Update README.md

README.md

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 ---
-title: RAGmod4
-emoji: 😻
-colorFrom: yellow
-colorTo: yellow
+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: RAGmod4
+short_description: GPT-5.1 RAG chatbot for URLs, files, and text sources
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🔍 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
+