Quick Start Install and launch OpenRAG with all its web interfaces in 5 minutes flat .
Prerequisites
Docker Compose 2.26+ Included with modern Docker
16 GB RAM minimum 32 GB recommended with GPU
50 GB storage For Docker images + LLM model (4.9 GB)
Important: The system requires 16 GB RAM minimum to run the llama3.1:8b LLM. See Detailed Requirements for more information.Installation in 4 Steps 1. Clone the Repository git clone https://github.com/3ntrop1a/openrag.git cd openrag
2. Launch All Services # Start the 10 microservices sudo docker-compose up -d
First startup: Downloading Docker images and LLM model (4.9 GB). Allow 10-15 minutes depending on your connection.
3. Verify Everything is Started # View the status of the 10 services sudo docker-compose ps
You should see 10 services with Up status:
NAME STATUS openrag-api Up openrag-orchestrator Up openrag-embedding Up openrag-frontend-user Up # NEW: User chat openrag-frontend-admin Up # NEW: Admin panel openrag-postgres Up openrag-redis Up openrag-minio Up openrag-qdrant Up openrag-ollama Up
4. Download the LLM Model If you’re using Ollama (default configuration):
docker exec -it openrag-ollama ollama pull llama3.1:8b
Lightweight alternatives: llama3.1:3b (2GB), gemma:2b (1.5GB), phi3:mini (2.3GB)
Downloading the llama3.1:8b model takes 4.9 GB . Allow 5-10 minutes depending on your connection.
Access Web Interfaces Open your browser and test the interfaces:
First Test Option 1: Via Chat Interface (Recommended)
Ask a test question
In the chat, type: What is OpenRAG and how does it work?
Click “Send” or press Enter.
Observe the response
The system will:
Search in documents (100-200 ms)
Generate a response with the LLM (5-15 s after first load)
Display sources below with relevance scores
Important: The first query takes 50-75 seconds (loading LLM model). Subsequent ones are much faster (5-15s).Option 2: Via REST API (curl)
Check API health
curl http://localhost:8000/health | jq
Expected response: { "status" : "healthy" , "timestamp" : "2026-02-18T..." , "version" : "1.1.0" , "services" : { "database" : "healthy" , "redis" : "healthy" , "vector_store" : "healthy" , "orchestrator" : "healthy" } }
Do a simple search (without LLM)
curl -X POST http://localhost:8000/query \ -H "Content-Type: application/json" \ -d '{ "query": "configuration settings", "collection_id": "default", "max_results": 3, "use_llm": false }' | jq
Returns similar documents with relevance scores.
Make a query with LLM
curl -X POST http://localhost:8000/query \ -H "Content-Type: application/json" \ -d '{ "query": "What are the main features described in the documentation?", "collection_id": "default", "max_results": 5, "use_llm": true }' | jq -r '.answer'
First query: 50-75 seconds (loading llama3.1:8b into RAM)Subsequent queries: 5-15 seconds
Upload Your Own Documents Via Admin Interface (Recommended)
Go to Upload
Click “Upload” in the sidebar
Select a PDF file
Click “Browse files”
Choose a PDF
Fill in metadata (optional)
Click “Upload”
Verify processing
Go to “Documents” section
Check status (processing → processed)
Allow 10-30 seconds per document depending on size
Via API curl -X POST http://localhost:8000/documents/upload \ -F "file=@my_document.pdf" \ -F "collection_id=default" \ -F "metadata={ \" category \" : \" guide \" , \" source \" : \" documentation \" }"
MinIO Access (File Storage) URL: http://localhost:9001 Credentials: admin / admin123456Important: Change this password before any production deployment!
Useful Commands View Logs in Real-Time # All services sudo docker-compose logs -f # A specific service sudo docker-compose logs -f orchestrator sudo docker-compose logs -f ollama
Restart a Service sudo docker-compose restart orchestrator
Stop Everything Clean Completely (Including Data) sudo docker-compose down -v # Also removes volumes
The -v option removes all volumes, including your documents and indexed data!
Next Steps Quick Troubleshooting Services won’t start # Check logs sudo docker-compose logs -f # Check disk space (minimum 50 GB) df -h # Check RAM (minimum 16 GB) free -h
Ollama not responding # Check if model is downloaded docker exec -it openrag-ollama ollama list # If absent, download it docker exec -it openrag-ollama ollama pull llama3.1:8b
Queries very slow (>75s) No results for queries # Check if documents are processed curl http://localhost:8000/documents | jq '.documents[] | {filename, status}' # Status "processed" = ready # Status "processing" = in progress (wait 10-30s)