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[Backport 2.x] tune titan embedding model blueprint for v2 #3099

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Oct 17, 2024
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[Backport 2.x] tune titan embedding model blueprint for v2 #3099

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52 changes: 50 additions & 2 deletions docs/remote_inference_blueprints/bedrock_connector_titan_embedding_blueprint.md
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Expand Up @@ -16,10 +16,9 @@ PUT /_cluster/settings
```

## 2. Create connector for Amazon Bedrock:

### 2.1 Titan text embedding model v1
If you are using self-managed Opensearch, you should supply AWS credentials:

If you are using Titan Text Embedding V2, change "model" to `amazon.titan-embed-text-v2:0`
```json
POST /_plugins/_ml/connectors/_create
{
Expand Down Expand Up @@ -131,6 +130,55 @@ Sample response:
"connector_id": "nzh9PIsBnGXNcxYpPEcv"
}
```
### 2.2 Titan text embedding model v2

Follow Titan text embedding model v1, just change "model" to `amazon.titan-embed-text-v2:0` and configure extra parameters and request body as:


```
POST /_plugins/_ml/connectors/_create
{
"name": "Amazon Bedrock Connector: embedding",
"description": "The connector to bedrock Titan embedding model",
"version": 1,
"protocol": "aws_sigv4",
"parameters": {
"region": "<PLEASE ADD YOUR AWS REGION HERE>",
"service_name": "bedrock",
"model": "amazon.titan-embed-text-v2:0",
"dimensions": 1024,
"normalize": true,
"embeddingTypes": ["float"]
},
"credential": {
"access_key": "<PLEASE ADD YOUR AWS ACCESS KEY HERE>",
"secret_key": "<PLEASE ADD YOUR AWS SECRET KEY HERE>",
"session_token": "<PLEASE ADD YOUR AWS SECURITY TOKEN HERE>"
},
"actions": [
{
"action_type": "predict",
"method": "POST",
"url": "https://bedrock-runtime.${parameters.region}.amazonaws.com/model/${parameters.model}/invoke",
"headers": {
"content-type": "application/json",
"x-amz-content-sha256": "required"
},
"request_body": "{ \"inputText\": \"${parameters.inputText}\", \"dimensions\": ${parameters.dimensions}, \"normalize\": ${parameters.normalize}, \"embeddingTypes\": ${parameters.embeddingTypes} }",
"pre_process_function": "connector.pre_process.bedrock.embedding",
"post_process_function": "connector.post_process.bedrock.embedding"
}
]
}
```

Note:
1. neural-search plugin only support one embedding for one document now. So you should configure one embedding type in `embeddingTypes`
2. similar to v1, you should use `roleArn` in credential part on AWS OpenSearch Service
3. `binary` type not supported yet in built-in post process function `connector.post_process.bedrock.embedding`. You can use this painless script
```
"post_process_function":"\n def name = \"sentence_embedding\";\n def dataType = \"FLOAT32\";\n if (params.embeddingsByType == null || params.embeddingsByType.length == 0) {\n return params.message;\n }\n def shape = [params.embeddingsByType.length];\n def json = \"{\" +\n \"\\\"name\\\":\\\"\" + name + \"\\\",\" +\n \"\\\"data_type\\\":\\\"\" + dataType + \"\\\",\" +\n \"\\\"shape\\\":\" + shape + \",\" +\n \"\\\"data\\\":\" + params.embeddingsByType +\n \"}\";\n return json;\n "
```

## 3. Create model group:

Expand Down
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