These are two prototypes to explore the HBS Faculty Directory using retrieval augmented generation (RAG).

The backend under app.py is a llamaindex chat engine that queries a vector store in chroma_db

The main guidance-refinement prototype is user-query-form/ which uses a form input to build the RAG query, and then LLM-driven suggested questions for follow up responses.

chat-app/ contains a chat app that takes the input and uses that for queries to the llamaindex chat engine, and directly displays the responses.

Install git-lfs, which is used to store the vector DB. Then clone this repo.

Make a copy of .env-example to .env and add your OpenAI key.

Start a virtual environment with venv, running python -m venv venv and then start it with source venv/bin/activate.

install dependencies pip install -r requirements.txt

In each of the prototype directories user-query-form and chat-app, install the respective app with npm install.

Navigate to the frontend app in user-query-form/ and run npm run build. Start the server with python3 app.py

If instead you are running the chat app, navigate to chat-app/ and npm run build, then start the server with python3 chat_server.py.