Update README.md

README.md

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+---
+title: Multi-Source RAG Assistant
+emoji: 📚
+colorFrom: indigo
+colorTo: blue
+sdk: streamlit
+sdk_version: "1.33.0"
+app_file: app.py
+pinned: false
+---
+
 # 📚 Multi-Source RAG Assistant
 
-This app lets you chat with:
+This app lets users interact with:
 - 🧾 PDF documents
-- 📊 CSV datasets (with EDA)
-- 🌐 Website content
+- 📊 CSV datasets (with automatic EDA)
+- 🌐 Any website (scraped and embedded into a vector database)
+
+💡 Powered by:
+- Google Gemini API
+- FAISS vector search
+- LangChain framework
+
+## 🔧 How It Works
+
+1. Select input type from sidebar: **PDF**, **CSV**, or **Website URL**.
+2. Upload or input accordingly.
+3. Ask questions — the assistant answers using context-aware RAG via Gemini.
+
+## 🚀 Getting Started
+
+Before using the app:
+- Enter your **Gemini API Key** in the sidebar.
+
+## 🧠 Tech Stack
+
+- `Streamlit` for UI
+- `LangChain` for RAG logic
+- `FAISS` for vector storage
+- `SentenceTransformers` for embeddings
+- `Google Generative AI (Gemini)` for LLM-powered answers
 
-💡 Powered by Google's Gemini API, FAISS, and LangChain.
+---
 
-## Features
-- Dynamic input selection (PDF, CSV, or URL)
-- Text chunking and semantic vector search
-- Gemini-powered answers based on context
+### 🛠 Developed by Abhijeet Singh
 
-## Requirements
-- You need a Gemini API key
+Hosted with ❤️ on [Hugging Face Spaces](https://huggingface.co/spaces)