📖 What is TUG (TheUltimateRAG)?

TUG (TheUltimateRAG) is a real-world, production-grade RAG framework, not just another tutorial or demo project.

It is designed to solve common problems developers face when moving from simple prototypes to scalable AI systems, such as:

• Multi-user data separation
• Long-term memory handling
• Organizational knowledge sharing
• Clean, modular architecture

Whether you’re building:

• A corporate knowledge assistant
• A legal or research AI
• A personal second-brain
• Or a multi-tenant SaaS AI platform

👉 TUG (TheUltimateRAG) gives you a strong, extensible backend foundation.

For a complete walkthrough, architecture deep-dives, and usage examples,
📘 visit the official documentation:
https://ultimaterag.vercel.app/

🔐 Environment Configuration for TheUltimateRAG

To run TheUltimateRAG correctly, you must create and configure a .env file.
This file stores environment-specific settings such as API keys, database configs, and runtime options.

The project uses Pydantic Settings + python-dotenv, so all variables defined in .env are automatically loaded at startup.

📁 Step 1: Create the .env File

At the root of the project, create a file named:

.env

⚙️ Step 2: Required & Optional Environment Variables

Below is a complete reference of supported environment variables, grouped by purpose.

You only need to configure the parts relevant to your setup.

🧩 Core Application Settings

APP_NAME=TheUltimateRAG
APP_ENV=development        # development | production
DEBUG=true

🤖 LLM & Embedding Providers

LLM_PROVIDER=openai        # openai | ollama | anthropic
EMBEDDING_PROVIDER=openai # openai | ollama | huggingface
MODEL_NAME=gpt-3.5-turbo

🔑 API Keys (Required Based on Provider)

OpenAI

OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxx

Anthropic

ANTHROPIC_API_KEY=sk-ant-xxxxxxxxxxxx

⚠️ Note: If LLM_PROVIDER or EMBEDDING_PROVIDER is set to openai, OPENAI_API_KEY must be provided, otherwise a warning will be shown.

🧠 Ollama Configuration (Local Models)

OLLAMA_BASE_URL=http://localhost:11434

Use this only if you are running Ollama locally.

🗂️ Vector Database Configuration

ChromaDB (Default – Local)

VECTOR_DB_TYPE=chroma
VECTOR_DB_PATH=./chroma_db_data
EMBEDDING_DIMENSION=1536

PostgreSQL + PGVector

VECTOR_DB_TYPE=postgres
POSTGRES_HOST=localhost
POSTGRES_PORT=5432
POSTGRES_DB=vector_db
POSTGRES_USER=postgres
POSTGRES_PASSWORD=postgres

🧠 Memory & Conversation Storage (Redis)

MEMORY_WINDOW_SIZE=10
MEMORY_WINDOW_LIMIT=10

REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_DB=0
REDIS_USER=default
REDIS_PASSWORD=

The system automatically builds the Redis connection URL internally.

🔄 How .env Is Loaded

The project uses:

• python-dotenv
• pydantic-settings

load_dotenv()
settings = Settings()

So no manual loading is required.

✅ Minimal .env (Quick Start)

If you want to get started quickly, this is enough:

OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxx
LLM_PROVIDER=openai
EMBEDDING_PROVIDER=openai
VECTOR_DB_TYPE=chroma

⚠️ Important Notes

• Do NOT commit .env to Git
• Add .env to .gitignore
• Use different .env files for dev & prod if needed

📘 Need More Help?

For advanced configuration, architecture details, and examples, visit:

👉 https://theultimaterag.vercel.app/