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metadata 
title: Studyrag
emoji: 🏃
colorFrom: blue
colorTo: green
sdk: docker
pinned: false

Studyson — RAG Document QA & Summarization

A full-stack Retrieval-Augmented Generation (RAG) app for document Q&A, conversational chat, and summarization. Built with FastAPI, LlamaIndex, Groq, and a persistent Chroma vector store.

Features

  • Multi-format ingestion — PDF, DOCX, TXT, and Markdown files
  • Web scraping — Index any HTML page (with timeout, size cap, and content-type guard)
  • Conversational chat — Multi-turn Q&A with per-session memory
  • Persistent vector store — Chroma on disk; index survives restarts
  • Smart summarization — Length-controlled summaries across all indexed documents
  • Source citations — Verifiable snippets with similarity scores
  • Real-time streaming — Token-by-token Server-Sent Events
  • Markdown rendering — Chat answers render with code blocks, lists, and headings

Tech Stack

Layer Library
Web framework FastAPI >=0.118
RAG orchestration LlamaIndex >=0.14
LLM Groq llama-3.3-70b-versatile
Embeddings FastEmbed BAAI/bge-small-en-v1.5
Vector store Chroma >=0.6 (persistent)
Document parsing PyMuPDF · pypdf · python-docx
HTTP client httpx (async, with timeouts)
Frontend Vanilla JS + marked + DOMPurify

API Endpoints

Method Endpoint Description
GET / Web UI
POST /upload Upload PDF, DOCX, TXT, or MD
POST /scrape_and_index Scrape and index a URL
POST /stream_query SSE streaming Q&A (per-session chat memory)
POST /query One-shot Q&A with source citations
POST /summarize Summarize all indexed content
POST /reset Drop the index and clear all sessions
GET /status System status, indexed docs, active model

Configuration

Set via HF Space secrets or a .env file locally:

Variable Default Purpose
GROQ_API_KEY (required) Groq API key
GROQ_MODEL llama-3.3-70b-versatile Groq chat model
EMBED_MODEL BAAI/bge-small-en-v1.5 Embedding model
MAX_FILE_SIZE 20971520 (20 MB) Upload size limit
MAX_SCRAPE_BYTES 5242880 (5 MB) Scrape body cap
SIMILARITY_TOP_K 4 Retrieval top-k

Local Development 

git clone <repo-url>
cd studyrag
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt
cp .env.example .env  # add your GROQ_API_KEY
uvicorn app.main:app --reload --port 7860

Docker 

docker compose up --build

Volumes persist uploads/, chroma_store/, and the FastEmbed model cache across restarts.

Deploying on Hugging Face Spaces

  1. Push this repo to GitHub
  2. Go to huggingface.co → your profile → New Space
  3. Select Docker SDK, link your GitHub repo
  4. Add GROQ_API_KEY under Settings → Variables and secrets
  5. The Space auto-builds and serves on port 7860

Note: The Chroma store and uploads persist within the Space filesystem but are wiped on a factory reset.

Acknowledgments