Amazon Bedrock offers a powerful and convenient way to access leading foundation models (FMs) from AI21 Labs (Jurassic-2), Anthropic (Claude), Stability AI, and Amazon itself. These models are readily available and can be integrated into your applications without needing to manage the underlying infrastructure. This means you don't have to worry about deploying, scaling, or maintaining the models. You simply call them through a well-defined API. This article will guide you through the process of calling Amazon Bedrock-provided models, specifically focusing on AI21 Labs' Jurassic-2 and Anthropic's Claude, using both the AWS SDK and the AWS CLI. Understanding how to programmatically interact with these models opens up a wide range of possibilities for building AI-powered applications, from text generation and summarization to code completion and chatbot development. This ease of access significantly lowers the barrier to entry for leveraging cutting-edge AI technology.
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Before diving into the code, it's crucial to ensure you have the necessary prerequisites in place. First and foremost, you need an active AWS account with access to Amazon Bedrock. You can sign up for an AWS account on the AWS website. Once you have an account, you'll need to configure your AWS credentials. This typically involves installing the AWS CLI and configuring it with your access key ID, secret access key, and default region. You can install the AWS CLI by following the instructions on the AWS documentation page. After installing the CLI, use the aws configure command to enter your credentials and default region. Choosing the correct region is essential, as Bedrock might not be available in all regions. Check the AWS documentation or the AWS console to determine which regions support Bedrock and select one accordingly. Finally, ensure that your IAM user or role has the necessary permissions to access Bedrock. This usually involves attaching the AmazonBedrockFullAccess policy or a more restrictive policy that allows specific actions related to model invocation. You can manage IAM roles and policies through the AWS IAM console. Without the proper permissions, you will encounter authorization errors when attempting to call the Bedrock models.
The AWS SDK provides a programmatic interface for interacting with AWS services, including Amazon Bedrock. For Python, the SDK is known as boto3. You'll need to install boto3 and then use it to create a Bedrock client and invoke the desired model. Here’s a detailed breakdown:
First, ensure you have Python installed. Then, you can install boto3 using pip, the Python package installer. Open your terminal or command prompt and run the following command: pip install boto3. This command will download and install the latest version of boto3 along with its dependencies. After the installation completes, you'll be able to import boto3 into your Python scripts and start interacting with AWS services. It’s crucial to keep boto3 updated to ensure you have the latest features, bug fixes, and security patches. You can update boto3 using the command pip install --upgrade boto3. Regularly updating boto3 can help prevent compatibility issues and ensure you are using the most efficient and secure version of the SDK.
Once boto3 is installed, you can create a Bedrock client. This client will be used to make requests to the Bedrock API. Here's how you can create the client in your Python code:
import boto3
bedrock_runtime = boto3.client(
service_name='bedrock-runtime',
region_name='us-east-1' # Replace with your desired AWS region
)
In this code snippet, we import the boto3 library and then create a client for the bedrock-runtime service. The region_name parameter specifies the AWS region where you want to access Bedrock. Make sure to replace us-east-1 with the appropriate region for your account. The created bedrock_runtime object is your gateway to interacting with Bedrock's models. You can now use this client to invoke various models, such as Jurassic-2 or Claude. Creating the client is a one-time setup step, and you can reuse the same client object for multiple invocations of different models or the same model with different parameters.
Now that you have a Bedrock client, let's see how to invoke the Jurassic-2 model. The invocation process involves constructing a request payload with the necessary parameters and then sending it to the Bedrock API. Here's an example Python code snippet:
import json
body = json.dumps({
"prompt": "Write a short summary about the Amazon rainforest.",
"maxTokens": 200,
"temperature": 0.7,
"topP": 1
})
modelId = 'ai21.j2-ultra-v1'
accept = 'application/json'
contentType = 'application/json'
response = bedrock_runtime.invoke_model(body=body, modelId=modelId, accept=accept, contentType=contentType)
response_body = json.loads(response['body'].read().decode('utf-8'))
print(response_body['completions'][0]['data']['text'])
In this example, we first define the request body as a JSON object containing the prompt, maxTokens, temperature, and topP parameters. The prompt is the text you want the model to complete or generate output for. The maxTokens parameter specifies the maximum number of tokens that the model should generate in its response. The temperature parameter controls the randomness of the output, with higher values (e.g., 1.0) leading to more random and creative outputs, and lower values (e.g., 0.2) leading to more deterministic and predictable outputs. The topP parameter is another way to control the randomness of the output, by selecting the most likely tokens from the probability distribution. After defining the request body, we specify the modelId as ai21.j2-ultra-v1, which identifies the Jurassic-2 Ultra model from AI21 Labs. The accept and contentType headers are set to application/json to indicate that we are sending and receiving JSON data. Finally, we call the invoke_model method of the bedrock_runtime client, passing in these parameters. The response from the Bedrock API is then parsed and the generated text is extracted and printed to the console.
Invoking Claude is similar to invoking Jurassic-2, but the request payload structure is different. Claude models typically use a different set of parameters, and the structure of the request and response JSON is also different. Here’s an example of how to invoke the Claude model using the bedrock-runtime client:
import json
body = json.dumps({
"prompt": "Human: Write a poem about the ocean.\n\nAssistant:",
"max_tokens_to_sample": 200,
"temperature": 0.5,
"top_p": 1,
"stop_sequences": ["\n\nHuman:"]
})
modelId = 'anthropic.claude-v2'
accept = 'application/json'
contentType = 'application/json'
response = bedrock_runtime.invoke_model(body=body, modelId=modelId, accept=accept, contentType=contentType)
response_body = json.loads(response['body'].read().decode('utf-8'))
print(response_body['completion'])
In this example, the prompt parameter includes the special tags Human: and Assistant:. Claude models are trained to follow this dialogue format allowing the model to understand the structure of the conversation. The max_tokens_to_sample parameter specifies the maximum number of tokens that Claude should generate in its response. The temperature and top_p parameters control the randomness of the output. The stop_sequences parameter specifies a list of sequences that signal the model to stop generating output. In this case, we use "\n\nHuman:" as a stop sequence to prevent Claude from generating a response that continues the dialogue. The modelId is set to 'anthropic.claude-v2', which identifies the Claude v2 model. The response from the Bedrock API is parsed and the generated text is extracted from the completion field.
The AWS CLI provides a command-line interface for interacting with AWS services. You can use the CLI to invoke Bedrock models directly from your terminal or command prompt. This is particularly useful for scripting and automation.
The basic structure of the AWS CLI command for invoking Bedrock models is as follows:
aws bedrock-runtime invoke-model \
--model-id <model_id> \
--content-type <content_type> \
--accept <accept> \
--body <file://path/to/request.json> \
--region <your_aws_region> \
output.json
Let's break down each component of this command:
aws bedrock-runtime invoke-model: This specifies the AWS service and the action you want to perform (invoking a model).--model-id <model_id>: This specifies the ID of the Bedrock model you want to invoke. For example, ai21.j2-ultra-v1 for Jurassic-2 or anthropic.claude-v2 for Claude.--content-type <content_type>: This specifies the content type of the request body. Typically, this is application/json.--accept <accept>: This specifies the content type of the response you expect to receive. Typically, this is also application/json.--body <file://path/to/request.json>: This specifies the path to a file containing the JSON payload for the request. The file:// prefix indicates that the body is read from a file.--region <your_aws_region>: This specifies the AWS region where you want to access Bedrock.output.json: This specifies the file where the response from the Bedrock API will be saved.To invoke Jurassic-2 with the AWS CLI, you first need to create a JSON file containing the request payload. For example, you can create a file named jurassic2_request.json with the following content:
{
"prompt": "Write a short story about a cat who goes on an adventure.",
"maxTokens": 250,
"temperature": 0.8,
"topP": 0.9
}
Then, you can use the following CLI command to invoke the Jurassic-2 model:
aws bedrock-runtime invoke-model \
--model-id ai21.j2-ultra-v1 \
--content-type application/json \
--accept application/json \
--body file://jurassic2_request.json \
--region us-east-1 \
jurassic2_response.json
After running this command, the response from the Bedrock API will be saved in the jurassic2_response.json file. You can then parse this file to extract the generated text.
Similarly, to invoke Claude with the AWS CLI, you need to create a JSON file containing the request payload. For example, you can create a file named claude_request.json with the following content:
{
"prompt": "Human: What are the benefits of eating a healthy diet?\n\nAssistant:",
"max_tokens_to_sample": 200,
"temperature": 0.6,
"top_p": 0.9,
"stop_sequences": ["\n\nHuman:"]
}
Then, you can use the following CLI command to invoke the Claude model:
aws bedrock-runtime invoke-model \
--model-id anthropic.claude-v2 \
--content-type application/json \
--accept application/json \
--body file://claude_request.json \
--region us-east-1 \
claude_response.json
After running this command, the response from the Bedrock API will be saved in the claude_response.json file. You can then parse this file to extract the generated text.
When working with Amazon Bedrock, you may encounter errors due to various reasons such as incorrect credentials, invalid request parameters, or service limitations. Proper error handling is crucial for building robust applications that can gracefully handle these situations. Ensure you validate your inputs and handle exceptions accordingly
AccessDeniedException: This error indicates that your IAM user or role does not have the necessary permissions to access Bedrock. Ensure that you have attached the AmazonBedrockFullAccess policy or a more restrictive policy that grants the required permissions.ValidationException: This error indicates that the request payload is invalid. Check the request parameters and ensure that they are in the correct format and within the allowed ranges. For example, make sure that the maxTokens parameter is within the allowed limits.ModelNotReadyException: This error indicates that the model is not yet ready to be invoked. This can happen if you are trying to invoke a model that has just been made available or if there is a temporary issue with the model service. Wait a few minutes and try again.ThrottlingException: This error indicates that you have exceeded the rate limit for the Bedrock API. Reduce the number of requests you are making or implement a retry mechanism with exponential backoff.To effectively troubleshoot issues, it's important to implement proper logging and monitoring. You can use AWS CloudWatch to monitor the performance of your Bedrock applications and track any errors that occur. You can also add logging statements to your code to capture relevant information about the requests and responses. Detailed logs can help you identify the root cause of errors and optimize the performance of your applications. Regularly reviewing your CloudWatch metrics and logs can also help you proactively identify potential issues before they impact your users.
By following the steps outlined in this article, you can successfully call Amazon Bedrock-provided models like Jurassic-2 and Claude using both the AWS SDK and the AWS CLI. This knowledge empowers you to integrate these powerful foundation models into your applications, unlocking a world of possibilities for AI-powered services and solutions. Remember to carefully configure your AWS environment, handle errors gracefully, and leverage logging and monitoring to ensure the robustness and reliability of your applications. With Amazon Bedrock and its diverse range of models, you're well-equipped to embark on an exciting journey of AI innovation. Keep exploring other models and parameters to find those that work best for your use cases.
