Invoke Amazon Titan Text Embeddings on Amazon Bedrock - Amazon Bedrock

Invoke Amazon Titan Text Embeddings on Amazon Bedrock

The following code examples show how to:

  • Get started creating your first embedding.

  • Create embeddings configuring the number of dimensions and normalization (V2 only).

Java
SDK for Java 2.x
Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository.

Create your first embedding with Titan Text Embeddings V2.

// Generate and print an embedding with Amazon Titan Text Embeddings. import org.json.JSONObject; import org.json.JSONPointer; import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider; import software.amazon.awssdk.core.SdkBytes; import software.amazon.awssdk.core.exception.SdkClientException; import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient; public class InvokeModel { public static String invokeModel() { // Create a Bedrock Runtime client in the AWS Region you want to use. // Replace the DefaultCredentialsProvider with your preferred credentials provider. var client = BedrockRuntimeClient.builder() .credentialsProvider(DefaultCredentialsProvider.create()) .region(Region.US_EAST_1) .build(); // Set the model ID, e.g., Titan Text Embeddings V2. var modelId = "amazon.titan-embed-text-v2:0"; // The InvokeModel API uses the model's native payload. // Learn more about the available inference parameters and response fields at: // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-text.html var nativeRequestTemplate = "{ \"inputText\": \"{{inputText}}\" }"; // The text to convert into an embedding. var inputText = "Please recommend books with a theme similar to the movie 'Inception'."; // Embed the prompt in the model's native request payload. String nativeRequest = nativeRequestTemplate.replace("{{inputText}}", inputText); try { // Encode and send the request to the Bedrock Runtime. var response = client.invokeModel(request -> request .body(SdkBytes.fromUtf8String(nativeRequest)) .modelId(modelId) ); // Decode the response body. var responseBody = new JSONObject(response.body().asUtf8String()); // Retrieve the generated text from the model's response. var text = new JSONPointer("/embedding").queryFrom(responseBody).toString(); System.out.println(text); return text; } catch (SdkClientException e) { System.err.printf("ERROR: Can't invoke '%s'. Reason: %s", modelId, e.getMessage()); throw new RuntimeException(e); } } public static void main(String[] args) { invokeModel(); } }

Invoke Titan Text Embeddings V2 configuring the number of dimensions and normalization.

/** * Invoke Amazon Titan Text Embeddings V2 with additional inference parameters. * * @param inputText - The text to convert to an embedding. * @param dimensions - The number of dimensions the output embeddings should have. * Values accepted by the model: 256, 512, 1024. * @param normalize - A flag indicating whether or not to normalize the output embeddings. * @return The {@link JSONObject} representing the model's response. */ public static JSONObject invokeModel(String inputText, int dimensions, boolean normalize) { // Create a Bedrock Runtime client in the AWS Region of your choice. var client = BedrockRuntimeClient.builder() .region(Region.US_WEST_2) .build(); // Set the model ID, e.g., Titan Embed Text v2.0. var modelId = "amazon.titan-embed-text-v2:0"; // Create the request for the model. var nativeRequest = """ { "inputText": "%s", "dimensions": %d, "normalize": %b } """.formatted(inputText, dimensions, normalize); // Encode and send the request. var response = client.invokeModel(request -> { request.body(SdkBytes.fromUtf8String(nativeRequest)); request.modelId(modelId); }); // Decode the model's response. var modelResponse = new JSONObject(response.body().asUtf8String()); // Extract and print the generated embedding and the input text token count. var embedding = modelResponse.getJSONArray("embedding"); var inputTokenCount = modelResponse.getBigInteger("inputTextTokenCount"); System.out.println("Embedding: " + embedding); System.out.println("\nInput token count: " + inputTokenCount); // Return the model's native response. return modelResponse; }
  • For API details, see InvokeModel in AWS SDK for Java 2.x API Reference.

Python
SDK for Python (Boto3)
Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository.

Create your first embedding with Amazon Titan Text Embeddings.

# Generate and print an embedding with Amazon Titan Text Embeddings V2. import boto3 import json # Create a Bedrock Runtime client in the AWS Region of your choice. client = boto3.client("bedrock-runtime", region_name="us-east-1") # Set the model ID, e.g., Titan Text Embeddings V2. model_id = "amazon.titan-embed-text-v2:0" # The text to convert to an embedding. input_text = "Please recommend books with a theme similar to the movie 'Inception'." # Create the request for the model. native_request = {"inputText": input_text} # Convert the native request to JSON. request = json.dumps(native_request) # Invoke the model with the request. response = client.invoke_model(modelId=model_id, body=request) # Decode the model's native response body. model_response = json.loads(response["body"].read()) # Extract and print the generated embedding and the input text token count. embedding = model_response["embedding"] input_token_count = model_response["inputTextTokenCount"] print("\nYour input:") print(input_text) print(f"Number of input tokens: {input_token_count}") print(f"Size of the generated embedding: {len(embedding)}") print("Embedding:") print(embedding)
  • For API details, see InvokeModel in AWS SDK for Python (Boto3) API Reference.

For a complete list of AWS SDK developer guides and code examples, see Using Amazon Bedrock with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions.