1. [Create a standalone connector](#creating-a-standalone-connector): A standalone connector can be reused and shared by multiple remote models but requires access to both the model and connector in OpenSearch and the third-party platform, such as OpenAI or Amazon SageMaker, that the connector is accessing. Standalone connectors are saved in a connector index.
2. [Create a connector for a specific remote model](#creating-a-connector-for-a-specific-model): Alternatively, you can create a connector that can only be used with the remote model for which it was created. To access such a connector, you only need access to the model itself because the connection is established inside the model. These connectors are saved in the model index.
As of OpenSearch 2.9, connectors have been tested for the following ML services, though it is possible to create connectors for other platforms not listed here:
- [Amazon SageMaker](https://aws.amazon.com/sagemaker/) allows you to host and manage the lifecycle of text embedding models, powering semantic search queries in OpenSearch. When connected, Amazon SageMaker hosts your models and OpenSearch is used to query inferences. This benefits Amazon SageMaker users who value its functionality, such as model monitoring, serverless hosting, and workflow automation for continuous training and deployment.
- [Cohere](https://cohere.com/) allows you to use data from OpenSearch to power the Cohere large language models.
- The [Bedrock Titan Embeddings](https://aws.amazon.com/bedrock/titan/) model can drive semantic search and retrieval-augmented generation in OpenSearch.
All connectors consist of a JSON blueprint created by machine learning (ML) developers. The blueprint allows administrators and data scientists to make connections between OpenSearch and an AI service or model-serving technology.
You can find blueprints for each connector in the [ML Commons repository](https://github.com/opensearch-project/ml-commons/tree/2.x/docs/remote_inference_blueprints).
Admins are only required to enter their `credential` settings, such as `"openAI_key"`, for the service they are connecting to. All other parameters are defined within the [blueprint]({{site.url}}{{site.baseurl}}/ml-commons-plugin/remote-models/blueprints/).
Standalone connectors can be used by multiple models. To create a standalone connector, send a request to the `connectors/_create` endpoint and provide all of the parameters described in [Connector blueprints]({{site.url}}{{site.baseurl}}/ml-commons-plugin/remote-models/blueprints/):
To create a connector for a specific model, provide all of the parameters described in [Connector blueprints]({{site.url}}{{site.baseurl}}/ml-commons-plugin/remote-models/blueprints/) within the `connector` object of a request to the `models/_register` endpoint:
After creating the connector, you can retrieve the `task_id` and `connector_id` to register and deploy the model and then use the Predict API, similarly to a standalone connector.
- To learn more about using models in OpenSearch, see [Using ML models within OpenSearch]({{site.url}}{{site.baseurl}}/ml-commons-plugin/using-ml-models/).
- To learn more about model access control and model groups, see [Model access control]({{site.url}}{{site.baseurl}}/ml-commons-plugin/model-access-control/).
