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---
title: "RAG Chatbot — GPT-5.1 + URLs / Files / Text"
emoji: 🧠
colorFrom: indigo
colorTo: blue
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false
short_description: GPT-5.1 RAG chatbot for URLs, files, and text sources
---
# 🔍 RAG Chatbot — GPT-5.1 + URLs / Files / Text
A universal Retrieval-Augmented Generation (RAG) chatbot powered by **OpenAI GPT-5.1** and **Gradio 5.49.1**, enabling users to inject their own knowledge in real time — via **URLs**, **uploaded files**, or **raw text blocks** — and query it conversationally.
---
## 🚀 Features
- **OpenAI API key input** stored per session (no backend storage)
- **GPT-5.1** model for chat; **text-embedding-3-large** for vector retrieval
- Accepts:
- Live **URLs** (auto-fetched text)
- Uploaded **files** (`.txt`, `.md`, `.csv`, `.json`)
- Custom **text areas** for notes or raw data
- Interactive **presets** that auto-fill working examples (ZEN sites, policy QA, etc.)
- **Retrieval debugging info** displayed transparently for testing relevance
- **No external DB** — fully in-memory RAG layer for portability
- Works in **Hugging Face Space**, **Google Colab**, or **local Python**
---
## 🧩 How It Works
1. Enter your **OpenAI API key** and click **Save**.
2. Add knowledge sources (URLs, uploads, or text).
3. Click **Build / Refresh Knowledge Base**.
- Text is chunked → embedded via `text-embedding-3-large`.
- Stored in memory as a lightweight vector index.
4. Ask questions — the system retrieves relevant chunks and constrains GPT-5.1 to answer **only from those** and your **system instructions**.
---
## 🧱 File Structure
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