Lexis.ai - Legal EXtraction & Intelligence System ⚖️

Lexis.ai is an industry-grade AI-powered legal document intelligence platform that combines OCR, LLM-based extraction, risk analytics, and conversational AI to help you analyze contracts and uncover hidden risks.

✨ Features

📄 Document Intelligence

• Smart Upload: Drag-and-drop document upload with automatic processing
• OCR Extraction: Extract text from images and PDFs using Tesseract
• AI Classification: Automatically classify documents (invoices, receipts, contracts)
• Structured Extraction: Extract key fields (vendor, amount, date, invoice number)
• Thumbnail Generation: Auto-generate document previews

💬 Conversational AI

• Chat with Documents: Ask natural language questions about your documents
• Context-Aware: Select multiple documents for cross-document queries
• Smart Responses: Powered by local LLM (Ollama) for privacy

📊 Analytics & Insights

• Financial Analytics: Track total spend, average amounts, highest transactions
• Vendor Analysis: See spending breakdown by vendor
• Monthly Trends: Visualize spending patterns over time
• Natural Language Queries: Ask questions like "What's my total spend with Acme Corp?"

⚡ Workflow Automation

• Pre-built Workflows: Invoice processing, receipt categorization, contract analysis
• Real-time Progress: Visual feedback for each workflow step
• Export Options: CSV, QuickBooks IIF, Excel formats

🎨 Beautiful UI

• Modern Design: Purple gradient theme with glassmorphism effects
• Smooth Animations: Framer Motion powered interactions
• Dark Mode: Full dark mode support
• Responsive: Works on desktop, tablet, and mobile

🏗️ Architecture

Frontend (React + TypeScript + Vite)

• Framework: React 18 with TypeScript
• Styling: Tailwind CSS + shadcn/ui components
• Animations: Framer Motion
• State Management: React Hooks
• Database: Supabase (PostgreSQL)
• Authentication: Supabase Auth

Backend (FastAPI + Python)

• Framework: FastAPI
• LLM: Ollama (local) with Gemma 3:4b model
• OCR: Tesseract + pytesseract
• PDF Processing: pdfplumber
• Image Processing: Pillow
• Cloud Integration: Vultr Object Storage (simulated)

AI/ML Stack

• Local LLM: Ollama (privacy-first, no external API calls)
• Model: Gemma 3:4b (efficient, fast, accurate)
• RAG: Retrieval-Augmented Generation for document Q&A
• Extraction: LLM-based structured data extraction

🚀 Quick Start

Prerequisites

• Node.js 18+ and npm
• Python 3.10+
• Ollama (for local LLM)
• Tesseract OCR
• Supabase Account (free tier works)

1. Clone the Repository

git clone https://github.com/yourusername/Lexis.ai.git
cd Lexis.ai

2. Backend Setup

cd backend

# Create virtual environment
python -m venv venv

# Activate virtual environment
# Windows:
venv\Scripts\activate
# Mac/Linux:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Copy environment template
copy .env.example .env  # Windows
# cp .env.example .env  # Mac/Linux

# Install Ollama and pull the model
ollama pull gemma3:4b

# Run the backend
uvicorn main:app --reload

Backend will run on http://localhost:8000

3. Frontend Setup

cd frontend

# Install dependencies
npm install

# Copy environment template
copy .env.example .env.local  # Windows
# cp .env.example .env.local  # Mac/Linux

# Edit .env.local and add your Supabase credentials
# VITE_SUPABASE_URL=your_supabase_url
# VITE_SUPABASE_PUBLISHABLE_KEY=your_supabase_anon_key

# Run the frontend
npm run dev

Frontend will run on http://localhost:8080

4. Supabase Setup

1. Create a free account at supabase.com
2. Create a new project
3. Run the SQL schema (see database/schema.sql)
4. Copy your project URL and anon key to .env.local

📦 Tech Stack

Frontend

• React 18
• TypeScript
• Vite
• Tailwind CSS
• shadcn/ui
• Framer Motion
• React Router
• Supabase Client

Backend

• FastAPI
• Python 3.10+
• Ollama (LLM)
• Tesseract OCR
• pdfplumber
• Pillow
• python-dotenv

Database & Auth

• Supabase (PostgreSQL)
• Supabase Auth

AI/ML

• Ollama
• Gemma 3:4b
• Tesseract OCR

🔒 Security

• ✅ No API Keys in Code: All secrets in environment variables
• ✅ Local LLM: Privacy-first with Ollama (no external API calls)
• ✅ Secure Auth: Supabase authentication with JWT
• ✅ CORS Protection: Configured for production
• ✅ .gitignore: Protects sensitive files

📝 Environment Variables

Backend (.env)

# Optional: Only if using Gemini instead of Ollama
GEMINI_API_KEY=your_key_here

# Ollama Configuration
OLLAMA_MODEL=gemma3:4b

Frontend (.env.local)

VITE_SUPABASE_URL=your_supabase_project_url
VITE_SUPABASE_PUBLISHABLE_KEY=your_supabase_anon_key

🎯 Key Features Explained

Document Processing Pipeline

1. Upload → User uploads document (PDF/Image)
2. OCR → Tesseract extracts text
3. Classification → LLM identifies document type
4. Extraction → LLM extracts structured fields
5. Storage → Saved to Supabase + Vultr backup
6. Ready → Available for chat and analytics

Chat System

• Uses RAG (Retrieval-Augmented Generation)
• Combines extracted data + OCR text for context
• Local LLM ensures privacy
• Supports multi-document queries

Analytics Engine

• Real-time calculation from extracted data
• Vendor aggregation
• Monthly trend analysis
• Natural language query support

🐛 Known Issues & Limitations

• Rate Limits: Demo has 3 uploads and 5 questions per document limit
• OCR Accuracy: Depends on image quality
• LLM Speed: Local Ollama may be slower than cloud APIs
• File Size: Large PDFs (>10MB) may take longer to process

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

This project is licensed under the MIT License.

🙏 Acknowledgments

• Ollama - Local LLM runtime
• Supabase - Backend as a Service
• Tesseract - OCR engine
• shadcn/ui - Beautiful UI components
• Vultr - Cloud infrastructure partner

📧 Contact

For questions or support, please open an issue on GitHub.

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