---
title: RAG Document Assistant
emoji: 📄
colorFrom: green
colorTo: blue
sdk: static
app_file: index.html
pinned: false
license: mit
short_description: Grounded, cited answers from your own documents.
tags:
  - rag
  - retrieval
  - embeddings
  - question-answering
---

# RAG Document Assistant

Ask questions about your own documents — answers are grounded in your text, cited to the source, and the assistant says **"I don't know"** instead of guessing. Runs entirely in your browser.

## What it does

1. Upload `.txt` / `.md` files (or **Load sample docs** — a fictional company handbook).
2. Build the index.
3. Ask a question → get an answer, the retrieval mode, and the exact source chunks it came from.

Try: *"What is the refund window?"*, *"How do I reset my password?"*, *"What are the support hours?"*

## How the RAG pipeline works

- **Chunking** — overlapping, sentence-aware windows so context isn't cut at boundaries.
- **Embeddings** — a TF-IDF vectoriser computed in-browser (transparent and instant).
- **Retrieval** — cosine similarity over chunk vectors returns the top matches.
- **Grounding** — answers are assembled only from retrieved chunks and cited; below a similarity threshold it returns an explicit "I don't know".

## About this demo

This live Space is the **extractive**, in-browser version (free to host as a Hugging Face **Static** Space, no key, no server). The full Python project adds neural `sentence-transformers` embeddings and optional grounded LLM generation (Gemini/OpenAI) with a graceful key-free fallback:

🔗 **Source:** https://github.com/furqunali/rag-document-assistant

Sample docs are fictional. Uploaded files are processed **in-memory** and never leave your device.

## Files

- `index.html` — UI
- `logic.js` — chunk → TF-IDF embed → cosine retrieve → grounded answer (also runnable under Node)

## Limitations

- Extractive mode returns the most relevant passage; the full version adds synthesised generative answers.
- TF-IDF is keyword-semantic; the Python version's neural embeddings capture meaning more deeply.
- Designed for demo-sized document sets held in memory.

## License

MIT.