MochaGPT - Starbucks Drinks GPT Action

Overview

This serverless Python API uses AWS services to provide data from a Starbucks drinks table to ChatGPT. The API support GET queries and utilize AWS Lambda, Amazon API Gateway, and DynamoDB.

Project Details

Data Preparation and Loading

The data for this project came from a kaggle data set: https://www.kaggle.com/datasets/starbucks/starbucks-menu

1. CSV File Analysis: Analyzed and combined two CSV files containing Starbucks drinks data using ChatGPT.
2. Data Cleaning: Cleaned and merged the data, ensuring consistency across the datasets.
3. DynamoDB Preparation:
   • Created table StarbucksDrinksCatalog and CloudWatch dashboard.
   • Chose BeverageID (a combination of Beverage and Beverage_prep) as the primary key.
   • Transformed the data into a format suitable for DynamoDB.
4. Data Loading:
   • Utilized Boto3 in Python to load the transformed data into DynamoDB.
   • Verified the integrity of the data in the DynamoDB table against the original CSV files.

AWS Configuration and Monitoring

• Set up AWS credentials for programmatic access using Boto3.
• Configured Amazon CloudWatch for monitoring DynamoDB and Lambda functions.

Lambda Function Development

• Developed an AWS Lambda function in Python to handle API requests.
• The function is capable of querying DynamoDB to retrieve random beverage data.
• Set up necessary permissions for Lambda to access DynamoDB.
• Successfully tested the Lambda function locally.

Lambda Deployment and API Gateway Integration

• Deployed the Lambda function to AWS.
• Created a Lambda URL for direct invocation of the function.
• Successfully integrated the Lambda function with Amazon API Gateway.
• Set up an API endpoint (/random) to access the Lambda function.
• Created an OpenAPI definition for the /random endpoint.

Future Updates

Monitor and Optimize

• Use CloudWatch to monitor API and Lambda performance.
• Optimize configurations for efficiency and cost-effectiveness.

Additional Features and Enhancements

• Consider adding more features to the API based on user feedback.
• Explore opportunities for scaling and improving the system architecture.