* For more information, see the following documentation topic: - * + *
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class InvokeModelAsync {
// snippet-start:[bedrock-runtime.java2.invoke_mistral_7b_async.main]
+
/**
* Asynchronously invokes the Mistral 7B model to run an inference based on the provided input.
*
@@ -56,15 +57,15 @@ public static List
+ * For more examples in different programming languages check out the Amazon Bedrock User Guide at:
+ * https://docs.aws.amazon.com/bedrock/latest/userguide/service_code_examples.html
+ */
+
+public class G1InvokeModelScenarios {
+ // snippet-start:[bedrock-runtime.java2.InvokeModel_TitanTextEmbeddingsG1_AdditionalFields]
+
+ /**
+ * Invoke Amazon Titan Text Embeddings G1 and print the response.
+ *
+ * @param inputText - The text to convert to an embedding.
+ * @return The {@link JSONObject} representing the model's response.
+ */
+ public static JSONObject invokeModel(String inputText) {
+
+ // 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 Text Embeddings G1.
+ var modelId = "amazon.titan-embed-text-v1";
+
+ // Format the request payload using Anthropic's native structure.
+ var nativeRequest = "{\"inputText\": \"" + inputText + "\"}";
+
+ // 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;
+ }
+ // snippet-end:[bedrock-runtime.java2.InvokeModel_TitanTextEmbeddingsG1_AdditionalFields]
+
+ public static void main(String[] args) throws IOException {
+ new Demo().run();
+ }
+
+ private static class Demo {
+ private final ScenarioRunner demo = new ScenarioRunner()
+ .addScenario("Create an embedding with custom inference parameters");
+
+ void run() throws IOException {
+ demo.printHeader();
+
+ var inputText = "Please recommend books with a theme similar to the movie 'Inception'.";
+ var response = runTextScenario(inputText);
+ demo.printCurrentResponse(response);
+
+ demo.printFooter();
+ }
+
+ private JSONObject runTextScenario(String inputText) {
+ demo.printScenarioHeader("Scenario - Create an embedding with custom inference parameters:");
+ System.out.printf("%nInput text: '%s'%n", inputText);
+ System.out.printf("Waiting for the response...%n");
+ return invokeModel(inputText);
+ }
+ }
+}
diff --git a/javav2/example_code/bedrock-runtime/src/main/java/com/example/bedrockruntime/models/amazon/embeddings/text/V2InvokeModelQuickstart.java b/javav2/example_code/bedrock-runtime/src/main/java/com/example/bedrockruntime/models/amazon/embeddings/text/V2InvokeModelQuickstart.java
new file mode 100644
index 00000000000..82344359541
--- /dev/null
+++ b/javav2/example_code/bedrock-runtime/src/main/java/com/example/bedrockruntime/models/amazon/embeddings/text/V2InvokeModelQuickstart.java
@@ -0,0 +1,44 @@
+// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+// SPDX-License-Identifier: Apache-2.0
+
+package com.example.bedrockruntime.models.amazon.embeddings.text;
+
+import org.json.JSONObject;
+import software.amazon.awssdk.core.SdkBytes;
+import software.amazon.awssdk.regions.Region;
+import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient;
+
+// snippet-start:[bedrock-runtime.java2.InvokeModel_TitanTextEmbeddingsV2_Quickstart]
+// Generate and print an embedding with Amazon Titan Text Embeddings V2.
+public class V2InvokeModelQuickstart {
+
+ public static void main(String[] args) {
+
+ // 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 Text Embeddings V2.
+ var modelId = "amazon.titan-embed-text-v2:0";
+
+ // The text to convert into an embedding.
+ var inputText = "Please recommend books with a theme similar to the movie 'Inception'.";
+
+ // Create a JSON payload using the model's native structure.
+ var request = new JSONObject().put("inputText", inputText);
+
+ // Encode and send the request.
+ var response = client.invokeModel(req -> req
+ .body(SdkBytes.fromUtf8String(request.toString()))
+ .modelId(modelId));
+
+ // Decode the model's native response body.
+ var nativeResponse = new JSONObject(response.body().asUtf8String());
+
+ // Extract and print the generated embedding.
+ var embedding = nativeResponse.getJSONArray("embedding");
+ System.out.println(embedding);
+ }
+}
+// snippet-end:[bedrock-runtime.java2.InvokeModel_TitanTextEmbeddingsV2_Quickstart]
diff --git a/javav2/example_code/bedrock-runtime/src/main/java/com/example/bedrockruntime/models/amazon/embeddings/text/V2InvokeModelScenarios.java b/javav2/example_code/bedrock-runtime/src/main/java/com/example/bedrockruntime/models/amazon/embeddings/text/V2InvokeModelScenarios.java
new file mode 100644
index 00000000000..d1eed194cf1
--- /dev/null
+++ b/javav2/example_code/bedrock-runtime/src/main/java/com/example/bedrockruntime/models/amazon/embeddings/text/V2InvokeModelScenarios.java
@@ -0,0 +1,103 @@
+// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+// SPDX-License-Identifier: Apache-2.0
+
+package com.example.bedrockruntime.models.amazon.embeddings.text;
+
+import com.example.bedrockruntime.libs.ScenarioRunner;
+import org.json.JSONObject;
+import software.amazon.awssdk.core.SdkBytes;
+import software.amazon.awssdk.regions.Region;
+import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient;
+
+import java.io.IOException;
+
+/**
+ * This program demonstrates how to use InvokeModel with Amazon Titan Text Embeddings V2 on Amazon Bedrock.
+ *
+ * For more examples in different programming languages check out the Amazon Bedrock User Guide at:
+ * https://docs.aws.amazon.com/bedrock/latest/userguide/service_code_examples.html
+ */
+
+public class V2InvokeModelScenarios {
+ // snippet-start:[bedrock-runtime.java2.InvokeModel_TitanTextEmbeddingsV2_AdditionalFields]
+
+ /**
+ * 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;
+ }
+ // snippet-end:[bedrock-runtime.java2.InvokeModel_TitanTextEmbeddingsV2_AdditionalFields]
+
+ public static void main(String[] args) throws IOException {
+ new Demo().run();
+ }
+
+ private static class Demo {
+ private final ScenarioRunner demo = new ScenarioRunner()
+ .addScenario("Create an embedding with custom inference parameters");
+
+ void run() throws IOException {
+ demo.printHeader();
+
+ var inputText = "Please recommend books with a theme similar to the movie 'Inception'.";
+ var dimensions = 256;
+ var normalize = true;
+ var response = runTextScenario(inputText, dimensions, normalize);
+ demo.printCurrentResponse(response);
+
+ demo.printFooter();
+ }
+
+ private JSONObject runTextScenario(String inputText, int dimensions, boolean normalize) {
+ demo.printScenarioHeader("Scenario - Create an embedding with custom inference parameters:");
+
+ System.out.printf("%nInput text: '%s'%n", inputText);
+ System.out.printf("Dimensions: '%d'%n", dimensions);
+ System.out.printf("Normalize: '%b'%n%n", normalize);
+
+ System.out.printf("Waiting for the response...%n");
+ return invokeModel(inputText, dimensions, normalize);
+ }
+ }
+}
diff --git a/javav2/example_code/bedrock-runtime/src/test/java/amazontitan/TestTitanEmbeddingsG1.java b/javav2/example_code/bedrock-runtime/src/test/java/amazontitan/TestTitanEmbeddingsG1.java
new file mode 100644
index 00000000000..2daaaa212af
--- /dev/null
+++ b/javav2/example_code/bedrock-runtime/src/test/java/amazontitan/TestTitanEmbeddingsG1.java
@@ -0,0 +1,29 @@
+// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+// SPDX-License-Identifier: Apache-2.0
+
+package amazontitan;
+
+import com.example.bedrockruntime.models.amazon.embeddings.text.G1InvokeModelQuickstart;
+import org.junit.jupiter.api.Tag;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.TestInstance;
+
+import static com.example.bedrockruntime.models.amazon.embeddings.text.G1InvokeModelScenarios.invokeModel;
+import static org.junit.jupiter.api.Assertions.assertDoesNotThrow;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+
+@Tag("IntegrationTest")
+@TestInstance(TestInstance.Lifecycle.PER_METHOD)
+public class TestTitanEmbeddingsG1 {
+ @Test
+ void Quickstart() {
+ assertDoesNotThrow(() -> G1InvokeModelQuickstart.main(null));
+ }
+
+ @Test
+ void InvokeModel() {
+ var inputText = "A text input";
+ var response = invokeModel(inputText);
+ assertFalse(response.getJSONArray("embedding").isEmpty());
+ }
+}
diff --git a/javav2/example_code/bedrock-runtime/src/test/java/amazontitan/TestTitanEmbeddingsV2.java b/javav2/example_code/bedrock-runtime/src/test/java/amazontitan/TestTitanEmbeddingsV2.java
new file mode 100644
index 00000000000..6313aac2567
--- /dev/null
+++ b/javav2/example_code/bedrock-runtime/src/test/java/amazontitan/TestTitanEmbeddingsV2.java
@@ -0,0 +1,31 @@
+// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+// SPDX-License-Identifier: Apache-2.0
+
+package amazontitan;
+
+import com.example.bedrockruntime.models.amazon.embeddings.text.V2InvokeModelQuickstart;
+import org.junit.jupiter.api.Tag;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.TestInstance;
+
+import static com.example.bedrockruntime.models.amazon.embeddings.text.V2InvokeModelScenarios.invokeModel;
+import static org.junit.jupiter.api.Assertions.assertDoesNotThrow;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+
+@Tag("IntegrationTest")
+@TestInstance(TestInstance.Lifecycle.PER_METHOD)
+public class TestTitanEmbeddingsV2 {
+ @Test
+ void Quickstart() {
+ assertDoesNotThrow(() -> V2InvokeModelQuickstart.main(null));
+ }
+
+ @Test
+ void InvokeModel() {
+ var inputText = "A text input";
+ var dimensions = 256;
+ var normalize = true;
+ var response = invokeModel(inputText, dimensions, normalize);
+ assertFalse(response.getJSONArray("embedding").isEmpty());
+ }
+}
diff --git a/python/example_code/bedrock-agent/requirements.txt b/python/example_code/bedrock-agent/requirements.txt
index 6c7a79a78b3..4b8fab22567 100644
--- a/python/example_code/bedrock-agent/requirements.txt
+++ b/python/example_code/bedrock-agent/requirements.txt
@@ -1,6 +1,9 @@
-boto3==1.33.8
-botocore==1.33.8
+boto3==1.34.98
+botocore==1.34.98
+certifi==2023.11.17
+charset-normalizer==3.3.2
colorama==0.4.6
+idna==3.6
iniconfig==2.0.0
jmespath==1.0.1
packaging==23.2
@@ -8,6 +11,7 @@ pluggy==1.3.0
pytest==7.4.3
python-dateutil==2.8.2
PyYAML==6.0.1
-s3transfer==0.8.2
+requests==2.31.0
+s3transfer==0.10.1
six==1.16.0
urllib3==2.0.7
diff --git a/python/example_code/bedrock-runtime/README.md b/python/example_code/bedrock-runtime/README.md
index 2d3fd43ff1b..4972e809683 100644
--- a/python/example_code/bedrock-runtime/README.md
+++ b/python/example_code/bedrock-runtime/README.md
@@ -39,6 +39,8 @@ python -m pip install -r requirements.txt
### Invoke model examples
- [AI21 Labs Jurassic-2: Text generation](bedrock_runtime_wrapper.py#L79)
+- [Amazon Titan Text Embeddings G1](models/amazon/titan_embeddings_text/g1_invoke_model_quickstart.py#L4)
+- [Amazon Titan Text Embeddings V2](models/amazon/titan_embeddings_text/g1_invoke_model_quickstart.py#L4)
- [Amazon Titan: Image generation](bedrock_runtime_wrapper.py#L238)
- [Anthropic Claude 2: Real-time response stream processing](bedrock_runtime_wrapper.py#L283)
- [Anthropic Claude 2: Text generation](bedrock_runtime_wrapper.py#L39)
diff --git a/python/example_code/bedrock-runtime/models/amazon/titan_embeddings_text/g1_invoke_model_quickstart.py b/python/example_code/bedrock-runtime/models/amazon/titan_embeddings_text/g1_invoke_model_quickstart.py
new file mode 100644
index 00000000000..4bcc9bec446
--- /dev/null
+++ b/python/example_code/bedrock-runtime/models/amazon/titan_embeddings_text/g1_invoke_model_quickstart.py
@@ -0,0 +1,32 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+# snippet-start:[python.example_code.bedrock-runtime.InvokeModel_TitanTextEmbeddingsG1_Quickstart]
+# Generate and print an embedding with Amazon Titan Text Embeddings G1
+
+import boto3
+import json
+
+# Create a Bedrock Runtime client in the AWS Region of your choice.
+client = boto3.client("bedrock-runtime", region_name="us-west-2")
+
+# Set the model ID, e.g., Titan Text Embeddings G1.
+model_id = "amazon.titan-embed-text-v1"
+
+# 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.
+request = {"inputText": input_text}
+
+# Encode and send the request.
+response = client.invoke_model(body=json.dumps(request), modelId=model_id)
+
+# Decode the model's native response body.
+model_response = json.loads(response["body"].read())
+
+# Extract and print the generated embedding.
+embedding = model_response["embedding"]
+print(embedding)
+
+# snippet-end:[python.example_code.bedrock-runtime.InvokeModel_TitanTextEmbeddingsG1_Quickstart]
diff --git a/python/example_code/bedrock-runtime/models/amazon/titan_embeddings_text/g1_invoke_model_scenarios.py b/python/example_code/bedrock-runtime/models/amazon/titan_embeddings_text/g1_invoke_model_scenarios.py
new file mode 100644
index 00000000000..5bc7e9866bc
--- /dev/null
+++ b/python/example_code/bedrock-runtime/models/amazon/titan_embeddings_text/g1_invoke_model_scenarios.py
@@ -0,0 +1,92 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Purpose
+
+This class demonstrates how to use InvokeModel with Amazon Titan Text Embeddings G1
+on Amazon Bedrock.
+
+For more examples in different programming languages check out the Amazon Bedrock User Guide at:
+https://docs.aws.amazon.com/bedrock/latest/userguide/service_code_examples.html
+"""
+
+import boto3
+import json
+import pprint
+import sys
+
+# Add relative path to include demo_tools in this code example without needing to set up.
+sys.path.append("../..")
+import demo_tools.question as q
+
+
+# snippet-start:[python.example_code.bedrock-runtime.InvokeModel_TitanTextEmbeddingsG1_AdditionalFields]
+def invoke_model(input_text, client=None):
+ """
+ Invoke Amazon Titan Text Embeddings G1 and print the response.
+
+ :param input_text: The text to convert to an embedding.
+ :param client: An optional Bedrock Runtime client instance.
+ Defaults to None if not provided.
+ :return: The model's response object.
+ """
+
+ # Create a Bedrock Runtime client if not provided.
+ client = client or boto3.client("bedrock-runtime", region_name="us-west-2")
+
+ # Set the model ID, e.g., Titan Text Embeddings G1.
+ model_id = "amazon.titan-embed-text-v1"
+
+ # Create the request for the model.
+ request = {"inputText": input_text}
+
+ # Encode and send the request.
+ response = client.invoke_model(
+ body=json.dumps(request),
+ modelId=model_id,
+ )
+
+ # Decode the response
+ 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(f"Embedding: {embedding}\n")
+ print(f"Input token count: {input_token_count}")
+
+ return model_response
+
+
+# snippet-end:[python.example_code.bedrock-runtime.InvokeModel_TitanTextEmbeddingsG1_AdditionalFields]
+
+
+def run_demo():
+ print(f"{'=' * 80}\nWelcome to the Amazon Bedrock demo!")
+
+ print(f"{'=' * 80}\nDemo: Create an embedding\n{'-' * 80}")
+ print("Type an input text you'd like to turn into an embedding:")
+ input_text = q.ask("", q.non_empty)
+
+ print(f"{'-' * 80}")
+ print(f"Embedding the text: '{input_text}'")
+
+ print(f"{'-' * 80}")
+ response = invoke_model(input_text)
+
+ input(f"{'-' * 80}\nPress Enter to see the detailed response...")
+ print("Returned response:")
+ pprint.pprint(response)
+
+ print(f"{'=' * 80}\nThanks for running the Amazon Bedrock demo!")
+ print("For more examples in many different programming languages check out:")
+ print(
+ "https://docs.aws.amazon.com/bedrock/latest/userguide/service_code_examples.html"
+ )
+ print("=" * 80)
+
+
+if __name__ == "__main__":
+ run_demo()
diff --git a/python/example_code/bedrock-runtime/models/amazon/titan_embeddings_text/v2_invoke_model_quickstart.py b/python/example_code/bedrock-runtime/models/amazon/titan_embeddings_text/v2_invoke_model_quickstart.py
new file mode 100644
index 00000000000..a29bdc75af0
--- /dev/null
+++ b/python/example_code/bedrock-runtime/models/amazon/titan_embeddings_text/v2_invoke_model_quickstart.py
@@ -0,0 +1,32 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+# snippet-start:[python.example_code.bedrock-runtime.InvokeModel_TitanTextEmbeddingsV2_Quickstart]
+# 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-west-2")
+
+# 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.
+request = {"inputText": input_text}
+
+# Encode and send the request.
+response = client.invoke_model(body=json.dumps(request), modelId=model_id)
+
+# Decode the model's native response body.
+model_response = json.loads(response["body"].read())
+
+# Extract and print the generated embedding.
+embedding = model_response["embedding"]
+print(embedding)
+
+# snippet-end:[python.example_code.bedrock-runtime.InvokeModel_TitanTextEmbeddingsV2_Quickstart]
diff --git a/python/example_code/bedrock-runtime/models/amazon/titan_embeddings_text/v2_invoke_model_scenarios.py b/python/example_code/bedrock-runtime/models/amazon/titan_embeddings_text/v2_invoke_model_scenarios.py
new file mode 100644
index 00000000000..e33916569b7
--- /dev/null
+++ b/python/example_code/bedrock-runtime/models/amazon/titan_embeddings_text/v2_invoke_model_scenarios.py
@@ -0,0 +1,107 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Purpose
+
+This class demonstrates how to use InvokeModel with Amazon Titan Text Embeddings v2
+on Amazon Bedrock.
+
+For more examples in different programming languages check out the Amazon Bedrock User Guide at:
+https://docs.aws.amazon.com/bedrock/latest/userguide/service_code_examples.html
+"""
+
+import boto3
+import json
+import pprint
+import sys
+
+# Add relative path to include demo_tools in this code example without needing to set up.
+sys.path.append("../..")
+import demo_tools.question as q
+
+
+# snippet-start:[python.example_code.bedrock-runtime.InvokeModel_TitanTextEmbeddingsV2_AdditionalFields]
+def invoke_model(input_text, dimensions, normalize, client=None):
+ """
+ Invoke Amazon Titan Text Embeddings v2 with additional inference parameters.
+
+ :param input_text: The text to convert to an embedding.
+ :param dimensions: The number of dimensions the output embeddings should have.
+ Accepted values: 256, 512, 1024. Default: 1024.
+ :param normalize: A flag indicating whether or not to normalize the output embeddings.
+ Default: True.
+ :param client: An optional Bedrock Runtime client instance.
+ Defaults to None if not provided.
+ :return: The model's response object.
+ """
+
+ # Create a Bedrock Runtime client if not provided.
+ client = client or boto3.client("bedrock-runtime", region_name="us-west-2")
+
+ # Set the model ID, e.g., Titan Embed Text v2.0.
+ model_id = "amazon.titan-embed-text-v2:0"
+
+ # Create the request for the model.
+ request = {
+ "inputText": input_text,
+ "dimensions": dimensions,
+ "normalize": normalize,
+ }
+
+ # Encode and send the request.
+ response = client.invoke_model(
+ body=json.dumps(request),
+ modelId=model_id,
+ )
+
+ # Decode the response
+ 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(f"Embedding: {embedding}\n")
+ print(f"Input token count: {input_token_count}")
+
+ return model_response
+
+
+# snippet-end:[python.example_code.bedrock-runtime.InvokeModel_TitanTextEmbeddingsV2_AdditionalFields]
+
+
+def run_demo():
+ print(f"{'=' * 80}\nWelcome to the Amazon Bedrock demo!")
+
+ print(f"{'=' * 80}\nDemo: Create an embedding\n{'-' * 80}")
+ print("Type an input text you'd like to turn into an embedding:")
+ input_text = q.ask("", q.non_empty)
+
+ valid_dimensions = [256, 512, 1024]
+ dimensions = valid_dimensions[
+ q.choose("Select the number of dimensions: ", valid_dimensions)
+ ]
+ normalize = q.ask("Normalize [y/N]: ", q.is_yesno)
+
+ print(f"{'-' * 80}")
+ print(f"Embedding the text: '{input_text}'")
+ print(f"Number of dimensions: {dimensions} - Normalized: {normalize}")
+
+ print(f"{'-' * 80}")
+ response = invoke_model(input_text, dimensions, normalize)
+
+ input(f"{'-' * 80}\nPress Enter to see the detailed response...")
+ print("Returned response:")
+ pprint.pprint(response)
+
+ print(f"{'=' * 80}\nThanks for running the Amazon Bedrock demo!")
+ print("For more examples in many different programming languages check out:")
+ print(
+ "https://docs.aws.amazon.com/bedrock/latest/userguide/service_code_examples.html"
+ )
+ print("=" * 80)
+
+
+if __name__ == "__main__":
+ run_demo()
diff --git a/python/example_code/bedrock-runtime/models/meta/llama3/invoke_model_quickstart.py b/python/example_code/bedrock-runtime/models/meta/llama3/invoke_model_quickstart.py
index 9aeceb38549..39c44211628 100644
--- a/python/example_code/bedrock-runtime/models/meta/llama3/invoke_model_quickstart.py
+++ b/python/example_code/bedrock-runtime/models/meta/llama3/invoke_model_quickstart.py
@@ -7,6 +7,7 @@
import boto3
import json
+
# Create a Bedrock Runtime client in the AWS Region of your choice.
client = boto3.client("bedrock-runtime", region_name="us-west-2")
diff --git a/python/example_code/bedrock-runtime/requirements.txt b/python/example_code/bedrock-runtime/requirements.txt
index 346abda2894..53e4580c098 100644
--- a/python/example_code/bedrock-runtime/requirements.txt
+++ b/python/example_code/bedrock-runtime/requirements.txt
@@ -1,13 +1,13 @@
-boto3==1.34.55
-botocore==1.34.55
+boto3==1.34.98
+botocore==1.34.98
colorama==0.4.6
iniconfig==2.0.0
jmespath==1.0.1
-packaging==23.2
-pluggy==1.3.0
-pytest==7.4.3
-pytest-asyncio==0.21.1
-python-dateutil==2.8.2
-s3transfer==0.10.0
+packaging==24.0
+pluggy==1.5.0
+pytest==8.1.1
+pytest-asyncio==0.23.6
+python-dateutil==2.9.0.post0
+s3transfer==0.10.1
six==1.16.0
-urllib3==2.0.7
+urllib3==2.2.1
diff --git a/python/example_code/bedrock-runtime/test/models/amazon_titan/test_invoke_model.py b/python/example_code/bedrock-runtime/test/models/amazon_titan/test_invoke_model.py
new file mode 100644
index 00000000000..9497d9f068c
--- /dev/null
+++ b/python/example_code/bedrock-runtime/test/models/amazon_titan/test_invoke_model.py
@@ -0,0 +1,63 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Unit tests for:
+- models/amazon/titan_embeddings_text/g1_invoke_model_scenarios.py
+- models/amazon/titan_embeddings_text/v2_invoke_model_scenarios.py
+"""
+
+import boto3
+import io
+import json
+import pytest
+
+from models.amazon.titan_embeddings_text.g1_invoke_model_scenarios import (
+ invoke_model as invoke_embeddings_g1,
+)
+from models.amazon.titan_embeddings_text.v2_invoke_model_scenarios import (
+ invoke_model as invoke_embeddings_v2,
+)
+
+EMBEDDINGS_G1_MODEL_ID = "amazon.titan-embed-text-v1"
+EMBEDDINGS_V2_MODEL_ID = "amazon.titan-embed-text-v2:0"
+
+client = boto3.client(service_name="bedrock-runtime", region_name="us-west-2")
+
+
+def test_invoke_embeddings_g1(make_stubber):
+ input_text = "An input text."
+ expected_params = {
+ "modelId": EMBEDDINGS_G1_MODEL_ID,
+ "body": json.dumps({"inputText": input_text}),
+ }
+ response = {
+ "contentType": "application/json",
+ "body": io.BytesIO(
+ json.dumps({"embedding": [], "inputTextTokenCount": 1}).encode("utf-8")
+ ),
+ }
+ make_stubber(client).stub_invoke_model(expected_params, response)
+ result = invoke_embeddings_g1(input_text, client)
+ assert result is not None
+
+
+def test_invoke_embeddings_v2(make_stubber):
+ input_text = "An input text."
+ dimensions = 1024
+ normalize = True
+ expected_params = {
+ "modelId": EMBEDDINGS_V2_MODEL_ID,
+ "body": json.dumps(
+ {"inputText": input_text, "dimensions": dimensions, "normalize": normalize}
+ ),
+ }
+ response = {
+ "contentType": "application/json",
+ "body": io.BytesIO(
+ json.dumps({"embedding": [], "inputTextTokenCount": 1}).encode("utf-8")
+ ),
+ }
+ make_stubber(client).stub_invoke_model(expected_params, response)
+ result = invoke_embeddings_v2(input_text, dimensions, normalize, client)
+ assert result is not None
diff --git a/python/example_code/bedrock-runtime/test/models/amazon_titan/test_invoke_model_quickstarts.py b/python/example_code/bedrock-runtime/test/models/amazon_titan/test_invoke_model_quickstarts.py
new file mode 100644
index 00000000000..4b8bf3ca4aa
--- /dev/null
+++ b/python/example_code/bedrock-runtime/test/models/amazon_titan/test_invoke_model_quickstarts.py
@@ -0,0 +1,40 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Integration tests for:
+- models/amazon/titan_embeddings_text/g2_invoke_model_quickstart.py
+- models/amazon/titan_embeddings_text/v2_invoke_model_quickstart.py
+"""
+
+import pytest
+import subprocess
+import sys
+
+
+@pytest.mark.integ
+def test_titan_embeddings_text_g1_quickstart():
+ result = subprocess.run(
+ [
+ sys.executable,
+ "models/amazon/titan_embeddings_text/g1_invoke_model_quickstart.py",
+ ],
+ capture_output=True,
+ text=True,
+ )
+ assert result.stdout != ""
+ assert result.returncode == 0
+
+
+@pytest.mark.integ
+def test_titan_embeddings_text_v2_quickstart():
+ result = subprocess.run(
+ [
+ sys.executable,
+ "models/amazon/titan_embeddings_text/v2_invoke_model_quickstart.py",
+ ],
+ capture_output=True,
+ text=True,
+ )
+ assert result.stdout != ""
+ assert result.returncode == 0
diff --git a/python/test_tools/bedrock_runtime_stubber.py b/python/test_tools/bedrock_runtime_stubber.py
index 6ac95919308..a87a0ba90c9 100644
--- a/python/test_tools/bedrock_runtime_stubber.py
+++ b/python/test_tools/bedrock_runtime_stubber.py
@@ -29,6 +29,11 @@ def __init__(self, client, use_stubs=True):
"""
super().__init__(client, use_stubs)
+ def stub_invoke_model(self, expected_params, response, error_code=None):
+ self._stub_bifurcator(
+ "invoke_model", expected_params, response, error_code=error_code
+ )
+
def stub_invoke_mistral_7b(self, prompt, error_code=None):
expected_params = {
"modelId": "mistral.mistral-7b-instruct-v0:2",