University Honors

This project outlines the development of a journaling application designed to enhance deep self reflection with LLMs. This application addresses the existing privacy and ethical risks prevalent in existing cloud-based journaling chatbots. The app is built as a native macOS application using Flutter for the frontend graphical user interface and Python for the backend engine. OLLAMA to securely host local large language models (LLMs) and integrated into the backend through Langchain. Local storage is managed through SQLite for data serialization and FAISS vector stores for Retrieval-Augmented Generation (RAG). The application guarantees absolute data privacy with zero external network dependencies. The system functions as a reflective mirror following the principles of Acceptance and Commitment Therapy (ACT). Core feature capabilities include value systems analysis, temporal cognitive tracking, and contradiction awareness. Through natural language processing, the AI extracts virtues and behavioral metrics from daily entries. This data is fed through the RAG pipeline to generate highly personalized prompts that highlight past versus present value-behavior inconsistencies. The goal of prompting is to enhance user introspection without generating clinical advice through the LLM. The future goal of this application is to serve as a therapeutic aid for clinicians to analyze the progress of patients.