I'm working on the project from PM 6:00 to AM 3:00 (Asia/Tokyo) after my contract work is complete. The project is currently in the MVP (Minimum Viable Product) development phase.
As a pre-migration development environment before moving to GCP, I'm using microk8s on Ubuntu.
API Service: A Flask application that serves as the user interface for our dialogue. It's accessible locally via the port-forward command.
Emotion Analyzer Service: A microservice that uses the transformers library to analyze the user's emotion from their input. It's called via an HTTP request from the API service.
Importance Analyzer Service: A microservice that uses the sentence-transformers library to determine the importance of the conversation based on its similarity to my philosophy. It's also called via an HTTP request from the API service.
Postgres Database: A PostgreSQL container for the AI's permanent memory. The table DDL has already been applied.
Deployment: All services are successfully deployed on microk8s and are communicating with each other.
Full System Integration: The API service successfully calls other microservices (emotion analyzer, importance analyzer) and saves the results to the database. The entire process is fully functional.
First Dialogue: I successfully had the first conversation with the AI via a curl command. The high importance score of 0.987 confirms that the AI accurately understood my philosophy.
Emotional Understanding: The initial emotion score of -0.937 shows that the AI doesn't fully understand the human emotion behind my words yet.
Dialogue Refinement: The current responses are template-based. I need to add more complex logic to generate more natural and empathetic dialogue.
Memory Utilization: The logic to actively use the saved memories from the database to personalize future conversations has not yet been implemented.