siltanukifilie/rwanda-tax-ai

--- title: Rwanda Tax AI Assistant sdk: streamlit app_file: app.py python_version: "3.10" --- ## Rwanda Tax AI — RAG App Scaffold This repo is a starting folder structure for a Retrieval-Augmented Generation (RAG) app. ## Structure - `data/raw/`: source documents (PDFs, docs, text files) - `data/processed/`: derived artifacts (chunks, embeddings, vector index, metadata) - `src/`: library code (loaders, chunking, indexing, retrieval, evaluation, etc.) - `app.py`: Streamlit UI (optional) - `api/`: FastAPI backend (Vercel-compatible) - `frontend/`: ChatGPT-style static frontend (HTML/CSS/JS) - `requirements.txt`: Python dependencies ## Quickstart (Windows / PowerShell) ```bash python -m venv .venv .\.venv\Scripts\Activate.ps1 pip install -r requirements.txt python app.py ``` ## Full-stack ChatGPT-style web app (FastAPI + HTML/CSS/JS) ### 1) Build the FAISS artifacts (only once, or whenever documents change) Make sure you already extracted PDFs to `data/processed/*.txt`, then: ```bash python .\src\build_faiss_index.py ``` This produces: - `chunks.pkl` - `faiss.index` ### 2) Start the backend (FastAPI) ```bash python -m pip install -r requirements.txt python -m uvicorn api.main:app --reload --host 127.0.0.1 --port 8000 ``` Health check: `GET 127.0.0.1` ### 3) Open the frontend Open `frontend/index.html` in your browser. If your backend URL is different, edit `API_URL` in `frontend/app.js`. API response format used by the frontend: `{ "answer": "..." }` ## Next steps - Put documents into `data/raw/` - Add modules under `src/` for: - loading + cleaning text - chunking - embedding - vector indexing + persistence into `data/processed/` - retrieval + answer generation