An AI-powered legal advisory system leveraging Cohere's LLM capabilities for advanced legal assistance.

• Document embedding and semantic search
• Legal document analysis
• Query understanding and processing
• Vector database storage for legal knowledge
• Client Consultation Agent (Model A) implementation
• Redis caching for efficient embedding storage and retrieval

The easiest way to run the system is with Docker:

1. Clone the repository:

   git clone https://github.com/VarnitOS/GenAiGenesis.git
   cd GenAiGenesis
   

2. Copy the environment example and add your API keys:

   cp backend/.env.example backend/.env
   

   Edit the backend/.env file and add your Cohere API key.

3. Run the system with Docker:

   ./run.sh
   

4. Access the system:

   • API: http://localhost:8080
   • Redis GUI: http://localhost:8001

1. Set up Python environment (Python 3.9+ required)

   cd backend
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt
   

2. Set up Redis:

   docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest
   

3. Run the Flask app:

   cd backend
   PYTHONPATH=/path/to/GenAiGenesis/backend python -m app.flask_app
   

• GET /: Welcome message
• GET /test: Test endpoint
• POST /api/embed: Generate embedding for a query
• POST /api/generate: Generate text using Cohere
• POST /api/client/understand: Analyze client query
• POST /api/client/respond: Generate response to client query

The system uses a modular design with three main components:

• Client Consultation Agent (Model A)
• Redis for embedding caching and vector storage
• Flask API for client access

The Redis integration provides:

• Persistent caching of embeddings
• Improved performance with reduced API calls
• Failover to in-memory cache if Redis is unavailable

This repository contains multiple components:

• backend/: The LegalMind AI backend system with Model A (Client Consultation Agent)

MIT