What is Casecanopy

CaseCanopy is a comprehensive legal AI system that integrates Retrieval Augmented Generation (RAG) for legal precedent search with automated document generation, providing a modern platform for legal insights and access.

Use cases

Use cases for CaseCanopy include generating legal documents, conducting legal research, predicting case outcomes, and providing legal insights across jurisdictions.

How to use

To use CaseCanopy, clone the repository, set up the AI agent, backend, frontend, and RAG components by following the provided setup instructions. Ensure all necessary environment variables are configured.

Key features

Key features include AI-powered legal document generation (petitions, RTIs, complaints), case law and legal precedent search, outcome prediction, user authentication, document management, and a responsive frontend UI.

Where to use

CaseCanopy can be used in various legal fields, including law firms, legal aid organizations, and educational institutions, to enhance legal research and document preparation.

CaseCanopy

Overview

CaseCanopy is an AI-powered legal platform that bridges the justice gap by enabling cross-jurisdiction legal precedent discovery and outcome prediction. It provides equal access to legal insights through a modern, responsive web application.

Project Structure

• agentic-ai/: Python FastAPI service for legal document generation using AI.
• backend/: Go (Gin) backend for user, admin, file, and document management.
• frontend/: Next.js/React frontend for user interaction and legal research.
• RAG/: Python Flask server for retrieval-augmented generation (LangChain-based).

Key Features

• AI-powered legal document generation (petitions, RTIs, complaints, etc.)
• Case law and legal precedent search
• Outcome prediction and legal insights
• User authentication and admin approval
• Document upload, management, and PDF generation
• Modern, responsive frontend UI

Setup Instructions

1. Clone the repository:

git clone https://github.com/Arpit529Srivastava/Case_Canopy.git
cd Case_Canopy

2. Set up the AI Agent:

cd ai_agent
   # Follow instructions in ai_agent/README.md

3. Set up the Backend:

cd backend
go mod tidy
go run main.go
# Server runs on :8000, requires MongoDB running locally

4. Set up the Frontend:

cd frontend
npm install
npm run dev
# App runs on http://localhost:3000

5. Set up the RAG:

cd RAG
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python app.py
# Server runs on http://localhost:8000

# in other terminal tab run:
source venv/bin/activate
pip install -r requirements.txt
python analyzer.py

Environment Variables

1. agentic-ai/.env

OPENAI_API_KEY=your_openai_api_key_here

2. RAG/.env

OPENAI_API_KEY=your_openai_api_key_here
MODEL_NAME=gpt-4o-mini 
QDRANT_URL=your_link
QDRANT_API_KEY=your_qdrant_api_key_here

3. backend/.env

SMTP_HOST=smtp.gmail.com
SMTP_PORT=587
SMTP_USER=your_email
SMTP_PASS=generate_password_and paste_here
JWT_SECRET=your_token
GEMINI_API_KEY=your_api_key

4. frontend/.env.local

MONGODB_URI=you_uri
JWT_SECRET=secret_token

License

• This project is licensed under the MIT License.