⚖️ Legal Eagle: AI-Powered Contract Assistant

Legal Eagle is a Retrieval-Augmented Generation (RAG) application designed to analyze complex legal documents. It allows users to upload PDF contracts and ask natural language questions, retrieving precise clauses and generating expert-level summaries using Llama-3.

🧠 How It Works (The RAG Pipeline)

• Ingestion: The app reads the uploaded PDF and splits text into semantic chunks (recursive character splitting).

• Embedding: It uses all-MiniLM-L6-v2 to convert text chunks into dense vector embeddings.

• Storage: Embeddings are stored locally in ChromaDB, a high-performance vector database.

• Retrieval: When a user asks a question, the system performs a semantic similarity search to find the top 3 most relevant contract clauses.

• Generation: These clauses are fed into Llama-3.1-8b (via Groq), which answers the question based strictly on the provided context.

🛠️ Tech Stack

• LLM Engine: Llama-3.1-8b (via Groq API)

• Vector Database: ChromaDB

• Orchestration: LangChain

• Embeddings: Sentence-Transformers (HuggingFace)

• Frontend: Streamlit

• Language: Python 3.10+

📂 Project Structure

legal-eagle/

├── app.py               # The main Streamlit application

├── ingest.py            # Standalone script for batch ingestion (optional)

├── query_engine.py      # Standalone CLI for testing retrieval logic

├── requirements.txt     # Python dependencies

└── README.md            # Documentation

🚀 Setup & Installation

Prerequisites

• Python 3.10 or higher installed.

• A free Groq API Key.

1. Clone the Repository

git clone [https://github.com/yourusername/legal-eagle.git](https://github.com/yourusername/legal-eagle.git)

cd legal-eagle

2. Install Dependencies

pip install streamlit langchain langchain-community langchain-chroma langchain-huggingface pypdf chromadb groq sentence-transformers

3. Configure API Key

Open app.py and paste your Groq API key:

GROQ_API_KEY = "gsk_..."

⚡ Usage

Run the App:

streamlit run app.py

Upload: Use the sidebar to upload a PDF contract (e.g., NDA, Lease).

Process: Click "Process Document" to build the vector database.

Chat: Ask questions like:

• "What is the termination notice period?"

• "Who is liable for water damage?"

🔮 Future Improvements

[ ] Multi-File Support: Allow chatting with multiple PDFs simultaneously.

[ ] Citation Highlighting: Show exactly which page the answer came from.

[ ] Chat History: Save session history to a database.

📄 License

MIT