n8n Bootcamp Repository

This repository will be made publicly available and will serve as a hub for various n8n-related projects. Our goal is to provide a comprehensive resource for both beginners and experienced users looking to leverage the power of n8n.

Available Docker Compose Flavors

This repository offers multiple Docker Compose configurations to suit different needs:

1. Standard Setup (01_n8n-postgres-redis)

A streamlined workflow automation environment with core components:

• n8n: Workflow automation engine with webhook support
• Postgres: Relational database for persistent storage of workflows and data
• Redis: In-memory data store for queue management and improved performance

Best for: General automation workflows, production deployments, and standard n8n implementations.

2. AI-Enhanced Setup (02_n8n-postgres-redis-ollama-qdrant)

A comprehensive environment with built-in AI capabilities:

• n8n: Core workflow automation engine
• Postgres: Relational database for workflow data storage
• Redis: In-memory data store for queues and caching
• Ollama: Local LLM runtime for on-premise AI capabilities without external API dependencies
• Qdrant: Vector database for semantic search and AI-powered similarity queries

Best for: AI-enhanced workflows, semantic search implementations, and projects requiring local LLM processing.

3. LightRAG Setup (03_n8n-postgres-redis-lightrag)

A specialized environment for Retrieval-Augmented Generation (RAG) implementations:

• n8n: Core workflow automation engine
• Postgres: Relational database for workflow data storage
• Redis: In-memory data store and database for LightRAG
• LightRAG: Retrieval-Augmented Generation system for AI-powered document search and knowledge retrieval

Best for: Knowledge-intensive workflows, document processing, graph-based RAG implementations, and integration with OpenAI models.

Component Overview

Below is a brief introduction to each component and its purpose:

• n8n: Workflow automation tool for connecting apps and automating tasks.
• Ollama: Local LLM (Large Language Model) runtime for AI-powered features and chatbots.
• Postgres: Relational database for storing workflow data and application state.
• Redis: In-memory data store for caching, queues, and fast data access.
• Qdrant: Vector database for semantic search and AI-powered similarity queries.
• LightRAG: Retrieval-Augmented Generation system with graph representation for AI-powered document search and knowledge retrieval, using OpenAI models.

Each component is included in the docker-compose setups to provide a complete environment for building, testing, and running advanced automation workflows with AI and data capabilities.

Getting Started

To use any of the provided Docker Compose configurations:

1. Navigate to the desired setup directory:

   cd local-docker-options/01_n8n-postgres-redis
   # OR
   cd local-docker-options/02_n8n-postgres-redis-ollama-qdrant
   # OR
   cd local-docker-options/03_n8n-postgres-redis-lightrag

2. Create the required data directories (see each directory's README for specific folders)

   For Standard Setup:

   mkdir -p n8n_data postgres_data redis_data

   For AI-Enhanced Setup:

   mkdir -p shared n8n_data postgres_data redis_data qdrant_data ollama_data

   For LightRAG Setup:

   mkdir -p n8n_data postgres_data redis_data
   mkdir -p lightrag_data/rag_storage lightrag_data/inputs lightrag_data/tiktoken

3. Create a .env file with the necessary environment variables (see README in each directory for details)

4. Start the services:

   docker-compose up -d

5. Access the n8n web interface at http://localhost:5678 (or your configured port)

6. To stop the services:

   docker-compose down

Each configuration directory contains its own README with more detailed information about the specific setup and environment variables needed.

System Requirements

• Docker and Docker Compose installed on your system
• Minimum 4GB RAM (8GB recommended for AI-enhanced setup)
• At least 10GB of free disk space (more if using multiple LLM models with Ollama)
• Internet connection for initial container downloads

Use Cases

• Standard Setup: Perfect for building automations that connect various services, process data, or create custom API workflows.
• AI-Enhanced Setup: Ideal for:
  • Automated content generation
  • Document analysis and summarization
  • Sentiment analysis
  • Knowledge base creation with semantic search
  • Text classification and extraction
  • AI-powered decision making
• LightRAG Setup: Specialized for:
  • Graph-based knowledge representation
  • Advanced document retrieval systems
  • Question-answering over documents
  • Integration with OpenAI models for LLM and embeddings
  • Custom RAG implementations via n8n API integration
  • Interactive knowledge exploration via LightRAG UI

Contributing

Contributions to this repository are welcome. Please feel free to submit pull requests or open issues for bugs, feature requests, or documentation improvements.