.inference.put(
task_type: "text_embedding",
inference_id: "azure_ai_studio_embeddings",
body: {
"service": "azureaistudio",
"service_settings": {
"api_key": "Azure-AI-Studio-API-key",
"target": "Target-Uri",
"provider": "openai",
"endpoint_type": "token"
}
}
)
$resp = $client->inference()->put([
"task_type" => "text_embedding",
"inference_id" => "azure_ai_studio_embeddings",
"body" => [
"service" => "azureaistudio",
"service_settings" => [
"api_key" => "Azure-AI-Studio-API-key",
"target" => "Target-Uri",
"provider" => "openai",
"endpoint_type" => "token",
],
],
]);
curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"azureaistudio","service_settings":{"api_key":"Azure-AI-Studio-API-key","target":"Target-Uri","provider":"openai","endpoint_type":"token"}}' "$ELASTICSEARCH_URL/_inference/text_embedding/azure_ai_studio_embeddings"
Request examples
A text embedding task
Run `PUT _inference/text_embedding/azure_ai_studio_embeddings` to create an inference endpoint that performs a text_embedding task. Note that you do not specify a model here, as it is defined already in the Azure AI Studio deployment.
{
"service": "azureaistudio",
"service_settings": {
"api_key": "Azure-AI-Studio-API-key",
"target": "Target-Uri",
"provider": "openai",
"endpoint_type": "token"
}
}
Run `PUT _inference/completion/azure_ai_studio_completion` to create an inference endpoint that performs a completion task.
{
"service": "azureaistudio",
"service_settings": {
"api_key": "Azure-AI-Studio-API-key",
"target": "Target-URI",
"provider": "databricks",
"endpoint_type": "realtime"
}
}