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[Backport 2.x] add demo notebook for creating connector #2199

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Mar 12, 2024
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[Backport 2.x] add demo notebook for creating connector #2199

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2 changes: 1 addition & 1 deletion docs/remote_inference_blueprints/sagemaker_connector_blueprint.md
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Expand Up @@ -47,7 +47,7 @@ POST /_plugins/_ml/connectors/_create
"content-type": "application/json"
},
"url": "<PLEASE ADD YOUR Sagemaker MODEL INFERENCE ENDPOINT URL>",
"request_body": "${parameters.inputs}",
"request_body": "${parameters.input}",
"pre_process_function": "connector.pre_process.default.embedding",
"post_process_function": "connector.post_process.default.embedding"
}
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1,103 changes: 1,103 additions & 0 deletions docs/tutorials/aws/AIConnectorHelper.ipynb
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2 changes: 2 additions & 0 deletions docs/tutorials/aws/semantic_search_with_bedrock_cohere_embedding_model.md
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> The easiest way for setting up embedding model on your Amazon OpenSearch cluster is using [AWS CloudFormation](https://docs.aws.amazon.com/opensearch-service/latest/developerguide/cfn-template.html)

> Another easy way is using python, check [AIConnectorHelper](./AIConnectorHelper.ipynb)

> This tutorial explains detail steps if you want to configure everything manually.

> Bedrock has [quota limit](https://docs.aws.amazon.com/bedrock/latest/userguide/quotas.html). You can purchase [Provisioned Throughput](https://docs.aws.amazon.com/bedrock/latest/userguide/prov-throughput.html) to increase quota limit.
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2 changes: 2 additions & 0 deletions docs/tutorials/aws/semantic_search_with_bedrock_titan_embedding_model.md
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> The easiest way for setting up embedding model on your Amazon OpenSearch cluster is using [AWS CloudFormation](https://docs.aws.amazon.com/opensearch-service/latest/developerguide/cfn-template.html)

> Another easy way is using python, check [AIConnectorHelper](./AIConnectorHelper.ipynb)

> This tutorial explains detail steps if you want to configure everything manually.

> Bedrock has [quota limit](https://docs.aws.amazon.com/bedrock/latest/userguide/quotas.html). You can purchase [Provisioned Throughput](https://docs.aws.amazon.com/bedrock/latest/userguide/prov-throughput.html) to increase quota limit.
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2 changes: 2 additions & 0 deletions docs/tutorials/aws/semantic_search_with_cohere_embedding_model.md
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> The easiest way for setting up embedding model on your Amazon OpenSearch cluster is using [AWS CloudFormation](https://docs.aws.amazon.com/opensearch-service/latest/developerguide/cfn-template.html)

> Another easy way is using python, check [AIConnectorHelper](./AIConnectorHelper.ipynb)

> This tutorial explains detail steps if you want to configure everything manually. You can also connect to other service with similar way.

This doc introduces how to build semantic search in Amazon managed OpenSearch with [Cohere embedding model](https://docs.cohere.com/reference/embed).
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2 changes: 2 additions & 0 deletions docs/tutorials/aws/semantic_search_with_openai_embedding_model.md
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@@ -1,5 +1,7 @@
# Topic

> An easy way is using python, check [AIConnectorHelper](./AIConnectorHelper.ipynb)

This doc introduces how to build semantic search in Amazon managed OpenSearch with [OpenAI embedding model](https://platform.openai.com/docs/guides/embeddings).
If you are not using Amazon OpenSearch, you can refer to [openai_connector_embedding_blueprint](https://github.com/opensearch-project/ml-commons/blob/2.x/docs/remote_inference_blueprints/openai_connector_embedding_blueprint.md) and [OpenSearch semantic search](https://opensearch.org/docs/latest/search-plugins/semantic-search/).

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4 changes: 3 additions & 1 deletion docs/tutorials/aws/semantic_search_with_sagemaker_embedding_model.md
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> The easiest way for setting up embedding model on your Amazon OpenSearch cluster is using [AWS CloudFormation](https://docs.aws.amazon.com/opensearch-service/latest/developerguide/cfn-template.html)

> Another easy way is using python, check [AIConnectorHelper](./AIConnectorHelper.ipynb)

> This tutorial explains detail steps if you want to configure everything manually.

This doc introduces how to build semantic search in Amazon managed OpenSearch with embedding model running on [Sagemaker](https://aws.amazon.com/sagemaker/).
Expand Down Expand Up @@ -272,7 +274,7 @@ Sample output
POST /_plugins/_ml/models/NxU9Qo0BTaDH9c7t1Bca/_predict
{
"parameters": {
"inputs": ["hello world", "how are you"]
"input": ["hello world", "how are you"]
}
}
```
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