⚖️ LDA (Legal Document Analyzer)

AI Agent to analyze legal documents, contracts, and agreements — providing detailed legal risk assessments and insights.

Features | Tech Stack | Installation | Project Structure

🌟 Features

• Agent-based architecture
  • Analysis Agent: Document analysis with in-context learning from previous analyses and a built-in knowledge base
  • Chat Agent: RAG-powered follow-up Q&A over your document (FAISS + HuggingFace embeddings)
• Multi-model cascade via Groq with automatic fallback (primary → secondary → tertiary → fallback)
• Chat sessions: Create multiple analysis sessions; each session stores document, analysis, and follow-up messages in Supabase
• Document sources: Upload your own PDF or use the built-in sample contract for quick testing
• PDF handling: Upload up to 20MB, max 50 pages; validation for file type and legal-document content
• Daily analysis limit: Configurable cap (default 15/day) with countdown in the sidebar
• Secure auth: Supabase Auth (sign up / sign in), session validation, and configurable session timeout
• Session history: View, switch, and delete past sessions; document text persisted for follow-up chat across reloads
• Modern UI: Responsive Streamlit app with sidebar session list, user greeting, and real-time feedback

🛠️ Tech Stack

• Frontend: Streamlit (1.42+)
• AI / LLM
  • Document analysis: Groq with multi-model fallback via ModelManager
    • Primary: meta-llama/llama-4-maverick-17b-128e-instruct
    • Secondary: llama-3.3-70b-versatile
    • Tertiary: llama-3.1-8b-instant
    • Fallback: llama3-70b-8192
  • Follow-up chat: RAG with LangChain, HuggingFace embeddings (all-MiniLM-L6-v2), FAISS vector store, and Groq (llama-3.3-70b-versatile)
• Database: Supabase (PostgreSQL)
  • Tables: users, chat_sessions, chat_messages
• Auth: Supabase Auth, Gotrue
• PDF: PDFPlumber (text extraction), filetype (file validation)
• Libraries: LangChain, LangChain Community, LangChain HuggingFace, LangChain Text Splitters, sentence-transformers, FAISS (CPU)

🚀 Installation

Requirements 📋

• Python 3.8+
• Streamlit 1.42+
• Supabase account
• Groq API key
• PDFPlumber, filetype

Getting Started 📝

1. Clone the repository:

git clone https://github.com/yourusername/lda.git
cd lda

2. Install dependencies:

pip install -r requirements.txt

3. Required environment variables (in .streamlit/secrets.toml):

SUPABASE_URL = "your-supabase-url"
SUPABASE_KEY = "your-supabase-key"
GROQ_API_KEY = "your-groq-api-key"

4. Set up Supabase database schema:

The application uses three tables: users, chat_sessions, and chat_messages. Use the SQL script at public/db/script.sql to create them.

(You can turn off email confirmation on signup in Supabase: Authentication → Providers → Email → Confirm email.)

5. Run the application:

streamlit run src/main.py

📁 Project Structure

lda/
├── requirements.txt
├── README.md
├── src/
│   ├── main.py                 # Application entry point; chat UI and session flow
│   ├── auth/
│   │   ├── auth_service.py     # Supabase auth, sessions, chat message persistence
│   │   └── session_manager.py  # Session init, timeout, create/delete chat sessions
│   ├── components/
│   │   ├── analysis_form.py    # Document source (upload/sample), analysis form, analysis trigger
│   │   ├── auth_pages.py       # Login / signup pages
│   │   ├── footer.py           # Footer component
│   │   ├── header.py           # User greeting
│   │   └── sidebar.py          # Session list, new session, daily limit, logout
│   ├── config/
│   │   ├── app_config.py       # App name, limits (upload, pages, analysis, timeout)
│   │   ├── prompts.py          # Legal specialist prompts for document analysis
│   │   └── sample_data.py      # Sample legal contract for "Use Sample Contract"
│   ├── services/
│   │   └── ai_service.py       # Analysis + chat entry points; vector store caching
│   ├── agents/
│   │   ├── analysis_agent.py   # Document analysis, rate limits, knowledge base, in-context learning
│   │   ├── chat_agent.py       # RAG pipeline (embeddings, FAISS, query contextualization)
│   │   └── model_manager.py    # Groq multi-model cascade and fallback
│   └── utils/
│       ├── validators.py       # Email, password, PDF file and content validation
│       └── pdf_extractor.py    # PDF text extraction and validation
├── public/
│   └── db/
│       ├── script.sql          # Supabase schema (users, chat_sessions, chat_messages)
└── .streamlit/
    └── config.toml             # Streamlit theme and server configuration

📄 License

This project is licensed under the MIT License.