This repository primarily serves as a practical demonstrator for applying Retrieval-Augmented Generation (RAG) to manipulate personal data. Furthermore, it serves as an invaluable tool for my ongoing research, providing a hands-on experience in effectively leveraging Language Model Learning (LLM) within an applied context.
'Rag Diary' is constructed as a self-reflection aid, enabling users to assess their past life events more profoundly. It serves to merge users' queries with language model generated responses, juxtaposing their questions against the data populated from their localized diary entries or vector database.
It is crucial to note that 'Rag Diary' is not designed to replicate therapist functions. Users are discouraged from relying on suggestions output by the Language Learning Model (LLM) regarding life choices. The responses provided by the LLM primarily focus on encouraging further self-analysis and facilitating metabolic activities such as controlled breathing, regular exercising, and meditation.
This project requires an openai api key and python 3.10+
Clone the repo into a folder.
cd
into the repo create a new virtural environment
python -m venv ./venv
activate the virtural environment.
. ./venv/bin/activate
for windows
./venv/scripts/Activate
Once in the python virtural environment install the dependencies.
pip install .
This command will run the build from the pyproject.toml
file.
After installation activate the venv you can run the python module
python -m rag_diary --help
This will show you a list of available command to use. The Repo is in development. These commands are subject to change
python -m rag_diary new-entry "You diary input goes here. for now paste text into cli to update vector db"
python -m rag_diary query-retriver-agent "What did I do on January 1st"