| --- |
| 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. |
|
|
