Study Tools

This repository contains an experimental study bot that ingests PDF course material, stores it in a vector database and generates language‑aware summaries, flashcards and chat‑style Q&A. The code base is in flux and will be refactored into a modular tutoring system.

Current State

• build_index.py loads PDFs from docs/ and creates a Chroma vector store.
• summarize_improved.py summarises every chunk with OpenAI models, caches the results and tree‑merges them into a summary.md / optional PDF.
• chat.py provides a CLI chat interface backed by the vector store.
• flashcards.py turns retrieved chunks into an Anki deck.
• Several legacy scripts exist and can be ignored during refactor.

Setup

1. Install Python 3.11+.
2. pip install llama-index-core llama-index-llms-openai chromadb tiktoken genanki tenacity tqdm pypandoc
3. Export your OpenAI API key: export OPENAI_API_KEY=sk-...
4. Optionally set the model via environment variable or edit config.json.

Usage

Build the index:

python build_index.py

Generate summaries (cached, async):

python summarize_improved.py --no-pdf

Chat with the material:

python chat.py

Create flashcards:

python flashcards.py

To ingest new PDFs, place them under docs/ (or a sub‑folder per module) and rerun build_index.py.

Architecture Overview

1. Ingestor – splits PDFs into overlapping text chunks and stores them in Chroma.
2. Summariser – summarises each chunk and reduces by module.
3. Merger – combines module summaries into a final exam guide.
4. Chat/Q&A – retrieves relevant chunks for user questions.
5. Flashcard Builder – converts chunk summaries into Anki cards.

The refactor aims to replace ad‑hoc scripts with reusable modules and a CLI entry point. JSON based "Learning Units" (see agents.md) will track progress and relations between pieces of knowledge.