Bedrock Embedding

Embed data using AWS Bedrock's API.

Configure via UI

1. Select Bedrock Embedding from the Embedding Model dropdown.
2. Enter your AWS Region, AWS access key ID and AWS secret access key.
3. Select your preferred model:

• Titan Embedding (Default)
• Titan Embedding G1 Text 02
• Cohere Embed English V3
• Cohere Embed Multilingual V3

Configure via API / Client

For API / Client, use the model IDs:

• amazon.titan-embed-text-v1
• amazon.titan-embed-g1-text-02
• cohere.embed-english-v3
• cohere.embed-multilingual-v3

Python Client

pipeline = {
  'name': 'test-pipeline',
  'transform_config': {...},
  'embedding_config': {
      'type': 'BEDROCK_EMBEDDING',
      'component': {
          'region_name': 'us-east-1',
          'aws_access_key_id': '<aws_access_key_id>',
          'aws_secret_access_key': '<aws_secret_access_key>',
          'model': 'amazon.titan-embed-text-v1',
      },
  },
  'data_sink_id': data_sink.id
}

pipeline = client.pipelines.upsert_pipeline(request=pipeline)

TypeScript Client

const pipeline = {
    'name': 'test-pipeline',
    'transform_config': {...},
    'embedding_config': {
        'type': 'BEDROCK_EMBEDDING',
        'component': {
            'region_name': 'us-east-1',
            'aws_access_key_id': '<aws_access_key_id>',
            'aws_secret_access_key': '<aws_secret_access_key>',
            'model': 'amazon.titan-embed-text-v1',
        },
    },
  'dataSinkId': data_sink.id
}

await client.pipelines.upsertPipeline({
projectId: projectId,
body: pipeline
})