Automating Code Generation with AWS Lambda and Bedrock

In modern software development, the ability to quickly generate code snippets or templates can significantly boost productivity. Whether it's for prototyping, scaffolding new projects, or automating repetitive tasks, having a tool that can generate code based on given instructions can be invaluable. In this article, we'll explore how to leverage AWS Lambda and Bedrock, an AI-powered language model, to automatically generate code snippets based on provided instructions.

Introduction

This tutorial focuses on building an AWS Lambda function that interacts with Bedrock, an AI language model developed by OpenAI, to generate code snippets. The generated code will then be saved to an Amazon S3 bucket for further use.

Prerequisites

Before we dive into the implementation, make sure you have the following prerequisites:

• An AWS account with permissions to create Lambda functions and S3 buckets.
• Basic knowledge of Python programming.
• Familiarity with AWS Lambda and S3 services.

Setup

1. Installing Dependencies

To interact with AWS services and Bedrock, we'll use the boto3 library. Install it using pip:

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pip install boto3

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2. Configuration

Ensure you have the necessary IAM permissions to access Lambda, S3, and Bedrock services.

Understanding the Code

Let's dissect the provided Python code:

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import json
import boto3
import botocore.config
from datetime import datetime

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• We import necessary libraries, including json, boto3 for AWS SDK, botocore.config for configuring the AWS client, and datetime for timestamp manipulation.

generate_code_using_bedrock()

This function takes a message and a programming language as input and generates code using Bedrock.

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def generate_code_using_bedrock(message: str, language: str) -> str:
    prompt_text = f"Human: Write {language} code for the following instructions{message}\\nAssistant:"

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• prompt_text is the instruction given to the AI model, specifying the desired language and the instructions.

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    body = {
        "prompt": prompt_text,
        "max_tokens_to_sample": 2048,
        "temperature": 0.1,
        "top_p": 0.2,
        "top_k": 250,
        "stop_sequences": ["\\n\\nHuman"]
    }

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• We define the parameters for interacting with the Bedrock model. These parameters include the prompt text, maximum tokens to sample, temperature, top_p, top_k, and stop sequences.

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    try:
        bedrock = boto3.client("bedrock-runtime", region_name="us-west-2", config=botocore.config.Config(read_timeout=300, retries={'max_attempts': 3}))

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• We create a client for accessing the Bedrock runtime API using boto3.

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        response = bedrock.invoke_model(body=json.dumps(body), accept="application/json", contentType="application/json", modelId="anthropic.claude-v2")

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• We invoke the Bedrock model with the specified parameters.

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        response_content = response["body"].read().decode("utf-8")
        response_data = json.loads(response_content)
        code = response_data["completion"].strip()

        return code

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• We extract the generated code from the response and return it.

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    except Exception as e:
        print("Error generating the code", e)
        raise e  # Re-raise the exception to get more details

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• We handle exceptions and print error messages if code generation fails.

save_code_to_s3_bucket()

This function saves the generated code to an S3 bucket.

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def save_code_to_s3_bucket(code: str, s3_bucket: str, s3_key: str):
    s3 = boto3.client("s3")

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• We create an S3 client using boto3.

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    try:
        Body = bytes(json.dumps(code, default=str).encode())
        s3.put_object(Bucket=s3_bucket, Key=s3_key, Body=Body)
        print("Code saved to S3")

    except Exception as e:
        print("Error while saving to S3", e)

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• We serialize the code to JSON format and save it to the specified S3 bucket.

lambda_handler()

This is the main Lambda handler function.

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def lambda_handler(event, context):
    event_data = json.loads(event["body"])
    message = event_data["message"]
    language = event_data["key"]

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• We extract the message and programming language from the incoming event.

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    generated_code = generate_code_using_bedrock(message, language)

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• We call the generate_code_using_bedrock() function to generate code.

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    if generated_code:
        current_time = datetime.now().strftime("%H%M%S")
        s3_key = f"code-output/{current_time}.py"
        s3_bucket = "raneem-code-generation-bucket"
        save_code_to_s3_bucket(generated_code, s3_bucket, s3_key)

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• If code generation is successful, we generate a timestamp and construct an S3 key for the output file. Then, we save the code to the specified S3 bucket.

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        return {
            'statusCode': 200,
            'body': json.dumps(generated_code)
        }

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• We return a success response with the generated code.

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    else:
        print("No code was generated")
        return {
            'statusCode': 500,
            'body': json.dumps('Error generating code')
        }

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• If code generation fails, we return an error response.

Conclusion

In this tutorial, we've learned how to leverage AWS Lambda and Bedrock to automatically generate code snippets based on provided instructions. By integrating these services, developers can streamline their workflow and improve productivity by automating code generation tasks. Feel free to customize and extend this solution according to your specific requirements.