---
title: Document RAG Chatbot
emoji: 🤖
colorFrom: indigo
colorTo: purple
sdk: docker
app_file: flask_app.py
pinned: false
short_description: 'An intelligent chatbot that understands your documents'
---

# Document RAG Chatbot

An intelligent, context-aware chatbot that understands your documents.  
Upload a PDF or text file, and it will answer questions using only the information inside — no hallucinations, no fluff.  

Built with **Flask**, **LangChain**, and **Google Gemini**, this project demonstrates a clean, modular approach to **Retrieval-Augmented Generation (RAG)** in action.

---

## Highlights

- Upload and analyze **PDF** or **TXT** files  
- Uses **FAISS** for fast semantic search  
- Embeddings powered by **HuggingFace Sentence Transformer**  
- Answers generated by **Gemini 2.5 Flash**  
- Works with **frontend-provided API key** (no server-side storage)  
- Clean, responsive interface for smooth chat interaction  

---

## Tech Stack

| Component | Technology |
|------------|-------------|
| Backend | Flask |
| Language Model | Google Gemini |
| Vector Store | FAISS |
| Embeddings | HuggingFace all-MiniLM-L6-v2 |
| Frontend | HTML, CSS, JavaScript |
| Framework | LangChain |

---

## Getting Started

### 1️⃣ Clone the Repository
```bash
git clone https://huggingface.co/spaces/<your-username>/Document-RAG-System
cd Document-RAG-System
````

### 2️⃣ Set Up Your Environment

```bash
python -m venv venv
# Activate
venv\Scripts\activate     # Windows
source venv/bin/activate  # macOS/Linux
```

### 3️⃣ Install Dependencies

```bash
pip install -r requirements.txt
```

---

##  Run the App

```bash
python flask_app.py
```

Once running, open your browser and go to:
👉 **[http://127.0.0.1:5000/](http://127.0.0.1:5000/)**

---

## Get Your Gemini API Key

1. Visit [Google AI Studio](https://aistudio.google.com/app/apikey)
2. Generate a **Gemini API Key**
3. Paste it in the “API Key” field on the webpage
4. Save and start chatting!

Your key is never stored — it’s used only in your current session.

---

## How It Works

Here’s what happens behind the scenes:

1. You upload your document.
2. The file is split into small chunks (for efficient retrieval).
3. Each chunk is embedded into a vector using HuggingFace embeddings.
4. FAISS indexes these vectors for quick similarity search.
5. When you ask a question, relevant chunks are retrieved and sent to Gemini.
6. Gemini generates a focused, contextual answer — grounded in your document.

That’s **Retrieval-Augmented Generation (RAG)** in a nutshell.

---

## Example Use Cases

- Summarize long reports  
- Extract key information from research papers  
- Study assistant for textbooks  
- Legal, medical, or technical document Q&A  
- Analyze and interpret **crypto project whitepapers** — understand tokenomics, roadmap, and team details before investing  


---

## Customization

You can tweak:

* `chunk_size` and `chunk_overlap` in `chatbot.py`
* The **system message** for tone or depth
* The **Gemini model** (to `gemini-2.5-flash`, `gemini-1.5-pro`, etc.)

---

## Author

**Williams Odunayo**
*Machine Learning Engineer | Builder of useful AI systems*😉
🔗 [GitHub](https://github.com/Wills17) • [LinkedIn](https://linkedin.com/in/williamsodunayo)


---

## License

Released under the **MIT License**.
Free to use, modify, and build upon - attribution is appreciated.