OpenRAG

OpenRAG is a comprehensive Retrieval-Augmented Generation platform that enables intelligent document search and AI-powered conversations. Users can upload, process, and query documents through a chat interface backed by large language models and semantic search capabilities. The system utilizes Langflow for document ingestion, retrieval workflows, and intelligent nudges, providing a seamless RAG experience. Built with Starlette, Next.js, OpenSearch, and Langflow integration.

📋 Table of Contents

• Quick Start
• Development
• Configuration
• Docker Deployment
• Troubleshooting

🚀 Quick Start

Prerequisites

• Docker or Podman with Compose installed
• Make (for development commands)

1. Environment Setup

# Clone and setup environment
git clone <repository-url>
cd openrag
make setup  # Creates .env and installs dependencies

2. Configure Environment

Edit .env with your API keys and credentials:

# Required
OPENAI_API_KEY=your_openai_api_key
OPENSEARCH_PASSWORD=your_secure_password
LANGFLOW_SUPERUSER=admin
LANGFLOW_SUPERUSER_PASSWORD=your_secure_password
LANGFLOW_CHAT_FLOW_ID=your_chat_flow_id
LANGFLOW_INGEST_FLOW_ID=your_ingest_flow_id
NUDGES_FLOW_ID=your_nudges_flow_id

3. Start OpenRAG

# Full stack with GPU support
make dev

# Or CPU only
make dev-cpu

Access the services:

• Frontend: http://localhost:3000
• Backend API: http://localhost:8000
• Langflow: http://localhost:7860
• OpenSearch: http://localhost:9200
• OpenSearch Dashboards: http://localhost:5601

🛠️ Development

Development Commands

All development tasks are managed through the Makefile. Run make help to see all available commands.

Environment Management

# Setup development environment
make setup                    # Initial setup: creates .env, installs dependencies

# Start development environments
make dev                     # Full stack with GPU support
make dev-cpu                 # Full stack with CPU only
make dev-local               # Infrastructure only (for local development)
make infra                   # Alias for dev-local

# Container management
make stop                    # Stop all containers
make restart                 # Restart all containers
make clean                   # Stop and remove containers/volumes
make status                  # Show container status
make health                  # Check service health

Local Development

For faster development iteration, run infrastructure in Docker and backend/frontend locally:

# Terminal 1: Start infrastructure
make dev-local

# Terminal 2: Run backend locally
make backend

# Terminal 3: Run frontend locally  
make frontend

Dependency Management

make install                 # Install all dependencies
make install-be             # Install backend dependencies (uv)
make install-fe             # Install frontend dependencies (npm)

Building and Testing

# Build Docker images
make build                   # Build all images
make build-be               # Build backend image only
make build-fe               # Build frontend image only

# Testing and quality
make test                   # Run backend tests
make lint                   # Run linting checks

Debugging

# View logs
make logs                   # All container logs
make logs-be                # Backend logs only
make logs-fe                # Frontend logs only
make logs-lf                # Langflow logs only
make logs-os                # OpenSearch logs only


#### Database Operations

```bash
# Reset OpenSearch indices
make db-reset               # Delete and recreate indices

⚙️ Configuration

Environment Variables

OpenRAG uses environment variables for configuration. All variables should be set in your .env file.

Required Variables

Variable	Description
OPENAI_API_KEY	Your OpenAI API key
OPENSEARCH_PASSWORD	Password for OpenSearch admin user
LANGFLOW_SUPERUSER	Langflow admin username
LANGFLOW_SUPERUSER_PASSWORD	Langflow admin password
LANGFLOW_CHAT_FLOW_ID	ID of your Langflow chat flow
LANGFLOW_INGEST_FLOW_ID	ID of your Langflow ingestion flow
NUDGES_FLOW_ID	ID of your Langflow nudges/suggestions flow

Ingestion Configuration

Variable	Description
DISABLE_INGEST_WITH_LANGFLOW	Disable Langflow ingestion pipeline (default: false)

• false or unset: Uses Langflow pipeline (upload → ingest → delete)
• true: Uses traditional OpenRAG processor for document ingestion

Optional Variables

Variable	Description
LANGFLOW_PUBLIC_URL	Public URL for Langflow (default: http://localhost:7860)
GOOGLE_OAUTH_CLIENT_ID / GOOGLE_OAUTH_CLIENT_SECRET	Google OAuth authentication
MICROSOFT_GRAPH_OAUTH_CLIENT_ID / MICROSOFT_GRAPH_OAUTH_CLIENT_SECRET	Microsoft OAuth
WEBHOOK_BASE_URL	Base URL for webhook endpoints
AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY	AWS integrations
SESSION_SECRET	Session management (default: auto-generated, change in production)
LANGFLOW_KEY	Explicit Langflow API key (auto-generated if not provided)
LANGFLOW_SECRET_KEY	Secret key for Langflow internal operations

🐳 Docker Deployment

Standard Deployment

# Build and start all services
docker compose build
docker compose up -d

CPU-Only Deployment

For environments without GPU support:

docker compose -f docker-compose-cpu.yml up -d

Force Rebuild

If you need to reset state or rebuild everything:

docker compose up --build --force-recreate --remove-orphans

Service URLs

After deployment, services are available at:

• Frontend: http://localhost:3000
• Backend API: http://localhost:8000
• Langflow: http://localhost:7860
• OpenSearch: http://localhost:9200
• OpenSearch Dashboards: http://localhost:5601

🔧 Troubleshooting

Podman on macOS

If using Podman on macOS, you may need to increase VM memory:

podman machine stop
podman machine rm
podman machine init --memory 8192   # 8 GB example
podman machine start

Common Issues

1. OpenSearch fails to start: Check that OPENSEARCH_PASSWORD is set and meets requirements
2. Langflow connection issues: Verify LANGFLOW_SUPERUSER credentials are correct
3. Out of memory errors: Increase Docker memory allocation or use CPU-only mode
4. Port conflicts: Ensure ports 3000, 7860, 8000, 9200, 5601 are available

Development Tips

• Use make infra + make backend + make frontend for faster development iteration
• Run make help to see all available commands
• Check .env.example for complete environment variable documentation