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[Backport 2.x] Add bedrock multimodal build-in function usage example in doc #3128

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Oct 21, 2024
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[Backport 2.x] Add bedrock multimodal build-in function usage example in doc #3128

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40 changes: 38 additions & 2 deletions ..._inference_blueprints/bedrock_connector_titan_multimodal_embedding_blueprint.md
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Original file line number Diff line number Diff line change
Expand Up @@ -17,8 +17,11 @@ PUT /_cluster/settings

## 2. Create connector for Amazon Bedrock:

If you are using self-managed Opensearch, you should supply AWS credentials:
If you are using self-managed Opensearch, you should supply AWS credentials.
You have two different approaches to specify the pre&post process function and request body in the API:

**Use build-in function**
> You can use this starting from OpenSearch 2.16
```json
POST /_plugins/_ml/connectors/_create
{
Expand Down Expand Up @@ -46,6 +49,21 @@ POST /_plugins/_ml/connectors/_create
"content-type": "application/json",
"x-amz-content-sha256": "required"
},
"request_body": "{\"inputText\": \"${parameters.inputText:-null}\", \"inputImage\": \"${parameters.inputImage:-null}\"}",
"pre_process_function": "connector.pre_process.bedrock.multimodal_embedding",
"post_process_function": "connector.post_process.bedrock.embedding"
}
]
}
```
**Use painless script**
```json
POST /_plugins/_ml/connectors/_create
{
... //same above
"actions": [
{
... // same above
"request_body": "{ \"inputText\": \"${parameters.inputText:-null}\", \"inputImage\": \"${parameters.inputImage:-null}\" }",
"pre_process_function": "\n StringBuilder parametersBuilder = new StringBuilder(\"{\");\n if (params.text_docs.length > 0 && params.text_docs[0] != null) {\n parametersBuilder.append(\"\\\"inputText\\\":\");\n parametersBuilder.append(\"\\\"\");\n parametersBuilder.append(params.text_docs[0]);\n parametersBuilder.append(\"\\\"\");\n \n if (params.text_docs.length > 1 && params.text_docs[1] != null) {\n parametersBuilder.append(\",\");\n }\n }\n \n \n if (params.text_docs.length > 1 && params.text_docs[1] != null) {\n parametersBuilder.append(\"\\\"inputImage\\\":\");\n parametersBuilder.append(\"\\\"\");\n parametersBuilder.append(params.text_docs[1]);\n parametersBuilder.append(\"\\\"\");\n }\n parametersBuilder.append(\"}\");\n \n return \"{\" +\"\\\"parameters\\\":\" + parametersBuilder + \"}\";",
"post_process_function": "\n def name = \"sentence_embedding\";\n def dataType = \"FLOAT32\";\n if (params.embedding == null || params.embedding.length == 0) {\n return null;\n }\n def shape = [params.embedding.length];\n def json = \"{\" +\n \"\\\"name\\\":\\\"\" + name + \"\\\",\" +\n \"\\\"data_type\\\":\\\"\" + dataType + \"\\\",\" +\n \"\\\"shape\\\":\" + shape + \",\" +\n \"\\\"data\\\":\" + params.embedding +\n \"}\";\n return json;\n "
Expand All @@ -55,8 +73,11 @@ POST /_plugins/_ml/connectors/_create
```

If using the AWS Opensearch Service, you can provide an IAM role arn that allows access to the bedrock service.
Refer to this [AWS doc](https://docs.aws.amazon.com/opensearch-service/latest/developerguide/ml-amazon-connector.html)
Refer to this [AWS doc](https://docs.aws.amazon.com/opensearch-service/latest/developerguide/ml-amazon-connector.html)
You have two different approaches to specify the pre&post process function and request body in the API:

**Use build-in function**
> You can use this starting from OpenSearch 2.16
```json
POST /_plugins/_ml/connectors/_create
{
Expand All @@ -82,6 +103,21 @@ POST /_plugins/_ml/connectors/_create
"content-type": "application/json",
"x-amz-content-sha256": "required"
},
"request_body": "{\"inputText\": \"${parameters.inputText:-null}\", \"inputImage\": \"${parameters.inputImage:-null}\"}",
"pre_process_function": "connector.pre_process.bedrock.multimodal_embedding",
"post_process_function": "connector.post_process.bedrock.embedding"
}
]
}
```
**Use painless script**
```json
POST /_plugins/_ml/connectors/_create
{
... //same above
"actions": [
{
... //same above
"request_body": "{ \"inputText\": \"${parameters.inputText:-null}\", \"inputImage\": \"${parameters.inputImage:-null}\" }",
"pre_process_function": "\n StringBuilder parametersBuilder = new StringBuilder(\"{\");\n if (params.text_docs.length > 0 && params.text_docs[0] != null) {\n parametersBuilder.append(\"\\\"inputText\\\":\");\n parametersBuilder.append(\"\\\"\");\n parametersBuilder.append(params.text_docs[0]);\n parametersBuilder.append(\"\\\"\");\n \n if (params.text_docs.length > 1 && params.text_docs[1] != null) {\n parametersBuilder.append(\",\");\n }\n }\n \n \n if (params.text_docs.length > 1 && params.text_docs[1] != null) {\n parametersBuilder.append(\"\\\"inputImage\\\":\");\n parametersBuilder.append(\"\\\"\");\n parametersBuilder.append(params.text_docs[1]);\n parametersBuilder.append(\"\\\"\");\n }\n parametersBuilder.append(\"}\");\n \n return \"{\" +\"\\\"parameters\\\":\" + parametersBuilder + \"}\";",
"post_process_function": "\n def name = \"sentence_embedding\";\n def dataType = \"FLOAT32\";\n if (params.embedding == null || params.embedding.length == 0) {\n return null;\n }\n def shape = [params.embedding.length];\n def json = \"{\" +\n \"\\\"name\\\":\\\"\" + name + \"\\\",\" +\n \"\\\"data_type\\\":\\\"\" + dataType + \"\\\",\" +\n \"\\\"shape\\\":\" + shape + \",\" +\n \"\\\"data\\\":\" + params.embedding +\n \"}\";\n return json;\n "
Expand Down
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