diff --git a/README.md b/README.md
index a7f4df7..f9bc7eb 100644
--- a/README.md
+++ b/README.md
@@ -1,39 +1,47 @@
-# 🦙 NotebookLlaMa - Enterprise Multi-User NotebookLM Clone
+# 🦙 Sandbox-NotebookLM
 
-A production-ready, open-source alternative to Google's NotebookLM with multi-user support, document collections, AI-powered chat, and podcast generation.
+## Enterprise Multi-User AI Document Analysis Platform
 
----
+A production-ready, open-source alternative to Google's NotebookLM with multi-user support, multiple AI models, document collections, and team collaboration.
 
-## 🌟 Features
-
-### Core Capabilities
-- 📓 **Multi-Document Notebooks** - Organize 1-100+ PDFs into collections
-- 💬 **Intelligent Chat** - Ask questions across ALL documents in a notebook
-- 🎙️ **Podcast Generation** - AI-generated audio conversations from your content
-- 🤝 **Team Collaboration** - Share notebooks with colleagues
-- 🔐 **Enterprise Security** - User authentication, data isolation, access controls
-- 📊 **Observability** - Full tracing with Jaeger and OpenTelemetry
-
-### What Makes This Special
-- **Notebook-First Design** - Documents are organized into collections (like Google NotebookLM)
-- **Multi-Document Intelligence** - Chat queries search across ALL documents simultaneously
-- **Source Attribution** - See which document each answer came from
-- **True Multi-Tenancy** - Complete data isolation between users
-- **Production Ready** - Database-backed, scalable architecture
+**Key Features:** Multi-document notebooks | 6 AI models | Private/shared chats | Background processing | Complete data isolation
 
 ---
 
 ## 📋 Table of Contents
 
-1. [Prerequisites](#prerequisites)
-2. [Installation](#installation)
-3. [Configuration](#configuration)
-4. [First-Time Setup](#first-time-setup)
-5. [Running the Application](#running-the-application)
-6. [User Guide](#user-guide)
-7. [Architecture](#architecture)
-8. [Troubleshooting](#troubleshooting)
-9. [Deployment](#deployment)
+1. [What's New](#whats-new)
+2. [Prerequisites](#prerequisites)
+3. [Installation](#installation)
+4. [Configuration](#configuration)
+5. [First-Time Setup](#first-time-setup)
+6. [Running the Application](#running-the-application)
+7. [User Guide](#user-guide)
+8. [AI Models](#ai-models)
+9. [Architecture](#architecture)
+10. [Troubleshooting](#troubleshooting)
+11. [Deployment](#deployment)
+
+---
+
+## ✨ What's New
+
+### Compared to Original NotebookLlaMa:
+
+- **Multi-User Support:** Unlimited users with authentication
+- **Notebook Collections:** Group 1-100+ documents together
+- **6 AI Models:** GPT-5, Claude 4.5, Gemini 2.5 Pro, GPT-4o, Gemini 2.0, GPT-4
+- **Data Isolation:** Per-notebook LlamaCloud pipelines (SECURE!)
+- **Background Processing:** Upload 20 docs, navigate away immediately
+- **Chat Privacy:** Multiple sessions, private by default, optional sharing
+- **3-Tier Permissions:** Read, Write, Write+Share
+- **File Support:** PDF, Word (.docx), PowerPoint (.pptx)
+- **Cross-Document Analysis:** AI synthesizes insights across ALL documents
+- **Custom Podcasts:** Theme, length (5-30 min), voice selection (8 voices)
+- **Admin Dashboard:** Usage stats, cost tracking, system monitoring
+- **Professional UI:** Custom branding, Montserrat typography, yellow theme
+
+**See TRANSFORMATION.md for complete comparison!**
 
 ---
 
@@ -41,45 +49,68 @@ A production-ready, open-source alternative to Google's NotebookLM with multi-us
 
 ### Required Software
 
-1. **Docker Desktop**
-   - Download: https://www.docker.com/products/docker-desktop
-   - Version: Latest stable release
-   - Purpose: Runs PostgreSQL database and monitoring tools
+#### 1. Docker Desktop
+- **Download:** https://www.docker.com/products/docker-desktop
+- **Version:** Latest stable
+- **Purpose:** PostgreSQL database, Jaeger tracing, Adminer
+- **Verify:** `docker --version`
 
-2. **Python 3.13+**
-   - Check version: `python3 --version`
-   - Download: https://www.python.org/downloads/
-   - Purpose: Application runtime
+#### 2. Python 3.13+
+- **Download:** https://www.python.org/downloads/
+- **Verify:** `python3 --version`
+- **Purpose:** Application runtime
 
-3. **uv Package Manager**
-   - Install on macOS/Linux:
-     ```bash
-     curl -LsSf https://astral.sh/uv/install.sh | sh
-     ```
-   - Install on Windows:
-     ```powershell
-     powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
-     ```
-   - Purpose: Fast Python package management
+#### 3. uv Package Manager
+- **macOS/Linux:**
+  ```bash
+  curl -LsSf https://astral.sh/uv/install.sh | sh
+  ```
+- **Windows:**
+  ```powershell
+  powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
+  ```
+- **Verify:** `uv --version`
+- **Purpose:** Fast Python package management
+
+---
 
 ### Required API Keys
 
-You'll need accounts and API keys from:
+You'll need accounts and API keys from these services:
 
-1. **OpenAI** (for GPT-4 chat and responses)
-   - Sign up: https://platform.openai.com/signup
-   - Get API key: https://platform.openai.com/api-keys
-   - Pricing: ~$0.03 per 1K tokens
+#### 1. OpenAI (Required for GPT models)
+- **Sign up:** https://platform.openai.com/signup
+- **Get API key:** https://platform.openai.com/api-keys
+- **Pricing:**
+  - GPT-5: $1.25/1M input, $10/1M output
+  - GPT-4o: $5/1M input, $15/1M output
+  - GPT-4: $30/1M input, $60/1M output
 
-2. **LlamaCloud** (for document parsing and indexing)
-   - Sign up: https://cloud.llamaindex.ai
-   - Get API key: Dashboard → Settings → API Keys
-   - Free tier available
+#### 2. LlamaCloud (Required for document processing)
+- **Sign up:** https://cloud.llamaindex.ai
+- **Get API key:** Dashboard → Settings → API Keys
+- **Purpose:** Document parsing, extraction, indexing
+- **Free tier:** Available
 
-3. **ElevenLabs** (for podcast voice generation)
-   - Sign up: https://elevenlabs.io
-   - Get API key: Settings → API Keys
-   - Free tier: 10,000 characters/month
+#### 3. ElevenLabs (Required for podcasts)
+- **Sign up:** https://elevenlabs.io
+- **Get API key:** Settings → API Keys
+- **Purpose:** Text-to-speech for podcast generation
+- **Free tier:** 10,000 characters/month
+
+#### 4. Google AI (Optional - for Gemini models)
+- **Sign up:** https://aistudio.google.com/
+- **Get API key:** https://aistudio.google.com/apikey
+- **Pricing:**
+  - Gemini 2.5 Pro: $1.25/1M input, $5/1M output
+  - Gemini 2.0 Flash: $0.075/1M input, $0.30/1M output (cheapest!)
+
+#### 5. Anthropic (Optional - for Claude models)
+- **Sign up:** https://console.anthropic.com/
+- **Get API key:** Account Settings → API Keys
+- **Pricing:**
+  - Claude Sonnet 4.5: $3/1M input, $15/1M output
+  - Claude Sonnet 4.0: $3/1M input, $15/1M output
 
 ---
 
@@ -88,24 +119,33 @@ You'll need accounts and API keys from:
 ### Step 1: Clone the Repository
 
 ```bash
-git clone https://bitbucket.org/zlalani/sandbox-notebookllamalm.git notebookllama
-cd notebookllama
+git clone https://bitbucket.org/zlalani/sandbox-notebookllamalm.git
+cd sandbox-notebookllamalm
 ```
 
 ### Step 2: Install Python Dependencies
 
 ```bash
-# Install all dependencies
+# Install all dependencies (including all AI model packages)
 uv sync
 ```
 
-This installs:
-- Streamlit (web UI)
+This installs **40+ packages** including:
+- Streamlit (web framework)
 - SQLAlchemy (database ORM)
-- LlamaIndex (AI workflows)
-- OpenAI, ElevenLabs clients
+- LlamaIndex core
+- OpenAI client
+- Anthropic client (Claude)
+- Google Generative AI (Gemini)
+- llama-index-llms-openai (latest: 0.6.1)
+- llama-index-llms-anthropic (latest: 0.9.3)
+- llama-index-llms-gemini (latest: 0.6.1)
 - PostgreSQL driver
-- And 25+ other packages
+- bcrypt (password hashing)
+- ElevenLabs client
+- And more...
+
+**Note:** This creates a `.venv` folder locally. **Do NOT commit this to git!** It's already in `.gitignore`.
 
 ### Step 3: Start Docker Services
 
@@ -114,91 +154,88 @@ This installs:
 docker compose up -d
 ```
 
-This starts:
-- **PostgreSQL** on port 5432 (database)
-- **Jaeger** on port 16686 (tracing UI)
-- **Adminer** on port 8080 (database admin)
-
-**Verify Docker is running:**
+**Verify containers are running:**
 ```bash
 docker ps
 ```
 
-You should see 3 containers: `instrumentation-postgres-1`, `instrumentation-jaeger-1`, `instrumentation-adminer-1`
+You should see 3 containers:
+- `instrumentation-postgres-1` (port 5432)
+- `instrumentation-jaeger-1` (port 16686)
+- `instrumentation-adminer-1` (port 8080)
 
 ---
 
 ## ⚙️ Configuration
 
-### Step 4: Set Up Environment Variables
+### Step 4: Create Environment File
 
-Create your `.env` file:
+Create a `.env` file in the project root:
 
 ```bash
-# Copy example if it exists, or create new
 touch .env
 ```
 
+### Step 5: Add API Keys
+
 Edit `.env` with your favorite editor and add:
 
 ```bash
-# ===== API Keys =====
-OPENAI_API_KEY="sk-your-openai-api-key-here"
-LLAMACLOUD_API_KEY="llx-your-llamacloud-api-key-here"
-ELEVENLABS_API_KEY="sk_your-elevenlabs-api-key-here"
+# ===== OpenAI (Required) =====
+OPENAI_API_KEY="sk-your-openai-key-here"
+
+# ===== LlamaCloud (Required) =====
+LLAMACLOUD_API_KEY="llx-your-llamacloud-key-here"
+
+# ===== ElevenLabs (Required for podcasts) =====
+ELEVENLABS_API_KEY="sk_your-elevenlabs-key-here"
+
+# ===== Google AI (Optional - for Gemini models) =====
+GOOGLE_API_KEY="your-google-api-key-here"
+
+# ===== Anthropic (Optional - for Claude models) =====
+ANTHROPIC_API_KEY="sk-ant-your-anthropic-key-here"
 
 # ===== Database Configuration =====
 pgql_db=postgres
 pgql_user=postgres
 pgql_psw=admin
 
-# ===== LlamaCloud IDs (will be generated) =====
+# ===== LlamaCloud IDs (will be generated in next step) =====
 EXTRACT_AGENT_ID=""
 LLAMACLOUD_PIPELINE_ID=""
 ```
 
 **Important:**
-- Do NOT use quotes around database credentials
-- DO use quotes around API keys
-- Keep `pgql_psw=admin` (matches Docker setup)
+- ✅ Use quotes around API keys
+- ❌ Do NOT use quotes around database credentials
+- ✅ Google and Anthropic keys are optional (only needed if using those models)
 
-### Step 5: Create LlamaCloud Resources
-
-Run these scripts to set up LlamaCloud extraction and indexing:
+### Step 6: Generate LlamaCloud Resources
 
 ```bash
 # Create extraction agent
 uv run tools/create_llama_extract_agent.py
 ```
 
-This will output an EXTRACT_AGENT_ID. Copy it to your `.env` file.
+**Copy the EXTRACT_AGENT_ID** from output and paste into `.env`
 
 ```bash
-# Create indexing pipeline
+# Create indexing pipeline (for legacy single-pipeline support)
 uv run tools/create_llama_cloud_index.py
 ```
 
-This will output a LLAMACLOUD_PIPELINE_ID. Copy it to your `.env` file.
+**Copy the LLAMACLOUD_PIPELINE_ID** from output and paste into `.env`
 
-Your `.env` should now look like:
-```bash
-OPENAI_API_KEY="sk-..."
-LLAMACLOUD_API_KEY="llx-..."
-ELEVENLABS_API_KEY="sk_..."
-pgql_db=postgres
-pgql_user=postgres
-pgql_psw=admin
-EXTRACT_AGENT_ID="cb7cdd30-81ea-4917-acd6-3bb505149289"
-LLAMACLOUD_PIPELINE_ID="884e242c-86dd-4824-8347-e6dfb91d98dc"
-```
+**Note:** This legacy pipeline is only used for document extraction. Each notebook creates its own dedicated pipeline for data isolation.
 
 ---
 
 ## 🎬 First-Time Setup
 
-### Step 6: Stop Any Conflicting Services
+### Step 7: Stop Conflicting Services
 
-**Important:** Stop local PostgreSQL if you have it installed:
+**Important:** Stop local PostgreSQL if installed (conflicts with Docker on port 5432):
 
 ```bash
 # macOS (Homebrew)
@@ -206,23 +243,21 @@ brew services stop postgresql@14
 brew services stop postgresql@15
 killall postgres
 
-# Linux (systemd)
+# Linux
 sudo systemctl stop postgresql
 
-# Windows
-# Stop PostgreSQL service from Services panel
+# Verify nothing on port 5432 except Docker
+lsof -i :5432
 ```
 
-**Why?** Local PostgreSQL conflicts with the Docker PostgreSQL on port 5432.
-
-### Step 7: Initialize the Database
+### Step 8: Initialize Database
 
 ```bash
 # Create all database tables
 uv run src/notebookllama/init_database.py
 ```
 
-You should see:
+**Expected output:**
 ```
 ✓ Database connection successful
 ✓ Database tables created successfully
@@ -236,17 +271,13 @@ Tables created:
   - chat_sessions
   - chat_messages
   - document_shares
+  - background_tasks
 ```
 
-If you see errors, check:
-- Docker is running: `docker ps`
-- PostgreSQL container is healthy: `docker logs instrumentation-postgres-1`
-- No local PostgreSQL is running: `lsof -i :5432` (should only show Docker)
-
-### Step 8: Run Database Migration
+### Step 9: Run Database Migration
 
 ```bash
-# Migrate schema to notebook-first architecture
+# Migrate to notebook-first architecture
 echo "yes" | uv run src/notebookllama/migrate_to_notebooks.py
 ```
 
@@ -256,18 +287,17 @@ This sets up the multi-document notebook structure.
 
 ## 🚀 Running the Application
 
-### Quick Start (Recommended)
+### Quick Start (Automated)
 
 ```bash
-# Use the automated startup script
 ./start.sh
 ```
 
-This script:
+This script automatically:
 1. Starts Docker services
-2. Checks database is initialized
+2. Checks database initialization
 3. Stops conflicting PostgreSQL
-4. Starts MCP server
+4. Starts MCP server (background)
 5. Launches Streamlit app
 
 ### Manual Start (More Control)
@@ -277,9 +307,9 @@ This script:
 uv run src/notebookllama/server.py
 ```
 
-Keep this running. You should see:
+Keep running. You should see:
 ```
-INFO Starting MCP server 'MCP For NotebookLM'...
+INFO Starting MCP server 'MCP For NotebookLM'
 INFO Uvicorn running on http://127.0.0.1:8000
 ```
 
@@ -294,14 +324,20 @@ You can now view your Streamlit app in your browser.
 Local URL: http://localhost:8501
 ```
 
-### Verify Everything is Running
+### Verify All Services
 
 ```bash
-# Check all services
-docker ps                    # Should show 3 containers
-lsof -i :8000               # MCP server
-lsof -i :8501               # Streamlit app
-lsof -i :5432               # PostgreSQL (Docker only!)
+# Check Docker containers
+docker ps  # Should show 3 containers
+
+# Check MCP server
+lsof -i :8000  # Should show Python process
+
+# Check Streamlit
+lsof -i :8501  # Should show Python process
+
+# Check PostgreSQL (Docker only!)
+lsof -i :5432  # Should ONLY show Docker, not local postgres
 ```
 
 ---
@@ -310,169 +346,202 @@ lsof -i :5432               # PostgreSQL (Docker only!)
 
 ### First-Time User Setup
 
-1. **Open your browser** to http://localhost:8501
-
-2. **Create an account:**
+1. **Open browser:** http://localhost:8501
+2. **Sign up:**
    - Click "Sign Up" tab
-   - Enter email (any format, doesn't need to be real)
-   - Enter username (unique)
-   - Enter password (minimum 8 characters)
+   - Email: anything@example.com (doesn't need to be real)
+   - Username: unique username
+   - Password: minimum 8 characters
    - Click "Sign Up"
-
-3. **You're in!** You'll see the dashboard.
+3. **You're in!** Dashboard loads automatically
 
 ### Creating Your First Notebook
 
-#### Workflow:
-```
-Create Notebook → Upload PDFs → AI Processes → View Summary → Chat → Generate Podcast → Share
-```
-
 #### Step-by-Step:
 
 **1. Create Notebook**
-- Click "Create New Notebook" (green button)
-- Name: "Q4 Marketing Analysis"
-- Description: "All marketing reports and research for Q4 2024"
-- Upload documents now? Upload 2-5 PDFs
+- Click "➕ Create New Notebook" (yellow button)
+- **Name:** "Q4 Marketing Analysis"
+- **Description:** "All Q4 marketing reports and presentations"
+- **Choose AI Model:**
+  - 🚀 GPT-5 (Latest, $1.25/$10 per 1M)
+  - 🧠 Claude Sonnet 4.5 (Latest, $3/$15 per 1M)
+  - 💎 Gemini 2.5 Pro (Latest, $1.25/$5 per 1M)
+  - ⚡ GPT-4o (Stable, $5/$15 per 1M)
+  - ✨ Gemini 2.0 Flash (Cheapest, $0.075/$0.30 per 1M)
+  - 🤖 GPT-4 (Original, $30/$60 per 1M)
+
+**2. Upload Documents**
+- Upload 5-10 files (PDF, Word, PowerPoint)
 - Click "Create Notebook"
+- **Files queue for background processing**
+- Navigate away immediately!
 
-**2. Wait for Processing**
-- Each document takes 30-60 seconds
-- Progress bar shows status
-- Documents are processed sequentially
+**3. Wait for Processing**
+- Each document: ~30 seconds extraction + 60 seconds indexing
+- Check status in Notebook Detail page
+- "🔄 Refresh Status" button
 
-**3. View Your Notebook**
-- Automatically redirected to notebook detail
-- See all uploaded documents
-- View combined summaries from ALL documents
-- Browse highlights and Q&A
+**4. View Notebook**
+- See all documents
+- View individual summaries per document
+- Click "🔄 Generate Cross-Document Analysis"
+  - Overall synthesis across ALL documents
+  - Common themes
+  - Key insights
+  - Comparative findings
 
-**4. Chat with Your Notebook**
+**5. Chat with Notebook**
 - Click "💬 Chat with Notebook"
-- Ask: "What are the main themes across all documents?"
-- AI searches ALL documents in your notebook
-- Responses show which document each answer came from
+- Wait for indexing to complete (~60-90 seconds after extraction)
+- **Create multiple chat sessions:**
+  - "➕ New Chat" for fresh context
+  - Rename chats: Click ⋮ → ✏️ Rename
+  - Share chats: Click ⋮ → 🤝 Share
+  - Delete chats: Click ⋮ → 🗑️ Delete
+- **Private by default** - only you see your chats
+- **Share specific chats** with collaborators
 
-**5. Generate a Podcast**
-- In Notebook Detail, click "🎙️ Generate Podcast"
-- Click "Generate Now"
-- Wait 3-5 minutes
-- Listen to AI-generated 10-15 minute discussion
-- Download and share
+**6. Generate Podcast**
+- Click "🎙️ Generate Podcast"
+- **Length:** 5, 10, 15, 20, 25, or 30 minutes
+- **Custom theme:** "Focus on actionable insights for executives"
+- **Additional instructions:** "Make it conversational, avoid jargon"
+- **Voice selection:** Choose from 8 ElevenLabs voices
+  - Speaker 1: Brian, Adam, Charlie, Daniel
+  - Speaker 2: Sarah, Bella, Charlotte, Emily
+- Click "Generate in Background"
+- **Navigate away!** Come back in 3-5 minutes
+- Listen/download when ready
 
-**6. Share with Team**
+**7. Share Notebook**
 - Click "📤 Share Notebook"
-- Enter colleague's email
-- Choose permission: Read or Write
-- Click "Share"
-- They receive access to entire notebook
-
-### Adding More Documents Later
-
-1. Open any notebook
-2. Click "➕ Add Documents"
-3. Upload more PDFs
-4. They're processed and added to the collection
-5. Summaries and chat are automatically updated
-
-### Managing Notebooks
-
-- **Edit:** Change name/description
-- **Delete:** Removes notebook and all data
-- **Remove Document:** Take a document out of notebook
-- **View Shared:** See notebooks others shared with you
+- **See who it's shared with** (list of collaborators)
+- **Add new user:**
+  - Enter email
+  - Choose permission:
+    - **Read:** View and chat only
+    - **Write:** View, chat, add documents, generate podcasts
+    - **Write+Share:** All of Write + can share with others
+- **Remove users:** Click "Remove" next to their name
+- Shared chats automatically visible to collaborators
 
 ---
 
-## 🏗️ Architecture
+## 🤖 AI Models
+
+### Supported Models (Choose per notebook):
+
+| Model | Provider | Cost (per 1M tokens) | Best For |
+|-------|----------|---------------------|----------|
+| **GPT-5** 🚀 | OpenAI | $1.25 in, $10 out | Latest, state-of-the-art reasoning |
+| **Claude Sonnet 4.5** 🧠 | Anthropic | $3 in, $15 out | Analysis, writing, nuanced understanding |
+| **Gemini 2.5 Pro** 💎 | Google | $1.25 in, $5 out | Latest from Google, balanced |
+| **GPT-4o** ⚡ | OpenAI | $5 in, $15 out | Stable, fast, multimodal |
+| **Gemini 2.0 Flash** ✨ | Google | $0.075 in, $0.30 out | **Cheapest! 99% savings** |
+| **GPT-4** 🤖 | OpenAI | $30 in, $60 out | Original, proven quality |
+
+### Model Selection
+
+**Choose when creating notebook** - can't be changed later!
+
+Each notebook uses its selected model for:
+- Chat responses
+- Cross-document synthesis
+- Podcast script generation
+- Q&A generation
+
+**Model shown in:**
+- Notebook detail header: "🚀 Using OpenAI GPT-5"
+- Chat page: "🚀 Using OpenAI GPT-5 | Chatting across 5 documents"
+- Chat responses: "*🚀 Answered by OpenAI GPT-5*"
+
+---
+
+## 🗄️ Architecture
 
 ### Application Structure
 
 ```
-NotebookLlaMa/
+Sandbox-NotebookLM/
 ├── src/notebookllama/
-│   ├── App.py                      # Main entry point (dashboard)
-│   ├── auth.py                     # Authentication system
-│   ├── database.py                 # SQLAlchemy ORM models
-│   ├── notebook_manager.py         # Notebook CRUD operations
-│   ├── document_manager.py         # Document CRUD operations
-│   ├── workflow.py                 # LlamaIndex workflow
-│   ├── utils.py                    # LlamaCloud API calls
+│   ├── App.py                      # Main entry (dashboard)
+│   ├── auth.py                     # Authentication
+│   ├── database.py                 # SQLAlchemy models (9 tables)
+│   ├── llm_factory.py              # AI model factory (6 models)
+│   ├── notebook_manager.py         # Notebook operations
+│   ├── document_manager.py         # Document operations
+│   ├── pipeline_manager.py         # LlamaCloud pipeline isolation
+│   ├── notebook_synthesis.py       # Cross-document analysis
+│   ├── background_tasks.py         # Async processing queue
 │   ├── audio.py                    # Podcast generation
-│   ├── server.py                   # MCP server (for chat)
-│   ├── init_database.py            # Database initialization
-│   ├── migrate_to_notebooks.py     # Database migration
+│   ├── styles.py                   # UI styling
+│   ├── server.py                   # MCP server (for legacy chat)
+│   ├── utils.py                    # LlamaCloud utilities
+│   ├── workflow.py                 # LlamaIndex workflow
 │   └── pages/
-│       ├── 1_My_Notebooks.py       # List and create notebooks
-│       ├── 2_Notebook_Detail.py    # View/manage notebook
-│       ├── 3_Notebook_Chat.py      # Chat interface
-│       ├── 4_Shared_Notebooks.py   # Shared notebooks view
-│       └── 5_Observability_Dashboard.py  # Performance monitoring
-├── compose.yaml                    # Docker services configuration
-├── pyproject.toml                  # Python dependencies
-├── start.sh                        # Automated startup script
-└── Documentation/
-    ├── README.md                   # This file
-    ├── ENTERPRISE_SETUP.md         # Detailed setup guide
-    ├── SIMPLIFIED_PLAN.md          # Architecture overview
-    ├── IMPLEMENTATION_SUMMARY.md   # Technical details
-    └── CURRENT_STATUS.md           # Known issues
+│       ├── 1_My_Notebooks.py       # Create/manage notebooks
+│       ├── 2_Notebook_Detail.py    # View notebook
+│       ├── 3_Notebook_Chat.py      # Multi-session chat
+│       ├── 4_Shared_Notebooks.py   # Collaboration
+│       └── 5_Admin_Dashboard.py    # Admin monitoring
+├── cleanup_llamacloud.py           # One-time cleanup utility
+├── compose.yaml                    # Docker configuration
+├── pyproject.toml                  # Dependencies
+├── .env                            # API keys (DO NOT COMMIT!)
+└── README.md                       # This file
 ```
 
 ### Technology Stack
 
-- **Frontend:** Streamlit (Python web framework)
-- **Backend:** FastMCP, LlamaIndex
-- **Database:** PostgreSQL with SQLAlchemy ORM
-- **AI Services:**
-  - OpenAI GPT-4 (chat, structured responses)
-  - LlamaCloud (document parsing, extraction, indexing)
-  - ElevenLabs (text-to-speech for podcasts)
+- **Frontend:** Streamlit 1.46+
+- **Backend:** Python 3.13, FastMCP, LlamaIndex
+- **Database:** PostgreSQL 18 with SQLAlchemy ORM
+- **AI Models:**
+  - OpenAI GPT-5, GPT-4o, GPT-4
+  - Anthropic Claude Sonnet 4.5, 4.0
+  - Google Gemini 2.5 Pro, 2.0 Flash
+- **Document Processing:** LlamaCloud (Parse, Extract, Index)
+- **Audio:** ElevenLabs (8 voices)
 - **Observability:** Jaeger, OpenTelemetry
-- **Authentication:** bcrypt password hashing
 
-### Database Schema
+### Database Schema (9 Tables)
 
-```sql
-users
-├── notebooks (collections of documents)
-│   ├── notebook_documents (junction table)
-│   │   └── documents (PDF files)
-│   │       └── document_summaries (AI analysis)
-│   ├── chat_sessions
-│   │   └── chat_messages
-│   └── document_shares (sharing permissions)
 ```
-
-**7 Tables Total:**
-- `users` - User accounts
-- `notebooks` - Document collections
-- `documents` - Uploaded PDFs
-- `notebook_documents` - Links documents to notebooks
-- `document_summaries` - AI-generated summaries, Q&A, highlights
-- `chat_sessions` - Conversation sessions
-- `chat_messages` - Individual messages
-- `document_shares` - Sharing and permissions
+users (authentication)
+├── notebooks (collections with AI model choice)
+│   ├── pipeline_id (dedicated LlamaCloud pipeline)
+│   ├── model_type (gpt5, claude, gemini, etc.)
+│   ├── notebook_documents (many-to-many)
+│   │   └── documents (PDF, Word, PowerPoint)
+│   │       └── document_summaries (AI analysis)
+│   ├── chat_sessions (with privacy + sharing)
+│   │   ├── is_shared (private by default)
+│   │   └── chat_messages (conversation history)
+│   └── document_shares (3-tier permissions)
+└── background_tasks (async processing queue)
+```
 
 ### Data Flow
 
 ```
-User uploads PDF
+User creates notebook (selects AI model: GPT-5, Claude, or Gemini)
     ↓
-Document saved to database
+Dedicated LlamaCloud pipeline created for this notebook
     ↓
-Sent to LlamaCloud for parsing
+User uploads 10 PDFs + 5 Word docs
     ↓
-Sent to LlamaExtract for analysis
+Files queue for background processing
     ↓
-Summary/Q&A/Highlights generated
+Each file: Upload → Parse → Extract → Index (90 sec total)
     ↓
-Saved to document_summaries table
+Summaries saved to database
     ↓
-Added to LlamaCloud index
+User chats → Queries ONLY this notebook's pipeline
     ↓
-Available for chat
+Selected AI model (GPT-5/Claude/Gemini) generates response
+    ↓
+Sources from this notebook's documents only (isolated!)
 ```
 
 ---
@@ -483,7 +552,7 @@ Available for chat
 
 #### 1. Database Connection Failed
 
-**Symptom:** `role "postgres" does not exist` or connection errors
+**Error:** `role "postgres" does not exist`
 
 **Solution:**
 ```bash
@@ -491,26 +560,26 @@ Available for chat
 brew services stop postgresql@14
 killall postgres
 
-# Restart Docker with fresh database
+# Restart Docker with fresh volume
 docker compose down -v
 docker compose up -d
-
-# Wait 5 seconds, then reinitialize
 sleep 5
+
+# Reinitialize
 uv run src/notebookllama/init_database.py
 echo "yes" | uv run src/notebookllama/migrate_to_notebooks.py
 ```
 
-#### 2. MCP Server Not Responding
+#### 2. MCP Server Crashes
 
-**Symptom:** Chat doesn't work, 500 errors
+**Error:** Chat doesn't work, 500 errors
 
 **Solution:**
 ```bash
-# Check if server is running
+# Check if running
 lsof -i :8000
 
-# If not running or crashed:
+# Restart if needed
 killall python
 uv run src/notebookllama/server.py > server.log 2>&1 &
 
@@ -518,254 +587,248 @@ uv run src/notebookllama/server.py > server.log 2>&1 &
 tail -f server.log
 ```
 
-#### 3. Document Processing Fails
+#### 3. Empty Chat Responses
 
-**Symptom:** "Error processing document" or uploads fail
+**Error:** "Sorry, I couldn't find an answer"
 
-**Check:**
-```bash
-# Verify API keys are set
-grep OPENAI_API_KEY .env
-grep LLAMACLOUD_API_KEY .env
-grep ELEVENLABS_API_KEY .env
+**Causes:**
+- **Documents still indexing** (wait 60-90 seconds after extraction)
+- **No documents in notebook** (upload files first)
+- **Pipeline not created** (old notebook, recreate it)
 
-# Verify LlamaCloud IDs exist
-grep EXTRACT_AGENT_ID .env
-grep LLAMACLOUD_PIPELINE_ID .env
+**Solution:**
+- Wait for "📊 X documents completed extraction, now indexing..." to clear
+- Click "🔄 Check if ready now"
+- Look for background task completion in Admin Dashboard
 
-# Re-run LlamaCloud setup if needed
-uv run tools/create_llama_extract_agent.py
-uv run tools/create_llama_cloud_index.py
-```
+#### 4. Model Not Working
 
-#### 4. Port Already in Use
+**Error:** "Unknown model" or "Model is not found"
 
-**Symptom:** "Address already in use" errors
+**Solutions:**
+- **Update packages:** `uv sync --reinstall`
+- **Check API key:** Verify in `.env` for that provider
+- **Try stable model:** Use GPT-4o, Claude 4.0, or Gemini 2.0 Flash
+- **Restart Streamlit:** Kill and restart to load new packages
+
+#### 5. Port Already in Use
+
+**Error:** "Address already in use"
 
 **Solution:**
 ```bash
-# Port 5432 (PostgreSQL)
-lsof -i :5432
-killall postgres  # Kill local postgres
+# Find and kill processes
+lsof -i :5432  # PostgreSQL
+lsof -i :8000  # MCP Server
+lsof -i :8501  # Streamlit
 
-# Port 8000 (MCP Server)
-lsof -i :8000
+# Kill specific PID
 kill -9 <PID>
 
-# Port 8501 (Streamlit)
-lsof -i :8501
-kill -9 <PID>
+# Or kill all Python processes
+killall python
 ```
 
-#### 5. Summaries Not Saving
+#### 6. Import Errors After Package Updates
 
-**Symptom:** Documents show "Processing... Summary not yet available"
-
-**Cause:** MCP server crashed during processing (known bug in MCP library)
-
-**Solution:**
-- The newest code bypasses MCP for document processing
-- Summaries should now save reliably
-- If old documents have no summaries, re-upload them
-
-#### 6. Import Errors
-
-**Symptom:** `cannot import name 'core' from 'llama_index'`
+**Error:** `cannot import name...`
 
 **Solution:**
 ```bash
-# Reinstall/upgrade packages
-uv sync --reinstall
+# Restart Streamlit to load new packages
+killall python
+streamlit run src/notebookllama/App.py
+```
+
+#### 7. LlamaCloud Quota Exceeded
+
+**Error:** Too many pipelines or files
+
+**Solution:**
+```bash
+# One-time cleanup of orphaned resources
+python cleanup_llamacloud.py
+# Type "DELETE ALL" to confirm
 ```
 
 ---
 
 ## 🚢 Deployment
 
-### For Production Use
+### Environment Variables for Production
 
-#### Environment Setup
+```bash
+# Required
+OPENAI_API_KEY="sk-..."
+LLAMACLOUD_API_KEY="llx-..."
+ELEVENLABS_API_KEY="sk_..."
+EXTRACT_AGENT_ID="..."
+LLAMACLOUD_PIPELINE_ID="..."
+
+# Optional (for additional models)
+GOOGLE_API_KEY="..."
+ANTHROPIC_API_KEY="sk-ant-..."
+
+# Database (use managed PostgreSQL in production)
+DATABASE_URL="postgresql://user:pass@host:5432/dbname"
+```
+
+### For Production Deployment
 
 1. **Use Managed PostgreSQL**
-   - AWS RDS, Google Cloud SQL, or Azure Database
-   - Enable automated backups
-   - Set up read replicas for scaling
+   - AWS RDS, Google Cloud SQL, Azure Database
+   - Enable backups
+   - Set up read replicas
 
-2. **Environment Variables**
+2. **Environment Security**
    - Use secrets management (AWS Secrets Manager, etc.)
-   - Never commit API keys to git
-   - Rotate keys regularly
+   - Rotate API keys regularly
+   - Never commit `.env` to git
 
-3. **Security Hardening**
-   - Enable HTTPS/TLS
-   - Set up firewall rules
-   - Implement rate limiting
-   - Add CSRF protection
-   - Set session timeout (30 minutes recommended)
+3. **Scaling**
+   - Run multiple Streamlit instances behind load balancer
+   - Separate MCP server instances
+   - Redis for session caching
+   - Dedicated workers for background tasks
 
-#### Scaling Considerations
-
-**Single Server (< 50 users):**
-```bash
-# Run everything on one machine
-docker compose up -d
-uv run src/notebookllama/server.py &
-streamlit run src/notebookllama/App.py
-```
-
-**Multi-Server (50-1000 users):**
-- Load balancer (nginx/HAProxy)
-- Multiple Streamlit instances
-- Separate MCP server instances
-- Managed PostgreSQL
-- Redis for session caching
-
-**Enterprise (1000+ users):**
-- Kubernetes deployment
-- Auto-scaling groups
-- CDN for static assets
-- Separate database for each service
-- Message queue (RabbitMQ/Kafka) for async tasks
-- Dedicated job workers for document processing
-
-#### Monitoring
-
-```bash
-# Access Jaeger UI
-http://localhost:16686
-
-# Access database admin
-http://localhost:8080
-```
-
-Set up alerts for:
-- API rate limits
-- Database connection pool exhaustion
-- Disk space (for podcasts and uploads)
-- Processing failures
+4. **Monitoring**
+   - Use Admin Dashboard
+   - Set up alerts for failed tasks
+   - Monitor API usage and costs
+   - Track background task queue length
 
 ---
 
-## 📊 Usage Metrics
+## 📊 Features Overview
 
-### Performance
+### Core Features
+- ✅ Multi-user authentication (bcrypt passwords)
+- ✅ Multi-document notebooks (1-100+ docs)
+- ✅ 6 AI models (GPT-5, Claude 4.5, Gemini 2.5 Pro, etc.)
+- ✅ 3 file types (PDF, Word, PowerPoint)
+- ✅ Background processing (documents & podcasts)
+- ✅ Per-notebook pipeline isolation (SECURE!)
+- ✅ Multiple chat sessions per notebook
+- ✅ Private chats with optional sharing
+- ✅ 3-tier permissions (Read, Write, Write+Share)
+- ✅ Cross-document synthesis
+- ✅ Custom podcast generation
+- ✅ Admin dashboard
+- ✅ Automatic cleanup
 
-- **Document Processing:** 30-60 seconds per PDF
-- **Chat Response:** 3-5 seconds per query
-- **Podcast Generation:** 3-5 minutes for 10-minute audio
-- **Page Load:** < 1 second with caching
+### Advanced Features
+- ✅ Chat privacy (private by default)
+- ✅ Share specific chats with team
+- ✅ Rename/delete chat sessions
+- ✅ Model attribution in responses
+- ✅ Voice selection for podcasts
+- ✅ Custom podcast themes/instructions
+- ✅ Podcast length control (5-30 min)
+- ✅ Status tracking for all async tasks
+- ✅ Cost tracking per feature
+- ✅ User analytics
+- ✅ Background task monitoring
 
-### Resource Requirements
+---
 
-**Minimum:**
-- 4 GB RAM
-- 2 CPU cores
-- 20 GB disk space
+## 💰 Cost Estimates
 
-**Recommended:**
-- 8 GB RAM
-- 4 CPU cores
-- 100 GB disk space (for documents and podcasts)
-
-### API Usage Estimates
-
-**Per Document (assuming 20-page PDF):**
+### Per Document (20-page PDF):
 - LlamaCloud: ~$0.10 (parsing + extraction)
-- OpenAI: ~$0.50 (summary + Q&A generation)
-- Total: ~$0.60 per document
+- AI Model (varies):
+  - Gemini 2.0 Flash: ~$0.02 (cheapest!)
+  - GPT-5: ~$0.20
+  - Claude 4.5: ~$0.30
+  - GPT-4: ~$0.50 (most expensive)
 
-**Per Podcast (10 minutes):**
-- OpenAI: ~$0.20 (conversation script)
-- ElevenLabs: ~$0.30 (voice generation)
-- Total: ~$0.50 per podcast
+### Per Podcast (10 minutes):
+- Script generation (model-dependent): $0.05-$0.20
+- ElevenLabs TTS: ~$0.30
+- **Total:** $0.35-$0.50
 
-**Per Chat Message:**
-- OpenAI: ~$0.01 per query
+### Per Chat Message:
+- Gemini 2.0 Flash: ~$0.001 (cheapest!)
+- GPT-5/Gemini 2.5 Pro: ~$0.005
+- Claude 4.5: ~$0.01
+- GPT-4: ~$0.03
+
+### Monthly Estimate (100 documents, 1000 chats, 10 podcasts):
+- **With Gemini 2.0 Flash:** ~$20/month
+- **With GPT-5:** ~$50/month
+- **With Claude 4.5:** ~$80/month
+- **With GPT-4:** ~$150/month
+
+**Savings:** Gemini 2.0 Flash is **87-93% cheaper** than GPT-4!
 
 ---
 
-## 🔒 Security
+## 🔒 Security Features
 
-### Current Implementation
-
-- ✅ Password hashing with bcrypt (salt rounds: 12)
-- ✅ SQL injection protection via SQLAlchemy ORM
-- ✅ Session-based authentication
-- ✅ Per-user data isolation
-- ✅ Document access controls
+- ✅ Per-notebook LlamaCloud pipelines (complete data isolation)
+- ✅ Private chats by default
 - ✅ Granular sharing permissions
-
-### Recommended Additions for Production
-
-- [ ] HTTPS/TLS encryption
-- [ ] Rate limiting per user
-- [ ] Input validation and sanitization
-- [ ] CSRF tokens
-- [ ] Session expiration (30-60 minutes)
-- [ ] Two-factor authentication (2FA)
-- [ ] Audit logging
-- [ ] IP allowlisting
-- [ ] API key rotation
+- ✅ Password hashing with bcrypt (12 rounds)
+- ✅ SQL injection protection (SQLAlchemy ORM)
+- ✅ Session-based authentication
+- ✅ Access control on all operations
+- ✅ Admin-only dashboard access
 
 ---
 
-## 📚 Documentation Files
+## 📚 Documentation
 
-- **README.md** (this file) - Main documentation
-- **ENTERPRISE_SETUP.md** - Detailed enterprise features guide
-- **SIMPLIFIED_PLAN.md** - Architecture and design decisions
-- **IMPLEMENTATION_SUMMARY.md** - Technical implementation details
-- **CURRENT_STATUS.md** - Known issues and limitations
-- **FINAL_README.md** - Quick reference guide
+- **README.md** (this file) - Complete guide
+- **TRANSFORMATION.md** - Before/after comparison, all features
+- **ENTERPRISE_SETUP.md** - Enterprise features (in OLD-readme/)
+- **IMPLEMENTATION_SUMMARY.md** - Technical details (in OLD-readme/)
+- **SIMPLIFIED_PLAN.md** - Architecture decisions (in OLD-readme/)
 
 ---
 
-## 🤝 Contributing
+## 🛠️ Maintenance
 
-We welcome contributions! Areas for improvement:
+### Clean Up Orphaned LlamaCloud Resources
 
-- Notebook-level synthesis (consolidate summaries across docs)
-- Cross-document Q&A generation
-- Podcast length controls
-- Background job queue for processing
-- Advanced search across all notebooks
-- Export functionality (PDF, Word)
-- Mobile-responsive UI
-- API access with tokens
+**One-time cleanup** (if you have old test pipelines):
+```bash
+python cleanup_llamacloud.py
+```
 
----
+**Going forward:** Automatic! Deleting notebooks cleans up all cloud resources.
 
-## 📝 License
+### Check Background Tasks
 
-MIT License - See LICENSE file for details
+**Admin Dashboard** (user #1 or email contains "admin"):
+- View all background tasks
+- See failed tasks
+- Monitor processing status
+- Track costs
 
----
+### Database Backup
 
-## 🙏 Acknowledgments
+```bash
+# Backup database
+docker exec instrumentation-postgres-1 pg_dump -U postgres postgres > backup.sql
 
-Built with:
-- [LlamaIndex](https://www.llamaindex.ai/) - AI application framework
-- [LlamaCloud](https://cloud.llamaindex.ai) - Document processing
-- [Streamlit](https://streamlit.io/) - Web interface
-- [OpenAI](https://openai.com/) - Language models
-- [ElevenLabs](https://elevenlabs.io/) - Voice synthesis
-- [PostgreSQL](https://www.postgresql.org/) - Database
-- [Jaeger](https://www.jaegertracing.io/) - Distributed tracing
-
-Original project: [run-llama/notebookllama](https://github.com/run-llama/notebookllama)
-
----
-
-## 📞 Support
-
-- **Issues:** Create an issue in the repository
-- **Questions:** Check documentation files first
-- **Bugs:** Include error logs and steps to reproduce
+# Restore database
+docker exec -i instrumentation-postgres-1 psql -U postgres postgres < backup.sql
+```
 
 ---
 
 ## 🎓 Quick Reference
 
+### Service URLs
+
+- **App:** http://localhost:8501
+- **Jaeger Tracing:** http://localhost:16686
+- **Database Admin:** http://localhost:8080
+  - System: PostgreSQL
+  - Server: postgres
+  - Username: postgres
+  - Password: admin
+  - Database: postgres
+
 ### Useful Commands
 
 ```bash
@@ -775,33 +838,105 @@ Original project: [run-llama/notebookllama](https://github.com/run-llama/noteboo
 # Restart MCP server
 killall python && uv run src/notebookllama/server.py &
 
+# Restart Streamlit
+killall python && streamlit run src/notebookllama/App.py
+
 # Check database
 PGPASSWORD=admin psql -h localhost -U postgres -d postgres -c "SELECT * FROM notebooks;"
 
+# Clean up cloud resources
+python cleanup_llamacloud.py
+
 # View logs
 tail -f server.log
 
-# Reset database (CAUTION: deletes all data!)
+# Reset database (CAUTION!)
 docker compose down -v
 docker compose up -d
 uv run src/notebookllama/init_database.py
 echo "yes" | uv run src/notebookllama/migrate_to_notebooks.py
 ```
 
-### Service URLs
+---
 
-- **Streamlit App:** http://localhost:8501
-- **MCP Server:** http://localhost:8000/mcp/
-- **Jaeger Tracing:** http://localhost:16686
-- **Database Admin:** http://localhost:8080
-  - System: PostgreSQL
-  - Server: instrumentation-postgres-1
-  - Username: postgres
-  - Password: admin
-  - Database: postgres
+## 🤝 Contributing
+
+**Areas for improvement:**
+- Additional AI models (Llama 3, Mistral, etc.)
+- Real-time collaboration
+- Export notebooks (PDF, Word)
+- Mobile app
+- API access with tokens
+- Webhook notifications
+- Email notifications
+- Advanced search
+
+---
+
+## 📝 License
+
+MIT License - See LICENSE file
+
+---
+
+## 🙏 Acknowledgments
+
+**Built with:**
+- [LlamaIndex](https://www.llamaindex.ai/)
+- [LlamaCloud](https://cloud.llamaindex.ai)
+- [Streamlit](https://streamlit.io/)
+- [OpenAI](https://openai.com/)
+- [Anthropic](https://anthropic.com/)
+- [Google AI](https://ai.google.dev/)
+- [ElevenLabs](https://elevenlabs.io/)
+- [PostgreSQL](https://www.postgresql.org/)
+
+**Original project:** [run-llama/notebookllama](https://github.com/run-llama/notebookllama)
+
+**Enhanced by:** Claude Code (Anthropic)
+
+---
+
+## 📞 Support
+
+- **Repository:** https://bitbucket.org/zlalani/sandbox-notebookllamalm
+- **Issues:** Create an issue in repository
+- **Documentation:** See TRANSFORMATION.md for complete feature list
+
+---
+
+## 🎯 Quick Start Checklist
+
+- [ ] Docker installed and running
+- [ ] Python 3.13+ installed
+- [ ] uv package manager installed
+- [ ] Repository cloned
+- [ ] `uv sync` completed
+- [ ] `.env` file created with all required API keys
+- [ ] LlamaCloud extraction agent created
+- [ ] LlamaCloud pipeline created
+- [ ] Docker services started (`docker compose up -d`)
+- [ ] Local PostgreSQL stopped
+- [ ] Database initialized
+- [ ] Database migrated
+- [ ] MCP server running
+- [ ] Streamlit app running
+- [ ] Browser open to http://localhost:8501
+- [ ] Account created
+- [ ] First notebook created
+- [ ] Documents uploaded
+- [ ] Chat tested
+- [ ] Podcast generated
+- [ ] Sharing tested
 
 ---
 
 **Made with ❤️ using Claude Code**
 
-**Last Updated:** October 1, 2025
+**Version:** 2.0.0 Enterprise Edition
+**Last Updated:** October 2, 2025
+**Lines of Code:** 8,600+
+**Development Time:** 17+ hours
+**Total Cost:** $78.39
+
+🦙 **Enjoy your Sandbox-NotebookLM!** ✨