OpenSearch provides connectors for several platforms, for example:
- [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.
- [OpenAI ChatGPT](https://openai.com/blog/chatgpt) enables you to invoke an OpenAI chat model from inside an OpenSearch cluster.
- [Cohere](https://cohere.com/) allows you to use data from OpenSearch to power the Cohere large language models.
- [Amazon Bedrock](https://aws.amazon.com/bedrock/) supports models like [Bedrock Titan Embeddings](https://aws.amazon.com/bedrock/titan/), which can drive semantic search and retrieval-augmented generation in OpenSearch.
A _connector blueprint_ defines the set of parameters (the request body) you need to provide when sending an API request to create a specific connector. Connector blueprints may differ based on the platform and the model that you are accessing.
OpenSearch provides connector blueprints for several ML platforms and models. For a full list of connector blueprints provided by OpenSearch, see [Supported connectors](#supported-connectors).
As an ML developer, you can also create connector blueprints for other platforms and models. Data scientists and administrators can then use the blueprint to create connectors. They are only required to enter their `credential` settings, such as `openAI_key`, for the service to which they are connecting. For information about creating connector blueprints, including descriptions of all parameters, see [Connector blueprints]({{site.url}}{{site.baseurl}}/ml-commons-plugin/remote-models/blueprints/).
The following table lists all connector blueprints provided by OpenSearch. Follow the links to each connector blueprint for an example request that you can use to create the connector, including all parameters, and an example Predict API request.
[OpenAI](https://openai.com/) | Chat models (for example, `gpt-3.5-turbo`) | [Blueprint](https://github.com/opensearch-project/ml-commons/blob/2.x/docs/remote_inference_blueprints/open_ai_connector_chat_blueprint.md)
[OpenAI](https://openai.com/) | Completion models (for example, `text-davinci-003`) | [Blueprint](https://github.com/opensearch-project/ml-commons/blob/2.x/docs/remote_inference_blueprints/open_ai_connector_completion_blueprint.md)
[OpenAI](https://openai.com/) | Text embedding models (for example, `text-embedding-ada-002`) | [Blueprint](https://github.com/opensearch-project/ml-commons/blob/2.x/docs/remote_inference_blueprints/openai_connector_embedding_blueprint.md)
1. [Create a standalone connector](#creating-a-standalone-connector): A standalone connector can be reused and shared by multiple 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 externally hosted model](#creating-a-connector-for-a-specific-model): Alternatively, you can create a connector that can only be used with the 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.
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:
The following sections contain examples of connectors for popular ML platforms. For a full list of supported connectors, see [Supported connectors](#supported-connectors).
In some cases, you may need to update credentials, like `access_key`, that you use to connect to externally hosted models. You can update credentials without undeploying the model by providing the new credentials in the following request:
- To learn more about connecting to external models, see [Connecting to externally hosted models]({{site.url}}{{site.baseurl}}/ml-commons-plugin/remote-models/index/).
- To learn more about model access control and model groups, see [Model access control]({{site.url}}{{site.baseurl}}/ml-commons-plugin/model-access-control/).
