serverless-aws-bedrock-ai-api-gateway-aws-lambda

My code snippet of keeping note of how to deploy a serverless AI model using Amazon Bedrock, API Gateway, AWS Lambda, and Postman to generate text-based responses from an AI model (Cohere model). This project showcases the use of AWS services to build a scalable and efficient solution for AI model deployment.

Key Technologies & Services Used:

• Amazon API Gateway: Used to create the REST API to interact with the Lambda function.
• AWS Lambda: Serverless compute service used to run the AI model inference logic.
• Amazon Bedrock: Provides foundation models (e.g., Cohere model) to perform AI tasks such as generating responses.
• IAM (Identity and Access Management): Manages permissions and roles for Lambda to interact with other AWS services.
• Insomania/Postman: Tool to test the API and Lambda function by sending requests and receiving responses.

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Prerequisites

• AWS account with necessary permissions to create Lambda functions, IAM roles, and API Gateway.
• Postman for testing the API endpoints.
• Familiarity with AWS services, including Lambda, API Gateway, IAM, and Amazon Bedrock.

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Steps to Deploy

Step 1: Request Access to AI Model in Amazon Bedrock

1. Go to Amazon Bedrock in your AWS account and navigate to Base models under Foundation models.
2. Request access to Cohere model, which will be used for generating text-based responses based on prompts.
3. Ensure that the model is available and ready to be used for this project.

Step 2: Create IAM Role for Lambda Function

1. In the IAM console, go to Roles and create a new role with the name ChatBot-Lambda-role-access.
2. Select Lambda as the use case.
3. Add the following policies to the role:
   • Bedrock permissions (to interact with the Cohere model).
   • CloudWatch Logs permissions (for logging Lambda function execution).
4. Create the role and note down the role ARN.

Step 3: Create Lambda Function

1. Go to AWS Lambda and create a new function using Python runtime.
2. Use the existing IAM role created in Step 2 (ChatBot-Lambda-role-access).
3. Set the timeout configuration to 1-2 minutes and adjust the memory (e.g., 500 MB).
4. Click Create function.

Step 4: Create API Gateway

1. Go to Amazon API Gateway and select Create API.
2. Build a new REST API and create a resource with the path /ask.
3. Enable CORS (Cross-Origin Resource Sharing) for the resource.
4. Create a POST method and link it to the Lambda function.
5. Ensure Lambda Proxy Integration is enabled and select the correct region for Lambda.
6. Deploy the API by creating a new stage (e.g., dev).

Step 5: Write and Deploy Lambda Code

1. Write the Lambda code in a Python file (e.g., lambda_function.py).
2. Refer to the file lambda_function.py for the implementation.
3. Deploy the code to your Lambda function using the AWS Console or CLI.

Step 6: Test the Lambda Function

1. Copy the Invoke URL from the API Gateway stage deployment.
2. Use Postman to send a POST request to the endpoint:
   • Append /ask to the Invoke URL.
   • Example payload:

     {
       "Prompt": "Who is Andy Ng?"
     }

3. Validate the response returned by the API.

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References

• Cohere Model Parameters Documentation
• AWS Lambda Documentation
• Amazon API Gateway Documentation