Personalized product recommendations generated by AI for individual consumers, and an ad ranking system that helps multi-tenant businesses optimize their ad placements. "With this businesses can drive sales by leveraging intelligent product recommendations and targeted ad placements, all in one place".
Project Structure and steps:
Week 1-2:
Set up the basic Flask application and database structure using MongoDB.
Set up Elasticsearch on AWS and establish connection with the Flask application.
Implement basic search functionality using Elasticsearch.
Week 3-4:
Implement language models using Langchain and integrate them with Elasticsearch.
Implement vector databases to enhance search accuracy.
Integrate the vector database with Elasticsearch.
Week 5-6:
Implement a recommendation engine using the language models and vector database.
Integrate the recommendation engine with the Flask application and Elasticsearch.
Implement a caching mechanism to reduce search time and enhance performance.
Week 7-8:
Implement user authentication and authorization using AWS Cognito.
Implement data encryption and decryption using AWS Key Management Service (KMS).
Implement automated backups using AWS Backup or similar tools.
Week 9-10:
Implement AWS Elastic Load Balancer to distribute traffic to multiple instances of the Flask application.
Implement AWS CloudFront to improve content delivery and caching.
Implement AWS Route 53 for DNS management.